Observability patterns for OLAP systems: instrumenting ClickHouse at scale

How to instrument ClickHouse for performance and cost visibility: practical observability patterns for OLAP in 2026

Hook: If your analytics cluster unexpectedly ballooned cloud bills last month, or teams keep filing tickets about slow dashboards and “mystery” long-running queries — you are not alone. Modern OLAP workloads are high-volume, multi-tenant, and bursty, and without targeted observability you lack the signals to fix performance, control cost, or attribute usage to teams.

In 2026, ClickHouse is a default choice for high-throughput analytics. The company’s rapid growth and large funding rounds through late 2025 have accelerated features, but the operational burden for analytics platforms grew too — especially around tracing, storage transparency, and tenant-level cost attribution. This article gives pragmatic, production-ready observability patterns for ClickHouse: query tracing, storage metrics, user-level dashboards, and alerting strategies that analytics ops and DevOps teams can implement this quarter.

Quick summary — what you'll get

• Concrete data sources and SQL queries to extract observability from ClickHouse.
• How to implement distributed query tracing and correlate traces with ClickHouse internals.
• Storage and S3 cost visibility patterns with sample queries and formulas.
• Dashboard layouts and Prometheus alert rules for high-signal alerts.
• Operational best practices for multi-tenant clusters and scaling telemetry ingest.

Why observability for OLAP matters in 2026

Large investments into ClickHouse and other analytical engines in late 2025 and early 2026 (for example, major funding events) are driving adoption across engineering organizations. With that adoption comes two realities:

• Operators now run larger clusters, often mixing hot on-node storage with colder object stores (S3/Azure/GCS), which changes cost and performance profiles.
• Analytics queries are multi-tenant and unpredictable, making per-query and per-user visibility essential for performance SLAs and cost control.

Observability data sources inside ClickHouse

Start with the system tables and metrics ClickHouse exposes. The most actionable sources are:

• system.query_log — persistent log of queries, durations, user, client, query_id.
• system.events and system.profile_events — low-level counters (RowsRead, BytesRead, MemoryPeak, etc.).
• system.parts and system.merges — storage layout, parts size, merge backlog.
• system.metrics — runtime counters exposed via HTTP /metrics for Prometheus.
• ClickHouse HTTP headers and settings — e.g., X-ClickHouse-Query-Id and query tags that your apps can set.

Example — get the top long-running queries

SELECT query_id, user, query, elapsed, read_rows, read_bytes
FROM system.query_log
WHERE type = 'QueryFinish'
ORDER BY elapsed DESC
LIMIT 50;

Designing traceable queries

For meaningful correlation between application-level traces and ClickHouse internals, follow a simple contract:

1. Generate a canonical query_id at the application layer (UUID or trace ID).
2. Propagate that ID to ClickHouse using the HTTP header X-ClickHouse-Query-Id or the client setting.
3. Instrument the application with OpenTelemetry (or your APM) and attach the same query_id as an attribute on the DB span.
4. Correlate traces with system.query_log and system.profile_events by matching query_id.

Why this works

ClickHouse already records query_id in its query log; by reusing a single canonical ID you can correlate latency and resource usage (profile events) with distributed traces. This yields immediate benefits: you can see not just that a query was slow, but whether the slowness was CPU-bound, IO-bound, or network-bound — and which downstream services or dashboards triggered it.

Sample instrumentation (Python + OpenTelemetry)

from opentelemetry import trace
import requests, uuid

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span('analytics.query') as span:
    qid = str(uuid.uuid4())
    span.set_attribute('clickhouse.query_id', qid)
    headers = {'X-ClickHouse-Query-Id': qid}
    r = requests.post('http://clickhouse:8123', params={'query':'SELECT count() FROM events'}, headers=headers)
    print(r.text)

Extracting profile events and low-level resource usage

Profile events provide the fine-grained counters you need to diagnose bottlenecks.

SELECT query_id, name, value
FROM system.query_thread_log
WHERE query_id = 'your-query-id';

-- or aggregated profile events
SELECT
  q.query_id,
  pe.name,
  sum(pe.value) AS total
FROM system.query_log AS q
JOIN system.profile_events AS pe ON pe.query_id = q.query_id
WHERE q.query_id = '...'
GROUP BY q.query_id, pe.name;

Key profile events to monitor: ReadRows, ReadBytes, WriteRows, WriteBytes, MemoryTracking, ContextLockWaitMicroseconds, MergeAndFetchFromRemoteFiles.

