ClickHouse is a column-oriented database management system known for its high performance in handling analytical queries. Distributing data across shards and replicating these shards are key strategies that ClickHouse employs to ensure data redundancy and balance the load across nodes. However, scenarios can arise where a replica of a shard lacks the latest data. This article delves into the workings of distributed queries in ClickHouse, specifically focusing on how queries are handled when a shard's replica doesn't have up-to-date data.

Overview of ClickHouse Distributed Queries

When handling a distributed query, ClickHouse distributes the task across multiple servers (nodes) where data resides. Each server is responsible for a subset (shard) of the entire data set. These shards can have replicas across different nodes to ensure fault tolerance and availability. The distributed query engine in ClickHouse coordinates the execution of these queries across the cluster, aggregates results, and returns them to the client.

Handling Stale Data on Replicas

Scenario

When a replica does not have the latest data, it typically means it has missed commits that other replicas have incorporated. ClickHouse is designed to detect such scenarios and adjust its behavior accordingly.

Query Execution

1. Consistency Check: When a distributed query is executed, ClickHouse performs a consistency check to determine the state of each replica.
2. Choosing Replicas: ClickHouse selects the replica with the latest data for query execution. The choice of replica helps ensure the query results reflect the most up-to-date state of the dataset.
3. Failover Mechanism: If all replicas of a shard are outdated or unavailable, ClickHouse can be configured to either:
   • Abort the query with an error, indicating that the necessary data is not available.
   • Proceed with the available but outdated data, if strict consistency is not critical for the use case.

Technical Mechanism

• ZooKeeper Coordination: ClickHouse uses ZooKeeper for coordination between cluster nodes. This service helps nodes be aware of the data state across replicas by keeping a consensus on the metadata.
• Replication Logs: ClickHouse maintains logs of operations (replication logs) which are used by replicas to catch up. When a replica heals itself, it reads these logs to synchronize with the latest data state.
• Consistency Level Configuration: Users can configure the level of consistency required for reads and writes globally or for specific queries using settings like $allow_experimental_query_deduplication$ or $prefer_kafka_raft$.

Example

Consider a ClickHouse cluster with two shards, each having two replicas:

• Shard_S1: Replica_R1 (Node1, up-to-date), Replica_R2 (Node2, outdated)
• Shard_S2: Replica_R3 (Node3, up-to-date), Replica_R4 (Node4, up-to-date)

A distributed query is executed. ClickHouse determines the state of each replica and chooses Replica_R1 and Replica_R3 for the query as they are both up to date. Replica_R2 is bypassed due to its outdated state.

Table: Summarizing Replica Selection

Additional Considerations

Load Balancing

When multiple replicas are equally up-to-date, ClickHouse can employ load balancing mechanisms to distribute query loads across these replicas to optimize resources.

Network Latency

Replica selection can be influenced by network latency. Nodes closest to the client or with better network paths might be prioritized to help improve query performance.

Future Improvements

As ClickHouse evolves, features enhancing smart replica selection, such as real-time replication progress awareness and performance-based replica scoring, are of interest. Contributions in these areas can further enhance how ClickHouse handles distributed queries with stale replicas.

Conclusion

ClickHouse’s ability to manage distributed queries effectively, even when some replicas are outdated, is integral to its performance as a high-speed analytical database. By leveraging data consistency checks, failover strategies, and real-time synchronization via ZooKeeper and replication logs, ClickHouse ensures the availability and accuracy of query results, maintaining its reputation as a robust data processing engine.