Adding upstream version 1.34.4.

Signed-off-by: Daniel Baumann <daniel@debian.org>

plugins/processors/starlark/testdata/schema_sizing.star

@@ -0,0 +1,96 @@
+# Produces a new Line of statistics about the Fields
+# Drops the original metric
+#
+# Example Input:
+# logstash,environment_id=EN456,property_id=PR789,request_type=ingress,stack_id=engd asn=1313i,cache_response_code=202i,colo_code="LAX",colo_id=12i,compute_time=28736i,edge_end_timestamp=1611085500320i,edge_start_timestamp=1611085496208i,id="1b5c67ed-dfd0-4d30-99bd-84f0a9c5297b_76af1809-29d1-4b35-a0cf-39797458275c",parent_ray_id="00",processing_details="ok",rate_limit_id=0i,ray_id="76af1809-29d1-4b35-a0cf-39797458275c",request_bytes=7777i,request_host="engd-08364a825824e04f0a494115.reactorstream.dev",request_id="1b5c67ed-dfd0-4d30-99bd-84f0a9c5297b",request_result="succeeded",request_uri="/ENafcb2798a9be4bb7bfddbf35c374db15",response_code=200i,subrequest=false,subrequest_count=1i,user_agent="curl/7.64.1" 1611085496208
+#
+# Example Output:
+# sizing,measurement=logstash,environment_id=EN456,property_id=PR789,request_type=ingress,stack_id=engd tag_count=4,tag_key_avg_length=11.25,tag_value_avg_length=5.25,int_key_avg_length=13.4,int_avg_length=4.9,int_count=10,bool_key_avg_length=10,bool_avg_length=5,bool_count=1,str_key_avg_length=10.5,str_avg_length=25.4,str_count=10 1611085496208
+
+def apply(metric):
+    new_metric = Metric("sizing")
+    num_tags = len(metric.tags.items())
+    new_metric.fields["tag_count"] = float(num_tags)
+    new_metric.fields["tag_key_avg_length"] = sum(map(len, metric.tags.keys())) / num_tags
+    new_metric.fields["tag_value_avg_length"] = sum(map(len, metric.tags.values())) / num_tags
+
+    new_metric.tags["measurement"] =  metric.name
+
+    new_metric.tags.update(metric.tags)
+
+    ints, floats, bools, strs = [], [], [], []
+    for field in metric.fields.items():
+        key, value = field[0], field[1]
+        if type(value) == "int":
+            ints.append(field)
+        elif type(value) == "float":
+            floats.append(field)
+        elif type(value) == "bool":
+            bools.append(field)
+        elif type(value) == "string":
+            strs.append(field)
+
+    if len(ints) > 0:
+        int_keys = [i[0] for i in ints]
+        int_vals = [i[1] for i in ints]
+        produce_pairs(new_metric, int_keys, "int", key=True)
+        produce_pairs(new_metric, int_vals, "int")
+    if len(floats) > 0:
+        float_keys = [i[0] for i in floats]
+        float_vals = [i[1] for i in floats]
+        produce_pairs(new_metric, float_keys, "float", key=True)
+        produce_pairs(new_metric, float_vals, "float")
+    if len(bools) > 0:
+        bool_keys = [i[0] for i in bools]
+        bool_vals = [i[1] for i in bools]
+        produce_pairs(new_metric, bool_keys, "bool", key=True)
+        produce_pairs(new_metric, bool_vals, "bool")     
+    if len(strs) > 0:
+        str_keys = [i[0] for i in strs]
+        str_vals = [i[1] for i in strs]
+        produce_pairs(new_metric, str_keys, "str", key=True)
+        produce_pairs(new_metric, str_vals, "str")
+
+    new_metric.time = metric.time
+    return new_metric
+
+def produce_pairs(metric, li, field_type, key=False):
+    lens = elem_lengths(li)
+    counts = count_lengths(lens)
+    metric.fields["{}_count".format(field_type)]               = float(len(li))
+    if key:
+        metric.fields["{}_key_avg_length".format(field_type)]  = float(mean(lens))     
+    else:
+        metric.fields["{}_avg_length".format(field_type)]      = float(mean(lens))
+
+
+def elem_lengths(li):
+    if type(li[0]) in ("int", "float", "bool"):
+        return [len(str(elem)) for elem in li]
+    else:
+        return [len(elem) for elem in li]
+
+def count_lengths(li):
+    # Returns dict of counts of each occurrence of length in a list of lengths
+    lens = []
+    counts = []
+    for elem in li:
+        if elem not in lens:
+            lens.append(elem)
+            counts.append(1)
+        else:
+            index = lens.index(elem)
+            counts[index] += 1
+    return dict(zip(lens, counts))
+
+def map(f, li):
+    return [f(x) for x in li]
+
+def sum(li):
+    sum = 0
+    for i in li:
+        sum += i
+    return sum
+
+def mean(li):
+    return sum(li)/len(li)