Skip to main content
Prometheus is a widely-used open-source monitoring and alerting toolkit that collects and stores time series data from various sources. The Prometheus Message Decoder in Pinot provides native support for ingesting Prometheus-formatted metrics data into pinot tables.

​
Prometheus Message Format

<metric_name>{<label1>=<value1>,<label2>=<value2>,...} <metric_value> <timestamp>
This format encapsulates the four main components of our schema:
  • <metric_name> corresponds to the metric field (STRING)
  • {<label1>=<value1>,<label2>=<value2>,...} represents the labels field (JSON)
  • <metric_value> maps to the value field (DOUBLE)
  • <timestamp> corresponds to the ts field (TIMESTAMP)

​
Table schema

ColumnTypeField typeDescription
metricSTRINGDimensionThe name of the Prometheus metric
labelsJSONDimensionA JSON object containing key-value pairs of Prometheus labels
valueDOUBLEMetricThe numeric value of the metric
tsTIMESTAMPDate-TimeThe timestamp when the metric was recorded, in milliseconds since the Unix epoch
  • The schema created for ingesting Prometheus metrics MUST adhere to the format specified above, particularly with respect to the field names and data types.
  • If you require different field names in your Pinot table, you can add transform functions to alias the names.
    • However, the incoming data must match this specified schema for the Prometheus Message Decoder to function correctly.

​
Schema Configuration

{
  "schemaName": "events",
  "dimensionFieldSpecs": [
    {
      "name": "metric",
      "dataType": "STRING"
    },
    {
      "name": "labels",
      "dataType": "JSON"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "value",
      "dataType": "DOUBLE"
    }
  ],
"dateTimeFieldSpecs": [{
    "name": "ts",
    "dataType": "TIMESTAMP",
    "format" : "1:MILLISECONDS:EPOCH",
    "granularity": "1:MILLISECONDS"
  }]
}

​
Configuration Example

When ingesting a Prometheus Message formatted payload from a stream, the decoder used for the stream must be ai.startree.pinot.plugin.inputformat.prometheus.PrometheusMessageDecoder 
{
  "tableName": "events",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "ts",
    "schemaName": "events",
    "replicasPerPartition": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "streamConfigs": {
      "streamType": "kafka",
      "stream.kafka.consumer.type": "lowlevel",
      "stream.kafka.topic.name": "events",
      "stream.kafka.decoder.class.name": "ai.startree.pinot.plugin.inputformat.prometheus.PrometheusMessageDecoder",
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
      "stream.kafka.broker.list": "localhost:9092",
      "realtime.segment.flush.threshold.rows": "0",
      "realtime.segment.flush.threshold.time": "24h",
      "realtime.segment.flush.threshold.segment.size": "50M",
      "stream.kafka.consumer.prop.auto.offset.reset": "smallest"
    }
  }
}
In the above sample, the Kafka consumer factory used is org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory and the decoder associated with this stream is ai.startree.pinot.plugin.inputformat.prometheus.PrometheusMessageDecoder".