ClickHouse Pro-ClickHouse optimization assistant

Example

Schema/ordering example: create an event table optimized for recent time window aggregations: CREATE TABLE events ( event_time DateTime, user_id UInt64, region String, event_type LowCardinality(String), value Float32 ) ENGINE = MergeTree() ORDER BY (toYYYYMMDD(event_time), user_id) SETTINGS index_granularity = 8192; Materialized view for daily aggregates: CREATE MATERIALIZED VIEW mv_daily TO daily_agg AS SELECT toDate(event_time) AS day, user_id, count() AS cnt FROM events GROUP BY day, user_id;

Scenario

A BI dashboard that needs minute-granularity user activity for the last 7 days. ClickHouse Pro chooses ORDER BY to colocate rows for fast range scans (time first, then user), reduces cardinality with LowCardinality types, picks compression codecs per column (e.g., DoubleDelta for timestamps, LZ4/ ZSTD with tuned level for strings), and creates targeted materialized views/aggregates. The result: heavy GROUP BYs that previously took multiple seconds drop to tens or hundreds of milliseconds; storage cost falls due to better compression; stress tests show predictable tail latency.

Example

Replicated table example for high availability: CREATE TABLE hits ON CLUSTER analytics_cluster ( timestamp DateTime, user_id UInt64, url String ) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/hits','{replica}') ORDER BY (toDate(timestamp), user_id); Typical cluster building blocks include shards (horizontal partitioning), replicas per shard (HA + read scaling), and a coordination service (ClickHouse Keeper or ZooKeeper).

Scenario

Global analytics system: ClickHouse Pro designs a 6-node cluster with 3 shards × 2 replicas. Shards colocate data for different customer sets for query locality; replicas provide fast failover and read throughput. For cross-region needs, ClickHouse Pro recommends async replication to remote read-only replicas, and designs delayed replicas for safe repair. Operational policies include small maintenance windows, rolling upgrades using ON CLUSTER DDLs, monitoring of replication lags (system.replicas), and automated rebalancing scripts. This yields a resilient cluster that scales linearly for reads and can survive a datacenter outage with minimal data loss.

Example

Kafka streaming ingestion pattern: -- Kafka source table: CREATE TABLE kafka_events ( event_time DateTime, user_id UInt64, payload String ) ENGINE = Kafka('kafka-broker:9092','topic-name','group-analytics','JSONEachRow'); -- Materialized view to move from Kafka to MergeTree: CREATE MATERIALIZED VIEW to_events_mv TO events AS SELECT * FROM kafka_events; Additional patterns: Buffer engine for burst smoothing, small batch inserts using max_insert_block_size and insert_distributed_sync settings, and use of producers with the ClickHouse native protocol for high throughput.

Scenario

Streaming telemetry at 200k events/sec: ClickHouse Pro sets up a Kafka → ClickHouse pipeline using the Kafka engine plus a materialized view targetting a MergeTree table. To avoid backpressure and tiny commits, it configures batch sizes (max_insert_block_size), tunes commit intervals, and uses Buffer or a small intermediate queue for burst absorption. For exactly-once / ordering-sensitive cases, ClickHouse Pro recommends idempotent writes (dedup keys using CollapsingMergeTree or ReplacingMergeTree with sign column) or external coordination and documents tradeoffs between latency and delivery guarantees. Result: reliable sustained ingestion with bounded write latency and predictable compaction behavior.