ClickHouse · MergeTree · KeeperClickHouse, high-throughput analytics on large datasets.
In short: ClickHouse is an open-source, column-oriented OLAP database management system built for real-time analytics over large datasets. Its MergeTree storage engine, vectorized execution, and compression support high-throughput analytical scans for observability, time-series, and reporting workloads.
High-speed OLAP solutions with ClickHouse for real-time analytics, time-series data, and interactive dashboards. Achieve lightning-fast query performance at scale.
JUSDB_CLICKHOUSE_PRODClickHouse 24 · MergeTree
Sharded + replicated · Keeper
0
0.1B
20ms
8.0×
[OK] mergetree: background merge complete, 412→1 part
[INF] mutation: ALTER UPDATE on events 78% applied
[OK] replicated: keeper quorum healthy, 0 lag
[INF] mat-view: hourly_rollup refreshed in 1.2s
Representative fleet view · illustrative metrics
Illustrative operating profile - example fleet and outcome figures, not audited customer results.
0B+
Rows Scanned / sec
0.99%
Uptime SLA
0×
Median Query Speedup
0×
Avg Compression Ratio
ClickHouse engineering
Specialized in high-performance ClickHouse deployments for real-time analytics and data warehousing - from ClickHouse consulting and engine-selection advisory to ClickHouse on Kubernetes.
MergeTree Profiling & Tuning
Optimize ClickHouse MergeTree table engines, index granularities, and PREWHERE clauses for sub-second aggregations.
Materialized Views Design
Design real-time aggregating materialized views and specialized projection strategies to pre-compute queries.
ClickHouse + Kafka Integration
High-throughput data ingestion using the Kafka Engine table integration - at-least-once delivery with deduplicated writes (ReplicatedMergeTree block dedup) for effectively-once semantics.
Compression Codec Optimization
Optimize ZSTD, LZ4, and specialized DoubleDelta codecs to maximize storage savings and reduce IO bottlenecks.
Sharding & ZooKeeper/Keeper
Configure ReplicatedMergeTree distributed tables, sharding keys, and ClickHouse Keeper for reliable horizontal scaling.
Observability & Profiling
Query execution plan profiling (`EXPLAIN PIPELINE`) and deep system.query_log auditing.
ClickHouse expertise
for real-time analytics
We tune MergeTree engines, design aggregating materialized views, and dial in compression codecs, then benchmark representative aggregations against the workload's latency and concurrency targets. Dig into our ClickHouse performance tuning work for the full methodology.
Analytical Performance
Illustrative target50×
Median speedup
11×
Compression ratio
Illustrative query optimization scenarios
12,000ms
200ms
Read all 6.2B rows - no PRIMARY KEY ordering
The fix
Tuned ORDER BY key + added minmax skip index on event_date
8,400ms
140ms
Partitioned by user_id - every part touched
The fix
Re-partitioned by toYYYYMM(event_date), prunes to 1 part
21,000ms
95ms
Hash JOIN of two 4B-row tables blew up memory
The fix
Replaced with dictionary lookup + denormalized fact table
0.00%
Cluster Uptime
<0s
Failover RTO
0ms
Replica Lag
Resilience by design. Replicated by design.
ReplicatedMergeTree tables coordinate through ClickHouse Keeper, so every shard keeps redundant replicas with automatic recovery - a lost node rejoins and re-syncs without manual intervention.
A merge-storm P1, handled against the contracted response target.
When too-many-parts stalls inserts and a merge storm pins CPU, a named ClickHouse engineer responds - not a ticket queue. We throttle merges, fix partitioning, and clear the backlog online, with a blameless postmortem after.
Dashboard p99 > 12s - analysts blocked
Named OLAP engineer in under 15 min, not a ticket queue
Full scan - query ignored ORDER BY key, no skip index
Added minmax skip index + tuned ORDER BY, no downtime
Scan pruned, p99 12s → 200ms - total 14 min
Pre-Migration Assessment
Postgres / Druid analytics → ClickHouse
Estimated cutover window: < 10 minutes
Move to ClickHouse with a controlled cutover
Postgres, MySQL or a legacy warehouse → ClickHouse. We model the right MergeTree schema, backfill with parallel inserts, stream live changes via Kafka, and cut over once row counts reconcile. See our ClickHouse migration service for the full cutover playbook.
ClickHouse Ecosystem & Integration Tools We Work With
Complete ClickHouse ecosystem and integration tools
ClickHouse services FAQ
Direct answers about fit, architecture, tuning, and migration.
What ClickHouse services does JusDB provide?
JusDB provides ClickHouse architecture and schema design, MergeTree and query tuning, materialized-view and projection design, Kafka ingestion, high availability, migration, Kubernetes deployment, observability, and production support for self-managed ClickHouse and ClickHouse Cloud environments.
When is ClickHouse a good fit?
ClickHouse is a strong fit for analytical workloads that scan and aggregate large volumes of event, observability, time-series, or product-analytics data. It is not normally a direct replacement for a transactional OLTP database; many architectures stream data from PostgreSQL, MySQL, or Kafka into ClickHouse for analytics.
How do you improve slow ClickHouse queries?
We review table engines, ORDER BY and partition keys, data-skipping indexes, PREWHERE use, projections, materialized views, join strategy, compression codecs, and system.query_log evidence. We also check part counts, merge pressure, memory limits, remote storage latency, and shard balance before recommending changes.
Can JusDB migrate data to ClickHouse with minimal downtime?
Yes. A typical plan creates the target schema, backfills historical data, streams ongoing changes through Kafka or another CDC path, reconciles counts and checksums, and then cuts readers over with a rollback window. The exact method depends on the source database, data model, write rate, and freshness target.
ClickHouse information, checked against primary documentation
JusDB reviews technology-specific claims against the vendor or project's official documentation. Performance examples without a linked case study are labeled illustrative; actual results depend on workload, data model, version, topology, infrastructure, and test method.
Technically reviewed by the JusDB Database Reliability Engineering team on .
Explore Our ClickHouse Services
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