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qryn-otel-collector

Open Telemetry distribution for qryn

About

The qryn-otel-collector is designed to store observability data (Traces, Logs, Metrics) from multiple vendors/platforms into ClickHouse using qryn fingerprinting and table formats transparently accessible through qryn via LogQL, PromQL, Tempo and Pyroscope queries.

Popular ingestion formats (out of many more):

Usage

otel-collector:
    container_name: otel-collector
    image: ghcr.io/metrico/qryn-otel-collector:latest
    volumes:
      - ./otel-collector-config.yaml:/etc/otel/config.yaml
    ports:
      - "3100:3100"     # Loki/Logql HTTP receiver
      - "3200:3200"     # Loki/Logql gRPC receiver
      - "8088:8088"     # Splunk HEC receiver
      - "5514:5514"     # Syslog TCP Rereceiverceiver
      - "24224:24224"   # Fluent Forward receiver
      - "4317:4317"     # OTLP gRPC receiver
      - "4318:4318"     # OTLP HTTP receiver
      - "14250:14250"   # Jaeger gRPC receiver
      - "14268:14268"   # Jaeger thrift HTTP receiver
      - "9411:9411"     # Zipkin Trace receiver
      - "11800:11800"   # Skywalking gRPC receiver
      - "12800:12800"   # Skywalking HTTP receiver
      
      - "8086:8086"     # InfluxDB Line proto HTTP

    restart: on-failure

Config Template view

The following template enables popular log, metric and tracing ingestion formats supported by qryn

receivers:
  loki:
    use_incoming_timestamp: true
    protocols:
      http:
        endpoint: 0.0.0.0:3100
      grpc:
        endpoint: 0.0.0.0:3200
  syslog:
    protocol: rfc5424
    tcp:
      listen_address: "0.0.0.0:5514"
  fluentforward:
    endpoint: 0.0.0.0:24224
  splunk_hec:
    endpoint: 0.0.0.0:8088
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
  jaeger:
    protocols:
      grpc:
        endpoint: 0.0.0.0:14250
      thrift_http:
        endpoint: 0.0.0.0:14268
  zipkin:
    endpoint: 0.0.0.0:9411
  skywalking:
    protocols:
      grpc:
        endpoint: 0.0.0.0:11800
      http:
        endpoint: 0.0.0.0:12800
  prometheus:
    config:
      scrape_configs:
        - job_name: 'otel-collector'
          scrape_interval: 5s
          static_configs:
            - targets: ['exporter:8080']
  influxdb:
    endpoint: 0.0.0.0:8086
connectors:
  servicegraph:
    latency_histogram_buckets: [ 100us, 1ms, 2ms, 6ms, 10ms, 100ms, 250ms ]
    dimensions: [ cluster, namespace ]
    store:
      ttl: 2s
      max_items: 1000
    cache_loop: 2m
    store_expiration_loop: 2s
    virtual_node_peer_attributes:
      - db.name
      - rpc.service
  spanmetrics:
    namespace: span.metrics
    exemplars:
      enabled: false
    dimensions_cache_size: 1000
    aggregation_temporality: 'AGGREGATION_TEMPORALITY_CUMULATIVE'
    metrics_flush_interval: 30s
    metrics_expiration: 5m
    events:
      enabled: false
processors:
  batch:
    send_batch_size: 10000
    timeout: 5s
  memory_limiter:
    check_interval: 2s
    limit_mib: 1800
    spike_limit_mib: 500
  resourcedetection/system:
    detectors: ['system']
    system:
      hostname_sources: ['os']
  resource:
    attributes:
      - key: service.name
        value: "serviceName"
        action: upsert
  metricstransform:
    transforms:
      - include: calls_total
        action: update
        new_name: traces_spanmetrics_calls_total
      - include: latency
        action: update
        new_name: traces_spanmetrics_latency
exporters:
  qryn:
    dsn: tcp://clickhouse-server:9000/qryn?username=default&password=*************
    timeout: 10s
    sending_queue:
      queue_size: 100
    retry_on_failure:
      enabled: true
      initial_interval: 5s
      max_interval: 30s
      max_elapsed_time: 300s
    logs:
       format: raw
  otlp/spanmetrics:
    endpoint: localhost:4317
    tls:
      insecure: true
extensions:
  health_check:
  pprof:
  zpages:

service:
  extensions: [pprof, zpages, health_check]
  pipelines:
    logs:
      receivers: [fluentforward, otlp, loki, syslog, splunk_hec]
      processors: [memory_limiter, resourcedetection/system, resource, batch]
      exporters: [qryn]
    traces:
      receivers: [otlp, jaeger, zipkin, skywalking]
      processors: [memory_limiter, resourcedetection/system, resource, batch]
      exporters: [qryn, spanmetrics, servicegraph]
    metrics:
      receivers: [prometheus, influxdb, spanmetrics, servicegraph]
      processors: [memory_limiter, resourcedetection/system, resource, batch]
      exporters: [qryn]

Kafka Receiver

In order to correctly set labels when using Kafka (or other generic receiver) you will have to elect fields as labels.

For example this processor copies severity json field to the severity label:

processors:
  logstransform:
    operators:
      - type: copy
        from: 'body.severity'
        to: 'attributes.severity'

Use the label processor inside the pipeline you want:

  pipelines:
    logs:
      receivers: [kafka]
      processors: [logstransform, memory_limiter, batch]
      exporters: [qryn]

Kafka Example

A stream containing {"severity":"info", "data": "a"} should produce the following fingerprint and log:

┌───────date─┬──────────fingerprint─┬─labels──────────────┬─name─┐
│ 2023-10-05 │ 11473756280579456548 │ {"severity":"info"} │      │
└────────────┴──────────────────────┴─────────────────────┴──────┘

┌──────────fingerprint─┬────────timestamp_ns─┬─value─┬─string─────────────────────────┐
│ 11473756280579456548 │ 1696502612955383384 │     0 │ {"data":"a","severity":"info"} │
└──────────────────────┴─────────────────────┴───────┴────────────────────────────────┘