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Merging upstream version 24.0.0.

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Signed-off-by: Daniel Baumann <daniel@debian.org>
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Daniel Baumann 2025-02-13 21:36:08 +01:00
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CHANGELOG.md
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Changelog Changelog
========= =========
## [v23.17.0] - 2024-05-19
### :boom: BREAKING CHANGES
- due to [`77d21d9`](https://github.com/tobymao/sqlglot/commit/77d21d9379c3f130b803ea651ec3d36256bb84a4) - parse : operator as JSONExtract (similar to Snowflake) *(PR [#3508](https://github.com/tobymao/sqlglot/pull/3508) by [@georgesittas](https://github.com/georgesittas))*:
parse : operator as JSONExtract (similar to Snowflake) (#3508)
### :sparkles: New Features
- [`1125662`](https://github.com/tobymao/sqlglot/commit/11256629d74c4721ed13ed534509d266e260dde6) - add support for snowflake lambdas with type annotations closes … *(PR [#3506](https://github.com/tobymao/sqlglot/pull/3506) by [@tobymao](https://github.com/tobymao))*
### :bug: Bug Fixes
- [`77d21d9`](https://github.com/tobymao/sqlglot/commit/77d21d9379c3f130b803ea651ec3d36256bb84a4) - **databricks**: parse : operator as JSONExtract (similar to Snowflake) *(PR [#3508](https://github.com/tobymao/sqlglot/pull/3508) by [@georgesittas](https://github.com/georgesittas))*
## [v23.16.0] - 2024-05-18
### :boom: BREAKING CHANGES
- due to [`e281db8`](https://github.com/tobymao/sqlglot/commit/e281db8784682649be305e9a05c45211402f107c) - Add ALTER TABLE SET *(PR [#3485](https://github.com/tobymao/sqlglot/pull/3485) by [@VaggelisD](https://github.com/VaggelisD))*:
Add ALTER TABLE SET (#3485)
### :sparkles: New Features
- [`e281db8`](https://github.com/tobymao/sqlglot/commit/e281db8784682649be305e9a05c45211402f107c) - Add ALTER TABLE SET *(PR [#3485](https://github.com/tobymao/sqlglot/pull/3485) by [@VaggelisD](https://github.com/VaggelisD))*
- [`9aee21b`](https://github.com/tobymao/sqlglot/commit/9aee21b88e73809e2cdc4e48f04e16edcf1141d7) - add RETURNS NULL ON NULL and STRICT properties *(PR [#3504](https://github.com/tobymao/sqlglot/pull/3504) by [@georgesittas](https://github.com/georgesittas))*
- :arrow_lower_right: *addresses issue [#3503](https://github.com/tobymao/sqlglot/issues/3503) opened by [@krzysztof-kwitt](https://github.com/krzysztof-kwitt)*
### :wrench: Chores
- [`0896d11`](https://github.com/tobymao/sqlglot/commit/0896d113b94aaea82e90dd04cdf917dfa546d08e) - lint *(commit by [@tobymao](https://github.com/tobymao))*
## [v23.15.10] - 2024-05-17 ## [v23.15.10] - 2024-05-17
### :sparkles: New Features ### :sparkles: New Features
- [`89c1d3a`](https://github.com/tobymao/sqlglot/commit/89c1d3a4dd3387576c384413b3a8991a2dd030de) - **clickhouse**: support generate TimestampTrunc, Variance, Stddev *(PR [#3489](https://github.com/tobymao/sqlglot/pull/3489) by [@longxiaofei](https://github.com/longxiaofei))* - [`89c1d3a`](https://github.com/tobymao/sqlglot/commit/89c1d3a4dd3387576c384413b3a8991a2dd030de) - **clickhouse**: support generate TimestampTrunc, Variance, Stddev *(PR [#3489](https://github.com/tobymao/sqlglot/pull/3489) by [@longxiaofei](https://github.com/longxiaofei))*
@ -3668,3 +3698,5 @@ Changelog
[v23.15.8]: https://github.com/tobymao/sqlglot/compare/v23.15.7...v23.15.8 [v23.15.8]: https://github.com/tobymao/sqlglot/compare/v23.15.7...v23.15.8
[v23.15.9]: https://github.com/tobymao/sqlglot/compare/v23.15.8...v23.15.9 [v23.15.9]: https://github.com/tobymao/sqlglot/compare/v23.15.8...v23.15.9
[v23.15.10]: https://github.com/tobymao/sqlglot/compare/v23.15.9...v23.15.10 [v23.15.10]: https://github.com/tobymao/sqlglot/compare/v23.15.9...v23.15.10
[v23.16.0]: https://github.com/tobymao/sqlglot/compare/v23.15.10...v23.16.0
[v23.17.0]: https://github.com/tobymao/sqlglot/compare/v23.16.0...v23.17.0

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* SQLGlot does not aim to be a SQL validator - it is designed to be very forgiving. This makes the codebase more comprehensive and also gives more flexibility to its users, e.g. by allowing them to include trailing commas in their projection lists. * SQLGlot does not aim to be a SQL validator - it is designed to be very forgiving. This makes the codebase more comprehensive and also gives more flexibility to its users, e.g. by allowing them to include trailing commas in their projection lists.
What happened to sqlglot.dataframe?
* The PySpark dataframe api was moved to a standalone library called [sqlframe](https://github.com/eakmanrq/sqlframe) in v24. It now allows you to run queries as opposed to just generate SQL.
## Examples ## Examples
### Formatting and Transpiling ### Formatting and Transpiling
@ -501,6 +505,7 @@ See also: [Writing a Python SQL engine from scratch](https://github.com/tobymao/
* [Querybook](https://github.com/pinterest/querybook) * [Querybook](https://github.com/pinterest/querybook)
* [Quokka](https://github.com/marsupialtail/quokka) * [Quokka](https://github.com/marsupialtail/quokka)
* [Splink](https://github.com/moj-analytical-services/splink) * [Splink](https://github.com/moj-analytical-services/splink)
* [sqlframe](https://github.com/eakmanrq/sqlframe)
## Documentation ## Documentation

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@ -76,8 +76,8 @@
</span><span id="L-12"><a href="#L-12"><span class="linenos">12</span></a><span class="n">__version_tuple__</span><span class="p">:</span> <span class="n">VERSION_TUPLE</span> </span><span id="L-12"><a href="#L-12"><span class="linenos">12</span></a><span class="n">__version_tuple__</span><span class="p">:</span> <span class="n">VERSION_TUPLE</span>
</span><span id="L-13"><a href="#L-13"><span class="linenos">13</span></a><span class="n">version_tuple</span><span class="p">:</span> <span class="n">VERSION_TUPLE</span> </span><span id="L-13"><a href="#L-13"><span class="linenos">13</span></a><span class="n">version_tuple</span><span class="p">:</span> <span class="n">VERSION_TUPLE</span>
</span><span id="L-14"><a href="#L-14"><span class="linenos">14</span></a> </span><span id="L-14"><a href="#L-14"><span class="linenos">14</span></a>
</span><span id="L-15"><a href="#L-15"><span class="linenos">15</span></a><span class="n">__version__</span> <span class="o">=</span> <span class="n">version</span> <span class="o">=</span> <span class="s1">&#39;23.15.10&#39;</span> </span><span id="L-15"><a href="#L-15"><span class="linenos">15</span></a><span class="n">__version__</span> <span class="o">=</span> <span class="n">version</span> <span class="o">=</span> <span class="s1">&#39;23.17.0&#39;</span>
</span><span id="L-16"><a href="#L-16"><span class="linenos">16</span></a><span class="n">__version_tuple__</span> <span class="o">=</span> <span class="n">version_tuple</span> <span class="o">=</span> <span class="p">(</span><span class="mi">23</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> </span><span id="L-16"><a href="#L-16"><span class="linenos">16</span></a><span class="n">__version_tuple__</span> <span class="o">=</span> <span class="n">version_tuple</span> <span class="o">=</span> <span class="p">(</span><span class="mi">23</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</span></pre></div> </span></pre></div>
@ -97,7 +97,7 @@
<section id="version"> <section id="version">
<div class="attr variable"> <div class="attr variable">
<span class="name">version</span><span class="annotation">: str</span> = <span class="name">version</span><span class="annotation">: str</span> =
<span class="default_value">&#39;23.15.10&#39;</span> <span class="default_value">&#39;23.17.0&#39;</span>
</div> </div>
@ -109,7 +109,7 @@
<section id="version_tuple"> <section id="version_tuple">
<div class="attr variable"> <div class="attr variable">
<span class="name">version_tuple</span><span class="annotation">: object</span> = <span class="name">version_tuple</span><span class="annotation">: object</span> =
<span class="default_value">(23, 15, 10)</span> <span class="default_value">(23, 17, 0)</span>
</div> </div>

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docs/sqlglot/dataframe.html
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<style>/*! * Bootstrap Reboot v5.0.0 (https://getbootstrap.com/) * Copyright 2011-2021 The Bootstrap Authors * Copyright 2011-2021 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width:1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + .9vw)}@media (min-width:1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + .6vw)}@media (min-width:1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + .3vw)}@media (min-width:1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[data-bs-original-title],abbr[title]{-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;-webkit-text-decoration-skip-ink:none;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}b,strong{font-weight:bolder}small{font-size:.875em}mark{padding:.2em;background-color:#fcf8e3}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#0d6efd;text-decoration:underline}a:hover{color:#0a58ca}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em;direction:ltr;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em}pre code{font-size:inherit;color:inherit;word-break:normal}code{font-size:.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}kbd{padding:.2rem .4rem;font-size:.875em;color:#fff;background-color:#212529;border-radius:.2rem}kbd kbd{padding:0;font-size:1em;font-weight:700}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}tbody,td,tfoot,th,thead,tr{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]::-webkit-calendar-picker-indicator{display:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width:1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-text,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none!important}</style> <style>/*! * Bootstrap Reboot v5.0.0 (https://getbootstrap.com/) * Copyright 2011-2021 The Bootstrap Authors * Copyright 2011-2021 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width:1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + .9vw)}@media (min-width:1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + .6vw)}@media (min-width:1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + .3vw)}@media (min-width:1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[data-bs-original-title],abbr[title]{-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;-webkit-text-decoration-skip-ink:none;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}b,strong{font-weight:bolder}small{font-size:.875em}mark{padding:.2em;background-color:#fcf8e3}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#0d6efd;text-decoration:underline}a:hover{color:#0a58ca}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em;direction:ltr;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em}pre code{font-size:inherit;color:inherit;word-break:normal}code{font-size:.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}kbd{padding:.2rem .4rem;font-size:.875em;color:#fff;background-color:#212529;border-radius:.2rem}kbd kbd{padding:0;font-size:1em;font-weight:700}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}tbody,td,tfoot,th,thead,tr{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]::-webkit-calendar-picker-indicator{display:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width:1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-text,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none!important}</style>
@ -791,7 +791,7 @@
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">createDataFrame</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015153824&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015153824&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">]]</span>,</span><span class="param"> <span class="n">schema</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015110096&#39;</span><span class="o">&gt;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">samplingRatio</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">verifySchema</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span> <span class="name">createDataFrame</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666179102480&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666179102480&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">]]</span>,</span><span class="param"> <span class="n">schema</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666180910912&#39;</span><span class="o">&gt;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">samplingRatio</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">verifySchema</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span>
<label class="view-source-button" for="SparkSession.createDataFrame-view-source"><span>View Source</span></label> <label class="view-source-button" for="SparkSession.createDataFrame-view-source"><span>View Source</span></label>
@ -1848,7 +1848,7 @@
<input id="DataFrame.__init__-view-source" class="view-source-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="DataFrame.__init__-view-source" class="view-source-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<div class="attr function"> <div class="attr function">
<span class="name">DataFrame</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">spark</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657023744704&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">expression</span><span class="p">:</span> <span class="n"><a href="../expressions.html#Select">sqlglot.expressions.Select</a></span>,</span><span class="param"> <span class="n">branch_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">sequence_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">last_op</span><span class="p">:</span> <span class="n">sqlglot</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">operations</span><span class="o">.</span><span class="n">Operation</span> <span class="o">=</span> <span class="o">&lt;</span><span class="n">Operation</span><span class="o">.</span><span class="n">INIT</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">pending_hints</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">output_expression_container</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657023964576&#39;</span><span class="o">&gt;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span>)</span> <span class="name">DataFrame</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">spark</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666183911504&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">expression</span><span class="p">:</span> <span class="n"><a href="../expressions.html#Select">sqlglot.expressions.Select</a></span>,</span><span class="param"> <span class="n">branch_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">sequence_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">last_op</span><span class="p">:</span> <span class="n">sqlglot</span><span class="o">.</span><span class="n">dataframe</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">operations</span><span class="o">.</span><span class="n">Operation</span> <span class="o">=</span> <span class="o">&lt;</span><span class="n">Operation</span><span class="o">.</span><span class="n">INIT</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">pending_hints</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">output_expression_container</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666193925152&#39;</span><span class="o">&gt;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span>)</span>
<label class="view-source-button" for="DataFrame.__init__-view-source"><span>View Source</span></label> <label class="view-source-button" for="DataFrame.__init__-view-source"><span>View Source</span></label>
@ -2096,7 +2096,7 @@
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">sql</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">dialect</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014818464&#39;</span><span class="o">&gt;</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">optimize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>:</span></span> <span class="name">sql</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">dialect</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666179831456&#39;</span><span class="o">&gt;</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">optimize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>:</span></span>
<label class="view-source-button" for="DataFrame.sql-view-source"><span>View Source</span></label> <label class="view-source-button" for="DataFrame.sql-view-source"><span>View Source</span></label>
@ -2846,7 +2846,7 @@ is unlikely to come up.</p>
<div class="decorator">@operation(Operation.FROM)</div> <div class="decorator">@operation(Operation.FROM)</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">fillna</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657013789248&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">subset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span> <span class="name">fillna</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666179446304&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">subset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span>
<label class="view-source-button" for="DataFrame.fillna-view-source"><span>View Source</span></label> <label class="view-source-button" for="DataFrame.fillna-view-source"><span>View Source</span></label>
@ -2915,7 +2915,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@operation(Operation.FROM)</div> <div class="decorator">@operation(Operation.FROM)</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">replace</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">to_replace</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">subset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657013976720&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657013976720&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span> <span class="name">replace</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">to_replace</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>,</span><span class="param"> <span class="n">subset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666180055296&#39;</span><span class="o">&gt;</span><span class="p">],</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666180055296&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span>
<label class="view-source-button" for="DataFrame.replace-view-source"><span>View Source</span></label> <label class="view-source-button" for="DataFrame.replace-view-source"><span>View Source</span></label>
@ -3120,7 +3120,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@operation(Operation.NO_OP)</div> <div class="decorator">@operation(Operation.NO_OP)</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">repartition</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">numPartitions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657013976720&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657013976720&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span> <span class="name">repartition</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">numPartitions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666180055296&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666180055296&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#DataFrame">DataFrame</a></span>:</span></span>
<label class="view-source-button" for="DataFrame.repartition-view-source"><span>View Source</span></label> <label class="view-source-button" for="DataFrame.repartition-view-source"><span>View Source</span></label>
@ -3840,7 +3840,7 @@ and check if it matches the type of the value provided. If not then make it null
<input id="Column.__init__-view-source" class="view-source-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="Column.__init__-view-source" class="view-source-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<div class="attr function"> <div class="attr function">
<span class="name">Column</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">expression</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span></span>)</span> <span class="name">Column</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">expression</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span></span>)</span>
<label class="view-source-button" for="Column.__init__-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.__init__-view-source"><span>View Source</span></label>
@ -3884,7 +3884,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">ensure_col</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">ensure_col</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">value</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">,</span> <span class="n">NoneType</span><span class="p">]</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.ensure_col-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.ensure_col-view-source"><span>View Source</span></label>
@ -3905,7 +3905,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">ensure_cols</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">args</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n">List</span><span class="p">[</span><span class="n"><a href="#Column">Column</a></span><span class="p">]</span>:</span></span> <span class="name">ensure_cols</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">args</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n"><a href="../expressions.html#Expression">sqlglot.expressions.Expression</a></span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n">List</span><span class="p">[</span><span class="n"><a href="#Column">Column</a></span><span class="p">]</span>:</span></span>
<label class="view-source-button" for="Column.ensure_cols-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.ensure_cols-view-source"><span>View Source</span></label>
@ -3926,7 +3926,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">invoke_anonymous_function</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="n">func_name</span><span class="p">:</span> <span class="nb">str</span>,</span><span class="param"> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">]</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">invoke_anonymous_function</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="n">func_name</span><span class="p">:</span> <span class="nb">str</span>,</span><span class="param"> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">]</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.invoke_anonymous_function-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.invoke_anonymous_function-view-source"><span>View Source</span></label>
@ -3953,7 +3953,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">invoke_expression_over_column</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="n">callable_expression</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">invoke_expression_over_column</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="n">column</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">]</span>,</span><span class="param"> <span class="n">callable_expression</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.invoke_expression_over_column-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.invoke_expression_over_column-view-source"><span>View Source</span></label>
@ -3992,7 +3992,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">binary_op</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">klass</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="n">other</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">binary_op</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">klass</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="n">other</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.binary_op-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.binary_op-view-source"><span>View Source</span></label>
@ -4013,7 +4013,7 @@ and check if it matches the type of the value provided. If not then make it null
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">inverse_binary_op</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">klass</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="n">other</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">inverse_binary_op</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">klass</span><span class="p">:</span> <span class="n">Callable</span>,</span><span class="param"> <span class="n">other</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.inverse_binary_op-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.inverse_binary_op-view-source"><span>View Source</span></label>
@ -4607,7 +4607,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">isin</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">):</span></span> <span class="name">isin</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">):</span></span>
<label class="view-source-button" for="Column.isin-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.isin-view-source"><span>View Source</span></label>
@ -4628,7 +4628,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">between</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">lowerBound</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">upperBound</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657015887408&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">between</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">lowerBound</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span>,</span><span class="param"> <span class="n">upperBound</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666181935232&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.between-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.between-view-source"><span>View Source</span></label>
@ -4663,7 +4663,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">over</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">window</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014198032&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span> <span class="name">over</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="n">window</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178523520&#39;</span><span class="o">&gt;</span></span><span class="return-annotation">) -> <span class="n"><a href="#Column">Column</a></span>:</span></span>
<label class="view-source-button" for="Column.over-view-source"><span>View Source</span></label> <label class="view-source-button" for="Column.over-view-source"><span>View Source</span></label>
@ -4908,7 +4908,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">partitionBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span> <span class="name">partitionBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span>
<label class="view-source-button" for="Window.partitionBy-view-source"><span>View Source</span></label> <label class="view-source-button" for="Window.partitionBy-view-source"><span>View Source</span></label>
@ -4929,7 +4929,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="decorator">@classmethod</div> <div class="decorator">@classmethod</div>
<span class="def">def</span> <span class="def">def</span>
<span class="name">orderBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span> <span class="name">orderBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">cls</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span>
<label class="view-source-button" for="Window.orderBy-view-source"><span>View Source</span></label> <label class="view-source-button" for="Window.orderBy-view-source"><span>View Source</span></label>
@ -5171,7 +5171,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">partitionBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span> <span class="name">partitionBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span>
<label class="view-source-button" for="WindowSpec.partitionBy-view-source"><span>View Source</span></label> <label class="view-source-button" for="WindowSpec.partitionBy-view-source"><span>View Source</span></label>
@ -5198,7 +5198,7 @@ Sqlglot doesn't currently replicate this class so it only accepts a string</p>
<div class="attr function"> <div class="attr function">
<span class="def">def</span> <span class="def">def</span>
<span class="name">orderBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140657014627040&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span> <span class="name">orderBy</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="bp">self</span>,</span><span class="param"> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="o">&lt;</span><span class="n">MagicMock</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;140666178965504&#39;</span><span class="o">&gt;</span><span class="p">]]</span></span><span class="return-annotation">) -> <span class="n"><a href="#WindowSpec">WindowSpec</a></span>:</span></span>
<label class="view-source-button" for="WindowSpec.orderBy-view-source"><span>View Source</span></label> <label class="view-source-button" for="WindowSpec.orderBy-view-source"><span>View Source</span></label>

