Monitoring Ingestion Pipeline

Monitoring the ingestion pipeline with Grafana MCP

The ingestion pipeline (nodejs/) is PostHog's Node.js event processing layer.

It consumes events from Kafka (produced by the capture service), runs them through

processing steps (person resolution, group assignment, property overrides, etc.),

and produces enriched events to ClickHouse-bound Kafka topics.

A single codebase is deployed as many K8s Deployments via the posthog-app

Helm chart (golden-chart migration). Each deployment sets PLUGIN_SERVER_MODE

and is distinguished in metrics by two default Prometheus labels:

• ingestion_pipeline — values: analytics, heatmaps, clientwarnings, errortracking
• ingestion_lane — values: main, overflow, historical, async, turbo

The app label (set by K8s pod labels) matches the deployment name and is the most

universal scope filter across all telemetry domains.

This skill teaches how to discover live metrics using the Grafana MCP tools

rather than memorizing metric names that change as the code evolves.

Environment context

The Grafana MCP is connected to a single Grafana instance scoped to one environment.

If the user hasn't specified, ask which environment they want to investigate:

• prod-us — US production (us-east-1)
• prod-eu — EU production (eu-central-1)

Most ingestion app metrics are environment-specific by virtue of which Grafana

you're connected to — they don't carry an environment label. CloudWatch metrics

are also scoped to the connected Grafana's AWS account.

Cross-environment comparison requires switching Grafana instances (not possible in one session).

All datasource UIDs, dashboard UIDs, ingestion_pipeline/ingestion_lane label values,

and ClickHouse type label values are identical across prod-us and prod-eu.

Key differences:

• ingestion-analytics-turbo deployment exists only in prod-us.
• Both envs use a dedicated ingestion MSK cluster separate from the events cluster —

consumers point at msk-ingestion not events

(prod-us: c21; prod-eu: posthog-prod-eu-ingestion-2026-05-04).

• CloudWatch cluster IDs differ by region suffix (see topology sections below).

Observability landscape

Six telemetry domains, all validated identical across prod-us and prod-eu:

| Domain | Datasource UID | Discovery tool | Scope filter |

| ---------------------------------- | ------------------------------- | ------------------------------ | ----------------------------------------- |

| App metrics (VictoriaMetrics) | victoriametrics | list_prometheus_metric_names | See metric prefixes below |

| App metrics (realtime) | victoriametrics-realtime | same | same (lower retention, higher resolution) |

| Logs | P44D702D3E93867EC (Loki-logs) | list_loki_label_names | app=~"ingestion-.*" |

| Profiling | pyroscope | list_pyroscope_profile_types | See Pyroscope services below |

| CloudWatch (ElastiCache, MSK, RDS) | P034F075C744B399F | query_prometheus | env-specific cluster IDs |

| Dashboards | n/a | search_dashboards | query "ingestion" or deployment name |

Datasource notes:

• Do NOT use primary Loki (P8E80F9AEF21F6940) — it returns 502 intermittently in both envs. Always use Loki-logs (P44D702D3E93867EC).
• Do NOT use CloudWatch Root (PAAE47F430CFD1449) — it exists only in prod-us.

Stable waypoints

These facts change infrequently and are hard to discover dynamically.

Deployment roles

All PLUGIN_SERVER_MODE=ingestion-v2 deployments (the "analytics ingestion" family), plus specialized modes.