Storage metrics and cost visibility

Storage is now the dominant cost for analytic platforms that offload data to object stores. To get cost visibility:

• Track on-disk bytes via system.parts and per-table bytes via system.columns or system.table_size equivalents.
• Monitor S3 object count and bytes if using S3 tiering or ClickHouse's cloud object storage policies.
• Estimate egress costs from bytes transferred for distributed queries or cross-region reads.

SQL to compute per-table size

SELECT database, table, sum(bytes_on_disk) AS bytes_on_disk
FROM system.parts
WHERE active = 1
GROUP BY database, table
ORDER BY bytes_on_disk DESC
LIMIT 100;

Per-user storage usage (indirect)

ClickHouse doesn’t tag parts with the originating user. For per-user storage attribution you can use a hybrid approach:

• Enforce user-level ETL write patterns that tag data with a team column (tenant_id).
• Use query_log to capture write queries and map estimated bytes written to users.

-- Estimate bytes written per user last 30 days
SELECT user, sum(write_bytes) AS bytes_written
FROM system.query_log
WHERE type = 'QueryFinish'
  AND event_time >= now() - INTERVAL 30 DAY
GROUP BY user
ORDER BY bytes_written DESC;

Cost model — simple formula you can implement

To estimate cost per query or per user:

estimated_query_cost = (read_bytes * storage_read_price) + (compute_seconds * cpu_price) + (egress_bytes * egress_price)

Where:

• read_bytes: from profile events or query_log
• compute_seconds: approximate from elapsed * CPU utilization fraction
• egress_bytes: especially relevant for distributed queries across regions

User-level dashboards and reports

Building actionable dashboards for product analytics teams means giving them cost and performance signals tied to identity and workspace. Key panels to include:

• Query volume (QPS) by user/team — trend and heatmap.
• P99 / P95 / median query latency by team and query template.
• Top queries by read_bytes and compute time (cost drivers).
• Storage growth by table and estimated per-team storage allocation.
• Merge backlog and local disk utilization for capacity planning.

Example Grafana panels

1. Top 10 heavy queries — table with query_id, user, elapsed, read_bytes, estimated_cost.
2. Cost burn rate — time series of estimated daily cost using the cost model above.
3. Storage heatmap — per-database/table bytes_on_disk, with alerts for growth rate > threshold.

Alerting strategies that reduce noise

A major failure mode is alert fatigue. OLAP systems are noisy; focus alerts on high-confidence, actionable thresholds.

High-signal alert categories

• Performance — P99 query latency > SLA for > X minutes, or a sudden spike in long-running queries.
• Resource exhaustion — disk usage > 85%, memory pressure, process OOM events.
• Operational — high replication lag, # of failed merges, stuck mutations.
• Cost — daily estimated cost > projected budget or sudden increase in S3 reads/egress.

Prometheus-style alert examples

Assuming you export ClickHouse metrics to Prometheus (via /metrics or a ClickHouse exporter):

# Alert when the P99 query duration across cluster > 10s for 5m
- alert: ClickHouseHighP99
  expr: clickhouse_query_duration_seconds{quantile="0.99"} > 10
  for: 5m
  labels:
    severity: page
  annotations:
    summary: "ClickHouse cluster P99 query duration > 10s"

# Alert on disk usage per node
- alert: ClickHouseDiskUsageHigh
  expr: (clickhouse_disks_free_bytes / clickhouse_disks_total_bytes) < 0.15
  for: 2m
  labels:
    severity: page

Alert tuning tips

• Use sustained thresholds (for e.g., 5–10 minutes) to reduce noise from bursts.
• Group alerts by cluster or data center to avoid redundant pages when multiple replicas act up.
• Prefer alerts that map directly to runbooks — e.g., “DiskHigh” -> steps to add capacity or cleanup TTLs.

Scaling telemetry at scale — practical constraints

Telemetry can become a secondary source of overload. Plan for:

• Retention tiers: Keep raw query logs for a limited window (30–90 days) and retain aggregates for longer.
• Sampling: Sample traces for high-frequency queries, but always capture full traces for errors and slow queries.
• High-cardinality fields: Avoid exploding label cardinality in Prometheus — aggregate by user or team instead of raw query hash when possible.
• Dedicated observability cluster: For very large workloads, run a smaller ClickHouse cluster dedicated to analytics about the main cluster (meta-analytics).