2
docs/sqlglot/dialects.html
View file

@ -3,7 +3,7 @@
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@ -1346,6 +1346,7 @@ Default: 3</li>
<dd id="PRQL.Parser.UNION_MODIFIERS" class="variable"><a href="../parser.html#Parser.UNION_MODIFIERS">UNION_MODIFIERS</a></dd> <dd id="PRQL.Parser.UNION_MODIFIERS" class="variable"><a href="../parser.html#Parser.UNION_MODIFIERS">UNION_MODIFIERS</a></dd>
<dd id="PRQL.Parser.NO_PAREN_IF_COMMANDS" class="variable"><a href="../parser.html#Parser.NO_PAREN_IF_COMMANDS">NO_PAREN_IF_COMMANDS</a></dd> <dd id="PRQL.Parser.NO_PAREN_IF_COMMANDS" class="variable"><a href="../parser.html#Parser.NO_PAREN_IF_COMMANDS">NO_PAREN_IF_COMMANDS</a></dd>
<dd id="PRQL.Parser.JSON_ARROWS_REQUIRE_JSON_TYPE" class="variable"><a href="../parser.html#Parser.JSON_ARROWS_REQUIRE_JSON_TYPE">JSON_ARROWS_REQUIRE_JSON_TYPE</a></dd> <dd id="PRQL.Parser.JSON_ARROWS_REQUIRE_JSON_TYPE" class="variable"><a href="../parser.html#Parser.JSON_ARROWS_REQUIRE_JSON_TYPE">JSON_ARROWS_REQUIRE_JSON_TYPE</a></dd>
<dd id="PRQL.Parser.COLON_IS_JSON_EXTRACT" class="variable"><a href="../parser.html#Parser.COLON_IS_JSON_EXTRACT">COLON_IS_JSON_EXTRACT</a></dd>
<dd id="PRQL.Parser.VALUES_FOLLOWED_BY_PAREN" class="variable"><a href="../parser.html#Parser.VALUES_FOLLOWED_BY_PAREN">VALUES_FOLLOWED_BY_PAREN</a></dd> <dd id="PRQL.Parser.VALUES_FOLLOWED_BY_PAREN" class="variable"><a href="../parser.html#Parser.VALUES_FOLLOWED_BY_PAREN">VALUES_FOLLOWED_BY_PAREN</a></dd>
<dd id="PRQL.Parser.SUPPORTS_IMPLICIT_UNNEST" class="variable"><a href="../parser.html#Parser.SUPPORTS_IMPLICIT_UNNEST">SUPPORTS_IMPLICIT_UNNEST</a></dd> <dd id="PRQL.Parser.SUPPORTS_IMPLICIT_UNNEST" class="variable"><a href="../parser.html#Parser.SUPPORTS_IMPLICIT_UNNEST">SUPPORTS_IMPLICIT_UNNEST</a></dd>
<dd id="PRQL.Parser.INTERVAL_SPANS" class="variable"><a href="../parser.html#Parser.INTERVAL_SPANS">INTERVAL_SPANS</a></dd> <dd id="PRQL.Parser.INTERVAL_SPANS" class="variable"><a href="../parser.html#Parser.INTERVAL_SPANS">INTERVAL_SPANS</a></dd>

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<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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@ -2539,6 +2540,7 @@ Default: True</li>
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<dd id="Python.Generator.renamecolumn_sql" class="function"><a href="../generator.html#Generator.renamecolumn_sql">renamecolumn_sql</a></dd> <dd id="Python.Generator.renamecolumn_sql" class="function"><a href="../generator.html#Generator.renamecolumn_sql">renamecolumn_sql</a></dd>
<dd id="Python.Generator.alterset_sql" class="function"><a href="../generator.html#Generator.alterset_sql">alterset_sql</a></dd>
<dd id="Python.Generator.altertable_sql" class="function"><a href="../generator.html#Generator.altertable_sql">altertable_sql</a></dd> <dd id="Python.Generator.altertable_sql" class="function"><a href="../generator.html#Generator.altertable_sql">altertable_sql</a></dd>
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@ -2631,6 +2633,7 @@ Default: True</li>
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<dd id="Python.Generator.copy_sql" class="function"><a href="../generator.html#Generator.copy_sql">copy_sql</a></dd> <dd id="Python.Generator.copy_sql" class="function"><a href="../generator.html#Generator.copy_sql">copy_sql</a></dd>
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2
docs/sqlglot/executor/table.html
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<title>sqlglot.executor.table API documentation</title> <title>sqlglot.executor.table API documentation</title>
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<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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<title>sqlglot.helper API documentation</title> <title>sqlglot.helper API documentation</title>
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@ -1893,7 +1893,7 @@ belong to some totally-ordered set.</p>
<section id="DATE_UNITS"> <section id="DATE_UNITS">
<div class="attr variable"> <div class="attr variable">
<span class="name">DATE_UNITS</span> = <span class="name">DATE_UNITS</span> =
<span class="default_value">{&#39;year_month&#39;, &#39;week&#39;, &#39;year&#39;, &#39;month&#39;, &#39;day&#39;, &#39;quarter&#39;}</span> <span class="default_value">{&#39;year_month&#39;, &#39;week&#39;, &#39;day&#39;, &#39;year&#39;, &#39;month&#39;, &#39;quarter&#39;}</span>
</div> </div>

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@ -577,7 +577,7 @@
<div class="attr variable"> <div class="attr variable">
<span class="name">ALL_JSON_PATH_PARTS</span> = <span class="name">ALL_JSON_PATH_PARTS</span> =
<input id="ALL_JSON_PATH_PARTS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="ALL_JSON_PATH_PARTS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<label class="view-value-button pdoc-button" for="ALL_JSON_PATH_PARTS-view-value"></label><span class="default_value">{&lt;class &#39;<a href="expressions.html#JSONPathFilter">sqlglot.expressions.JSONPathFilter</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathUnion">sqlglot.expressions.JSONPathUnion</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSubscript">sqlglot.expressions.JSONPathSubscript</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSelector">sqlglot.expressions.JSONPathSelector</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathWildcard">sqlglot.expressions.JSONPathWildcard</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSlice">sqlglot.expressions.JSONPathSlice</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathScript">sqlglot.expressions.JSONPathScript</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathRoot">sqlglot.expressions.JSONPathRoot</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathRecursive">sqlglot.expressions.JSONPathRecursive</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathKey">sqlglot.expressions.JSONPathKey</a>&#39;&gt;}</span> <label class="view-value-button pdoc-button" for="ALL_JSON_PATH_PARTS-view-value"></label><span class="default_value">{&lt;class &#39;<a href="expressions.html#JSONPathRecursive">sqlglot.expressions.JSONPathRecursive</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathKey">sqlglot.expressions.JSONPathKey</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathWildcard">sqlglot.expressions.JSONPathWildcard</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathFilter">sqlglot.expressions.JSONPathFilter</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathUnion">sqlglot.expressions.JSONPathUnion</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSubscript">sqlglot.expressions.JSONPathSubscript</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSelector">sqlglot.expressions.JSONPathSelector</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathSlice">sqlglot.expressions.JSONPathSlice</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathScript">sqlglot.expressions.JSONPathScript</a>&#39;&gt;, &lt;class &#39;<a href="expressions.html#JSONPathRoot">sqlglot.expressions.JSONPathRoot</a>&#39;&gt;}</span>
</div> </div>

2
docs/sqlglot/lineage.html
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@ -3,7 +3,7 @@
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2
docs/sqlglot/optimizer.html
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docs/sqlglot/optimizer/eliminate_joins.html
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docs/sqlglot/optimizer/eliminate_subqueries.html
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docs/sqlglot/optimizer/isolate_table_selects.html
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docs/sqlglot/optimizer/merge_subqueries.html
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@ -586,7 +586,7 @@ queries if it would result in multiple table selects in a single query:</p>
<div class="attr variable"> <div class="attr variable">
<span class="name">UNMERGABLE_ARGS</span> = <span class="name">UNMERGABLE_ARGS</span> =
<input id="UNMERGABLE_ARGS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="UNMERGABLE_ARGS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<label class="view-value-button pdoc-button" for="UNMERGABLE_ARGS-view-value"></label><span class="default_value">{&#39;prewhere&#39;, &#39;distribute&#39;, &#39;having&#39;, &#39;sort&#39;, &#39;qualify&#39;, &#39;offset&#39;, &#39;distinct&#39;, &#39;settings&#39;, &#39;pivots&#39;, &#39;connect&#39;, &#39;limit&#39;, &#39;sample&#39;, &#39;group&#39;, &#39;match&#39;, &#39;format&#39;, &#39;kind&#39;, &#39;into&#39;, &#39;cluster&#39;, &#39;options&#39;, &#39;windows&#39;, &#39;with&#39;, &#39;locks&#39;, &#39;laterals&#39;}</span> <label class="view-value-button pdoc-button" for="UNMERGABLE_ARGS-view-value"></label><span class="default_value">{&#39;locks&#39;, &#39;with&#39;, &#39;cluster&#39;, &#39;distinct&#39;, &#39;pivots&#39;, &#39;prewhere&#39;, &#39;windows&#39;, &#39;options&#39;, &#39;offset&#39;, &#39;sort&#39;, &#39;settings&#39;, &#39;match&#39;, &#39;connect&#39;, &#39;sample&#39;, &#39;laterals&#39;, &#39;format&#39;, &#39;having&#39;, &#39;group&#39;, &#39;limit&#39;, &#39;qualify&#39;, &#39;kind&#39;, &#39;into&#39;, &#39;distribute&#39;}</span>
</div> </div>

2
docs/sqlglot/optimizer/normalize.html
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@ -3,7 +3,7 @@
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2
docs/sqlglot/optimizer/normalize_identifiers.html
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@ -3,7 +3,7 @@
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docs/sqlglot/optimizer/pushdown_predicates.html
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docs/sqlglot/optimizer/pushdown_projections.html
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docs/sqlglot/optimizer/qualify.html
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docs/sqlglot/optimizer/qualify_columns.html
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docs/sqlglot/optimizer/qualify_tables.html
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docs/sqlglot/optimizer/scope.html
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6
docs/sqlglot/optimizer/simplify.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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<title>sqlglot.optimizer.simplify API documentation</title> <title>sqlglot.optimizer.simplify API documentation</title>
<style>/*! * Bootstrap Reboot v5.0.0 (https://getbootstrap.com/) * Copyright 2011-2021 The Bootstrap Authors * Copyright 2011-2021 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width:1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + .9vw)}@media (min-width:1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + .6vw)}@media (min-width:1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + .3vw)}@media (min-width:1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[data-bs-original-title],abbr[title]{-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;-webkit-text-decoration-skip-ink:none;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}b,strong{font-weight:bolder}small{font-size:.875em}mark{padding:.2em;background-color:#fcf8e3}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#0d6efd;text-decoration:underline}a:hover{color:#0a58ca}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em;direction:ltr;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em}pre code{font-size:inherit;color:inherit;word-break:normal}code{font-size:.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}kbd{padding:.2rem .4rem;font-size:.875em;color:#fff;background-color:#212529;border-radius:.2rem}kbd kbd{padding:0;font-size:1em;font-weight:700}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}tbody,td,tfoot,th,thead,tr{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]::-webkit-calendar-picker-indicator{display:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width:1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-text,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none!important}</style> <style>/*! 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@ -3006,7 +3006,7 @@ prefix are statically known.</p>
<div class="attr variable"> <div class="attr variable">
<span class="name">DATETRUNC_COMPARISONS</span> = <span class="name">DATETRUNC_COMPARISONS</span> =
<input id="DATETRUNC_COMPARISONS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="DATETRUNC_COMPARISONS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<label class="view-value-button pdoc-button" for="DATETRUNC_COMPARISONS-view-value"></label><span class="default_value">{&lt;class &#39;<a href="../expressions.html#LT">sqlglot.expressions.LT</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#GTE">sqlglot.expressions.GTE</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#EQ">sqlglot.expressions.EQ</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#LTE">sqlglot.expressions.LTE</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#NEQ">sqlglot.expressions.NEQ</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#GT">sqlglot.expressions.GT</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#In">sqlglot.expressions.In</a>&#39;&gt;}</span> <label class="view-value-button pdoc-button" for="DATETRUNC_COMPARISONS-view-value"></label><span class="default_value">{&lt;class &#39;<a href="../expressions.html#GTE">sqlglot.expressions.GTE</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#EQ">sqlglot.expressions.EQ</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#LTE">sqlglot.expressions.LTE</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#GT">sqlglot.expressions.GT</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#NEQ">sqlglot.expressions.NEQ</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#LT">sqlglot.expressions.LT</a>&#39;&gt;, &lt;class &#39;<a href="../expressions.html#In">sqlglot.expressions.In</a>&#39;&gt;}</span>
</div> </div>
@ -3086,7 +3086,7 @@ prefix are statically known.</p>
<section id="JOINS"> <section id="JOINS">
<div class="attr variable"> <div class="attr variable">
<span class="name">JOINS</span> = <span class="name">JOINS</span> =
<span class="default_value">{(&#39;&#39;, &#39;INNER&#39;), (&#39;RIGHT&#39;, &#39;OUTER&#39;), (&#39;RIGHT&#39;, &#39;&#39;), (&#39;&#39;, &#39;&#39;)}</span> <span class="default_value">{(&#39;RIGHT&#39;, &#39;&#39;), (&#39;RIGHT&#39;, &#39;OUTER&#39;), (&#39;&#39;, &#39;INNER&#39;), (&#39;&#39;, &#39;&#39;)}</span>
</div> </div>

