Evaluating ClickHouse for Preprod Observability: OLAP for Test Telemetry

Fight environment drift and exploding telemetry costs: why your preprod observability needs an OLAP rethink

Pain point: Your CI pipeline spins up hundreds of ephemeral preprod environments per day, each emitting high-cardinality logs and test telemetry that you must store, query, and troubleshoot — but production-like fidelity becomes cost-prohibitive and slow to query.

In 2026 the observability landscape is shifting: teams are moving high-volume test telemetry into purpose-built OLAP systems instead of legacy cloud data warehouses or ad-hoc log stores. This article assesses ClickHouse as an OLAP backend for preprod observability — comparing performance, cost, integrations, and operational trade-offs against incumbents like Snowflake.

Executive summary (most important takeaways first)

• Speed and cardinality: ClickHouse delivers low-latency ad-hoc queries on high-cardinality telemetry because of its columnar format, aggressive compression, and MergeTree engines tuned for time-series and logs.
• Cost advantage: For retention windows of days-to-weeks and telemetry with high row counts, ClickHouse (self-managed or cloud) typically costs less than Snowflake for storage + interactive queries — especially when using downsampling and TTLs.
• Integration fit: ClickHouse integrates well with modern observability pipelines: Fluent Bit/Vector for ingestion, Kafka/Redpanda for buffering, and Grafana or Superset for exploration. It fits CI/IaC workflows via Helm, Terraform providers, and GitOps patterns.
• Operational trade-offs: Snowflake wins for minimal ops, mature governance, and cross-team SQL semantics. ClickHouse often requires ops expertise or use of ClickHouse Cloud to reduce operational burden.
• 2026 trends: The market is accelerating toward specialized OLAP for telemetry; ClickHouse’s growth and funding in late 2025/early 2026 reflect this trend and improved cloud offerings.

Why OLAP for preprod telemetry now (2026 context)

Preprod observability has evolved beyond simple log retention. Modern test suites and environment fuzzing generate millions of structured events per day. Teams need:

• sub-second ad-hoc queries to debug CI failures and flaky tests,
• cheap storage for large but short-lived datasets from ephemeral environments,
• aggregations and rollups for SLA and deployment metrics, and
• tight integration with IaC and container workflows so telemetry streams originate from the environment lifecycle itself.

Cloud data warehouses such as Snowflake were designed for analytics at enterprise scale, but their separation of storage and compute and pricing models can be inefficient for the high-cardinality, high-ingest density, and short retention needs of preprod telemetry. In contrast, specialized columnar OLAP engines like ClickHouse are optimized for many small columns, fast scans, and compression — a natural fit for test telemetry.

What makes ClickHouse appealing as an OLAP backend for test telemetry

1. Architecture and engines tailored for time-series and logs

ClickHouse uses columnar storage and MergeTree-family table engines that are optimized for append-heavy workloads. You get:

• fast vectorized execution for large scans,
• efficient compression (LZ4, ZSTD),
• partitioning by time for fast TTL-driven retention, and
• materialized views for on-write downsampling/rollups.

2. High ingest throughput and low-latency queries

ClickHouse excels when your preprod agents emit millions of small events per second. It supports native Kafka/Streaming ingestion patterns, HTTP bulk inserts (JSONEachRow), and connectors from Vector/Fluent Bit. This reduces pipeline complexity and provides near real-time visibility into test runs.

3. Cost controls suited to ephemeral environments

Because ClickHouse compresses densely and can be configured to downsample/TTL data aggressively, storage costs for short-lived test telemetry drop significantly. Combined with spot instances or autoscaling in ClickHouse Cloud, you can maintain production-like fidelity for a fraction of the cost charged by generalized cloud warehouses.

4. Tooling and ecosystem (2026 maturity)

By 2026 ClickHouse has mature operators for Kubernetes, a managed cloud offering with region availability, and wide support in observability UIs like Grafana and Superset. The 2025–2026 investment cycle accelerated vendor integrations making ClickHouse a first-class observability target.

Where Snowflake still has advantages

• Simplicity and governance: Snowflake’s fully managed architecture reduces operational load and leverages mature access controls, data sharing, time-travel, and data marketplace features.
• SQL semantics and ecosystem: Many analytics teams already standardize on Snowflake SQL and BI connections, which reduces context switching.
• Predictable concurrency: Snowflake isolates compute for concurrency bursts; ClickHouse requires cluster sizing and operational tuning to handle extreme concurrency smoothly.

Practical evaluation plan: How to benchmark ClickHouse vs Snowflake for your preprod telemetry

Run a reproducible, small-scale benchmark that models your real telemetry and CI patterns. Don’t rely on vendor numbers — your cardinality and query shapes matter.

