Platform · ClickHouse

ClickHouse, ready for billions of rows.

Managed ClickHouse for analytical workloads — event ingestion, product analytics, observability, and customer-facing dashboards. Columnar storage with vectorized execution and S3-backed cold tiering.

Create a ClickHouse cluster

OLAP, without the operational tax

ClickHouse is a different beast from Postgres: column-oriented, vectorized, append-mostly. It rewards careful schema design — and punishes the wrong primary key by an order of magnitude. We ship sensible defaults for time-series, event, and metrics workloads, and we keep the operational surface (replication, merges, backups) behind a managed control plane so you focus on the queries.

MergeTree engine family

MergeTree, ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree. Pick the right shape for your data model.

Materialized views

Pre-aggregate on insert. Trade write throughput for query latency. We expose query-time observability so you can tune materializations.

Projections

Alternative orderings of the same table for different query patterns. No more "build a second table just for that one query".

S3 cold storage

Hot data on local NVMe; cold data tiered to S3 via storage policies. TTL moves data on schedule.

Compression

ZSTD by default; LZ4 for hot tables. Average compression ratio across customer workloads: ~9–11×.

HTTP and native protocols

Both protocols exposed. Use `clickhouse-client`, JDBC, the official Node/Python clients, or hit /clickhouse over HTTPS with Basic Auth.

Cluster specs
Max storage (Free)	10 GB compressed (~100 GB raw at 10× ratio)
Max storage (Contact-us)	On request — single-cluster scale to multi-PB
Query parallelism	All vCPUs on cluster, vectorized execution
Ingestion rate	Targets 100M+ rows/sec on Contact-us tier
Compression	ZSTD (default) or LZ4 per column
Hosting region	EU (France primary, Germany replica)

Quickstart

bash

Connect with clickhouse-client

# Get the connection string from the Luceris dashboard
clickhouse-client \
  --host olap-abc.eu.luceris.cloud \
  --port 9440 --secure \
  --user app --password '••••••••' \
  --database main \
  --query "
    CREATE TABLE events (
      ts        DateTime DEFAULT now(),
      user_id   UInt64,
      kind      LowCardinality(String),
      payload   JSON
    ) ENGINE = MergeTree
    ORDER BY (ts, user_id);"

Ideal use cases

Product analytics — event funnels, retention curves, cohort analysis
Observability — logs, metrics, traces at high ingestion rate
Customer-facing analytics dashboards — sub-second aggregates over billions of rows
Time-series storage for IoT or financial market data
Ad-hoc analysis over warehoused data with low p99 latency

Frequently asked questions

How do I size my cluster?

Start with the Free tier and ingest realistic sample data. We expose per-table compression ratios and merge times in the dashboard so you know what scale you actually need before committing to a paid tier.

Can I use Kafka to feed it?

Yes — ClickHouse has built-in Kafka engine tables that consume topics directly. We support both the native ClickHouse Kafka integration and external pipelines (Vector, Fluent Bit, Logstash) writing via HTTP.

What about replication?

On the Contact-us tier, ReplicatedMergeTree backed by Keeper (ClickHouse's built-in coordination layer). The Free tier is single-replica.

Analytical queries that finish before your coffee.

Free tier includes a ClickHouse cluster with 10 GB compressed storage and ZSTD compression.

Create a ClickHouse cluster Talk to us