Each golden-chart deployment runs in its own namespace matching the deployment name.

| Deployment name | Mode | Pipeline | Lane | Consumer group | Consume topic (EU example) |

| --------------------------------------- | ------------------------------ | ---------------- | ------------ | ------------------------------------- | ------------------------------------- |

| ingestion-analytics-main | ingestion-v2 | analytics | main | ingestion-analytics-main | ingestion-analytics-main-512 |

| ingestion-analytics-overflow | ingestion-v2 | analytics | overflow | ingestion-analytics-overflow | ingestion-analytics-overflow-128 |

| ingestion-analytics-historical | ingestion-v2 | analytics | historical | ingestion-analytics-historical | ingestion-analytics-historical-128 |

| ingestion-analytics-async | ingestion-v2 | analytics | async | ingestion-analytics-async | ingestion-analytics-async-8 |

| ingestion-analytics-turbo | ingestion-v2 | analytics | turbo | ingestion-analytics-turbo | ingestion-analytics-turbo-1024 |

| ingestion-clientwarnings-main | ingestion-v2 | clientwarnings | — | ingestion-clientwarnings-main | ingestion-clientwarnings-main-32 |

| ingestion-heatmaps-main | ingestion-v2 | heatmaps | — | ingestion-heatmaps-main | ingestion-heatmaps-main-128 |

| ingestion-errortracking-main | ingestion-errortracking | errortracking | — | ingestion-errortracking-main | ingestion-errortracking-main-128 |

| ingestion-errortracking-overflow | ingestion-errortracking | errortracking | — | ingestion-errortracking-overflow | ingestion-errortracking-overflow-32 |

| logs-ingestion | ingestion-logs | — | — | logs-ingestion | ingestion-logs |

| traces-ingestion | (traces) | — | — | traces-ingestion | ingestion-traces |

| recordings-blob-ingestion-v2 | recordings-blob-ingestion-v2 | — | — | session-recordings-blob-v2 | ingestion-sessionreplay-main-256 |

| recordings-blob-ingestion-v2-overflow | recordings-blob-ingestion-v2 | — | — | session-recordings-blob-v2-overflow | ingestion-sessionreplay-overflow-32 |

Notes:

• ingestion-analytics-turbo exists only in prod-us.
• Topic partition counts differ by env (e.g., main is 1024 in US, 512 in EU).
• Each deployment also has a DLQ topic (ingestion-analytics-main-dlq, etc.).
• KEDA autoscaling queries reference both old and new consumer group names during migration

(e.g., groupId=~"ingestion-events|ingestion-analytics-main").

Metric prefixes

Every prefix here can be discovered live with list_prometheus_metric_names

using datasourceUid: "victoriametrics" and regex: "<prefix>.*".

| Prefix | Domain | Key scope labels |

| -------------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------------------------- |

| ingestion_* | Core ingestion app metrics (~80 metrics) | app, ingestion_pipeline, ingestion_lane |

| ingestion_lag_ms* | Per-partition lag (primary lag signal) | groupId, partition |

| consumed_batch_* | Kafka consumer batch processing | topic, groupId |

| consumer_batch_* / consumer_background_* | Consumer loop health | topic, groupId |

| kafka_broker_* | librdkafka broker stats | broker_id, broker_name, consumer_group |

| kafka_consumer_* | Consumer rebalance, assignment | groupId, type |

| events_pipeline_* | Legacy pipeline step metrics | step_name |

| person_* | Person processing (~30 metrics) | db_write_mode, operation, method |

| group_* (non-AWS) | Group processing | operation |

| personhog_* | PersonHog gRPC client + service | method, source, client |

| overflow_redirect_* | Stateful overflow routing | type, result, decision, operation |

| cookieless_* | Cookieless mode | — |

| http_request_duration_seconds | HTTP health/readiness server | method, route, status_code |

| recording_blob_ingestion_v2_* | Session replay ingestion | app |

| logs_ingestion_* | Logs ingestion pipeline | app |

| error_tracking_* / cymbal_* | Error tracking pipeline | app |

| kminion_kafka_* | KMinion consumer group lag & topic offsets | group_id, topic_name, partition_id |

| aws_msk_kafka_* | MSK broker-side JMX metrics | environment |

| warpstream_agent_* | WarpStream agent metrics (~10 metrics) | virtual_cluster_id, agent_group, operation |

| kube_* / container_* | K8s resources | namespace=~"ingestion-.*", container=~"ingestion-.*" |

| pg_* / pgbouncer_* | Postgres exporter | varies |

| ClickHouseMetrics_* / ClickHouseProfileEvents_* / ClickHouseAsyncMetrics_* | ClickHouse cluster health | type (=cluster role) |

| kafka_connect_* | Kafka Connect bridge to ClickHouse | namespace, connector |

| posthog_celery_clickhouse_* | CH health monitors from Django celery | scenario |

Redis topology

Ingestion workers depend on up to five Redis instances.