Step 1 — Model your telemetry

1. Export a representative sample of test telemetry: traces of events, tags/labels (environment-id, build-id, test-suite, container-id), timings, and error codes.
2. Define cardinality tiers: low (service-level), medium (test-run), and high (per-container or per-VM).

Step 2 — Ingest pipeline parity

Use equivalent ingestion paths for fairness:

• ClickHouse: Ingest via Kafka engine → buffer → INSERT, or use Vector/Fluent Bit -> ClickHouse HTTP insert.
• Snowflake: Ingest via staged files (S3) + COPY INTO or Snowpipe for continuous ingestion.

Step 3 — Queries to simulate

Measure:

• point lookups for a single environment-id or build-id,
• high-cardinality group_bys across tag combinations,
• time-range scans with aggregations (count, p50, p95), and
• ad-hoc exploratory queries used in incident triage.

Step 4 — Cost model

Estimate 30/90/365-day retention scenarios, include storage, ingestion egress, compute time for queries, and any managed service charges (e.g., Snowflake credits). For ClickHouse, include node-hours, disk, and ops time (or ClickHouse Cloud costs).

Step 5 — Run and iterate

Automate your benchmark using GitHub Actions or GitLab CI. Capture metrics: ingest latency, query latency P50/P95/P99, CPU/memory utilization, and cost. Publish results internally so product and SRE teams can weigh trade-offs.

Design patterns and best practices for ClickHouse as preprod observability store

Schema design: wide vs narrow

Avoid extremely wide JSON columns for high-cardinality tags. Instead:

• store frequently queried tags as dedicated columns (environment_id, build_id, test_name),
• use a tags map for low-use metadata but avoid querying it in heavy scans, and
• apply low-cardinality encoding (LowCardinality(String)) for moderate-cardinality fields to reduce memory pressure.

Partitioning and TTL

Partition by date (e.g., toYYYYMMDD(timestamp)) and set TTLs to drop raw rows after a short window (7–30 days) while retaining downsampled aggregates in a separate table for longer-term metrics.

Materialized views for downsampling

Use materialized views that aggregate and insert into rollup tables on write. This makes P95/P99 queries over longer windows cheap and fast.

Buffering strategy

Use Kafka or Redpanda as a buffer between CI agents and ClickHouse. This decouples spikes during massive test runs and enables replays for debugging flaky test scenarios.

Alerting and anomaly detection

Export ClickHouse aggregates to Prometheus-compatible endpoints or use Grafana’s advanced analytics plugins for anomaly detection driven by ML models. By 2026 many teams run lightweight model scoring near the OLAP store to detect regressions quickly.

Example — Minimal ingestion and query flow

Below is a compact example showing a table definition, JSON insert, and a typical failure triage query.

-- Create a MergeTree table for test telemetry
CREATE TABLE preprod.test_telemetry (
  ts DateTime,
  env_id String,
  build_id String,
  test_name String,
  container_id String,
  status String,
  duration_ms UInt32,
  error_code Nullable(String)
) 
ENGINE = MergeTree()
PARTITION BY toYYYYMMDD(ts)
ORDER BY (env_id, build_id, ts)
TTL ts + INTERVAL 14 DAY
SETTINGS index_granularity = 8192;

-- Insert via HTTP JSONEachRow
curl -sS 'http://clickhouse:8123/?query=INSERT%20INTO%20preprod.test_telemetry%20FORMAT%20JSONEachRow' \
  -d '{"ts":"2026-01-18 12:34:00","env_id":"ci-123","build_id":"b-456","test_name":"login_flow","container_id":"c-789","status":"fail","duration_ms":3200,"error_code":"AUTH_TIMEOUT"}'

-- Typical triage query: find failing tests this build
SELECT test_name, count(*) AS failures, avg(duration_ms) as avg_ms
FROM preprod.test_telemetry
WHERE build_id = 'b-456' AND status = 'fail'
GROUP BY test_name
ORDER BY failures DESC
LIMIT 50;

Operational checklist before adopting ClickHouse

• Decide managed vs self-hosted: choose ClickHouse Cloud to reduce ops, or run an operator on Kubernetes if you need full control.
• Define retention and downsampling policies per environment type (short-lived ephemeral vs integration vs long-term audit).
• Implement a buffering layer (Kafka/Redpanda) between CI agents and ClickHouse to absorb bursts.
• Automate schema rollouts with Terraform or Helm in CI merge pipelines.
• Set resource quotas and autoscale rules to prevent query storms from impacting cluster health.

