46 lines
1.9 KiB
Text
46 lines
1.9 KiB
Text
#
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# This code assumes the value parser with data_type='string' is used
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# in the input collecting the JSON data. The entire JSON obj/doc will
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# be set to a Field named `value` with which this code will work.
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# JSON:
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# ```
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# {
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# "fields": {
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# "LogEndOffset": 339238,
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# "LogStartOffset": 339238,
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# "NumLogSegments": 1,
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# "Size": 0,
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# "UnderReplicatedPartitions": 0
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# },
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# "name": "partition",
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# "tags": {
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# "host": "CUD1-001559",
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# "jolokia_agent_url": "http://localhost:7777/jolokia",
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# "partition": "1",
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# "topic": "qa-kafka-connect-logs"
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# },
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# "timestamp": 1591124461
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# } ```
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#
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# Example Input:
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# json value="[{\"fields\": {\"LogEndOffset\": 339238, \"LogStartOffset\": 339238, \"NumLogSegments\": 1, \"Size\": 0, \"UnderReplicatedPartitions\": 0}, \"name\": \"partition\", \"tags\": {\"host\": \"CUD1-001559\", \"jolokia_agent_url\": \"http://localhost:7777/jolokia\", \"partition\": \"1\", \"topic\": \"qa-kafka-connect-logs\"}, \"timestamp\": 1591124461}]"
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# Example Output:
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# partition,host=CUD1-001559,jolokia_agent_url=http://localhost:7777/jolokia,partition=1,topic=qa-kafka-connect-logs LogEndOffset=339238i,LogStartOffset=339238i,NumLogSegments=1i,Size=0i,UnderReplicatedPartitions=0i 1591124461000000000
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load("json.star", "json")
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def apply(metric):
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j_list = json.decode(metric.fields.get('value')) # input JSON may be an arrow of objects
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metrics = []
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for obj in j_list:
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new_metric = Metric("partition") # We want a new InfluxDB/Telegraf metric each iteration
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for tag in obj["tags"].items(): # 4 Tags to iterate through
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new_metric.tags[str(tag[0])] = tag[1]
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for field in obj["fields"].items(): # 5 Fields to iterate through
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new_metric.fields[str(field[0])] = field[1]
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new_metric.time = int(obj["timestamp"] * 1e9)
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metrics.append(new_metric)
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return metrics
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