Part 10: Scaling & Operations

You’ve made it to the final part! We’ve covered everything from installation to advanced query tuning. Now, let’s talk about what happens when one server isn’t enough.

Replication

Replication provides high availability. In ClickHouse, this is done using the ReplicatedMergeTree engine, which uses ZooKeeper (or ClickHouse Keeper) to coordinate data consistency across replicas.

CREATE TABLE hits_replicated (
timestamp DateTime,
user_id UInt64
) ENGINE = ReplicatedMergeTree(‘/clickhouse/tables/{shard}/hits’, '{replica}')
ORDER BY timestamp;

Sharding

Sharding allows you to scale horizontally by distributing data across multiple nodes. You query a Distributed table, which acts as a router to the underlying local tables on each shard.

CREATE TABLE hits_distributed AS hits_replicated
ENGINE = Distributed(my_cluster, default, hits_replicated, rand());

Operations & Monitoring

Running a cluster requires visibility. ClickHouse exposes internal metrics via system tables (system.metrics, system.events) which can be scraped by Prometheus and visualized in Grafana.

Series Conclusion

Congratulations on completing the “ClickHouse: From Zero to Hero” series! You now have the knowledge to build, optimize, and scale a world-class real-time analytics platform. Go forth and query!