2
docs/sqlglot/optimizer/unnest_subqueries.html
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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docs/sqlglot/serde.html
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docs/sqlglot/tokens.html
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@ -8628,7 +8628,7 @@
<div class="attr variable"> <div class="attr variable">
<span class="name">COMMANDS</span> = <span class="name">COMMANDS</span> =
<input id="Tokenizer.COMMANDS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1"> <input id="Tokenizer.COMMANDS-view-value" class="view-value-toggle-state" type="checkbox" aria-hidden="true" tabindex="-1">
<label class="view-value-button pdoc-button" for="Tokenizer.COMMANDS-view-value"></label><span class="default_value">{&lt;<a href="#TokenType.EXECUTE">TokenType.EXECUTE</a>: &#39;EXECUTE&#39;&gt;, &lt;<a href="#TokenType.SHOW">TokenType.SHOW</a>: &#39;SHOW&#39;&gt;, &lt;<a href="#TokenType.FETCH">TokenType.FETCH</a>: &#39;FETCH&#39;&gt;, &lt;<a href="#TokenType.COMMAND">TokenType.COMMAND</a>: &#39;COMMAND&#39;&gt;}</span> <label class="view-value-button pdoc-button" for="Tokenizer.COMMANDS-view-value"></label><span class="default_value">{&lt;<a href="#TokenType.SHOW">TokenType.SHOW</a>: &#39;SHOW&#39;&gt;, &lt;<a href="#TokenType.EXECUTE">TokenType.EXECUTE</a>: &#39;EXECUTE&#39;&gt;, &lt;<a href="#TokenType.FETCH">TokenType.FETCH</a>: &#39;FETCH&#39;&gt;, &lt;<a href="#TokenType.COMMAND">TokenType.COMMAND</a>: &#39;COMMAND&#39;&gt;}</span>
</div> </div>

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docs/sqlglot/transforms.html
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docs/sqlglot/trie.html
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1
setup.py
View file

@ -33,7 +33,6 @@ setup(
"mypy", "mypy",
"pandas", "pandas",
"pandas-stubs", "pandas-stubs",
"pyspark",
"python-dateutil", "python-dateutil",
"pdoc", "pdoc",
"pre-commit", "pre-commit",

238
sqlglot/dataframe/README.md
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@ -1,238 +0,0 @@
# PySpark DataFrame SQL Generator
This is a drop-in replacement for the PySpark DataFrame API that will generate SQL instead of executing DataFrame operations directly. This, when combined with the transpiling support in SQLGlot, allows one to write PySpark DataFrame code and execute it on other engines like [DuckDB](https://duckdb.org/), [Presto](https://prestodb.io/), [Spark](https://spark.apache.org/), [Snowflake](https://www.snowflake.com/en/), and [BigQuery](https://cloud.google.com/bigquery/).
Currently many of the common operations are covered and more functionality will be added over time. Please [open an issue](https://github.com/tobymao/sqlglot/issues) or [PR](https://github.com/tobymao/sqlglot/pulls) with your feedback or contribution to help influence what should be prioritized next and make sure your use case is properly supported.
# How to use
## Instructions
* [Install SQLGlot](https://github.com/tobymao/sqlglot/blob/main/README.md#install) and that is all that is required to just generate SQL. [The examples](#examples) show generating SQL and then executing that SQL on a specific engine and that will require that engine's client library.
* Find/replace all `from pyspark.sql` with `from sqlglot.dataframe`.
* Prior to any `spark.read.table` or `spark.table` run `sqlglot.schema.add_table('<table_name>', <column_structure>, dialect="spark")`.
* The column structure can be defined the following ways:
* Dictionary where the keys are column names and values are string of the Spark SQL type name.
* Ex: `{'cola': 'string', 'colb': 'int'}`
* PySpark DataFrame `StructType` similar to when using `createDataFrame`.
* Ex: `StructType([StructField('cola', StringType()), StructField('colb', IntegerType())])`
* A string of names and types similar to what is supported in `createDataFrame`.
* Ex: `cola: STRING, colb: INT`
* [Not Recommended] A list of string column names without type.
* Ex: `['cola', 'colb']`
* The lack of types may limit functionality in future releases.
* See [Registering Custom Schema](#registering-custom-schema-class) for information on how to skip this step if the information is stored externally.
* If your output SQL dialect is not Spark, then configure the SparkSession to use that dialect
* Ex: `SparkSession().builder.config("sqlframe.dialect", "bigquery").getOrCreate()`
* See [dialects](https://github.com/tobymao/sqlglot/tree/main/sqlglot/dialects) for a full list of dialects.
* Add `.sql(pretty=True)` to your final DataFrame command to return a list of sql statements to run that command.
* In most cases a single SQL statement is returned. Currently the only exception is when caching DataFrames which isn't supported in other dialects.
* Ex: `.sql(pretty=True)`
## Examples
```python
import sqlglot
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dataframe.sql import functions as F
dialect = "spark"
sqlglot.schema.add_table(
'employee',
{
'employee_id': 'INT',
'fname': 'STRING',
'lname': 'STRING',
'age': 'INT',
},
dialect=dialect,
) # Register the table structure prior to reading from the table
spark = SparkSession.builder.config("sqlframe.dialect", dialect).getOrCreate()
df = (
spark
.table('employee')
.groupBy(F.col("age"))
.agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
)
print(df.sql(pretty=True))
```
```sparksql
SELECT
`employee`.`age` AS `age`,
COUNT(DISTINCT `employee`.`employee_id`) AS `num_employees`
FROM `employee` AS `employee`
GROUP BY
`employee`.`age`
```
## Registering Custom Schema Class
The step of adding `sqlglot.schema.add_table` can be skipped if you have the column structure stored externally like in a file or from an external metadata table. This can be done by writing a class that implements the `sqlglot.schema.Schema` abstract class and then assigning that class to `sqlglot.schema`.
```python
import sqlglot
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dataframe.sql import functions as F
from sqlglot.schema import Schema
class ExternalSchema(Schema):
...
sqlglot.schema = ExternalSchema()
spark = SparkSession() # Spark will be used by default is not specific in SparkSession config
df = (
spark
.table('employee')
.groupBy(F.col("age"))
.agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
)
print(df.sql(pretty=True))
```
## Example Implementations
### Bigquery
```python
from google.cloud import bigquery
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dataframe.sql import types
from sqlglot.dataframe.sql import functions as F
client = bigquery.Client()
data = [
(1, "Jack", "Shephard", 34),
(2, "John", "Locke", 48),
(3, "Kate", "Austen", 34),
(4, "Claire", "Littleton", 22),
(5, "Hugo", "Reyes", 26),
]
schema = types.StructType([
types.StructField('employee_id', types.IntegerType(), False),
types.StructField('fname', types.StringType(), False),
types.StructField('lname', types.StringType(), False),
types.StructField('age', types.IntegerType(), False),
])
sql_statements = (
SparkSession
.builder
.config("sqlframe.dialect", "bigquery")
.getOrCreate()
.createDataFrame(data, schema)
.groupBy(F.col("age"))
.agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
.sql()
)
result = None
for sql in sql_statements:
result = client.query(sql)
assert result is not None
for row in client.query(result):
print(f"Age: {row['age']}, Num Employees: {row['num_employees']}")
```
### Snowflake
```python
import os
import snowflake.connector
from sqlglot.dataframe.session import SparkSession
from sqlglot.dataframe import types
from sqlglot.dataframe import functions as F
ctx = snowflake.connector.connect(
user=os.environ["SNOWFLAKE_USER"],
password=os.environ["SNOWFLAKE_PASS"],
account=os.environ["SNOWFLAKE_ACCOUNT"]
)
cs = ctx.cursor()
data = [
(1, "Jack", "Shephard", 34),
(2, "John", "Locke", 48),
(3, "Kate", "Austen", 34),
(4, "Claire", "Littleton", 22),
(5, "Hugo", "Reyes", 26),
]
schema = types.StructType([
types.StructField('employee_id', types.IntegerType(), False),
types.StructField('fname', types.StringType(), False),
types.StructField('lname', types.StringType(), False),
types.StructField('age', types.IntegerType(), False),
])
sql_statements = (
SparkSession
.builder
.config("sqlframe.dialect", "snowflake")
.getOrCreate()
.createDataFrame(data, schema)
.groupBy(F.col("age"))
.agg(F.countDistinct(F.col("lname")).alias("num_employees"))
.sql()
)
try:
for sql in sql_statements:
cs.execute(sql)
results = cs.fetchall()
for row in results:
print(f"Age: {row[0]}, Num Employees: {row[1]}")
finally:
cs.close()
ctx.close()
```
### Spark
```python
from pyspark.sql.session import SparkSession as PySparkSession
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dataframe.sql import types
from sqlglot.dataframe.sql import functions as F
data = [
(1, "Jack", "Shephard", 34),
(2, "John", "Locke", 48),
(3, "Kate", "Austen", 34),
(4, "Claire", "Littleton", 22),
(5, "Hugo", "Reyes", 26),
]
schema = types.StructType([
types.StructField('employee_id', types.IntegerType(), False),
types.StructField('fname', types.StringType(), False),
types.StructField('lname', types.StringType(), False),
types.StructField('age', types.IntegerType(), False),
])
sql_statements = (
SparkSession()
.createDataFrame(data, schema)
.groupBy(F.col("age"))
.agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
.sql()
)
pyspark = PySparkSession.builder.master("local[*]").getOrCreate()
df = None
for sql in sql_statements:
df = pyspark.sql(sql)
assert df is not None
df.show()
```
# Unsupportable Operations
Any operation that lacks a way to represent it in SQL cannot be supported by this tool. An example of this would be rdd operations. Since the DataFrame API though is mostly modeled around SQL concepts most operations can be supported.

3
sqlglot/dataframe/__init__.py
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@ -1,3 +0,0 @@
"""
.. include:: ./README.md
"""

18
sqlglot/dataframe/sql/__init__.py
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@ -1,18 +0,0 @@
from sqlglot.dataframe.sql.column import Column
from sqlglot.dataframe.sql.dataframe import DataFrame, DataFrameNaFunctions
from sqlglot.dataframe.sql.group import GroupedData
from sqlglot.dataframe.sql.readwriter import DataFrameReader, DataFrameWriter
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dataframe.sql.window import Window, WindowSpec
__all__ = [
"SparkSession",
"DataFrame",
"GroupedData",
"Column",
"DataFrameNaFunctions",
"Window",
"WindowSpec",
"DataFrameReader",
"DataFrameWriter",
]

18
sqlglot/dataframe/sql/_typing.py
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@ -1,18 +0,0 @@
from __future__ import annotations
import datetime
import typing as t
from sqlglot import expressions as exp
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql.column import Column
from sqlglot.dataframe.sql.types import StructType
ColumnLiterals = t.Union[str, float, int, bool, t.List, t.Tuple, datetime.date, datetime.datetime]
ColumnOrName = t.Union[Column, str]
ColumnOrLiteral = t.Union[
Column, str, float, int, bool, t.List, t.Tuple, datetime.date, datetime.datetime
]
SchemaInput = t.Union[str, t.List[str], StructType, t.Dict[str, t.Optional[str]]]
OutputExpressionContainer = t.Union[exp.Select, exp.Create, exp.Insert]

342
sqlglot/dataframe/sql/column.py
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@ -1,342 +0,0 @@
from __future__ import annotations
import typing as t
import sqlglot
from sqlglot import expressions as exp
from sqlglot.dataframe.sql.types import DataType
from sqlglot.helper import flatten, is_iterable
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql._typing import ColumnOrLiteral
from sqlglot.dataframe.sql.window import WindowSpec
class Column:
def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
from sqlglot.dataframe.sql.session import SparkSession
if isinstance(expression, Column):
expression = expression.expression # type: ignore
elif expression is None or not isinstance(expression, (str, exp.Expression)):
expression = self._lit(expression).expression # type: ignore
elif not isinstance(expression, exp.Column):
expression = sqlglot.maybe_parse(expression, dialect=SparkSession().dialect).transform(
SparkSession().dialect.normalize_identifier, copy=False
)
if expression is None:
raise ValueError(f"Could not parse {expression}")
self.expression: exp.Expression = expression # type: ignore
def __repr__(self):
return repr(self.expression)
def __hash__(self):
return hash(self.expression)
def __eq__(self, other: ColumnOrLiteral) -> Column: # type: ignore
return self.binary_op(exp.EQ, other)
def __ne__(self, other: ColumnOrLiteral) -> Column: # type: ignore
return self.binary_op(exp.NEQ, other)
def __gt__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.GT, other)
def __ge__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.GTE, other)
def __lt__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.LT, other)
def __le__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.LTE, other)
def __and__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.And, other)
def __or__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Or, other)
def __mod__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Mod, other)
def __add__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Add, other)
def __sub__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Sub, other)
def __mul__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Mul, other)
def __truediv__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Div, other)
def __div__(self, other: ColumnOrLiteral) -> Column:
return self.binary_op(exp.Div, other)
def __neg__(self) -> Column:
return self.unary_op(exp.Neg)
def __radd__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Add, other)
def __rsub__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Sub, other)
def __rmul__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Mul, other)
def __rdiv__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Div, other)
def __rtruediv__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Div, other)
def __rmod__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Mod, other)
def __pow__(self, power: ColumnOrLiteral, modulo=None):
return Column(exp.Pow(this=self.expression, expression=Column(power).expression))
def __rpow__(self, power: ColumnOrLiteral):
return Column(exp.Pow(this=Column(power).expression, expression=self.expression))
def __invert__(self):
return self.unary_op(exp.Not)
def __rand__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.And, other)
def __ror__(self, other: ColumnOrLiteral) -> Column:
return self.inverse_binary_op(exp.Or, other)
@classmethod
def ensure_col(cls, value: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]) -> Column:
return cls(value)
@classmethod
def ensure_cols(cls, args: t.List[t.Union[ColumnOrLiteral, exp.Expression]]) -> t.List[Column]:
return [cls.ensure_col(x) if not isinstance(x, Column) else x for x in args]
@classmethod
def _lit(cls, value: ColumnOrLiteral) -> Column:
if isinstance(value, dict):
columns = [cls._lit(v).alias(k).expression for k, v in value.items()]
return cls(exp.Struct(expressions=columns))
return cls(exp.convert(value))
@classmethod
def invoke_anonymous_function(
cls, column: t.Optional[ColumnOrLiteral], func_name: str, *args: t.Optional[ColumnOrLiteral]
) -> Column:
columns = [] if column is None else [cls.ensure_col(column)]
column_args = [cls.ensure_col(arg) for arg in args]
expressions = [x.expression for x in columns + column_args]
new_expression = exp.Anonymous(this=func_name.upper(), expressions=expressions)
return Column(new_expression)
@classmethod
def invoke_expression_over_column(
cls, column: t.Optional[ColumnOrLiteral], callable_expression: t.Callable, **kwargs
) -> Column:
ensured_column = None if column is None else cls.ensure_col(column)
ensure_expression_values = {
k: (
[Column.ensure_col(x).expression for x in v]
if is_iterable(v)
else Column.ensure_col(v).expression
)
for k, v in kwargs.items()
if v is not None
}
new_expression = (
callable_expression(**ensure_expression_values)
if ensured_column is None
else callable_expression(
this=ensured_column.column_expression, **ensure_expression_values
)
)
return Column(new_expression)
def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
return Column(
klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
)
def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
return Column(
klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
)
def unary_op(self, klass: t.Callable, **kwargs) -> Column:
return Column(klass(this=self.column_expression, **kwargs))
@property
def is_alias(self):
return isinstance(self.expression, exp.Alias)
@property
def is_column(self):
return isinstance(self.expression, exp.Column)
@property
def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
return self.expression.unalias()
@property
def alias_or_name(self) -> str:
return self.expression.alias_or_name
@classmethod
def ensure_literal(cls, value) -> Column:
from sqlglot.dataframe.sql.functions import lit
if isinstance(value, cls):
value = value.expression
if not isinstance(value, exp.Literal):
return lit(value)
return Column(value)
def copy(self) -> Column:
return Column(self.expression.copy())
def set_table_name(self, table_name: str, copy=False) -> Column:
expression = self.expression.copy() if copy else self.expression
expression.set("table", exp.to_identifier(table_name))
return Column(expression)
def sql(self, **kwargs) -> str:
from sqlglot.dataframe.sql.session import SparkSession
return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
def alias(self, name: str) -> Column:
from sqlglot.dataframe.sql.session import SparkSession
dialect = SparkSession().dialect
alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
new_expression = exp.alias_(
self.column_expression,
alias.this if isinstance(alias, exp.Column) else name,
dialect=dialect,
)
return Column(new_expression)
def asc(self) -> Column:
new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
return Column(new_expression)
def desc(self) -> Column:
new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
return Column(new_expression)
asc_nulls_first = asc
def asc_nulls_last(self) -> Column:
new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
return Column(new_expression)
def desc_nulls_first(self) -> Column:
new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
return Column(new_expression)
desc_nulls_last = desc
def when(self, condition: Column, value: t.Any) -> Column:
from sqlglot.dataframe.sql.functions import when
column_with_if = when(condition, value)
if not isinstance(self.expression, exp.Case):
return column_with_if
new_column = self.copy()
new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
return new_column
def otherwise(self, value: t.Any) -> Column:
from sqlglot.dataframe.sql.functions import lit
true_value = value if isinstance(value, Column) else lit(value)
new_column = self.copy()
new_column.expression.set("default", true_value.column_expression)
return new_column
def isNull(self) -> Column:
new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
return Column(new_expression)
def isNotNull(self) -> Column:
new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
return Column(new_expression)
def cast(self, dataType: t.Union[str, DataType]) -> Column:
"""
Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
Sqlglot doesn't currently replicate this class so it only accepts a string
"""
from sqlglot.dataframe.sql.session import SparkSession
if isinstance(dataType, DataType):
dataType = dataType.simpleString()
return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))
def startswith(self, value: t.Union[str, Column]) -> Column:
value = self._lit(value) if not isinstance(value, Column) else value
return self.invoke_anonymous_function(self, "STARTSWITH", value)
def endswith(self, value: t.Union[str, Column]) -> Column:
value = self._lit(value) if not isinstance(value, Column) else value
return self.invoke_anonymous_function(self, "ENDSWITH", value)
def rlike(self, regexp: str) -> Column:
return self.invoke_expression_over_column(
column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
)
def like(self, other: str):
return self.invoke_expression_over_column(
self, exp.Like, expression=self._lit(other).expression
)
def ilike(self, other: str):
return self.invoke_expression_over_column(
self, exp.ILike, expression=self._lit(other).expression
)
def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
length = self._lit(length) if not isinstance(length, Column) else length
return Column.invoke_expression_over_column(
self, exp.Substring, start=startPos.expression, length=length.expression
)
def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols # type: ignore
expressions = [self._lit(x).expression for x in columns]
return Column.invoke_expression_over_column(self, exp.In, expressions=expressions) # type: ignore
def between(
self,
lowerBound: t.Union[ColumnOrLiteral],
upperBound: t.Union[ColumnOrLiteral],
) -> Column:
lower_bound_exp = (
self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
)
upper_bound_exp = (
self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
)
return Column(
exp.Between(
this=self.column_expression,
low=lower_bound_exp.expression,
high=upper_bound_exp.expression,
)
)
def over(self, window: WindowSpec) -> Column:
window_expression = window.expression.copy()
window_expression.set("this", self.column_expression)
return Column(window_expression)

