ClickHouse® Distributed Tables Explained

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 ClickHouse® 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 and let ClickHouse® handle the complexity behind the scenes.

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

Why Distributed Tables Exist

A single ClickHouse® server can process enormous amounts of data.

However, some workloads eventually require:

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

Rather than continuously upgrading a single machine, organizations often distribute data across multiple servers.

This approach introduces a challenge:

How do users query data spread across several nodes without manually querying each server?

Distributed tables solve this problem.

Understanding the Distributed Engine

The Distributed engine acts as a logical layer on top of existing tables.

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

Instead, it serves as a routing and query coordination layer.

Example:

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

In this example:

• analytics_cluster identifies the cluster configuration.
• analytics is the database name.
• events_local is the underlying table.
• cityHash64(user_id) determines how data is distributed.

The Distributed table contains no actual data.

The data lives inside the underlying local tables.

Local Tables vs Distributed Tables

A common source of confusion is the relationship between local and Distributed tables.

Consider:

Shard 1
└── events_local
 
Shard 2
└── events_local
 
Shard 3
└── events_local

Each server stores data inside its local table.

A Distributed table sits above them:

events_distributed
        │
 ┌──────┼──────┐
 │      │      │
Shard1 Shard2 Shard3

Users query:

SELECT *
FROM events_distributed;

ClickHouse® automatically communicates with the relevant nodes and combines the results.

Understanding Sharding

Distributed tables are often used alongside sharding.

Sharding means splitting data across multiple servers.

Example:

User 1  → Shard 1
User 2  → Shard 2
User 3  → Shard 3

A sharding key determines where data is stored.

Example:

cityHash64(user_id)

Using a consistent sharding key ensures that related data lands on predictable nodes.

A good sharding strategy helps:

• Balance storage
• Distribute workload
• Improve scalability
• Reduce bottlenecks

How Queries Work

Suppose a query is executed against a Distributed table:

SELECT
    country,
    count()
FROM events_distributed
GROUP BY country;

ClickHouse® performs several steps:

1. Sends the query to participating shards.
2. Executes local processing on each shard.
3. Collects partial results.
4. Merges results.
5. Returns the final response.

This parallel execution model is one of the reasons ClickHouse® scales so effectively.

Distributed Inserts

Distributed tables can also accept inserts.

Example:

INSERT INTO events_distributed
VALUES (...);

The Distributed engine evaluates the sharding key and forwards rows to the appropriate shard.

This simplifies application logic because clients do not need to determine where individual rows belong.

What Happens Without a Sharding Key?

A Distributed table may be created without specifying a meaningful sharding strategy.

However, this often leads to:

• Uneven distribution
• Hot shards
• Storage imbalance
• Poor scalability

Choosing an appropriate sharding key is one of the most important decisions when designing a distributed ClickHouse® cluster.

Common candidates include:

• user_id
• customer_id
• tenant_id
• organization_id

The best choice depends on query patterns and data distribution.

Distributed Tables and Replication

Distributed tables and replication are often confused, but they solve different problems.

Sharding

Sharding distributes data across multiple servers.

Goal:

Scale Out

Replication

Replication creates copies of data.

Goal:

High Availability

A typical production cluster may use both:

Shard 1
├── Replica A
└── Replica B
 
Shard 2
├── Replica A
└── Replica B

Distributed tables then provide a single entry point for querying the entire cluster.

Query Optimization Considerations

Distributed queries introduce network communication between nodes.

For this reason, efficient query design remains important.

Good practices include:

• Filter early
• Use appropriate sorting keys
• Limit unnecessary data movement
• Design effective sharding keys
• Avoid excessive cross-shard operations

The less data that must travel across the network, the better overall performance tends to be.

Common Mistakes

Assuming Distributed Tables Store Data

They do not.

The actual data resides in local tables.

Choosing Poor Sharding Keys

An uneven sharding strategy can create overloaded nodes.

Ignoring Query Patterns

Sharding should align with how data is queried.

Confusing Replication with Distribution

Replication improves availability.

Distribution improves scalability.

They address different architectural requirements.

Example Architecture

A typical setup might look like:

analytics_cluster
 
Shard 1
├── events_local
└── Replica
 
Shard 2
├── events_local
└── Replica
 
events_distributed

Applications interact with:

events_distributed

while ClickHouse® handles query routing and result aggregation automatically.

When Should You Use Distributed Tables?

Distributed tables make sense when:

• Data exceeds the capacity of a single server.
• Horizontal scaling is required.
• Multiple shards exist.
• Applications need a unified query interface.
• Large analytical workloads are distributed across nodes.

For smaller deployments running on a single server, Distributed tables are often unnecessary.

Best Practices

When using Distributed tables:

• Design sharding keys carefully.
• Understand your query patterns.
• Keep local tables consistent across nodes.
• Minimize unnecessary cross-shard operations.
• Combine sharding and replication appropriately.
• Monitor cluster health regularly.
• Test data distribution before production deployment.

Final Thoughts

Distributed tables are a fundamental building block for scaling ClickHouse® beyond a single server.

They provide a simple abstraction that allows users to query and insert data across multiple nodes without worrying about the underlying distribution mechanics.

However, understanding the distinction between local tables, Distributed tables, sharding, and replication is essential for building efficient clusters.

When designed correctly, Distributed tables enable ClickHouse® to scale analytical workloads across multiple servers while preserving the familiar SQL experience that users expect.

Exploring ClickHouse® for Your Analytics?

At Quantrail Data, we help teams run ClickHouse® reliably for real-time analytics – from Kubernetes deployments and migrations to performance tuning in production.

We see these challenges firsthand while supporting demanding analytics workloads. In one recent engagement, a customer achieved near bare-metal performance with ClickHouse® in production – a story we’ve shared here: Success Story: Quantrail Bare-Metal ClickHouse® Deployment

If you’re evaluating ClickHouse® or trying to get more out of an existing setup, we’re happy to share practical lessons from real-world deployments.

Contact

Quantrail Data

References

ClickHouse® Documentation – Distributed Table Engine

ClickHouse® Documentation – Cluster Discovery