Redis health is inferred from ingestion-side metrics and CloudWatch ElastiCache metrics.

| Redis instance | ElastiCache cluster (prod-us) | Env var | Use |

| --------------------- | --------------------------------- | -------------------------- | ------------------------------------ |

| Ingestion Redis | ingestion-prod-redis | INGESTION_REDIS_HOST | Overflow state, pub/sub coordination |

| PostHog/Primary Redis | posthog-solo | POSTHOG_REDIS_HOST | Billing/quota, restrictions, general |

| Cookieless Redis | cookieless-prod-redis | COOKIELESS_REDIS_HOST | Cookieless server hash mode |

| CDP Redis | cdp-delivery-prod-redis | CDP_REDIS_HOST | CDP Hog function delivery |

| Dedup Redis | ingestion-duplicates-prod-redis | DEDUPLICATION_REDIS_HOST | Event deduplication |

Ingestion-side Redis metrics: overflow_redirect_redis_*, cookieless_redis_error.

Infrastructure-side: CloudWatch datasource P034F075C744B399F.

prod-eu uses the same logical cluster names but different endpoint suffixes.

The prod-eu primary Redis is posthog-prod-redis-encripted (sic — the typo is in the actual cluster name).

The event restrictions Redis (ingestion-prod-redis) is a separate writable cluster from

the primary — capture-analytics and ingestion workers both use it via EVENT_RESTRICTIONS_REDIS_URL.

Kafka topology

Ingestion workers interact with three Kafka systems via separate producer/consumer configs:

• MSK ingestion cluster (consume side) — KAFKA_CONSUMER_METADATA_BROKER_LIST.

Capture produces here; ingestion workers consume. Carries all ingestion-analytics-*,

ingestion-errortracking-*, ingestion-heatmaps-*, ingestion-clientwarnings-* topics.

Both envs have a dedicated cluster (prod-us: c21, 12 brokers; prod-eu: posthog-prod-eu-ingestion-2026-05-04).

KMinion: kminion-msk-ingestion.

• WarpStream ingestion VC (output side) — KAFKA_WARPSTREAM_PRODUCER_METADATA_BROKER_LIST.

Ingestion workers produce ALL ClickHouse-bound output here (clickhouse_events_json,

clickhouse_person, clickhouse_groups, clickhouse_heatmap_events, clickhouse_ai_events_json, etc.).

In-cluster WarpStream agents (warpstream-ingestion-v2) with multi-AZ pools; plaintext, no TLS.

KMinion: kminion-warpstream-ingestion.

• MSK ingestion cluster (feedback/DLQ producer) — same physical cluster as item 1,

different producer config via KAFKA_INGESTION_PRODUCER_METADATA_BROKER_LIST.

Used for overflow/DLQ/async topics that route events BACK to the ingestion system.

• MSK (events/analytics) — the original events cluster. Still carries some legacy topics and

ClickHouse consumer groups during migration. prod-us: posthog-prod-us-events-2026-03-08

(12 brokers, kafka.m7g.8xlarge); prod-eu: posthog-prod-eu-events-2025-10-16 (15 brokers).

KMinion: kminion-msk-analytics.