862
sqlglot/dataframe/sql/dataframe.py
View file

@ -1,862 +0,0 @@
from __future__ import annotations
import functools
import logging
import typing as t
import zlib
from copy import copy
import sqlglot
from sqlglot import Dialect, expressions as exp
from sqlglot.dataframe.sql import functions as F
from sqlglot.dataframe.sql.column import Column
from sqlglot.dataframe.sql.group import GroupedData
from sqlglot.dataframe.sql.normalize import normalize
from sqlglot.dataframe.sql.operations import Operation, operation
from sqlglot.dataframe.sql.readwriter import DataFrameWriter
from sqlglot.dataframe.sql.transforms import replace_id_value
from sqlglot.dataframe.sql.util import get_tables_from_expression_with_join
from sqlglot.dataframe.sql.window import Window
from sqlglot.helper import ensure_list, object_to_dict, seq_get
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql._typing import (
ColumnLiterals,
ColumnOrLiteral,
ColumnOrName,
OutputExpressionContainer,
)
from sqlglot.dataframe.sql.session import SparkSession
from sqlglot.dialects.dialect import DialectType
logger = logging.getLogger("sqlglot")
JOIN_HINTS = {
"BROADCAST",
"BROADCASTJOIN",
"MAPJOIN",
"MERGE",
"SHUFFLEMERGE",
"MERGEJOIN",
"SHUFFLE_HASH",
"SHUFFLE_REPLICATE_NL",
}
class DataFrame:
def __init__(
self,
spark: SparkSession,
expression: exp.Select,
branch_id: t.Optional[str] = None,
sequence_id: t.Optional[str] = None,
last_op: Operation = Operation.INIT,
pending_hints: t.Optional[t.List[exp.Expression]] = None,
output_expression_container: t.Optional[OutputExpressionContainer] = None,
**kwargs,
):
self.spark = spark
self.expression = expression
self.branch_id = branch_id or self.spark._random_branch_id
self.sequence_id = sequence_id or self.spark._random_sequence_id
self.last_op = last_op
self.pending_hints = pending_hints or []
self.output_expression_container = output_expression_container or exp.Select()
def __getattr__(self, column_name: str) -> Column:
return self[column_name]
def __getitem__(self, column_name: str) -> Column:
column_name = f"{self.branch_id}.{column_name}"
return Column(column_name)
def __copy__(self):
return self.copy()
@property
def sparkSession(self):
return self.spark
@property
def write(self):
return DataFrameWriter(self)
@property
def latest_cte_name(self) -> str:
if not self.expression.ctes:
from_exp = self.expression.args["from"]
if from_exp.alias_or_name:
return from_exp.alias_or_name
table_alias = from_exp.find(exp.TableAlias)
if not table_alias:
raise RuntimeError(
f"Could not find an alias name for this expression: {self.expression}"
)
return table_alias.alias_or_name
return self.expression.ctes[-1].alias
@property
def pending_join_hints(self):
return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]
@property
def pending_partition_hints(self):
return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]
@property
def columns(self) -> t.List[str]:
return self.expression.named_selects
@property
def na(self) -> DataFrameNaFunctions:
return DataFrameNaFunctions(self)
def _replace_cte_names_with_hashes(self, expression: exp.Select):
replacement_mapping = {}
for cte in expression.ctes:
old_name_id = cte.args["alias"].this
new_hashed_id = exp.to_identifier(
self._create_hash_from_expression(cte.this), quoted=old_name_id.args["quoted"]
)
replacement_mapping[old_name_id] = new_hashed_id
expression = expression.transform(replace_id_value, replacement_mapping).assert_is(
exp.Select
)
return expression
def _create_cte_from_expression(
self,
expression: exp.Expression,
branch_id: t.Optional[str] = None,
sequence_id: t.Optional[str] = None,
**kwargs,
) -> t.Tuple[exp.CTE, str]:
name = self._create_hash_from_expression(expression)
expression_to_cte = expression.copy()
expression_to_cte.set("with", None)
cte = exp.Select().with_(name, as_=expression_to_cte, **kwargs).ctes[0]
cte.set("branch_id", branch_id or self.branch_id)
cte.set("sequence_id", sequence_id or self.sequence_id)
return cte, name
@t.overload
def _ensure_list_of_columns(self, cols: t.Collection[ColumnOrLiteral]) -> t.List[Column]: ...
@t.overload
def _ensure_list_of_columns(self, cols: ColumnOrLiteral) -> t.List[Column]: ...
def _ensure_list_of_columns(self, cols):
return Column.ensure_cols(ensure_list(cols))
def _ensure_and_normalize_cols(self, cols, expression: t.Optional[exp.Select] = None):
cols = self._ensure_list_of_columns(cols)
normalize(self.spark, expression or self.expression, cols)
return cols
def _ensure_and_normalize_col(self, col):
col = Column.ensure_col(col)
normalize(self.spark, self.expression, col)
return col
def _convert_leaf_to_cte(self, sequence_id: t.Optional[str] = None) -> DataFrame:
df = self._resolve_pending_hints()
sequence_id = sequence_id or df.sequence_id
expression = df.expression.copy()
cte_expression, cte_name = df._create_cte_from_expression(
expression=expression, sequence_id=sequence_id
)
new_expression = df._add_ctes_to_expression(
exp.Select(), expression.ctes + [cte_expression]
)
sel_columns = df._get_outer_select_columns(cte_expression)
new_expression = new_expression.from_(cte_name).select(
*[x.alias_or_name for x in sel_columns]
)
return df.copy(expression=new_expression, sequence_id=sequence_id)
def _resolve_pending_hints(self) -> DataFrame:
df = self.copy()
if not self.pending_hints:
return df
expression = df.expression
hint_expression = expression.args.get("hint") or exp.Hint(expressions=[])
for hint in df.pending_partition_hints:
hint_expression.append("expressions", hint)
df.pending_hints.remove(hint)
join_aliases = {
join_table.alias_or_name
for join_table in get_tables_from_expression_with_join(expression)
}
if join_aliases:
for hint in df.pending_join_hints:
for sequence_id_expression in hint.expressions:
sequence_id_or_name = sequence_id_expression.alias_or_name
sequence_ids_to_match = [sequence_id_or_name]
if sequence_id_or_name in df.spark.name_to_sequence_id_mapping:
sequence_ids_to_match = df.spark.name_to_sequence_id_mapping[
sequence_id_or_name
]
matching_ctes = [
cte
for cte in reversed(expression.ctes)
if cte.args["sequence_id"] in sequence_ids_to_match
]
for matching_cte in matching_ctes:
if matching_cte.alias_or_name in join_aliases:
sequence_id_expression.set("this", matching_cte.args["alias"].this)
df.pending_hints.remove(hint)
break
hint_expression.append("expressions", hint)
if hint_expression.expressions:
expression.set("hint", hint_expression)
return df
def _hint(self, hint_name: str, args: t.List[Column]) -> DataFrame:
hint_name = hint_name.upper()
hint_expression = (
exp.JoinHint(
this=hint_name,
expressions=[exp.to_table(parameter.alias_or_name) for parameter in args],
)
if hint_name in JOIN_HINTS
else exp.Anonymous(
this=hint_name, expressions=[parameter.expression for parameter in args]
)
)
new_df = self.copy()
new_df.pending_hints.append(hint_expression)
return new_df
def _set_operation(self, klass: t.Callable, other: DataFrame, distinct: bool):
other_df = other._convert_leaf_to_cte()
base_expression = self.expression.copy()
base_expression = self._add_ctes_to_expression(base_expression, other_df.expression.ctes)
all_ctes = base_expression.ctes
other_df.expression.set("with", None)
base_expression.set("with", None)
operation = klass(this=base_expression, distinct=distinct, expression=other_df.expression)
operation.set("with", exp.With(expressions=all_ctes))
return self.copy(expression=operation)._convert_leaf_to_cte()
def _cache(self, storage_level: str):
df = self._convert_leaf_to_cte()
df.expression.ctes[-1].set("cache_storage_level", storage_level)
return df
@classmethod
def _add_ctes_to_expression(cls, expression: exp.Select, ctes: t.List[exp.CTE]) -> exp.Select:
expression = expression.copy()
with_expression = expression.args.get("with")
if with_expression:
existing_ctes = with_expression.expressions
existsing_cte_names = {x.alias_or_name for x in existing_ctes}
for cte in ctes:
if cte.alias_or_name not in existsing_cte_names:
existing_ctes.append(cte)
else:
existing_ctes = ctes
expression.set("with", exp.With(expressions=existing_ctes))
return expression
@classmethod
def _get_outer_select_columns(cls, item: t.Union[exp.Expression, DataFrame]) -> t.List[Column]:
expression = item.expression if isinstance(item, DataFrame) else item
return [Column(x) for x in (expression.find(exp.Select) or exp.Select()).expressions]
@classmethod
def _create_hash_from_expression(cls, expression: exp.Expression) -> str:
from sqlglot.dataframe.sql.session import SparkSession
value = expression.sql(dialect=SparkSession().dialect).encode("utf-8")
return f"t{zlib.crc32(value)}"[:6]
def _get_select_expressions(
self,
) -> t.List[t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]]:
select_expressions: t.List[
t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]
] = []
main_select_ctes: t.List[exp.CTE] = []
for cte in self.expression.ctes:
cache_storage_level = cte.args.get("cache_storage_level")
if cache_storage_level:
select_expression = cte.this.copy()
select_expression.set("with", exp.With(expressions=copy(main_select_ctes)))
select_expression.set("cte_alias_name", cte.alias_or_name)
select_expression.set("cache_storage_level", cache_storage_level)
select_expressions.append((exp.Cache, select_expression))
else:
main_select_ctes.append(cte)
main_select = self.expression.copy()
if main_select_ctes:
main_select.set("with", exp.With(expressions=main_select_ctes))
expression_select_pair = (type(self.output_expression_container), main_select)
select_expressions.append(expression_select_pair) # type: ignore
return select_expressions
def sql(self, dialect: DialectType = None, optimize: bool = True, **kwargs) -> t.List[str]:
from sqlglot.dataframe.sql.session import SparkSession
dialect = Dialect.get_or_raise(dialect or SparkSession().dialect)
df = self._resolve_pending_hints()
select_expressions = df._get_select_expressions()
output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
for expression_type, select_expression in select_expressions:
select_expression = select_expression.transform(
replace_id_value, replacement_mapping
).assert_is(exp.Select)
if optimize:
select_expression = t.cast(
exp.Select, self.spark._optimize(select_expression, dialect=dialect)
)
select_expression = df._replace_cte_names_with_hashes(select_expression)
expression: t.Union[exp.Select, exp.Cache, exp.Drop]
if expression_type == exp.Cache:
cache_table_name = df._create_hash_from_expression(select_expression)
cache_table = exp.to_table(cache_table_name)
original_alias_name = select_expression.args["cte_alias_name"]
replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier( # type: ignore
cache_table_name
)
sqlglot.schema.add_table(
cache_table_name,
{
expression.alias_or_name: expression.type.sql(dialect=dialect)
for expression in select_expression.expressions
},
dialect=dialect,
)
cache_storage_level = select_expression.args["cache_storage_level"]
options = [
exp.Literal.string("storageLevel"),
exp.Literal.string(cache_storage_level),
]
expression = exp.Cache(
this=cache_table, expression=select_expression, lazy=True, options=options
)
# We will drop the "view" if it exists before running the cache table
output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
elif expression_type == exp.Create:
expression = df.output_expression_container.copy()
expression.set("expression", select_expression)
elif expression_type == exp.Insert:
expression = df.output_expression_container.copy()
select_without_ctes = select_expression.copy()
select_without_ctes.set("with", None)
expression.set("expression", select_without_ctes)
if select_expression.ctes:
expression.set("with", exp.With(expressions=select_expression.ctes))
elif expression_type == exp.Select:
expression = select_expression
else:
raise ValueError(f"Invalid expression type: {expression_type}")
output_expressions.append(expression)
return [expression.sql(dialect=dialect, **kwargs) for expression in output_expressions]
def copy(self, **kwargs) -> DataFrame:
return DataFrame(**object_to_dict(self, **kwargs))
@operation(Operation.SELECT)
def select(self, *cols, **kwargs) -> DataFrame:
cols = self._ensure_and_normalize_cols(cols)
kwargs["append"] = kwargs.get("append", False)
if self.expression.args.get("joins"):
ambiguous_cols = [
col
for col in cols
if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
]
if ambiguous_cols:
join_table_identifiers = [
x.this for x in get_tables_from_expression_with_join(self.expression)
]
cte_names_in_join = [x.this for x in join_table_identifiers]
# If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
# and therefore we allow multiple columns with the same name in the result. This matches the behavior
# of Spark.
resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
for ambiguous_col in ambiguous_cols:
ctes_with_column = [
cte
for cte in self.expression.ctes
if cte.alias_or_name in cte_names_in_join
and ambiguous_col.alias_or_name in cte.this.named_selects
]
# Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
# use the same CTE we used before
cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
if cte:
resolved_column_position[ambiguous_col] += 1
else:
cte = ctes_with_column[resolved_column_position[ambiguous_col]]
ambiguous_col.expression.set("table", cte.alias_or_name)
return self.copy(
expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
)
@operation(Operation.NO_OP)
def alias(self, name: str, **kwargs) -> DataFrame:
new_sequence_id = self.spark._random_sequence_id
df = self.copy()
for join_hint in df.pending_join_hints:
for expression in join_hint.expressions:
if expression.alias_or_name == self.sequence_id:
expression.set("this", Column.ensure_col(new_sequence_id).expression)
df.spark._add_alias_to_mapping(name, new_sequence_id)
return df._convert_leaf_to_cte(sequence_id=new_sequence_id)
@operation(Operation.WHERE)
def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
col = self._ensure_and_normalize_col(column)
return self.copy(expression=self.expression.where(col.expression))
filter = where
@operation(Operation.GROUP_BY)
def groupBy(self, *cols, **kwargs) -> GroupedData:
columns = self._ensure_and_normalize_cols(cols)
return GroupedData(self, columns, self.last_op)
@operation(Operation.SELECT)
def agg(self, *exprs, **kwargs) -> DataFrame:
cols = self._ensure_and_normalize_cols(exprs)
return self.groupBy().agg(*cols)
@operation(Operation.FROM)
def join(
self,
other_df: DataFrame,
on: t.Union[str, t.List[str], Column, t.List[Column]],
how: str = "inner",
**kwargs,
) -> DataFrame:
other_df = other_df._convert_leaf_to_cte()
join_columns = self._ensure_list_of_columns(on)
# We will determine actual "join on" expression later so we don't provide it at first
join_expression = self.expression.join(
other_df.latest_cte_name, join_type=how.replace("_", " ")
)
join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
self_columns = self._get_outer_select_columns(join_expression)
other_columns = self._get_outer_select_columns(other_df)
# Determines the join clause and select columns to be used passed on what type of columns were provided for
# the join. The columns returned changes based on how the on expression is provided.
if isinstance(join_columns[0].expression, exp.Column):
"""
Unique characteristics of join on column names only:
* The column names are put at the front of the select list
* The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
"""
table_names = [
table.alias_or_name
for table in get_tables_from_expression_with_join(join_expression)
]
potential_ctes = [
cte
for cte in join_expression.ctes
if cte.alias_or_name in table_names
and cte.alias_or_name != other_df.latest_cte_name
]
# Determine the table to reference for the left side of the join by checking each of the left side
# tables and see if they have the column being referenced.
join_column_pairs = []
for join_column in join_columns:
num_matching_ctes = 0
for cte in potential_ctes:
if join_column.alias_or_name in cte.this.named_selects:
left_column = join_column.copy().set_table_name(cte.alias_or_name)
right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
join_column_pairs.append((left_column, right_column))
num_matching_ctes += 1
if num_matching_ctes > 1:
raise ValueError(
f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
)
elif num_matching_ctes == 0:
raise ValueError(
f"Column {join_column.alias_or_name} does not exist in any of the tables."
)
join_clause = functools.reduce(
lambda x, y: x & y,
[left_column == right_column for left_column, right_column in join_column_pairs],
)
join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
# To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
select_column_names = [
(
column.alias_or_name
if not isinstance(column.expression.this, exp.Star)
else column.sql()
)
for column in self_columns + other_columns
]
select_column_names = [
column_name
for column_name in select_column_names
if column_name not in join_column_names
]
select_column_names = join_column_names + select_column_names
else:
"""
Unique characteristics of join on expressions:
* There is no deduplication of the results.
* The left join dataframe columns go first and right come after. No sort preference is given to join columns
"""
join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
if len(join_columns) > 1:
join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
join_clause = join_columns[0]
select_column_names = [column.alias_or_name for column in self_columns + other_columns]
# Update the on expression with the actual join clause to replace the dummy one from before
join_expression.args["joins"][-1].set("on", join_clause.expression)
new_df = self.copy(expression=join_expression)
new_df.pending_join_hints.extend(self.pending_join_hints)
new_df.pending_hints.extend(other_df.pending_hints)
new_df = new_df.select.__wrapped__(new_df, *select_column_names)
return new_df
@operation(Operation.ORDER_BY)
def orderBy(
self,
*cols: t.Union[str, Column],
ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
) -> DataFrame:
"""
This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
is unlikely to come up.
"""
columns = self._ensure_and_normalize_cols(cols)
pre_ordered_col_indexes = [
i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
]
if ascending is None:
ascending = [True] * len(columns)
elif not isinstance(ascending, list):
ascending = [ascending] * len(columns)
ascending = [bool(x) for i, x in enumerate(ascending)]
assert len(columns) == len(
ascending
), "The length of items in ascending must equal the number of columns provided"
col_and_ascending = list(zip(columns, ascending))
order_by_columns = [
(
exp.Ordered(this=col.expression, desc=not asc)
if i not in pre_ordered_col_indexes
else columns[i].column_expression
)
for i, (col, asc) in enumerate(col_and_ascending)
]
return self.copy(expression=self.expression.order_by(*order_by_columns))
sort = orderBy
@operation(Operation.FROM)
def union(self, other: DataFrame) -> DataFrame:
return self._set_operation(exp.Union, other, False)
unionAll = union
@operation(Operation.FROM)
def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
l_columns = self.columns
r_columns = other.columns
if not allowMissingColumns:
l_expressions = l_columns
r_expressions = l_columns
else:
l_expressions = []
r_expressions = []
r_columns_unused = copy(r_columns)
for l_column in l_columns:
l_expressions.append(l_column)
if l_column in r_columns:
r_expressions.append(l_column)
r_columns_unused.remove(l_column)
else:
r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
for r_column in r_columns_unused:
l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
r_expressions.append(r_column)
r_df = (
other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
)
l_df = self.copy()
if allowMissingColumns:
l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
return l_df._set_operation(exp.Union, r_df, False)
@operation(Operation.FROM)
def intersect(self, other: DataFrame) -> DataFrame:
return self._set_operation(exp.Intersect, other, True)
@operation(Operation.FROM)
def intersectAll(self, other: DataFrame) -> DataFrame:
return self._set_operation(exp.Intersect, other, False)
@operation(Operation.FROM)
def exceptAll(self, other: DataFrame) -> DataFrame:
return self._set_operation(exp.Except, other, False)
@operation(Operation.SELECT)
def distinct(self) -> DataFrame:
return self.copy(expression=self.expression.distinct())
@operation(Operation.SELECT)
def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
if not subset:
return self.distinct()
column_names = ensure_list(subset)
window = Window.partitionBy(*column_names).orderBy(*column_names)
return (
self.copy()
.withColumn("row_num", F.row_number().over(window))
.where(F.col("row_num") == F.lit(1))
.drop("row_num")
)
@operation(Operation.FROM)
def dropna(
self,
how: str = "any",
thresh: t.Optional[int] = None,
subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
) -> DataFrame:
minimum_non_null = thresh or 0 # will be determined later if thresh is null
new_df = self.copy()
all_columns = self._get_outer_select_columns(new_df.expression)
if subset:
null_check_columns = self._ensure_and_normalize_cols(subset)
else:
null_check_columns = all_columns
if thresh is None:
minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
else:
minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
if minimum_num_nulls > len(null_check_columns):
raise RuntimeError(
f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
)
if_null_checks = [
F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
]
nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
num_nulls = nulls_added_together.alias("num_nulls")
new_df = new_df.select(num_nulls, append=True)
filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
final_df = filtered_df.select(*all_columns)
return final_df
@operation(Operation.FROM)
def fillna(
self,
value: t.Union[ColumnLiterals],
subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
) -> DataFrame:
"""
Functionality Difference: If you provide a value to replace a null and that type conflicts
with the type of the column then PySpark will just ignore your replacement.
This will try to cast them to be the same in some cases. So they won't always match.
Best to not mix types so make sure replacement is the same type as the column
Possibility for improvement: Use `typeof` function to get the type of the column
and check if it matches the type of the value provided. If not then make it null.
"""
from sqlglot.dataframe.sql.functions import lit
values = None
columns = None
new_df = self.copy()
all_columns = self._get_outer_select_columns(new_df.expression)
all_column_mapping = {column.alias_or_name: column for column in all_columns}
if isinstance(value, dict):
values = list(value.values())
columns = self._ensure_and_normalize_cols(list(value))
if not columns:
columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
if not values:
values = [value] * len(columns)
value_columns = [lit(value) for value in values]
null_replacement_mapping = {
column.alias_or_name: (
F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
)
for column, value in zip(columns, value_columns)
}
null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
null_replacement_columns = [
null_replacement_mapping[column.alias_or_name] for column in all_columns
]
new_df = new_df.select(*null_replacement_columns)
return new_df
@operation(Operation.FROM)
def replace(
self,
to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
) -> DataFrame:
from sqlglot.dataframe.sql.functions import lit
old_values = None
new_df = self.copy()
all_columns = self._get_outer_select_columns(new_df.expression)
all_column_mapping = {column.alias_or_name: column for column in all_columns}
columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
if isinstance(to_replace, dict):
old_values = list(to_replace)
new_values = list(to_replace.values())
elif not old_values and isinstance(to_replace, list):
assert isinstance(value, list), "value must be a list since the replacements are a list"
assert len(to_replace) == len(
value
), "the replacements and values must be the same length"
old_values = to_replace
new_values = value
else:
old_values = [to_replace] * len(columns)
new_values = [value] * len(columns)
old_values = [lit(value) for value in old_values]
new_values = [lit(value) for value in new_values]
replacement_mapping = {}
for column in columns:
expression = Column(None)
for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
if i == 0:
expression = F.when(column == old_value, new_value)
else:
expression = expression.when(column == old_value, new_value) # type: ignore
replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
column.expression.alias_or_name
)
replacement_mapping = {**all_column_mapping, **replacement_mapping}
replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
new_df = new_df.select(*replacement_columns)
return new_df
@operation(Operation.SELECT)
def withColumn(self, colName: str, col: Column) -> DataFrame:
col = self._ensure_and_normalize_col(col)
existing_col_names = self.expression.named_selects
existing_col_index = (
existing_col_names.index(colName) if colName in existing_col_names else None
)
if existing_col_index:
expression = self.expression.copy()
expression.expressions[existing_col_index] = col.expression
return self.copy(expression=expression)
return self.copy().select(col.alias(colName), append=True)
@operation(Operation.SELECT)
def withColumnRenamed(self, existing: str, new: str):
expression = self.expression.copy()
existing_columns = [
expression
for expression in expression.expressions
if expression.alias_or_name == existing
]
if not existing_columns:
raise ValueError("Tried to rename a column that doesn't exist")
for existing_column in existing_columns:
if isinstance(existing_column, exp.Column):
existing_column.replace(exp.alias_(existing_column, new))
else:
existing_column.set("alias", exp.to_identifier(new))
return self.copy(expression=expression)
@operation(Operation.SELECT)
def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
all_columns = self._get_outer_select_columns(self.expression)
drop_cols = self._ensure_and_normalize_cols(cols)
new_columns = [
col
for col in all_columns
if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
]
return self.copy().select(*new_columns, append=False)
@operation(Operation.LIMIT)
def limit(self, num: int) -> DataFrame:
return self.copy(expression=self.expression.limit(num))
@operation(Operation.NO_OP)
def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
parameter_list = ensure_list(parameters)
parameter_columns = (
self._ensure_list_of_columns(parameter_list)
if parameters
else Column.ensure_cols([self.sequence_id])
)
return self._hint(name, parameter_columns)
@operation(Operation.NO_OP)
def repartition(
self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
) -> DataFrame:
num_partition_cols = self._ensure_list_of_columns(numPartitions)
columns = self._ensure_and_normalize_cols(cols)
args = num_partition_cols + columns
return self._hint("repartition", args)
@operation(Operation.NO_OP)
def coalesce(self, numPartitions: int) -> DataFrame:
num_partitions = Column.ensure_cols([numPartitions])
return self._hint("coalesce", num_partitions)
@operation(Operation.NO_OP)
def cache(self) -> DataFrame:
return self._cache(storage_level="MEMORY_AND_DISK")
@operation(Operation.NO_OP)
def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
"""
Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
"""
return self._cache(storageLevel)
class DataFrameNaFunctions:
def __init__(self, df: DataFrame):
self.df = df
def drop(
self,
how: str = "any",
thresh: t.Optional[int] = None,
subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
) -> DataFrame:
return self.df.dropna(how=how, thresh=thresh, subset=subset)
def fill(
self,
value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
) -> DataFrame:
return self.df.fillna(value=value, subset=subset)
def replace(
self,
to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
subset: t.Optional[t.Union[str, t.List[str]]] = None,
) -> DataFrame:
return self.df.replace(to_replace=to_replace, value=value, subset=subset)

