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Kanishga Subramani
Kanishga Subramani

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Day 29 of 100 Days of ClickHouse® - Understanding Distributed Tables

#clickhouse #database #dataengineering #analytics

Introduction

As datasets grow, a single ClickHouse® server may eventually become insufficient for handling storage, ingestion, or query workloads.

At that point, scaling horizontally becomes necessary.

This is where Distributed tables come into play.

Distributed tables provide a unified way to query data spread across multiple servers while maintaining a familiar SQL experience. Instead of worrying about which node contains specific data, users interact with a single logical table while ClickHouse® handles query routing and result aggregation behind the scenes.

In this article, we'll explore what Distributed tables are, how they work, when to use them, and the architectural concepts every ClickHouse® user should understand.

Why Distributed Tables Exist

A single ClickHouse® server is capable of processing billions of rows efficiently. However, as applications grow, organizations often encounter requirements such as:

  • More storage capacity
  • Higher ingestion throughput
  • Increased query processing power
  • High availability
  • Geographic distribution

Rather than continually upgrading a single machine, it's often more practical to distribute data across multiple servers.

The challenge then becomes simple:

How can users query data spread across several servers without manually connecting to each one?

Distributed tables solve this problem by providing a single logical entry point for the entire cluster.

Understanding the Distributed Engine

The Distributed engine acts as a logical layer over existing local tables.

Unlike MergeTree engines, a Distributed table does not store any data.

Instead, it routes queries and inserts to the appropriate servers.

Example:

CREATE TABLE events_distributed
AS events_local
ENGINE = Distributed(
    analytics_cluster,
    analytics,
    events_local,
    cityHash64(user_id)
);
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Here:

  • analytics_cluster is the cluster configuration defined in ClickHouse.
  • analytics is the database name.
  • events_local is the underlying local table.
  • cityHash64(user_id) is the sharding key.

The Distributed table itself contains no rows. All data remains inside the local MergeTree tables on each server.

Local Tables vs Distributed Tables

This distinction is one of the most important concepts to understand.

Suppose you have three shards.

Shard 1
└── events_local

Shard 2
└── events_local

Shard 3
└── events_local
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Each server stores its own portion of the data inside its local table.

Above them sits a Distributed table.

events_distributed
        │
 ┌──────┼──────┐
 │      │      │
Shard1 Shard2 Shard3
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Applications query the Distributed table:

SELECT *
FROM events_distributed;
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ClickHouse automatically sends the query to the relevant shards, collects the partial results, merges them, and returns a single response.

From the application's perspective, it behaves like querying a normal table.

Understanding Sharding

Distributed tables are typically used together with sharding.

Sharding means splitting data across multiple servers.

For example:

User 1 → Shard 1
User 2 → Shard 2
User 3 → Shard 3
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A sharding key determines where each row is stored.

Example:

cityHash64(user_id)
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A good sharding key helps:

  • Balance storage across servers
  • Distribute query workloads
  • Improve scalability
  • Prevent bottlenecks
  • Keep related data together

Choosing the right sharding strategy is one of the most important architectural decisions in a distributed ClickHouse deployment.

How Distributed Queries Work

Suppose a user runs:

SELECT
    country,
    count()
FROM events_distributed
GROUP BY country;
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ClickHouse performs the following steps:

  1. Sends the query to each participating shard.
  2. Executes the query locally on every shard.
  3. Collects the partial aggregation results.
  4. Merges the results.
  5. Returns the final output.

Because multiple servers process the query simultaneously, distributed queries can scale efficiently as data grows.

Distributed Inserts

Distributed tables can also receive inserts.

Example:

INSERT INTO events_distributed
VALUES (...);
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The Distributed engine evaluates the sharding key and automatically forwards each row to the appropriate shard.

Applications don't need to know where data belongs, making distributed ingestion much simpler.

Choosing the Right Sharding Key

Although a Distributed table can be created without a meaningful sharding strategy, doing so often leads to problems such as:

  • Uneven data distribution
  • Hot shards
  • Storage imbalance
  • Poor query performance
  • Limited scalability

Common sharding keys include:

  • user_id
  • customer_id
  • tenant_id
  • organization_id

The best choice depends on how your application queries data and how evenly values are distributed.

Distributed Tables vs Replication

These concepts are frequently confused, but they solve different problems.

Sharding

Sharding distributes data across multiple servers.

Goal: Scale horizontally.

Replication

Replication creates multiple copies of the same data.

Goal: High availability and fault tolerance.

A production cluster commonly combines both.

Shard 1
├── Replica A
└── Replica B

Shard 2
├── Replica A
└── Replica B
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Distributed tables provide a single interface that allows applications to query the entire cluster regardless of where the data is stored.

Query Optimization Tips

Distributed queries involve network communication between nodes.

To achieve the best performance:

  • Filter data as early as possible.
  • Choose efficient sorting keys.
  • Minimize unnecessary data movement.
  • Design effective sharding keys.
  • Avoid excessive cross-shard operations.

The less data transferred across the network, the faster distributed queries become.

Common Mistakes

Assuming Distributed Tables Store Data

They don't.

The data always resides in local MergeTree tables.

Choosing a Poor Sharding Key

An uneven sharding strategy can overload specific nodes while others remain underutilized.

Ignoring Query Patterns

Your sharding strategy should align with how the application queries data.

Confusing Replication with Distribution

Replication improves availability.

Distribution improves scalability.

Both are important, but they serve different purposes.

Example Architecture

A typical ClickHouse deployment may look like this:

analytics_cluster

Shard 1
├── events_local
└── Replica

Shard 2
├── events_local
└── Replica

events_distributed
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Applications interact only with:

events_distributed
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ClickHouse automatically handles query routing, distributed execution, and result aggregation.

When Should You Use Distributed Tables?

Distributed tables are useful when:

  • Your data no longer fits comfortably on a single server.
  • You need horizontal scalability.
  • Your cluster contains multiple shards.
  • Applications require a unified SQL interface.
  • Large analytical workloads need to be distributed across several nodes.

For single-node deployments, Distributed tables usually aren't necessary.

Best Practices

When designing distributed ClickHouse clusters:

  • Choose sharding keys carefully.
  • Understand your query patterns before distributing data.
  • Keep local table schemas identical across all nodes.
  • Minimize cross-shard communication whenever possible.
  • Combine sharding with replication for both scalability and availability.
  • Monitor cluster health regularly.
  • Validate data distribution before moving to production.

Final Thoughts

Distributed tables are one of the core building blocks that enable ClickHouse® to scale beyond a single server.

Rather than storing data themselves, they act as a routing layer that allows applications to query and insert data across multiple shards using a single SQL interface.

Understanding the relationship between local tables, Distributed tables, sharding, and replication is essential for designing efficient ClickHouse clusters.

With a well-planned sharding strategy and thoughtful cluster design, Distributed tables make it possible to scale analytical workloads across multiple servers while keeping the SQL experience simple and familiar.

Read more... https://www.quantrail-data.com/clickhouse-distributed-tables-explained

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