WarpStream virtual clusters — each VC is a separate logical cluster backed by S3, with its own

agent pool, KMinion instance, and topic namespace:

| VC name | Topics carried | KMinion instance |

| -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------- |

| warpstream-ingestion-v2 | clickhouse_events_json, clickhouse_person, clickhouse_person_distinct_id, clickhouse_groups, clickhouse_ai_events_json, clickhouse_heatmap_events, clickhouse_app_metrics2, clickhouse_tophog, clickhouse_ingestion_warnings, distinct_id_usage_events_json, log_entries, team_event_partitioned_events_json | kminion-warpstream-ingestion |

| warpstream-replay-v2 | ingestion-sessionreplay-main-*, clickhouse_session_replay_events, clickhouse_session_replay_features | kminion-warpstream-replay |

| warpstream-logs | ingestion-logs, clickhouse_logs | kminion-warpstream-logs |

| warpstream-traces | ingestion-traces, clickhouse_traces | kminion-warpstream-traces |

| warpstream-shared | clickhouse_document_embeddings, error tracking fingerprint/issue topics, document_embeddings_input | kminion-warpstream-shared |

| warpstream-calculated-events | clickhouse_precalculated_person_properties, clickhouse_prefiltered_events, cohort_membership_changed (US only) | kminion-warpstream-calculated-events |

| warpstream-cyclotron | CDP topics (cdp_cyclotron_hog*, cdp_internal_events, etc.) | kminion-warpstream-cyclotron |

| warpstream-warehouse-pipelines | data_warehouse_source_webhooks, data_warehouse_sources_jobs (US only) | kminion-warpstream-warehouse-pipelines |

WarpStream agent metrics: warpstream_agent_* prefix (~10 metrics).

Key: control_plane_operation_counter (by operation), file_cache_client_fetch_local_or_remote_counter (cache hit/miss).

Labels: virtual_cluster_id, agent_group (default, general, multi-az).

Dashboards: warpstream (Agent Overview), dbfj5c31spa1ogf (MSK vs WarpStream — Active Produce Topics).

US-only personal dashboards (not synced to EU): 8e93b023-… (CH Consumer Lag),

ws-coarse-lag-explore (Coarse Lag exploration).

Postgres topology

| DB | Aurora cluster (prod-us) | Ingestion PgBouncer |

| ----------- | ------------------------------------------------------------- | ------------------------------------------------------- |

| Main app DB | posthog-cloud-prod-us-east-1 (2x db.r8g.16xlarge) | ingestion-default-pgbouncer.posthog.svc.cluster.local |

| Persons DB | posthog-cloud-persons-prod-us-east-1 (3x db.r8g.24xlarge) | ingestion-events-pgbouncer.posthog.svc.cluster.local |

Postgres metrics via prometheus-postgres-exporter and prometheus-postgres-persons-exporter.

prod-eu: posthog-cloud-prod-eu-central-1 and posthog-cloud-persons-prod-eu-central-1.

ClickHouse topology

ClickHouse is the ultimate downstream consumer of events the ingestion pipeline produces.

Ingestion workers never talk to CH directly — they publish to Kafka topics which CH

consumes via its built-in Kafka engine and Kafka Connect (DuckLake).

CH health directly impacts perceived ingestion quality: if CH falls behind on consumption,

users see stale data.

Cluster roles (discovered via type label on ClickHouseMetrics_*):

| type label | Role | Notes |

| ----------------- | ----------------------------- | ------------------------------------------ |

| events | Main analytics events cluster | Consumes clickhouse_events_json |

| online | Online/fast queries cluster | Replicated from events |

| offline | Offline/batch queries cluster | Replicated from events |

| medium | Medium-sized tables | Persons, groups |

| small | Small/config tables | Infrequent writes |

| sessions | Session replay data | Consumes session recording topics |

| logs | Logs cluster | Consumes logs topics |

| logs-new-schema | Logs new schema migration | Migration target |

| ai-events | AI/LLM events | Consumes AI events topics |

| endpoints | API endpoints cluster | Lightweight |

| migrations | Migration-specific | Schema changes |

| aux / ops | Auxiliary/operations | Maintenance |

| batch-exports | Batch exports | prod-us has this; may not exist in prod-eu |

| test | Testing cluster | May not exist in all envs |

Most type label values are identical across prod-us and prod-eu. Minor differences

like batch-exports or test may exist only in one env.