1270
sqlglot/dataframe/sql/functions.py
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59
sqlglot/dataframe/sql/group.py
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@ -1,59 +0,0 @@
from __future__ import annotations
import typing as t
from sqlglot.dataframe.sql import functions as F
from sqlglot.dataframe.sql.column import Column
from sqlglot.dataframe.sql.operations import Operation, operation
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql.dataframe import DataFrame
class GroupedData:
def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
self._df = df.copy()
self.spark = df.spark
self.last_op = last_op
self.group_by_cols = group_by_cols
def _get_function_applied_columns(
self, func_name: str, cols: t.Tuple[str, ...]
) -> t.List[Column]:
func_name = func_name.lower()
return [getattr(F, func_name)(name).alias(f"{func_name}({name})") for name in cols]
@operation(Operation.SELECT)
def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
columns = (
[Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
if isinstance(exprs[0], dict)
else exprs
)
cols = self._df._ensure_and_normalize_cols(columns)
expression = self._df.expression.group_by(
*[x.expression for x in self.group_by_cols]
).select(*[x.expression for x in self.group_by_cols + cols], append=False)
return self._df.copy(expression=expression)
def count(self) -> DataFrame:
return self.agg(F.count("*").alias("count"))
def mean(self, *cols: str) -> DataFrame:
return self.avg(*cols)
def avg(self, *cols: str) -> DataFrame:
return self.agg(*self._get_function_applied_columns("avg", cols))
def max(self, *cols: str) -> DataFrame:
return self.agg(*self._get_function_applied_columns("max", cols))
def min(self, *cols: str) -> DataFrame:
return self.agg(*self._get_function_applied_columns("min", cols))
def sum(self, *cols: str) -> DataFrame:
return self.agg(*self._get_function_applied_columns("sum", cols))
def pivot(self, *cols: str) -> DataFrame:
raise NotImplementedError("Sum distinct is not currently implemented")

78
sqlglot/dataframe/sql/normalize.py
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@ -1,78 +0,0 @@
from __future__ import annotations
import typing as t
from sqlglot import expressions as exp
from sqlglot.dataframe.sql.column import Column
from sqlglot.dataframe.sql.util import get_tables_from_expression_with_join
from sqlglot.helper import ensure_list
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql.session import SparkSession
NORMALIZE_INPUT = t.TypeVar("NORMALIZE_INPUT", bound=t.Union[str, exp.Expression, Column])
def normalize(spark: SparkSession, expression_context: exp.Select, expr: t.List[NORMALIZE_INPUT]):
expr = ensure_list(expr)
expressions = _ensure_expressions(expr)
for expression in expressions:
identifiers = expression.find_all(exp.Identifier)
for identifier in identifiers:
identifier.transform(spark.dialect.normalize_identifier)
replace_alias_name_with_cte_name(spark, expression_context, identifier)
replace_branch_and_sequence_ids_with_cte_name(spark, expression_context, identifier)
def replace_alias_name_with_cte_name(
spark: SparkSession, expression_context: exp.Select, id: exp.Identifier
):
if id.alias_or_name in spark.name_to_sequence_id_mapping:
for cte in reversed(expression_context.ctes):
if cte.args["sequence_id"] in spark.name_to_sequence_id_mapping[id.alias_or_name]:
_set_alias_name(id, cte.alias_or_name)
break
def replace_branch_and_sequence_ids_with_cte_name(
spark: SparkSession, expression_context: exp.Select, id: exp.Identifier
):
if id.alias_or_name in spark.known_ids:
# Check if we have a join and if both the tables in that join share a common branch id
# If so we need to have this reference the left table by default unless the id is a sequence
# id then it keeps that reference. This handles the weird edge case in spark that shouldn't
# be common in practice
if expression_context.args.get("joins") and id.alias_or_name in spark.known_branch_ids:
join_table_aliases = [
x.alias_or_name for x in get_tables_from_expression_with_join(expression_context)
]
ctes_in_join = [
cte for cte in expression_context.ctes if cte.alias_or_name in join_table_aliases
]
if ctes_in_join[0].args["branch_id"] == ctes_in_join[1].args["branch_id"]:
assert len(ctes_in_join) == 2
_set_alias_name(id, ctes_in_join[0].alias_or_name)
return
for cte in reversed(expression_context.ctes):
if id.alias_or_name in (cte.args["branch_id"], cte.args["sequence_id"]):
_set_alias_name(id, cte.alias_or_name)
return
def _set_alias_name(id: exp.Identifier, name: str):
id.set("this", name)
def _ensure_expressions(values: t.List[NORMALIZE_INPUT]) -> t.List[exp.Expression]:
results = []
for value in values:
if isinstance(value, str):
results.append(Column.ensure_col(value).expression)
elif isinstance(value, Column):
results.append(value.expression)
elif isinstance(value, exp.Expression):
results.append(value)
else:
raise ValueError(f"Got an invalid type to normalize: {type(value)}")
return results

53
sqlglot/dataframe/sql/operations.py
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@ -1,53 +0,0 @@
from __future__ import annotations
import functools
import typing as t
from enum import IntEnum
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql.dataframe import DataFrame
from sqlglot.dataframe.sql.group import GroupedData
class Operation(IntEnum):
INIT = -1
NO_OP = 0
FROM = 1
WHERE = 2
GROUP_BY = 3
HAVING = 4
SELECT = 5
ORDER_BY = 6
LIMIT = 7
def operation(op: Operation):
"""
Decorator used around DataFrame methods to indicate what type of operation is being performed from the
ordered Operation enums. This is used to determine which operations should be performed on a CTE vs.
included with the previous operation.
Ex: After a user does a join we want to allow them to select which columns for the different
tables that they want to carry through to the following operation. If we put that join in
a CTE preemptively then the user would not have a chance to select which column they want
in cases where there is overlap in names.
"""
def decorator(func: t.Callable):
@functools.wraps(func)
def wrapper(self: DataFrame, *args, **kwargs):
if self.last_op == Operation.INIT:
self = self._convert_leaf_to_cte()
self.last_op = Operation.NO_OP
last_op = self.last_op
new_op = op if op != Operation.NO_OP else last_op
if new_op < last_op or (last_op == new_op == Operation.SELECT):
self = self._convert_leaf_to_cte()
df: t.Union[DataFrame, GroupedData] = func(self, *args, **kwargs)
df.last_op = new_op # type: ignore
return df
wrapper.__wrapped__ = func # type: ignore
return wrapper
return decorator

