Clustering

Clustering

Argus supports single-process and clustered (multi-process) deployments. The cluster label keeps them apart and is the only thing you must set per process.

Single process

One AutoShardedBot, one Argus, one endpoint exposing all shards. cluster_id optional (defaults to default).

Argus(bot)

Clustered

Run one Argus per process, each with a distinct cluster_id and port:

Argus(bot, cluster_id="0", port=9191)   # process 0, shards 0..n
Argus(bot, cluster_id="1", port=9192)   # process 1, shards n+1..m

State gauges carry the distinct cluster label; every counter and the duration histogram carry it too, so per-cluster breakdowns work directly:

sum by (cluster) (rate(discord_interactions_total[5m]))
sum by (cluster) (discord_guilds)

Counter rates aggregate across the fleet by simply dropping the by (cluster).

Prometheus scrape config

List every process; do not also set a cluster target label, or Prometheus renames Argus's own cluster label to exported_cluster to avoid the clash:

scrape_configs:
  - job_name: argus
    static_configs:
      - targets:
          - "host.docker.internal:9191"
          - "host.docker.internal:9192"

examples/clustered_bot.py shows the per-process pattern driven by env vars (CLUSTER_ID, ARGUS_PORT, SHARD_IDS, SHARD_COUNT).

Per-shard metrics

discord_shard_latency_seconds{shard} and discord_shard_up{shard} are per-shard; shard ids are globally unique across a clustered deploy (each process owns a disjoint range), so they need no cluster qualifier to disambiguate.