The Kafka consumer plugin polls a specified Kafka topic and adds messages to InfluxDB. The plugin assumes messages follow the line protocol. Consumer Group is used to talk to the Kafka cluster so multiple instances of telegraf can read from the same topic in parallel.
154 lines
3.2 KiB
Go
154 lines
3.2 KiB
Go
package kafka_consumer
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import (
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"os"
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"os/signal"
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"time"
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"github.com/influxdb/influxdb/tsdb"
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"github.com/influxdb/telegraf/plugins"
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"github.com/wvanbergen/kafka/consumergroup"
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"gopkg.in/Shopify/sarama.v1"
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)
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type Kafka struct {
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ConsumerGroupName string
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Topic string
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ZookeeperPeers []string
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Consumer *consumergroup.ConsumerGroup
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BatchSize int
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}
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var sampleConfig = `
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# topic to consume
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topic = "topic_with_metrics"
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# the name of the consumer group
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consumerGroupName = "telegraf_metrics_consumers"
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# an array of Zookeeper connection strings
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zookeeperPeers = ["localhost:2181"]
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# Batch size of points sent to InfluxDB
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batchSize = 1000`
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func (k *Kafka) SampleConfig() string {
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return sampleConfig
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}
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func (k *Kafka) Description() string {
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return "read metrics from a Kafka topic"
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}
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type Metric struct {
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Measurement string `json:"measurement"`
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Values map[string]interface{} `json:"values"`
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Tags map[string]string `json:"tags"`
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Time time.Time `json:"time"`
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}
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func (k *Kafka) Gather(acc plugins.Accumulator) error {
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var consumerErr error
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metricQueue := make(chan []byte, 200)
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if k.Consumer == nil {
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k.Consumer, consumerErr = consumergroup.JoinConsumerGroup(
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k.ConsumerGroupName,
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[]string{k.Topic},
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k.ZookeeperPeers,
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nil,
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)
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if consumerErr != nil {
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return consumerErr
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}
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c := make(chan os.Signal, 1)
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halt := make(chan bool, 1)
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signal.Notify(c, os.Interrupt)
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go func() {
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<-c
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halt <- true
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emitMetrics(k, acc, metricQueue)
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k.Consumer.Close()
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}()
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go readFromKafka(k.Consumer.Messages(), metricQueue, k.BatchSize, k.Consumer.CommitUpto, halt)
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}
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return emitMetrics(k, acc, metricQueue)
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}
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func emitMetrics(k *Kafka, acc plugins.Accumulator, metricConsumer <-chan []byte) error {
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timeout := time.After(1 * time.Second)
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for {
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select {
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case batch := <-metricConsumer:
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var points []tsdb.Point
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var err error
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if points, err = tsdb.ParsePoints(batch); err != nil {
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return err
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}
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for _, point := range points {
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acc.AddValuesWithTime(point.Name(), point.Fields(), point.Tags(), point.Time())
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}
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case <-timeout:
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return nil
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}
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}
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}
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const millisecond = 1000000 * time.Nanosecond
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type ack func(*sarama.ConsumerMessage) error
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func readFromKafka(kafkaMsgs <-chan *sarama.ConsumerMessage, metricProducer chan<- []byte, maxBatchSize int, ackMsg ack, halt <-chan bool) {
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batch := make([]byte, 0)
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currentBatchSize := 0
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timeout := time.After(500 * millisecond)
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var msg *sarama.ConsumerMessage
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for {
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select {
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case msg = <-kafkaMsgs:
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if currentBatchSize != 0 {
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batch = append(batch, '\n')
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}
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batch = append(batch, msg.Value...)
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currentBatchSize++
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if currentBatchSize == maxBatchSize {
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metricProducer <- batch
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currentBatchSize = 0
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batch = make([]byte, 0)
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ackMsg(msg)
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}
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case <-timeout:
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if currentBatchSize != 0 {
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metricProducer <- batch
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currentBatchSize = 0
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batch = make([]byte, 0)
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ackMsg(msg)
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}
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timeout = time.After(500 * millisecond)
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case <-halt:
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if currentBatchSize != 0 {
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metricProducer <- batch
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ackMsg(msg)
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}
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return
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}
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}
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}
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func init() {
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plugins.Add("kafka", func() plugins.Plugin {
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return &Kafka{}
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})
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}
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