108
sqlglot/dataframe/sql/readwriter.py
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@ -1,108 +0,0 @@
from __future__ import annotations
import typing as t
import sqlglot
from sqlglot import expressions as exp
from sqlglot.helper import object_to_dict
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql.dataframe import DataFrame
from sqlglot.dataframe.sql.session import SparkSession
class DataFrameReader:
def __init__(self, spark: SparkSession):
self.spark = spark
def table(self, tableName: str) -> DataFrame:
from sqlglot.dataframe.sql.dataframe import DataFrame
from sqlglot.dataframe.sql.session import SparkSession
sqlglot.schema.add_table(tableName, dialect=SparkSession().dialect)
return DataFrame(
self.spark,
exp.Select()
.from_(
exp.to_table(tableName, dialect=SparkSession().dialect).transform(
SparkSession().dialect.normalize_identifier
)
)
.select(
*(
column
for column in sqlglot.schema.column_names(
tableName, dialect=SparkSession().dialect
)
)
),
)
class DataFrameWriter:
def __init__(
self,
df: DataFrame,
spark: t.Optional[SparkSession] = None,
mode: t.Optional[str] = None,
by_name: bool = False,
):
self._df = df
self._spark = spark or df.spark
self._mode = mode
self._by_name = by_name
def copy(self, **kwargs) -> DataFrameWriter:
return DataFrameWriter(
**{
k[1:] if k.startswith("_") else k: v
for k, v in object_to_dict(self, **kwargs).items()
}
)
def sql(self, **kwargs) -> t.List[str]:
return self._df.sql(**kwargs)
def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
return self.copy(_mode=saveMode)
@property
def byName(self):
return self.copy(by_name=True)
def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
from sqlglot.dataframe.sql.session import SparkSession
output_expression_container = exp.Insert(
**{
"this": exp.to_table(tableName),
"overwrite": overwrite,
}
)
df = self._df.copy(output_expression_container=output_expression_container)
if self._by_name:
columns = sqlglot.schema.column_names(
tableName, only_visible=True, dialect=SparkSession().dialect
)
df = df._convert_leaf_to_cte().select(*columns)
return self.copy(_df=df)
def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
if format is not None:
raise NotImplementedError("Providing Format in the save as table is not supported")
exists, replace, mode = None, None, mode or str(self._mode)
if mode == "append":
return self.insertInto(name)
if mode == "ignore":
exists = True
if mode == "overwrite":
replace = True
output_expression_container = exp.Create(
this=exp.to_table(name),
kind="TABLE",
exists=exists,
replace=replace,
)
return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))

199
sqlglot/dataframe/sql/session.py
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@ -1,199 +0,0 @@
from __future__ import annotations
import typing as t
import uuid
from collections import defaultdict
import sqlglot
from sqlglot import Dialect, expressions as exp
from sqlglot.dataframe.sql import functions as F
from sqlglot.dataframe.sql.dataframe import DataFrame
from sqlglot.dataframe.sql.readwriter import DataFrameReader
from sqlglot.dataframe.sql.types import StructType
from sqlglot.dataframe.sql.util import get_column_mapping_from_schema_input
from sqlglot.helper import classproperty
from sqlglot.optimizer import optimize
from sqlglot.optimizer.qualify_columns import quote_identifiers
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql._typing import ColumnLiterals, SchemaInput
class SparkSession:
DEFAULT_DIALECT = "spark"
_instance = None
def __init__(self):
if not hasattr(self, "known_ids"):
self.known_ids = set()
self.known_branch_ids = set()
self.known_sequence_ids = set()
self.name_to_sequence_id_mapping = defaultdict(list)
self.incrementing_id = 1
self.dialect = Dialect.get_or_raise(self.DEFAULT_DIALECT)
def __new__(cls, *args, **kwargs) -> SparkSession:
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
@property
def read(self) -> DataFrameReader:
return DataFrameReader(self)
def table(self, tableName: str) -> DataFrame:
return self.read.table(tableName)
def createDataFrame(
self,
data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
schema: t.Optional[SchemaInput] = None,
samplingRatio: t.Optional[float] = None,
verifySchema: bool = False,
) -> DataFrame:
from sqlglot.dataframe.sql.dataframe import DataFrame
if samplingRatio is not None or verifySchema:
raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
if schema is not None and (
not isinstance(schema, (StructType, str, list))
or (isinstance(schema, list) and not isinstance(schema[0], str))
):
raise NotImplementedError("Only schema of either list or string of list supported")
if not data:
raise ValueError("Must provide data to create into a DataFrame")
column_mapping: t.Dict[str, t.Optional[str]]
if schema is not None:
column_mapping = get_column_mapping_from_schema_input(schema)
elif isinstance(data[0], dict):
column_mapping = {col_name.strip(): None for col_name in data[0]}
else:
column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
data_expressions = [
exp.tuple_(
*map(
lambda x: F.lit(x).expression,
row if not isinstance(row, dict) else row.values(),
)
)
for row in data
]
sel_columns = [
(
F.col(name).cast(data_type).alias(name).expression
if data_type is not None
else F.col(name).expression
)
for name, data_type in column_mapping.items()
]
select_kwargs = {
"expressions": sel_columns,
"from": exp.From(
this=exp.Values(
expressions=data_expressions,
alias=exp.TableAlias(
this=exp.to_identifier(self._auto_incrementing_name),
columns=[exp.to_identifier(col_name) for col_name in column_mapping],
),
),
),
}
sel_expression = exp.Select(**select_kwargs)
return DataFrame(self, sel_expression)
def _optimize(
self, expression: exp.Expression, dialect: t.Optional[Dialect] = None
) -> exp.Expression:
dialect = dialect or self.dialect
quote_identifiers(expression, dialect=dialect)
return optimize(expression, dialect=dialect)
def sql(self, sqlQuery: str) -> DataFrame:
expression = self._optimize(sqlglot.parse_one(sqlQuery, read=self.dialect))
if isinstance(expression, exp.Select):
df = DataFrame(self, expression)
df = df._convert_leaf_to_cte()
elif isinstance(expression, (exp.Create, exp.Insert)):
select_expression = expression.expression.copy()
if isinstance(expression, exp.Insert):
select_expression.set("with", expression.args.get("with"))
expression.set("with", None)
del expression.args["expression"]
df = DataFrame(self, select_expression, output_expression_container=expression) # type: ignore
df = df._convert_leaf_to_cte()
else:
raise ValueError(
"Unknown expression type provided in the SQL. Please create an issue with the SQL."
)
return df
@property
def _auto_incrementing_name(self) -> str:
name = f"a{self.incrementing_id}"
self.incrementing_id += 1
return name
@property
def _random_branch_id(self) -> str:
id = self._random_id
self.known_branch_ids.add(id)
return id
@property
def _random_sequence_id(self):
id = self._random_id
self.known_sequence_ids.add(id)
return id
@property
def _random_id(self) -> str:
id = "r" + uuid.uuid4().hex
self.known_ids.add(id)
return id
@property
def _join_hint_names(self) -> t.Set[str]:
return {"BROADCAST", "MERGE", "SHUFFLE_HASH", "SHUFFLE_REPLICATE_NL"}
def _add_alias_to_mapping(self, name: str, sequence_id: str):
self.name_to_sequence_id_mapping[name].append(sequence_id)
class Builder:
SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
def __init__(self):
self.dialect = "spark"
def __getattr__(self, item) -> SparkSession.Builder:
return self
def __call__(self, *args, **kwargs):
return self
def config(
self,
key: t.Optional[str] = None,
value: t.Optional[t.Any] = None,
*,
map: t.Optional[t.Dict[str, t.Any]] = None,
**kwargs: t.Any,
) -> SparkSession.Builder:
if key == self.SQLFRAME_DIALECT_KEY:
self.dialect = value
elif map and self.SQLFRAME_DIALECT_KEY in map:
self.dialect = map[self.SQLFRAME_DIALECT_KEY]
return self
def getOrCreate(self) -> SparkSession:
spark = SparkSession()
spark.dialect = Dialect.get_or_raise(self.dialect)
return spark
@classproperty
def builder(cls) -> Builder:
return cls.Builder()

9
sqlglot/dataframe/sql/transforms.py
View file

@ -1,9 +0,0 @@
import typing as t
from sqlglot import expressions as exp
def replace_id_value(node, replacement_mapping: t.Dict[exp.Identifier, exp.Identifier]):
if isinstance(node, exp.Identifier) and node in replacement_mapping:
node = node.replace(replacement_mapping[node].copy())
return node

212
sqlglot/dataframe/sql/types.py
View file

@ -1,212 +0,0 @@
import typing as t
class DataType:
def __repr__(self) -> str:
return self.__class__.__name__ + "()"
def __hash__(self) -> int:
return hash(str(self))
def __eq__(self, other: t.Any) -> bool:
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other: t.Any) -> bool:
return not self.__eq__(other)
def __str__(self) -> str:
return self.typeName()
@classmethod
def typeName(cls) -> str:
return cls.__name__[:-4].lower()
def simpleString(self) -> str:
return str(self)
def jsonValue(self) -> t.Union[str, t.Dict[str, t.Any]]:
return str(self)
class DataTypeWithLength(DataType):
def __init__(self, length: int):
self.length = length
def __repr__(self) -> str:
return f"{self.__class__.__name__}({self.length})"
def __str__(self) -> str:
return f"{self.typeName()}({self.length})"
class StringType(DataType):
pass
class CharType(DataTypeWithLength):
pass
class VarcharType(DataTypeWithLength):
pass
class BinaryType(DataType):
pass
class BooleanType(DataType):
pass
class DateType(DataType):
pass
class TimestampType(DataType):
pass
class TimestampNTZType(DataType):
@classmethod
def typeName(cls) -> str:
return "timestamp_ntz"
class DecimalType(DataType):
def __init__(self, precision: int = 10, scale: int = 0):
self.precision = precision
self.scale = scale
def simpleString(self) -> str:
return f"decimal({self.precision}, {self.scale})"
def jsonValue(self) -> str:
return f"decimal({self.precision}, {self.scale})"
def __repr__(self) -> str:
return f"DecimalType({self.precision}, {self.scale})"
class DoubleType(DataType):
pass
class FloatType(DataType):
pass
class ByteType(DataType):
def __str__(self) -> str:
return "tinyint"
class IntegerType(DataType):
def __str__(self) -> str:
return "int"
class LongType(DataType):
def __str__(self) -> str:
return "bigint"
class ShortType(DataType):
def __str__(self) -> str:
return "smallint"
class ArrayType(DataType):
def __init__(self, elementType: DataType, containsNull: bool = True):
self.elementType = elementType
self.containsNull = containsNull
def __repr__(self) -> str:
return f"ArrayType({self.elementType, str(self.containsNull)}"
def simpleString(self) -> str:
return f"array<{self.elementType.simpleString()}>"
def jsonValue(self) -> t.Dict[str, t.Any]:
return {
"type": self.typeName(),
"elementType": self.elementType.jsonValue(),
"containsNull": self.containsNull,
}
class MapType(DataType):
def __init__(self, keyType: DataType, valueType: DataType, valueContainsNull: bool = True):
self.keyType = keyType
self.valueType = valueType
self.valueContainsNull = valueContainsNull
def __repr__(self) -> str:
return f"MapType({self.keyType}, {self.valueType}, {str(self.valueContainsNull)})"
def simpleString(self) -> str:
return f"map<{self.keyType.simpleString()}, {self.valueType.simpleString()}>"
def jsonValue(self) -> t.Dict[str, t.Any]:
return {
"type": self.typeName(),
"keyType": self.keyType.jsonValue(),
"valueType": self.valueType.jsonValue(),
"valueContainsNull": self.valueContainsNull,
}
class StructField(DataType):
def __init__(
self,
name: str,
dataType: DataType,
nullable: bool = True,
metadata: t.Optional[t.Dict[str, t.Any]] = None,
):
self.name = name
self.dataType = dataType
self.nullable = nullable
self.metadata = metadata or {}
def __repr__(self) -> str:
return f"StructField('{self.name}', {self.dataType}, {str(self.nullable)})"
def simpleString(self) -> str:
return f"{self.name}:{self.dataType.simpleString()}"
def jsonValue(self) -> t.Dict[str, t.Any]:
return {
"name": self.name,
"type": self.dataType.jsonValue(),
"nullable": self.nullable,
"metadata": self.metadata,
}
class StructType(DataType):
def __init__(self, fields: t.Optional[t.List[StructField]] = None):
if not fields:
self.fields = []
self.names = []
else:
self.fields = fields
self.names = [f.name for f in fields]
def __iter__(self) -> t.Iterator[StructField]:
return iter(self.fields)
def __len__(self) -> int:
return len(self.fields)
def __repr__(self) -> str:
return f"StructType({', '.join(str(field) for field in self)})"
def simpleString(self) -> str:
return f"struct<{', '.join(x.simpleString() for x in self)}>"
def jsonValue(self) -> t.Dict[str, t.Any]:
return {"type": self.typeName(), "fields": [x.jsonValue() for x in self]}
def fieldNames(self) -> t.List[str]:
return list(self.names)

32
sqlglot/dataframe/sql/util.py
View file

@ -1,32 +0,0 @@
from __future__ import annotations
import typing as t
from sqlglot import expressions as exp
from sqlglot.dataframe.sql import types
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql._typing import SchemaInput
def get_column_mapping_from_schema_input(schema: SchemaInput) -> t.Dict[str, t.Optional[str]]:
if isinstance(schema, dict):
return schema
elif isinstance(schema, str):
col_name_type_strs = [x.strip() for x in schema.split(",")]
return {
name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip()
for name_type_str in col_name_type_strs
}
elif isinstance(schema, types.StructType):
return {struct_field.name: struct_field.dataType.simpleString() for struct_field in schema}
return {x.strip(): None for x in schema} # type: ignore
def get_tables_from_expression_with_join(expression: exp.Select) -> t.List[exp.Table]:
if not expression.args.get("joins"):
return []
left_table = expression.args["from"].this
other_tables = [join.this for join in expression.args["joins"]]
return [left_table] + other_tables

136
sqlglot/dataframe/sql/window.py
View file

@ -1,136 +0,0 @@
from __future__ import annotations
import sys
import typing as t
from sqlglot import expressions as exp
from sqlglot.dataframe.sql import functions as F
from sqlglot.helper import flatten
if t.TYPE_CHECKING:
from sqlglot.dataframe.sql._typing import ColumnOrName
class Window:
_JAVA_MIN_LONG = -(1 << 63) # -9223372036854775808
_JAVA_MAX_LONG = (1 << 63) - 1 # 9223372036854775807
_PRECEDING_THRESHOLD = max(-sys.maxsize, _JAVA_MIN_LONG)
_FOLLOWING_THRESHOLD = min(sys.maxsize, _JAVA_MAX_LONG)
unboundedPreceding: int = _JAVA_MIN_LONG
unboundedFollowing: int = _JAVA_MAX_LONG
currentRow: int = 0
@classmethod
def partitionBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
return WindowSpec().partitionBy(*cols)
@classmethod
def orderBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
return WindowSpec().orderBy(*cols)
@classmethod
def rowsBetween(cls, start: int, end: int) -> WindowSpec:
return WindowSpec().rowsBetween(start, end)
@classmethod
def rangeBetween(cls, start: int, end: int) -> WindowSpec:
return WindowSpec().rangeBetween(start, end)
class WindowSpec:
def __init__(self, expression: exp.Expression = exp.Window()):
self.expression = expression
def copy(self):
return WindowSpec(self.expression.copy())
def sql(self, **kwargs) -> str:
from sqlglot.dataframe.sql.session import SparkSession
return self.expression.sql(dialect=SparkSession().dialect, **kwargs)
def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
from sqlglot.dataframe.sql.column import Column
cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols # type: ignore
expressions = [Column.ensure_col(x).expression for x in cols]
window_spec = self.copy()
partition_by_expressions = window_spec.expression.args.get("partition_by", [])
partition_by_expressions.extend(expressions)
window_spec.expression.set("partition_by", partition_by_expressions)
return window_spec
def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
from sqlglot.dataframe.sql.column import Column
cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols # type: ignore
expressions = [Column.ensure_col(x).expression for x in cols]
window_spec = self.copy()
if window_spec.expression.args.get("order") is None:
window_spec.expression.set("order", exp.Order(expressions=[]))
order_by = window_spec.expression.args["order"].expressions
order_by.extend(expressions)
window_spec.expression.args["order"].set("expressions", order_by)
return window_spec
def _calc_start_end(
self, start: int, end: int
) -> t.Dict[str, t.Optional[t.Union[str, exp.Expression]]]:
kwargs: t.Dict[str, t.Optional[t.Union[str, exp.Expression]]] = {
"start_side": None,
"end_side": None,
}
if start == Window.currentRow:
kwargs["start"] = "CURRENT ROW"
else:
kwargs = {
**kwargs,
**{
"start_side": "PRECEDING",
"start": (
"UNBOUNDED"
if start <= Window.unboundedPreceding
else F.lit(start).expression
),
},
}
if end == Window.currentRow:
kwargs["end"] = "CURRENT ROW"
else:
kwargs = {
**kwargs,
**{
"end_side": "FOLLOWING",
"end": (
"UNBOUNDED" if end >= Window.unboundedFollowing else F.lit(end).expression
),
},
}
return kwargs
def rowsBetween(self, start: int, end: int) -> WindowSpec:
window_spec = self.copy()
spec = self._calc_start_end(start, end)
spec["kind"] = "ROWS"
window_spec.expression.set(
"spec",
exp.WindowSpec(
**{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
),
)
return window_spec
def rangeBetween(self, start: int, end: int) -> WindowSpec:
window_spec = self.copy()
spec = self._calc_start_end(start, end)
spec["kind"] = "RANGE"
window_spec.expression.set(
"spec",
exp.WindowSpec(
**{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
),
)
return window_spec