Two consumption paths from Kafka to ClickHouse:

ClickHouse now reads primarily from the WarpStream ingestion VC (where ingestion workers

produce their output), not directly from MSK.

• ClickHouse Kafka Engine — native CH feature. Metrics prefixed ClickHouseProfileEvents_Kafka*

(e.g., KafkaMessagesPolled, KafkaRowsRead, KafkaRowsRejected, KafkaCommitFailures).

Consumer groups: clickhouse_events_json (prod-us), group1 / group1_recent (prod-eu).

These groups exist on BOTH MSK analytics and WarpStream ingestion — use the correct KMinion

instance to distinguish: kminion-warpstream-ingestion for WarpStream, kminion-msk-analytics for MSK.

• Kafka Connect — runs in kafka-connect namespace, uses DuckLake sink connector.

Metrics prefixed kafka_connect_* and kafka_connect_ducklake_sink_task_metrics_*.

Consumer groups: connect-events-ducklake*.

Key health signals for ingestion operators:

• kminion_kafka_consumer_group_topic_lag{app_kubernetes_io_instance="kminion-warpstream-ingestion", group_id=~"clickhouse_events_json|group1|group1_recent", topic_name="clickhouse_events_json"} — lag between ingestion output and CH consumption on the WarpStream path (the primary path)
• kminion_kafka_consumer_group_topic_lag_seconds with same group filter — same in seconds
• ClickHouseProfileEvents_KafkaRowsRejected — rows CH couldn't parse/insert
• ClickHouseProfileEvents_FailedInsertQuery — insert failures (schema issues, too many parts, etc.)
• ClickHouseAsyncMetrics_MaxPartCountForPartition — rising part count = merge pressure
• ClickHouseMetrics_ReadonlyReplica — replicas that fell behind and went read-only
• ClickHouseAsyncMetrics_ReplicasMaxAbsoluteDelay — max replication delay
• posthog_celery_clickhouse_table_parts_count / _table_row_count — Django-side CH health monitors

Pyroscope services

Both old (ingestion/{name}) and new ({namespace}/{name}) formats coexist in Pyroscope

during the golden-chart migration. Prefer the new {namespace}/{name} format.

| Service name (new format) | Deployment |

| ------------------------------------------------------------------- | ------------------------- |

| ingestion-analytics-main/ingestion-analytics-main | Main analytics |

| ingestion-analytics-overflow/ingestion-analytics-overflow | Overflow lane |

| ingestion-analytics-historical/ingestion-analytics-historical | Historical lane |

| ingestion-analytics-async/ingestion-analytics-async | Async lane |

| ingestion-analytics-turbo/ingestion-analytics-turbo | Turbo lane (prod-us only) |

| ingestion-heatmaps-main/ingestion-heatmaps-main | Heatmaps |

| ingestion-clientwarnings-main/ingestion-clientwarnings-main | Client warnings |

| ingestion-errortracking-main/ingestion-errortracking-main | Error tracking |

| ingestion-errortracking-overflow/ingestion-errortracking-overflow | Error tracking overflow |

| logs-ingestion/logs-ingestion | Logs ingestion |

| traces-ingestion/traces-ingestion | Traces ingestion |

| recordings/recordings-blob-ingestion-v2 | Session replay |

| recordings/recordings-blob-ingestion-v2-overflow | Session replay overflow |

Profile types: process_cpu:cpu:nanoseconds:cpu:nanoseconds,

wall:wall:nanoseconds:wall:nanoseconds,

memory:inuse_space:bytes:inuse_space:bytes,

memory:inuse_objects:count:inuse_space:bytes.

