Sequin exposes a metrics endpoint, using Prometheus text format, which can be integrated into your existing observability workflows. By default, this endpoint is at /metrics on port 8376.
For production deployments, consider securing the metrics endpoint with HTTP Basic Authentication.Set SEQUIN_METRICS_USER and SEQUIN_METRICS_PASSWORD environment variables and Sequin will automatically require these credentials. See the Configuration reference for details.
Metrics are designed to answer:
  • Is Sequin keeping up with the database? (Ingestion metrics)
  • Is Sequin processing messages quickly? (Internal processing metrics)
  • Are sinks keeping up with Sequin? (Delivery metrics)
  • Is the Sequin service experiencing CPU or memory pressure? (VM metrics)
Sequin Grafana dashboard

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Metrics reference

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Ingestion metrics

  • sequin_ingestion_latency_us (Gauge) Measures latency between transaction commit and when Sequin first receives it. Calculated by instrumenting Sequin’s own heartbeat messages.
  • sequin_messages_ingested_count (Counter) Number of messages Sequin has received from a given replication slot.
  • sequin_replication_slot_size_mb (Gauge) Size of the Postgres replication slot in megabytes, calculated via: 
select pg_wal_lsn_diff(pg_current_wal_lsn(), confirmed_flush_lsn) as replication_lag_bytes
from pg_replication_slots
where slot_name = $1;
Indicates how much WAL has accumulated for the slot, helping determine if Sequin is keeping up with the database.

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Internal processing metrics

  • sequin_internal_latency_us (Histogram) Measures time between Sequin ingesting a message and beginning to process it.

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Delivery metrics

  • sequin_delivery_latency_us (Histogram) Measures time between Sequin beginning to deliver a message and a given sink’s successful acknowledgement. Some sinks are able to batch messages, in which case this metric measures the batch latency, rather than the per-message latency.
  • sequin_message_deliver_attempt_count (Counter) Incremented for each message Sequin tries to deliver, before receiving a success or failure response.
  • sequin_message_deliver_success_count (Counter) Incremented for each message that Sequin successfully delivers.
  • sequin_message_deliver_failure_count (Counter) Incremented for each message that Sequin fails to deliver to a sink. This is because the sink was either unreachable, timed out, or returned an error.

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Total latency

The total latency from database commit to message delivery is the sum of the ingestion latency, internal processing latency, and delivery latency.

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VM metrics

Sequin is implemented with Elixir. Elixir’s VM (the BEAM) exposes many metrics. The following are the ones we find most useful for monitoring Sequin:
  • erlang_vm_statistics_reductions_total (Counter) Measures how many VM instructions are executed across all processes. Shows intensity of CPU-bound work. Recommend using with a rate query rather than directly.
  • erlang_vm_memory_bytes_total (Gauge) Memory usage of the entire VM.

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Grafana dashboard

Sequin provides a Grafana dashboard template for monitoring Sequin. This can be imported directly into your Grafana instance for use, or referenced as a guide for building your own.