5
sqlglot/dialects/databricks.py
View file

@ -23,6 +23,7 @@ class Databricks(Spark):
class Parser(Spark.Parser): class Parser(Spark.Parser):
LOG_DEFAULTS_TO_LN = True LOG_DEFAULTS_TO_LN = True
STRICT_CAST = True STRICT_CAST = True
COLON_IS_JSON_EXTRACT = True
FUNCTIONS = { FUNCTIONS = {
**Spark.Parser.FUNCTIONS, **Spark.Parser.FUNCTIONS,
@ -58,7 +59,6 @@ class Databricks(Spark):
exp.DatetimeDiff: _timestamp_diff, exp.DatetimeDiff: _timestamp_diff,
exp.TimestampDiff: _timestamp_diff, exp.TimestampDiff: _timestamp_diff,
exp.DatetimeTrunc: timestamptrunc_sql(), exp.DatetimeTrunc: timestamptrunc_sql(),
exp.JSONExtract: lambda self, e: self.binary(e, ":"),
exp.Select: transforms.preprocess( exp.Select: transforms.preprocess(
[ [
transforms.eliminate_distinct_on, transforms.eliminate_distinct_on,
@ -88,6 +88,3 @@ class Databricks(Spark):
) -> str: ) -> str:
expression.set("this", True) # trigger ALWAYS in super class expression.set("this", True) # trigger ALWAYS in super class
return super().generatedasidentitycolumnconstraint_sql(expression) return super().generatedasidentitycolumnconstraint_sql(expression)
class Tokenizer(Spark.Tokenizer):
HEX_STRINGS = []

4
sqlglot/dialects/doris.py
View file

@ -3,7 +3,6 @@ from __future__ import annotations
from sqlglot import exp from sqlglot import exp
from sqlglot.dialects.dialect import ( from sqlglot.dialects.dialect import (
approx_count_distinct_sql, approx_count_distinct_sql,
arrow_json_extract_sql,
build_timestamp_trunc, build_timestamp_trunc,
rename_func, rename_func,
time_format, time_format,
@ -50,8 +49,7 @@ class Doris(MySQL):
exp.ArrayUniqueAgg: rename_func("COLLECT_SET"), exp.ArrayUniqueAgg: rename_func("COLLECT_SET"),
exp.CurrentTimestamp: lambda self, _: self.func("NOW"), exp.CurrentTimestamp: lambda self, _: self.func("NOW"),
exp.DateTrunc: lambda self, e: self.func("DATE_TRUNC", e.this, unit_to_str(e)), exp.DateTrunc: lambda self, e: self.func("DATE_TRUNC", e.this, unit_to_str(e)),
exp.JSONExtractScalar: arrow_json_extract_sql, exp.JSONExtractScalar: lambda self, e: self.func("JSON_EXTRACT", e.this, e.expression),
exp.JSONExtract: arrow_json_extract_sql,
exp.Map: rename_func("ARRAY_MAP"), exp.Map: rename_func("ARRAY_MAP"),
exp.RegexpLike: rename_func("REGEXP"), exp.RegexpLike: rename_func("REGEXP"),
exp.RegexpSplit: rename_func("SPLIT_BY_STRING"), exp.RegexpSplit: rename_func("SPLIT_BY_STRING"),

2
sqlglot/dialects/duckdb.py
View file

@ -233,7 +233,6 @@ class DuckDB(Dialect):
FUNCTIONS = { FUNCTIONS = {
**parser.Parser.FUNCTIONS, **parser.Parser.FUNCTIONS,
"ARRAY_HAS": exp.ArrayContains.from_arg_list,
"ARRAY_REVERSE_SORT": _build_sort_array_desc, "ARRAY_REVERSE_SORT": _build_sort_array_desc,
"ARRAY_SORT": exp.SortArray.from_arg_list, "ARRAY_SORT": exp.SortArray.from_arg_list,
"DATEDIFF": _build_date_diff, "DATEDIFF": _build_date_diff,
@ -381,6 +380,7 @@ class DuckDB(Dialect):
**generator.Generator.TRANSFORMS, **generator.Generator.TRANSFORMS,
exp.ApproxDistinct: approx_count_distinct_sql, exp.ApproxDistinct: approx_count_distinct_sql,
exp.Array: inline_array_unless_query, exp.Array: inline_array_unless_query,
exp.ArrayContainsAll: rename_func("ARRAY_HAS_ALL"),
exp.ArrayFilter: rename_func("LIST_FILTER"), exp.ArrayFilter: rename_func("LIST_FILTER"),
exp.ArraySize: rename_func("ARRAY_LENGTH"), exp.ArraySize: rename_func("ARRAY_LENGTH"),
exp.ArgMax: arg_max_or_min_no_count("ARG_MAX"), exp.ArgMax: arg_max_or_min_no_count("ARG_MAX"),

1
sqlglot/dialects/hive.py
View file

@ -572,6 +572,7 @@ class Hive(Dialect):
exp.NotForReplicationColumnConstraint: lambda *_: "", exp.NotForReplicationColumnConstraint: lambda *_: "",
exp.OnProperty: lambda *_: "", exp.OnProperty: lambda *_: "",
exp.PrimaryKeyColumnConstraint: lambda *_: "PRIMARY KEY", exp.PrimaryKeyColumnConstraint: lambda *_: "PRIMARY KEY",
exp.ParseJSON: lambda self, e: self.sql(e.this),
} }
PROPERTIES_LOCATION = { PROPERTIES_LOCATION = {

311
sqlglot/dialects/mysql.py
View file

@ -597,19 +597,21 @@ class MySQL(Dialect):
return offset, limit return offset, limit
def _parse_set_item_charset(self, kind: str) -> exp.Expression: def _parse_set_item_charset(self, kind: str) -> exp.Expression:
this = self._parse_string() or self._parse_id_var() this = self._parse_string() or self._parse_unquoted_field()
return self.expression(exp.SetItem, this=this, kind=kind) return self.expression(exp.SetItem, this=this, kind=kind)
def _parse_set_item_names(self) -> exp.Expression: def _parse_set_item_names(self) -> exp.Expression:
charset = self._parse_string() or self._parse_id_var() charset = self._parse_string() or self._parse_unquoted_field()
if self._match_text_seq("COLLATE"): if self._match_text_seq("COLLATE"):
collate = self._parse_string() or self._parse_id_var() collate = self._parse_string() or self._parse_unquoted_field()
else: else:
collate = None collate = None
return self.expression(exp.SetItem, this=charset, collate=collate, kind="NAMES") return self.expression(exp.SetItem, this=charset, collate=collate, kind="NAMES")
def _parse_type(self, parse_interval: bool = True) -> t.Optional[exp.Expression]: def _parse_type(
self, parse_interval: bool = True, fallback_to_identifier: bool = False
) -> t.Optional[exp.Expression]:
# mysql binary is special and can work anywhere, even in order by operations # mysql binary is special and can work anywhere, even in order by operations
# it operates like a no paren func # it operates like a no paren func
if self._match(TokenType.BINARY, advance=False): if self._match(TokenType.BINARY, advance=False):
@ -618,7 +620,9 @@ class MySQL(Dialect):
if isinstance(data_type, exp.DataType): if isinstance(data_type, exp.DataType):
return self.expression(exp.Cast, this=self._parse_column(), to=data_type) return self.expression(exp.Cast, this=self._parse_column(), to=data_type)
return super()._parse_type(parse_interval=parse_interval) return super()._parse_type(
parse_interval=parse_interval, fallback_to_identifier=fallback_to_identifier
)
def _parse_chr(self) -> t.Optional[exp.Expression]: def _parse_chr(self) -> t.Optional[exp.Expression]:
expressions = self._parse_csv(self._parse_conjunction) expressions = self._parse_csv(self._parse_conjunction)
@ -819,6 +823,303 @@ class MySQL(Dialect):
exp.DataType.Type.TIMESTAMPLTZ, exp.DataType.Type.TIMESTAMPLTZ,
} }
# https://dev.mysql.com/doc/refman/8.0/en/keywords.html
RESERVED_KEYWORDS = {
"accessible",
"add",
"all",
"alter",
"analyze",
"and",
"as",
"asc",
"asensitive",
"before",
"between",
"bigint",
"binary",
"blob",
"both",
"by",
"call",
"cascade",
"case",
"change",
"char",
"character",
"check",
"collate",
"column",
"condition",
"constraint",
"continue",
"convert",
"create",
"cross",
"cube",
"cume_dist",
"current_date",
"current_time",
"current_timestamp",
"current_user",
"cursor",
"database",
"databases",
"day_hour",
"day_microsecond",
"day_minute",
"day_second",
"dec",
"decimal",
"declare",
"default",
"delayed",
"delete",
"dense_rank",
"desc",
"describe",
"deterministic",
"distinct",
"distinctrow",
"div",
"double",
"drop",
"dual",
"each",
"else",
"elseif",
"empty",
"enclosed",
"escaped",
"except",
"exists",
"exit",
"explain",
"false",
"fetch",
"first_value",
"float",
"float4",
"float8",
"for",
"force",
"foreign",
"from",
"fulltext",
"function",
"generated",
"get",
"grant",
"group",
"grouping",
"groups",
"having",
"high_priority",
"hour_microsecond",
"hour_minute",
"hour_second",
"if",
"ignore",
"in",
"index",
"infile",
"inner",
"inout",
"insensitive",
"insert",
"int",
"int1",
"int2",
"int3",
"int4",
"int8",
"integer",
"intersect",
"interval",
"into",
"io_after_gtids",
"io_before_gtids",
"is",
"iterate",
"join",
"json_table",
"key",
"keys",
"kill",
"lag",
"last_value",
"lateral",
"lead",
"leading",
"leave",
"left",
"like",
"limit",
"linear",
"lines",
"load",
"localtime",
"localtimestamp",
"lock",
"long",
"longblob",
"longtext",
"loop",
"low_priority",
"master_bind",
"master_ssl_verify_server_cert",
"match",
"maxvalue",
"mediumblob",
"mediumint",
"mediumtext",
"middleint",
"minute_microsecond",
"minute_second",
"mod",
"modifies",
"natural",
"not",
"no_write_to_binlog",
"nth_value",
"ntile",
"null",
"numeric",
"of",
"on",
"optimize",
"optimizer_costs",
"option",
"optionally",
"or",
"order",
"out",
"outer",
"outfile",
"over",
"partition",
"percent_rank",
"precision",
"primary",
"procedure",
"purge",
"range",
"rank",
"read",
"reads",
"read_write",
"real",
"recursive",
"references",
"regexp",
"release",
"rename",
"repeat",
"replace",
"require",
"resignal",
"restrict",
"return",
"revoke",
"right",
"rlike",
"row",
"rows",
"row_number",
"schema",
"schemas",
"second_microsecond",
"select",
"sensitive",
"separator",
"set",
"show",
"signal",
"smallint",
"spatial",
"specific",
"sql",
"sqlexception",
"sqlstate",
"sqlwarning",
"sql_big_result",
"sql_calc_found_rows",
"sql_small_result",
"ssl",
"starting",
"stored",
"straight_join",
"system",
"table",
"terminated",
"then",
"tinyblob",
"tinyint",
"tinytext",
"to",
"trailing",
"trigger",
"true",
"undo",
"union",
"unique",
"unlock",
"unsigned",
"update",
"usage",
"use",
"using",
"utc_date",
"utc_time",
"utc_timestamp",
"values",
"varbinary",
"varchar",
"varcharacter",
"varying",
"virtual",
"when",
"where",
"while",
"window",
"with",
"write",
"xor",
"year_month",
"zerofill",
"cume_dist",
"dense_rank",
"empty",
"except",
"first_value",
"grouping",
"groups",
"intersect",
"json_table",
"lag",
"last_value",
"lateral",
"lead",
"nth_value",
"ntile",
"of",
"over",
"percent_rank",
"rank",
"recursive",
"row_number",
"system",
"window",
}
def array_sql(self, expression: exp.Array) -> str:
self.unsupported("Arrays are not supported by MySQL")
return self.function_fallback_sql(expression)
def arraycontainsall_sql(self, expression: exp.ArrayContainsAll) -> str:
self.unsupported("Array operations are not supported by MySQL")
return self.function_fallback_sql(expression)
def dpipe_sql(self, expression: exp.DPipe) -> str: def dpipe_sql(self, expression: exp.DPipe) -> str:
return self.func("CONCAT", *expression.flatten()) return self.func("CONCAT", *expression.flatten())

8
sqlglot/dialects/postgres.py
View file

@ -310,7 +310,6 @@ class Postgres(Dialect):
"EXEC": TokenType.COMMAND, "EXEC": TokenType.COMMAND,
"HSTORE": TokenType.HSTORE, "HSTORE": TokenType.HSTORE,
"INT8": TokenType.BIGINT, "INT8": TokenType.BIGINT,
"JSONB": TokenType.JSONB,
"MONEY": TokenType.MONEY, "MONEY": TokenType.MONEY,
"NAME": TokenType.NAME, "NAME": TokenType.NAME,
"OID": TokenType.OBJECT_IDENTIFIER, "OID": TokenType.OBJECT_IDENTIFIER,
@ -381,12 +380,12 @@ class Postgres(Dialect):
RANGE_PARSERS = { RANGE_PARSERS = {
**parser.Parser.RANGE_PARSERS, **parser.Parser.RANGE_PARSERS,
TokenType.AT_GT: binary_range_parser(exp.ArrayContains), TokenType.AT_GT: binary_range_parser(exp.ArrayContainsAll),
TokenType.DAMP: binary_range_parser(exp.ArrayOverlaps), TokenType.DAMP: binary_range_parser(exp.ArrayOverlaps),
TokenType.DAT: lambda self, this: self.expression( TokenType.DAT: lambda self, this: self.expression(
exp.MatchAgainst, this=self._parse_bitwise(), expressions=[this] exp.MatchAgainst, this=self._parse_bitwise(), expressions=[this]
), ),
TokenType.LT_AT: binary_range_parser(exp.ArrayContained), TokenType.LT_AT: binary_range_parser(exp.ArrayContainsAll, reverse_args=True),
TokenType.OPERATOR: lambda self, this: self._parse_operator(this), TokenType.OPERATOR: lambda self, this: self._parse_operator(this),
} }
@ -485,8 +484,7 @@ class Postgres(Dialect):
else f"{self.normalize_func('ARRAY')}[{self.expressions(e, flat=True)}]" else f"{self.normalize_func('ARRAY')}[{self.expressions(e, flat=True)}]"
), ),
exp.ArrayConcat: rename_func("ARRAY_CAT"), exp.ArrayConcat: rename_func("ARRAY_CAT"),
exp.ArrayContained: lambda self, e: self.binary(e, "<@"), exp.ArrayContainsAll: lambda self, e: self.binary(e, "@>"),
exp.ArrayContains: lambda self, e: self.binary(e, "@>"),
exp.ArrayOverlaps: lambda self, e: self.binary(e, "&&"), exp.ArrayOverlaps: lambda self, e: self.binary(e, "&&"),
exp.ArrayFilter: filter_array_using_unnest, exp.ArrayFilter: filter_array_using_unnest,
exp.ArraySize: lambda self, e: self.func("ARRAY_LENGTH", e.this, e.expression or "1"), exp.ArraySize: lambda self, e: self.func("ARRAY_LENGTH", e.this, e.expression or "1"),