Grafana dashboards

| UID | Title | Focus |

| ---------------------------------------- | ----------------------------------------- | ----------------------------------------------------- |

| ingestion-analytics | Ingestion - Analytics | Per-pipeline analytics breakdown |

| ingestion-health | Ingestion - Health | Health overview across all ingestion services |

| ingestion-pipelines | Ingestion - Pipelines | Per-lane pipeline step breakdown |

| ingestion-reliability | Ingestion - Reliability | Event trends, processing-lag SLOs, restarts, E2E lag |

| ingestion-person-processing | Ingestion -- Person Processing | Person store, merge, cache |

| ingestion-group-processing | Ingestion -- Group Processing | Group store |

| ingestion-sessionreplay | Ingestion - Session Replay | Replay blob pipeline |

| capture | Capture | Capture-side ingestion metrics (overview) |

| bexr9fnja75z4f | Kafka Deduplicator | Event deduplication pipeline |

| pl-ingestion-slas | Ingestion — SLIs / SLOs / SLAs | SLI/SLO view from ingestion_sli_* metrics (US only) |

| warpstream | Warpstream Agent Overview | Agent health, control plane ops, file cache |

| dbfj5c31spa1ogf | MSK vs Warpstream — Active Produce Topics | Side-by-side produce volume comparison |

| 8e93b023-a544-4a3b-8fac-123459d4eb84 | WarpStream: ClickHouse Consumer Lag | CH consumer lag on WarpStream topics (US only) |

| ws-coarse-lag-explore | WarpStream Coarse Lag — Explore | Agent-reported lag (US only, personal dashboard) |

| personhog-service | Personhog service | PersonHog latency decomposition |

| dbfgkwxs3gw8owd | KMinion Consumer Group Lag | Consumer lag by group (including CH groups) |

| logs | Logs (product) | Logs ingestion |

| vm-clickhouse-cluster-overview | ClickHouse (cluster overview) | QPS, memory, disk, replication, parts, merges |

| clickhouse-ingestion-overview-20260615 | ClickHouse Ingestion Layer Overview | Ingestion-layer CH cluster health and resources |

| clickhouse-keeper | ClickHouse Keeper | ZooKeeper replacement health |

| fe469e59-e10a-465a-9dac-ac8f6f82dc9a | ClickHouse Stuck Merge Loop Investigation | Merges that stop making progress |

| cdzv7o1635n9ca | Kafka Connect | Kafka Connect tasks, lag, DuckLake sink |

| fdrcd04np3hfkf | ClickHouse - Kafka consumption | CH Kafka engine consumption stats (EU only) |

Discovery workflows

Prometheus / VictoriaMetrics

• list_prometheus_metric_names — datasourceUid: "victoriametrics", regex: "ingestion_.*" to enumerate app metrics
• Pick a metric, then list_prometheus_label_names scoped to it — see available dimensions
• list_prometheus_label_values — discover actual values for a label

(e.g. labelName: "cause" on ingestion_event_dropped_total)

• query_prometheus with PromQL — always scope by app or ingestion_pipeline and set a time range

Repeat with other prefixes: consumed_batch_*, person_*, personhog_*,

overflow_redirect_*, ClickHouseMetrics_*, kafka_connect_*, etc.

Loki (logs)

• list_loki_label_names — datasourceUid: "P44D702D3E93867EC"
• list_loki_label_values for app — find ingestion containers (values like ingestion-analytics-main, logs-ingestion, etc.)
• query_loki_logs — e.g. {app=~"ingestion-.*"} |= "error" or {namespace="clickhouse"} |= "Exception"

Pyroscope (profiling)

• list_pyroscope_profile_types — data_source_uid: "pyroscope"
• fetch_pyroscope_profile — matchers: '{service_name="ingestion-analytics-main/ingestion-analytics-main"}',

profile_type: "process_cpu:cpu:nanoseconds:cpu:nanoseconds"

Dashboards

• search_dashboards — query "ingestion" or "clickhouse" or a specific deployment name
• get_dashboard_by_uid — use a known UID (e.g. "ingestion-health") to get panel details
• get_dashboard_panel_queries — extract PromQL from existing panels

Redis / ElastiCache

• Ingestion-side: discover overflow_redirect_redis_*, cookieless_redis_* metrics in VictoriaMetrics
• Infrastructure: CloudWatch datasource P034F075C744B399F for ElastiCache

(CPU, memory, connections, latency).

