# This is a modern starter configuration for the statsd_exporter. It uses native
# and classic histograms by default, as well as the fast glob matching without ordering.
defaults:
  # Convert timers into histograms, which can be aggregated across instances. See below for an example of using a summary with pre-computed quantiles instead.
  observer_type: histogram
  histogram_options:
    # Expose native histograms.
    # A bucket spread factor of 1.1 allows for pretty good precision in
    # measuring latencies, as long as they are not spread too widely.
    native_histogram_bucket_factor: 1.1
    # If latency is spread too widely, the resolution is automatically reduced,
    # keeping within the maximum bucket count. 160 is the default maximum bucket
    # count in OpenTelemetry, and is a good default for most use cases.
    native_histogram_max_buckets: 160
    # Also expose classic histograms, with a spread of latency buckets from 5ms to 2.5s.
    buckets: [.005, .01, .025, .05, .1, .25, .5, 1, 2.5 ]
  # Metric can be optionally exposed as a summary. Set reasonable defaults for
  # that case. Only used with `observer_type: summary`.
  summary_options:
    quantiles:
      - quantile: 0.99
        error: 0.001
      - quantile: 0.95
        error: 0.01
      - quantile: 0.9
        error: 0.05
      - quantile: 0.5
        error: 0.005
    max_age: 5m
    age_buckets: 2
    buf_cap: 1000
  # Match using glob patterns by default, it is much faster than regex.
  match_type: glob
  # Sepecific matches take precedence over *, ignoring the order of the mapping rules.
  # Regex matches are always evaluated after glob matches, and do honor the
  # order of mapping rules. Avoid using regex matches where possible.
  glob_disable_ordering: true
  # Do not expire metrics by default. When deployed as a sidecar, and restarted
  # together with application deployments, this matches the behavior of native
  # in-application instrumentation. Set a reasonable TTL if the exporter has its
  # own lifecycle.
  ttl: 0
mappings:
# Example 1: This will be a histogram using the bucket configuration set in `defaults`.
- match: "test.timing.*.*.*"
  name: "my_timer"
  help: "Latency of the application"
  labels:
    provider: "$2"
    outcome: "$3"
    job: "${1}_server"
# Example 2: Use a much tighter bucket spread. Use this to track latencies in a
# relatively narrow range. For classic histograms, we need to set the buckets
# around this band manually; native histograms are sparse and adapt automatically.
- match: "consistent_app.timing.*.*.*"
  name: "my_consistent_timer"
  help: "Latency of an application with a consistent latency pattern"
  histogram_options:
    native_histogram_bucket_factor: 1.005
    # For the sake of this example, set the buckets for a latency distribution
    # tightly clustered around 15ms.
    buckets: [0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02]
  labels:
    provider: "$2"
    outcome: "$3"
    job: "${1}_server"
# Example 3: This will be a summary using the summary_options set in `defaults`
#
# Note: Summary metrics are not recommended because they can not be aggregated over multiple instances.
- match: "other.distribution.*.*.*"
  observer_type: summary
  name: "other_distribution"
  labels:
    provider: "$2"
    outcome: "$3"
    job: "${1}_server_other"
# Optional: drop all unmapped metrics. Keep this as the last mapping rule. Any
# metric not matched otherwise will be dropped because "." matches all metrics.
# Without this rule, all metrics will be exposed, with the metric name
# automatically generated from the statsd metric name.
# - match: "."
#   match_type: regex
#   action: drop
#   name: "dropped"