62
sqlglot/dialects/snowflake.py
View file

@ -336,6 +336,7 @@ class Snowflake(Dialect):
class Parser(parser.Parser): class Parser(parser.Parser):
IDENTIFY_PIVOT_STRINGS = True IDENTIFY_PIVOT_STRINGS = True
DEFAULT_SAMPLING_METHOD = "BERNOULLI" DEFAULT_SAMPLING_METHOD = "BERNOULLI"
COLON_IS_JSON_EXTRACT = True
ID_VAR_TOKENS = { ID_VAR_TOKENS = {
*parser.Parser.ID_VAR_TOKENS, *parser.Parser.ID_VAR_TOKENS,
@ -482,44 +483,28 @@ class Snowflake(Dialect):
SCHEMA_KINDS = {"OBJECTS", "TABLES", "VIEWS", "SEQUENCES", "UNIQUE KEYS", "IMPORTED KEYS"} SCHEMA_KINDS = {"OBJECTS", "TABLES", "VIEWS", "SEQUENCES", "UNIQUE KEYS", "IMPORTED KEYS"}
NON_TABLE_CREATABLES = {"STORAGE INTEGRATION", "TAG", "WAREHOUSE", "STREAMLIT"}
LAMBDAS = {
**parser.Parser.LAMBDAS,
TokenType.ARROW: lambda self, expressions: self.expression(
exp.Lambda,
this=self._replace_lambda(
self._parse_conjunction(),
expressions,
),
expressions=[e.this if isinstance(e, exp.Cast) else e for e in expressions],
),
}
def _parse_create(self) -> exp.Create | exp.Command: def _parse_create(self) -> exp.Create | exp.Command:
expression = super()._parse_create() expression = super()._parse_create()
if isinstance(expression, exp.Create) and expression.kind == "TAG": if isinstance(expression, exp.Create) and expression.kind in self.NON_TABLE_CREATABLES:
# Replace the Table node with the enclosed Identifier # Replace the Table node with the enclosed Identifier
expression.this.replace(expression.this.this) expression.this.replace(expression.this.this)
return expression return expression
def _parse_column_ops(self, this: t.Optional[exp.Expression]) -> t.Optional[exp.Expression]:
this = super()._parse_column_ops(this)
casts = []
json_path = []
while self._match(TokenType.COLON):
path = super()._parse_column_ops(self._parse_field(any_token=True))
# The cast :: operator has a lower precedence than the extraction operator :, so
# we rearrange the AST appropriately to avoid casting the 2nd argument of GET_PATH
while isinstance(path, exp.Cast):
casts.append(path.to)
path = path.this
if path:
json_path.append(path.sql(dialect="snowflake", copy=False))
if json_path:
this = self.expression(
exp.JSONExtract,
this=this,
expression=self.dialect.to_json_path(exp.Literal.string(".".join(json_path))),
)
while casts:
this = self.expression(exp.Cast, this=this, to=casts.pop())
return this
# https://docs.snowflake.com/en/sql-reference/functions/date_part.html # https://docs.snowflake.com/en/sql-reference/functions/date_part.html
# https://docs.snowflake.com/en/sql-reference/functions-date-time.html#label-supported-date-time-parts # https://docs.snowflake.com/en/sql-reference/functions-date-time.html#label-supported-date-time-parts
def _parse_date_part(self: Snowflake.Parser) -> t.Optional[exp.Expression]: def _parse_date_part(self: Snowflake.Parser) -> t.Optional[exp.Expression]:
@ -716,6 +701,19 @@ class Snowflake(Dialect):
return exp.var("".join(part.text for part in parts if part)) return exp.var("".join(part.text for part in parts if part))
def _parse_lambda_arg(self) -> t.Optional[exp.Expression]:
this = super()._parse_lambda_arg()
if not this:
return this
typ = self._parse_types()
if typ:
return self.expression(exp.Cast, this=this, to=typ)
return this
class Tokenizer(tokens.Tokenizer): class Tokenizer(tokens.Tokenizer):
STRING_ESCAPES = ["\\", "'"] STRING_ESCAPES = ["\\", "'"]
HEX_STRINGS = [("x'", "'"), ("X'", "'")] HEX_STRINGS = [("x'", "'"), ("X'", "'")]
@ -744,6 +742,8 @@ class Snowflake(Dialect):
"TAG": TokenType.TAG, "TAG": TokenType.TAG,
"TIMESTAMP_TZ": TokenType.TIMESTAMPTZ, "TIMESTAMP_TZ": TokenType.TIMESTAMPTZ,
"TOP": TokenType.TOP, "TOP": TokenType.TOP,
"WAREHOUSE": TokenType.WAREHOUSE,
"STREAMLIT": TokenType.STREAMLIT,
} }
SINGLE_TOKENS = { SINGLE_TOKENS = {

5
sqlglot/dialects/spark2.py
View file

@ -262,7 +262,8 @@ class Spark2(Hive):
arg = expression.this arg = expression.this
is_json_extract = isinstance(arg, (exp.JSONExtract, exp.JSONExtractScalar)) is_json_extract = isinstance(arg, (exp.JSONExtract, exp.JSONExtractScalar))
if is_parse_json(arg) or is_json_extract: # We can't use a non-nested type (eg. STRING) as a schema
if expression.to.args.get("nested") and (is_parse_json(arg) or is_json_extract):
schema = f"'{self.sql(expression, 'to')}'" schema = f"'{self.sql(expression, 'to')}'"
return self.func("FROM_JSON", arg if is_json_extract else arg.this, schema) return self.func("FROM_JSON", arg if is_json_extract else arg.this, schema)
@ -283,4 +284,4 @@ class Spark2(Hive):
) )
class Tokenizer(Hive.Tokenizer): class Tokenizer(Hive.Tokenizer):
HEX_STRINGS = [("X'", "'")] HEX_STRINGS = [("X'", "'"), ("x'", "'")]

6
sqlglot/expressions.py
View file

@ -4808,11 +4808,11 @@ class ArrayConstructCompact(Func):
class ArrayContains(Binary, Func): class ArrayContains(Binary, Func):
pass _sql_names = ["ARRAY_CONTAINS", "ARRAY_HAS"]
class ArrayContained(Binary): class ArrayContainsAll(Binary, Func):
pass _sql_names = ["ARRAY_CONTAINS_ALL", "ARRAY_HAS_ALL"]
class ArrayFilter(Func): class ArrayFilter(Func):

107
sqlglot/parser.py
View file

@ -41,11 +41,17 @@ def build_like(args: t.List) -> exp.Escape | exp.Like:
def binary_range_parser( def binary_range_parser(
expr_type: t.Type[exp.Expression], expr_type: t.Type[exp.Expression], reverse_args: bool = False
) -> t.Callable[[Parser, t.Optional[exp.Expression]], t.Optional[exp.Expression]]: ) -> t.Callable[[Parser, t.Optional[exp.Expression]], t.Optional[exp.Expression]]:
return lambda self, this: self._parse_escape( def _parse_binary_range(
self.expression(expr_type, this=this, expression=self._parse_bitwise()) self: Parser, this: t.Optional[exp.Expression]
) ) -> t.Optional[exp.Expression]:
expression = self._parse_bitwise()
if reverse_args:
this, expression = expression, this
return self._parse_escape(self.expression(expr_type, this=this, expression=expression))
return _parse_binary_range
def build_logarithm(args: t.List, dialect: Dialect) -> exp.Func: def build_logarithm(args: t.List, dialect: Dialect) -> exp.Func:
@ -335,6 +341,8 @@ class Parser(metaclass=_Parser):
TokenType.TABLE, TokenType.TABLE,
TokenType.TAG, TokenType.TAG,
TokenType.VIEW, TokenType.VIEW,
TokenType.WAREHOUSE,
TokenType.STREAMLIT,
} }
CREATABLES = { CREATABLES = {
@ -418,6 +426,7 @@ class Parser(metaclass=_Parser):
TokenType.TRUE, TokenType.TRUE,
TokenType.TRUNCATE, TokenType.TRUNCATE,
TokenType.UNIQUE, TokenType.UNIQUE,
TokenType.UNNEST,
TokenType.UNPIVOT, TokenType.UNPIVOT,
TokenType.UPDATE, TokenType.UPDATE,
TokenType.USE, TokenType.USE,
@ -580,7 +589,7 @@ class Parser(metaclass=_Parser):
exp.Lambda, exp.Lambda,
this=self._replace_lambda( this=self._replace_lambda(
self._parse_conjunction(), self._parse_conjunction(),
{node.name for node in expressions}, expressions,
), ),
expressions=expressions, expressions=expressions,
), ),
@ -1160,6 +1169,9 @@ class Parser(metaclass=_Parser):
# Whether the -> and ->> operators expect documents of type JSON (e.g. Postgres) # Whether the -> and ->> operators expect documents of type JSON (e.g. Postgres)
JSON_ARROWS_REQUIRE_JSON_TYPE = False JSON_ARROWS_REQUIRE_JSON_TYPE = False
# Whether the `:` operator is used to extract a value from a JSON document
COLON_IS_JSON_EXTRACT = False
# Whether or not a VALUES keyword needs to be followed by '(' to form a VALUES clause. # Whether or not a VALUES keyword needs to be followed by '(' to form a VALUES clause.
# If this is True and '(' is not found, the keyword will be treated as an identifier # If this is True and '(' is not found, the keyword will be treated as an identifier
VALUES_FOLLOWED_BY_PAREN = True VALUES_FOLLOWED_BY_PAREN = True
@ -1631,6 +1643,7 @@ class Parser(metaclass=_Parser):
extend_props(self._parse_properties()) extend_props(self._parse_properties())
expression = self._match(TokenType.ALIAS) and self._parse_heredoc() expression = self._match(TokenType.ALIAS) and self._parse_heredoc()
extend_props(self._parse_properties())
if not expression: if not expression:
if self._match(TokenType.COMMAND): if self._match(TokenType.COMMAND):
@ -4155,7 +4168,9 @@ class Parser(metaclass=_Parser):
return self.UNARY_PARSERS[self._prev.token_type](self) return self.UNARY_PARSERS[self._prev.token_type](self)
return self._parse_at_time_zone(self._parse_type()) return self._parse_at_time_zone(self._parse_type())
def _parse_type(self, parse_interval: bool = True) -> t.Optional[exp.Expression]: def _parse_type(
self, parse_interval: bool = True, fallback_to_identifier: bool = False
) -> t.Optional[exp.Expression]:
interval = parse_interval and self._parse_interval() interval = parse_interval and self._parse_interval()
if interval: if interval:
# Convert INTERVAL 'val_1' unit_1 [+] ... [+] 'val_n' unit_n into a sum of intervals # Convert INTERVAL 'val_1' unit_1 [+] ... [+] 'val_n' unit_n into a sum of intervals
@ -4183,9 +4198,11 @@ class Parser(metaclass=_Parser):
if parser: if parser:
return parser(self, this, data_type) return parser(self, this, data_type)
return self.expression(exp.Cast, this=this, to=data_type) return self.expression(exp.Cast, this=this, to=data_type)
if not data_type.expressions: if not data_type.expressions:
self._retreat(index) self._retreat(index)
return self._parse_column() return self._parse_id_var() if fallback_to_identifier else self._parse_column()
return self._parse_column_ops(data_type) return self._parse_column_ops(data_type)
return this and self._parse_column_ops(this) return this and self._parse_column_ops(this)
@ -4364,7 +4381,10 @@ class Parser(metaclass=_Parser):
def _parse_struct_types(self, type_required: bool = False) -> t.Optional[exp.Expression]: def _parse_struct_types(self, type_required: bool = False) -> t.Optional[exp.Expression]:
index = self._index index = self._index
this = self._parse_type(parse_interval=False) or self._parse_id_var() this = (
self._parse_type(parse_interval=False, fallback_to_identifier=True)
or self._parse_id_var()
)
self._match(TokenType.COLON) self._match(TokenType.COLON)
column_def = self._parse_column_def(this) column_def = self._parse_column_def(this)
@ -4401,6 +4421,47 @@ class Parser(metaclass=_Parser):
return this return this
def _parse_colon_as_json_extract(
self, this: t.Optional[exp.Expression]
) -> t.Optional[exp.Expression]:
casts = []
json_path = []
while self._match(TokenType.COLON):
start_index = self._index
path = self._parse_column_ops(self._parse_field(any_token=True))
# The cast :: operator has a lower precedence than the extraction operator :, so
# we rearrange the AST appropriately to avoid casting the JSON path
while isinstance(path, exp.Cast):
casts.append(path.to)
path = path.this
if casts:
dcolon_offset = next(
i
for i, t in enumerate(self._tokens[start_index:])
if t.token_type == TokenType.DCOLON
)
end_token = self._tokens[start_index + dcolon_offset - 1]
else:
end_token = self._prev
if path:
json_path.append(self._find_sql(self._tokens[start_index], end_token))
if json_path:
this = self.expression(
exp.JSONExtract,
this=this,
expression=self.dialect.to_json_path(exp.Literal.string(".".join(json_path))),
)
while casts:
this = self.expression(exp.Cast, this=this, to=casts.pop())
return this
def _parse_column_ops(self, this: t.Optional[exp.Expression]) -> t.Optional[exp.Expression]: def _parse_column_ops(self, this: t.Optional[exp.Expression]) -> t.Optional[exp.Expression]:
this = self._parse_bracket(this) this = self._parse_bracket(this)
@ -4444,8 +4505,10 @@ class Parser(metaclass=_Parser):
) )
else: else:
this = self.expression(exp.Dot, this=this, expression=field) this = self.expression(exp.Dot, this=this, expression=field)
this = self._parse_bracket(this) this = self._parse_bracket(this)
return this
return self._parse_colon_as_json_extract(this) if self.COLON_IS_JSON_EXTRACT else this
def _parse_primary(self) -> t.Optional[exp.Expression]: def _parse_primary(self) -> t.Optional[exp.Expression]:
if self._match_set(self.PRIMARY_PARSERS): if self._match_set(self.PRIMARY_PARSERS):
@ -4680,18 +4743,21 @@ class Parser(metaclass=_Parser):
return self.expression(exp.SessionParameter, this=this, kind=kind) return self.expression(exp.SessionParameter, this=this, kind=kind)
def _parse_lambda_arg(self) -> t.Optional[exp.Expression]:
return self._parse_id_var()
def _parse_lambda(self, alias: bool = False) -> t.Optional[exp.Expression]: def _parse_lambda(self, alias: bool = False) -> t.Optional[exp.Expression]:
index = self._index index = self._index
if self._match(TokenType.L_PAREN): if self._match(TokenType.L_PAREN):
expressions = t.cast( expressions = t.cast(
t.List[t.Optional[exp.Expression]], self._parse_csv(self._parse_id_var) t.List[t.Optional[exp.Expression]], self._parse_csv(self._parse_lambda_arg)
) )
if not self._match(TokenType.R_PAREN): if not self._match(TokenType.R_PAREN):
self._retreat(index) self._retreat(index)
else: else:
expressions = [self._parse_id_var()] expressions = [self._parse_lambda_arg()]
if self._match_set(self.LAMBDAS): if self._match_set(self.LAMBDAS):
return self.LAMBDAS[self._prev.token_type](self, expressions) return self.LAMBDAS[self._prev.token_type](self, expressions)
@ -6182,8 +6248,10 @@ class Parser(metaclass=_Parser):
return None return None
right = self._parse_statement() or self._parse_id_var() right = self._parse_statement() or self._parse_id_var()
this = self.expression(exp.EQ, this=left, expression=right) if isinstance(right, (exp.Column, exp.Identifier)):
right = exp.var(right.name)
this = self.expression(exp.EQ, this=left, expression=right)
return self.expression(exp.SetItem, this=this, kind=kind) return self.expression(exp.SetItem, this=this, kind=kind)
def _parse_set_transaction(self, global_: bool = False) -> exp.Expression: def _parse_set_transaction(self, global_: bool = False) -> exp.Expression:
@ -6433,14 +6501,25 @@ class Parser(metaclass=_Parser):
return True return True
def _replace_lambda( def _replace_lambda(
self, node: t.Optional[exp.Expression], lambda_variables: t.Set[str] self, node: t.Optional[exp.Expression], expressions: t.List[exp.Expression]
) -> t.Optional[exp.Expression]: ) -> t.Optional[exp.Expression]:
if not node: if not node:
return node return node
lambda_types = {e.name: e.args.get("to") or False for e in expressions}
for column in node.find_all(exp.Column): for column in node.find_all(exp.Column):
if column.parts[0].name in lambda_variables: typ = lambda_types.get(column.parts[0].name)
if typ is not None:
dot_or_id = column.to_dot() if column.table else column.this dot_or_id = column.to_dot() if column.table else column.this
if typ:
dot_or_id = self.expression(
exp.Cast,
this=dot_or_id,
to=typ,
)
parent = column.parent parent = column.parent
while isinstance(parent, exp.Dot): while isinstance(parent, exp.Dot):

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