Operational best practices and guardrails

• Enforce query tags and limits: Require client libraries to set team or workspace tags; use ClickHouse quotas and settings profiles to limit resource use.
• Use Resource Groups / Query Settings: Prioritize mission-critical workloads using settings and resource constraints to isolate noisy tenants.
• Automate remediation: Auto-kill runaway queries beyond X minutes after notifying the owner, or throttle them when cluster is congested.
• Chargeback and showback: Run weekly cost reports and encourage teams to optimize queries by showing cost per dashboard or query.

Advanced strategies and 2026 trends

Recent developments through late 2025 and early 2026 accelerated a few trends that are already operational realities:

• Better native telemetry: ClickHouse and ecosystem tooling have added richer metrics endpoints and faster profile event export — use these to centralize profiling.
• OpenTelemetry adoption: Correlating DB query IDs with distributed traces became standard practice across analytics stacks in 2025–2026.
• Serverless and autoscaling: Hybrid clusters mix fixed-node compute with serverless query execution; this amplifies the need for cost per-query estimation.
• AI-assisted observability: Emerging tools automatically surface the root cause (e.g., mispartitioned table, missing filters) by combining query traces and profile events — be mindful of governance and privacy when you feed telemetry into LLMs; see a privacy policy template for guidance.

Plan for these by instrumenting now: tag queries, centralize logs, and build cost models you can refine with real telemetry.

Actionable checklist — what to implement this quarter

1. Instrument app code to set X-ClickHouse-Query-Id and collect OpenTelemetry spans (sample all errors and slow queries).
2. Export ClickHouse metrics to Prometheus and create baseline dashboards (latency, top consumers, disk, merges).
3. Implement a cost model that combines read_bytes, compute seconds, and egress, and surface it in user-level dashboards.
4. Set up 3–5 high-signal alerts: P99 latency, disk fullness, merge backlog, stuck mutations, and daily cost spike.
5. Create a runbook for noisy queries: automatic capture, owner notification, and soft-kill policy.

Case study: reducing cost by 30% in 60 days

Context: A SaaS analytics team saw a 40% month-over-month storage and compute increase. After implementing query tracing, the team identified the top 5 queries responsible for 65% of read bytes. By rewriting those queries, adding proper WHERE clauses and materialized views, and introducing per-team quotas, they reduced costs by 30% within two months.

ClickHouse's adoption and product velocity in 2025–26 make it essential to pair raw performance with observability that ties resource consumption back to teams and queries.

Conclusion & next steps

Observability for OLAP systems like ClickHouse is about connecting three layers: application traces, ClickHouse internals, and cloud cost metrics. By generating canonical query IDs, exporting profile events and system metrics, and building user-level dashboards and cost models, analytics ops teams can stop reacting and start optimizing.

Start small: enable query_id propagation and a handful of Prometheus metrics, then iterate. The investment pays for itself in fewer incidents, smaller bills, and faster dashboards.

Call to action

If you want a starter kit: download our ClickHouse observability checklist and sample Grafana dashboard JSON (production-ready) to deploy this week. Or contact our team to run a 2-week audit of your ClickHouse telemetry and cost model — we'll show where to save 20–40% on your next bill.

Related Reading

• Network Observability for Cloud Outages: What To Monitor to Detect Provider Failures Faster
• Edge+Cloud Telemetry: Integrating RISC-V NVLink-enabled Devices with Firebase
• Running a Bug Bounty for Your Cloud Storage Platform: Lessons
• Trust Scores for Security Telemetry Vendors in 2026
• From Microbatch to Mass Drop: What Fashion Brands Can Learn from a DIY Cocktail Success
• Best Tiny Gifts from CES 2026 Under $10: Perfect $1-Scale Stocking Stuffers
• Emergency Mobility: Using Your E‑Bike and a Portable Power Station During Blackouts
• Sportsbook Lines vs. Model Picks: Building an API Dashboard for Real-Time Edge
• How Gmail’s New AI Features Change Email Outreach for Nutrition Coaches