ClickHouse Memory Management

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Optimizing ClickHouse Memory Management to Prevent OOM Kill Events

ClickHouse is a high-performance analytical database designed for speed, but it can run into memory constraints that lead to unexpected process terminations. One of the primary reasons for such terminations is the Out-of-Memory (OOM) killer, a mechanism in Linux that terminates processes when the system runs out of memory. Understanding how to configure ClickHouse effectively can help prevent these situations.

Recognizing OOM Killer Interventions

When ClickHouse is terminated by the OOM killer, typical symptoms include:

  • ClickHouse suddenly stops or restarts.
  • System logs (accessible via dmesg, /var/log/kern.log, journalctl, or /var/log/messages, depending on the OS) contain messages indicating the OOM killer’s actions.

Configuring ClickHouse Memory Limits

To prevent excessive memory consumption, configure the following settings:

  • max_memory_usage: Limits memory usage for a single query.
  • max_server_memory_usage: Defines the total memory ClickHouse can use.
  • max_memory_usage_for_all_queries: Controls total memory used across all queries.
  • max_bytes_before_external_group_by & max_bytes_before_external_sort: Enables disk-based processing to reduce in-memory pressure.

These settings can be adjusted in config.xml or via SQL session parameters for temporary changes.

Example Configuration:

<!-- /etc/clickhouse-server/config.d/max_server_memory_usage.xml -->
<?xml version="1.0"?>
<clickhouse>
   <max_server_memory_usage>4000000000</max_server_memory_usage>
</clickhouse>

To use a memory-to-RAM ratio:

<?xml version="1.0"?>
<clickhouse>
   <max_server_memory_usage>0</max_server_memory_usage>
   <max_server_memory_usage_to_ram_ratio>0.75</max_server_memory_usage_to_ram_ratio>
</clickhouse>

OS-Level Optimizations

The operating system plays a critical role in memory management. Key optimizations include:

  • Managing the OOM Killer: While disabling it is possible, it’s not recommended as it helps maintain system stability. Instead, adjust oom_score_adj to prevent ClickHouse from being a primary target.
  • Swap Configuration: ClickHouse performs best with sufficient RAM, but a small swap space can prevent sudden failures.
  • Using cgroups: If running ClickHouse in a containerized environment, cgroups can limit memory usage and prevent overconsumption of system resources.

Query Optimization Strategies

Even with proper configuration, inefficient queries can cause high memory consumption. Optimize queries by:

  • Reducing Large Joins: Consider dictionary-based joins or restructuring queries.
  • Avoiding Large ORDER BY Operations: Sorting large datasets in memory is expensive—external sorting may be preferable.
  • Using Projections and Materialized Views: Pre-aggregated data can reduce query-time memory usage.

Monitoring Memory Usage

Proactive monitoring can prevent memory-related crashes before they happen. Use:

  • System tables (system.metrics, system.events) to track memory trends.
  • Prometheus & Grafana for real-time visualization and alerts.
  • ClickHouse logs (/var/log/clickhouse-server/clickhouse-server.log) to analyze memory patterns and optimize queries.

To debug memory issues before an OOM event, check logs with:

grep 'MemoryTracker' /var/log/clickhouse-server/clickhouse-server.log

Understanding ClickHouse Memory Behavior

ClickHouse’s memory consumption varies based on workload. In a well-tuned system:

  • ClickHouse may use 10-30% of RAM under normal conditions.
  • Memory spikes to 50-70% may occur during large queries.
  • The remaining RAM is often utilized by the OS page cache to enhance performance.

If ClickHouse is using less memory than expected, it could indicate an oversized instance or an underutilized page cache.

Conclusion

Preventing ClickHouse from being terminated due to memory issues requires a combination of configuration tuning, OS-level adjustments, and query optimization. By carefully setting memory limits, optimizing queries, and monitoring resource usage, you can ensure stable and efficient ClickHouse performance even under heavy workloads.

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References

https://chistadata.com/how-to-avoid-the-linux-oom-killer-on-clickhouse/#:~:text=Optimise%20queries%20to%20reduce%20memory,keys%20that%20consume%20less%20memory.

Rescuing ClickHouse® from the Linux OOM Killer

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