What makes ClickHouse so fast for analytics?

ClickHouse combines column-oriented storage, compression, parallel execution, MergeTree data parts, and sparse primary indexes that can skip granules when ORDER BY aligns with filters. Actual latency depends on schema, data distribution, query shape, concurrency, cache state, ingestion, and infrastructure.

What use cases is ClickHouse best suited for?

ClickHouse is commonly evaluated for logs, events, time-series analytics, observability, customer-facing dashboards, and aggregation-heavy reporting. It should be assessed carefully when the primary workload needs frequent point transactions, row-by-row updates, or relational constraints typical of an OLTP system.

Do you support ClickHouse Cloud?

Yes. JusDB can assess ClickHouse Cloud and self-managed deployments, including Kubernetes. Engagements can cover workload fit, schema and ORDER BY design, ingestion, sharding and replication, Keeper, query performance, cost evidence, migration, recovery, and operational handoff.

ClickHouse · MergeTree · Keeper

Columnar OLAP

ClickHouse, billions of rows per second.

In short: ClickHouse is an open-source, column-oriented OLAP database management system built for real-time analytics over very large datasets. Its MergeTree storage engine, vectorized query execution, and aggressive compression let it scan billions of rows per second. ClickHouse's scan speed makes it a popular choice for analytics, observability, and time-series data.

We design, tune, and operate ClickHouse clusters behind interactive dashboards and high-volume event pipelines. Our engineers handle schema modeling, cluster topology, ingestion, and query tuning.

JUSDB_CLICKHOUSE_PROD

LIVE

ClickHouse 24 · MergeTree

Sharded + replicated · Keeper

Tuned

Queries / sec

Rows scanned / sec

0.1B

Query p99

20ms

Compression

8.0×

Rows Scanned / sec

0.00B rows/s

[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

0B+

Rows Scanned / sec

0.99%

Uptime SLA

0×

Median Query Speedup

0×

Avg Compression Ratio

What we do

ClickHouse engineering

We specialize in high-performance ClickHouse deployments for real-time analytics and data warehousing. The work runs 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. Delivery is at-least-once. We add deduplicated writes via ReplicatedMergeTree block dedup, aiming at effectively-once semantics within the configured dedup window.

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 with EXPLAIN PIPELINE and deep system.query_log auditing.

OLAP performance

ClickHouse expertise
for real-time analytics

We tune MergeTree engines, design aggregating materialized views, and dial in compression codecs. The goal is billion-row aggregations that return in under a second without a separate warehouse. Dig into our ClickHouse performance tuning work for the full methodology.

ReplacingMergeTree & AggregatingMergeTree optimization

Distributed table sharding keys and global IN/JOIN efficiency

Kafka table engine integration (at-least-once ingestion with ReplicatedMergeTree block dedup for effectively-once)

Materialized Views state management and populating

PREWHERE filtering and skip indexes configuration

Integration with Grafana, Superset, and Custom BI

ClickHouse Keeper migration from ZooKeeper

System query log tracing and bottleneck profiling

Analytical Performance

After tuning

Skip-index pruning of parts0%

Projection-accelerated queries0%

Storage compression ratio0%

Materialized-view rollup coverage0%

50×

Median speedup

11×

Compression ratio

Real cases

Queries we've transformed

Full Scan, No ORDER BY

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

Wrong Partition Key

8,400ms

140ms

Partitioned by user_id — every part touched

The fix

Re-partitioned by toYYYYMM(event_date), prunes to 1 part

JOIN on Huge Tables

21,000ms

95ms

Hash JOIN of two 4B-row tables blew up memory

The fix

Replaced with dictionary lookup + denormalized fact table

Cluster ACTIVE3 shards × 2 replicas · Keeper

0.00%

Cluster Uptime

<0s

Failover RTO

0ms

Replica Lag

shard-01 · replica-a

REPLICATEDONLINE

shard-02 · replica-a

REPLICATEDONLINE

shard-03 · replica-b

REPLICATEDONLINE

High availability

Always on. Replicated by design.

ReplicatedMergeTree tables coordinate through ClickHouse Keeper, so replicas of the same shard stay in sync. In the topologies we design, each shard keeps redundant replicas with automatic recovery. A lost node rejoins and re-syncs without manual intervention.

ReplicatedMergeTree with multi-replica redundancy

ClickHouse Keeper (Raft) replacing ZooKeeper coordination

Automatic replica recovery and re-sync on node loss

Distributed tables with shard-aware query routing

Verified backups via clickhouse-backup and S3 snapshots

Incident response

A merge-storm P1, handled in under 15 minutes.

When too-many-parts stalls inserts and a merge storm pins CPU, a named ClickHouse engineer responds. You do not wait in a ticket queue. We throttle merges, fix partitioning, and clear the backlog online. A blameless postmortem follows.

P1 alert → named ClickHouse engineer paged in under 15 minutes

Root cause via system.query_log, system.merges & Grafana

Repartition + merge throttling — no insert downtime

Blameless postmortem with a prevention plan

Live incident replayP1 → resolved · ~14 min

00:00Alert fired

Dashboard p99 > 12s — analysts blocked

00:03On-call paged

Named OLAP engineer in under 15 min, not a ticket queue

00:07Root cause

Full scan — query ignored ORDER BY key, no skip index

00:11Fix applied

Added minmax skip index + tuned ORDER BY, no downtime

00:14Resolved

Scan pruned, p99 12s → 200ms — total 14 min

Pre-Migration Assessment

Postgres / Druid analytics → ClickHouse

READY

Schema & ORDER BY key design0%

Bulk load (clickhouse-client / S3)0%

MergeTree merge catch-up0%

Cutover readiness0%

Estimated cutover window: < 10 minutes

Migration

Move to ClickHouse without the downtime

Postgres, MySQL or a legacy warehouse → ClickHouse. We model the right MergeTree schema and backfill with parallel inserts. Live changes stream in via Kafka. We cut over once row counts reconcile. See our ClickHouse migration service for the full cutover playbook.

Schema modeling: MergeTree keys, codecs & materialized views

Parallel backfill plus Kafka engine incremental sync

Row-count and checksum reconciliation before cutover

ClickHouse Cloud, self-hosted & Kubernetes targets

Technology stack

ClickHouse Ecosystem & Integration Tools We Work With

Complete ClickHouse ecosystem and integration tools

ClickHouse

ClickHouse Cloud

Grafana

Tableau

Apache Superset

Kafka

Apache Spark

dbt

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