Cluster IDs: ingestion-prod-redis, posthog-solo (prod-us primary),

ingestion-duplicates-prod-redis, cookieless-prod-redis

Postgres / Aurora

• Ingestion-side: discover postgres_error_total, person_*, group_* metrics
• Infrastructure: prometheus-postgres-exporter and prometheus-postgres-persons-exporter metrics
• CloudWatch RDS: datasource P034F075C744B399F with cluster IDs

posthog-cloud-prod-us-east-1 / posthog-cloud-persons-prod-us-east-1

ClickHouse

• Cluster health: list_prometheus_metric_names with regex "ClickHouseMetrics_.*" or "ClickHouseProfileEvents_.*"

Scope with type label for cluster role (e.g. type="events")

• Kafka engine health: regex "ClickHouseProfileEvents_Kafka.*"
• Kafka Connect: regex "kafka_connect_.*" — scope with namespace="kafka-connect"
• Consumer lag (the bridge): kminion_kafka_consumer_group_topic_lag with

group_id=~"clickhouse_events_json|group1|group1_recent|connect-events-ducklake.*" and topic_name="clickhouse_events_json".

Important: scope with app_kubernetes_io_instance="kminion-warpstream-ingestion" for the

WarpStream path (primary) or "kminion-msk-analytics" for the MSK path.

• Logs: {namespace="clickhouse"} |= "Exception" or {namespace="kafka-connect"}
• Dashboards: vm-clickhouse-cluster-overview, clickhouse-ingestion-overview-20260615,

cdzv7o1635n9ca, 8e93b023-a544-4a3b-8fac-123459d4eb84 (WarpStream CH consumer lag)

Key metric domains

Categories of what to look for. Discover specific metrics live using the prefixes above.

Kafka consumer health — batch duration, messages consumed per batch, consumer group

assignment/rebalance events, consumer lag (via KMinion). Metrics: consumed_batch_*,

kafka_consumer_*, kminion_kafka_consumer_group_topic_lag* scoped by group_id.

Pipeline processing — step-level latency and error rates, pipeline result distribution

(ingested, filtered, dropped, DLQ'd). Metrics: events_pipeline_step_ms,

events_pipeline_step_error_total, ingestion_pipeline_results by result.

Person/group stores — person flush latency, cache hit rates, Postgres write latency,

merge failures, properties size. Metrics: person_*, group_*, personhog_*.

Outputs — Kafka production to ClickHouse-bound topics. Message size, latency, errors.

Metrics: ingestion_outputs_* by topic.

Overflow routing — stateful overflow decisions, Redis operations for overflow state.

Metrics: overflow_redirect_* by type, result, decision.

ClickHouse downstream health — CH cluster QPS, memory, disk, merge pressure,

replication lag, Kafka engine consumption (rows read/rejected/failed), Kafka Connect

task health and consumer lag. This tells you whether events are actually making it

to the query layer. Metrics: ClickHouseMetrics_*, ClickHouseProfileEvents_*,

ClickHouseAsyncMetrics_* scoped by type; kafka_connect_* scoped by namespace.

K8s resources — container_* and kube_* for CPU, memory, restarts, HPA state.

Scope: namespace=~"ingestion-.*", pod=~"ingestion-.*" (or namespace="clickhouse",

pod=~"chi-ingestion-.*" for CH ingestion pods).

Investigation playbooks

See references/investigation-playbooks.md

for step-by-step workflows covering: health checks, event drops, latency, consumer lag,

person processing (including the pganalyze query-level view of the persons DB),

Kafka/MSK issues, Redis, Postgres, session replay, ClickHouse downstream health,

single-partition lag (keyed-cost diagnosis, handing off to the querying-tophog

skill for actor identification), and cross-environment comparison.