ClickHouse is a highly performant open-source columnar database management system that enables analytics on large volumes of data. Its architecture is tailored for high-speed read and write operations and it can be scaled both vertically and horizontally. Among its features, replication is essential for ensuring data availability and fault tolerance, while distribution helps in managing large datasets by splitting them across multiple nodes. In this article, we will delve deeper into the roles and functioning of Replicated and Distributed tables in ClickHouse, with a focus on the new internal replication mechanism known as ClickHouse Keeper.

Replication and Its Importance

Replication in ClickHouse involves creating copies of data across multiple nodes. This serves multiple purposes:

• High Availability: By replicating data across different nodes, the system ensures that if one node fails, the data is still accessible from another node.
• Fault Tolerance: Replication helps in recovering quickly from hardware failures or other issues without data loss.
• Read Scalability: Multiple copies of the data allow more queries to be served parallelly, enhancing the read throughput.

In ClickHouse, replicated tables are managed through engines like ReplicatedMergeTree. This engine ensures that each replica remains consistent with others by using a replication log stored in ZooKeeper. Each replica pulls changes from this log and applies them to its dataset.

Enter ClickHouse Keeper

ClickHouse Keeper is an alternative to ZooKeeper, developed in-house for managing replication logs. It was introduced to overcome some of the limitations posed by ZooKeeper, such as the requirement of a separate deployment and management configurations, and Java dependency, which could complicate the operational overhead for ClickHouse users.

ClickHouse Keeper serves as a coordination service, which handles tasks like electing a leader among replicas, distributing configuration changes, and ensuring consistent states across replicas. It uses the RAFT consensus algorithm to ensure that all operations are performed reliably and in the correct order across the cluster.

Distributed Tables and Data Sharding

Distributed tables in ClickHouse do not store any data themselves. Instead, they provide an interface to access data distributed across multiple nodes, making it look like a single database table. This is particularly useful for querying large datasets that are horizontally partitioned across different nodes (sharding).

When a query is issued to a distributed table, ClickHouse sends subqueries to each shard that holds a part of the table, gathers the results from each shard, and combines them to produce the final result. This allows for linearly scaling the performance and storage.

Combining Replication and Distribution

ClickHouse allows combining replication and distribution by creating replicated tables on each shard. This model provides both horizontal scaling and high availability. Each shard handles a subset of the data and each subset is replicated across different nodes for reliability.

Technical Example

Consider a scenario where we need to set up a ClickHouse cluster with high availability and scalability. We could organize our database with several shards, each containing a replicated table. Below is a high-level illustration of the setup:

• Shard 1: Contains ReplicatedMergeTree tables on Nodes 1 and 2.
• Shard 2: Contains ReplicatedMergeTree tables on Nodes 3 and 4.

Queries to a distributed table that references these shards would be processed by fetching relevant data from each shard (and the corresponding replicas within those shards), ensuring that the query is processed in the most efficient manner possible.

Summary

The following table summarizes key points about ClickHouse replication and distribution:

In conclusion, ClickHouse combines advanced replication mechanisms via ClickHouse Keeper with powerful data distribution capabilities to provide a robust, scalable, and resilient analytics database solution. Whether managing massive amounts of data or ensuring continuous availability and performance in production environments, ClickHouse's architectural design offers compelling advantages for enterprises and individual users alike.