Integration recipes: CI, IaC, and container platforms

CI (GitHub Actions / GitLab)

1. Spin up ephemeral preprod environment via workflow using Terraform Cloud and Kubernetes provider.
2. Emit telemetry to a centralized Kafka cluster via Vector/Fluent Bit sidecar in each ephemeral pod.
3. Pipeline job waits for test telemetry summary in ClickHouse using a short query to verify no regressions before merge.

IaC (Terraform)

Use Terraform provider for ClickHouse or the ClickHouse Cloud module to declaratively manage database, users, and quotas. Put telemetry schema changes through the same PR review as application changes to prevent mismatch between environment and analytics schema.

Containers & Kubernetes

Deploy ClickHouse with the community operator or Altinity/ClickHouse Operator for production clusters. Use LocalSSD-backed storage for hot partitions, and object storage (S3) for cold backups and long-term snapshots.

Cost-control playbook

• Use short TTLs for raw telemetry and keep only aggregated rollups long-term.
• Leverage compression codecs (ZSTD) and partition pruning to reduce I/O and storage.
• Run benchmarking with spot instances or smaller nodes and autoscale when CI load spikes (via operator autoscaler).
• Enforce query limits and resource groups to isolate ad-hoc developer queries from ingestion nodes.

When to choose Snowflake instead

Choose Snowflake when your team values:

• minimal ops and enterprise governance over low-level performance tuning,
• tight integration with existing enterprise BI and data-sharing contracts, or
• when telemetry volumes are moderate and long-term analytics on combined production + preprod datasets justify Snowflake’s pricing model.

Real-world example: a 2026 preprod observability stack

One engineering org in 2026 implemented this stack for preprod observability:

• Ephemeral environments provisioned with Terraform + EKS via GitHub Actions,
• Telemetry emitted from tests via Vector -> Redpanda (buffer) -> ClickHouse cluster,
• Materialized views downsampling per-build metrics, 14-day raw retention, 365-day rollups,
• Grafana dashboards and Alertmanager for flaky-test alerts, and
• Automated prune of stale environment telemetry tied to Terraform destroy events.

The result: sub-second failure triage queries experienced by devs, a 40–60% reduction in telemetry storage cost compared to the org’s former Snowflake setup for their test telemetry workload, and faster mean-time-to-merge because CI checks could run cheap pre-merge telemetry checks.

Limitations and gotchas

• ClickHouse requires attention to schema and partitioning. Mistakes can lead to high memory usage or slower queries.
• Complex multi-tenant governance (cross-team data policies) is easier with Snowflake’s mature access features.
• Extreme concurrency with heavy ad-hoc analysts requires careful cluster sizing and resource isolation.

Tip: treat ClickHouse like a performance-sensitive data system — automate schema reviews, use CI-driven benchmarks, and codify retention policies in IaC.

Future predictions (2026–2028)

• OLAP engines optimized for observability will become standard — expect further ecosystem tooling that simplifies telemetry ingestion into ClickHouse-like stores.
• Managed ClickHouse offerings will add better autoscaling and spot-instance compatibility, narrowing the operational gap with Snowflake.
• Hybrid patterns — Snowflake for long-term business analytics plus ClickHouse for short-lived, high-cardinality telemetry — will become a common, cost-efficient architecture.

Actionable checklist to get started (next 7 days)

1. Export 1 week of representative preprod telemetry and load it into a dev ClickHouse instance (Docker or ClickHouse Cloud trial).
2. Run the benchmark queries from the evaluation plan for 30/90-day retention scenarios.
3. Implement an initial materialized view rollup and a 14-day TTL for raw data.
4. Integrate the triage query into a GitHub Action that gates merges on regression-free telemetry for a single critical pipeline.
5. Compare costs and developer feedback after one sprint and document results for your platform review board.

Final assessment

ClickHouse is a compelling OLAP choice for preprod observability when you need fast ad-hoc queries over high-cardinality, high-volume test telemetry at a controlled cost. In 2026 its ecosystem maturity and vendor investment make it a realistic choice for platform teams that are willing to accept some operational responsibility or use ClickHouse Cloud to reduce that burden.

Snowflake remains a strong option for organizations prioritizing minimal ops, complex governance, or joint long-term analytics across production and test datasets. In many cases the optimal architecture in 2026 is hybrid: ClickHouse for real-time preprod telemetry plus Snowflake for consolidated, curated analytics.

Call to action

Ready to evaluate ClickHouse for your preprod telemetry? Start with a reproducible benchmark. Try a ClickHouse Cloud trial or spin a dev cluster with our GitHub repo (links in your platform). If you want a ready-made benchmark and IaC templates — reach out to the preprod.cloud team and we’ll share a workshop that gets your CI gating and telemetry pipeline running in under a day.

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