Boost ClickHouse performance with mark cache: A complete guide

Introduction

ClickHouse is an open source columnar database management system known for its high-performance OLAP capabilities, and its caching mechanisms play a crucial role in query efficiency. One such caching feature is the mark cache, which significantly optimizes data retrieval from MergeTree tables. In this blog, we will explore what mark cache is, how it works, its use cases, ways to monitor it, and best practices for its efficient configuration.

What is mark cache?

Mark cache is an in-memory cache that contains marks, which are metadata pointers enabling ClickHouse to quickly locate specific data within compressed column files. These marks allow ClickHouse to access the relevant data blocks directly, avoiding the need to decompress the entire file.

How mark cache works

• Data files in ClickHouse are divided into granules, which are the smallest units of data retrieval.
• Each granule is associated with a mark that points to its location within the compressed file.
• Upon query execution, ClickHouse initially looks in the mark cache to quickly identify the file segments containing the needed data.
• If the marks are present in the cache, ClickHouse can skip reading irrelevant file portions, resulting in faster performance.
• If the marks are not found in the cache, ClickHouse retrieves them from disk, leading to higher latency.
• The mark cache has a configurable size limit. When the cache is full, the least recently used (LRU) marks are evicted to make room for new marks. This ensures that the cache remains efficient and contains the most frequently accessed marks.

Mark identification and granule access workflow

Efficient query execution in ClickHouse relies on its ability to quickly determine which granules need to be read from disk. This process involves several key steps—from query analysis to leveraging the mark cache. The following diagram provides a visual overview of how ClickHouse identifies relevant marks and accesses granules:

Use cases of mark cache in ClickHouse:

1. Faster query execution
   By storing metadata pointers to data granules in memory, mark cache allows ClickHouse to quickly locate and retrieve only the relevant data blocks needed for the query. This reduces the need to read and decompress entire files, resulting in significantly faster query execution times.
2. Performance improvement for repeated queries
   If the queries often access the same data, the marks for the required granules are likely to be cached. This means that subsequent queries can benefit from the cached marks, leading to consistently improved performance and reduced latency.
3. Real-time analytics
   With real-time requirements, the ability to quickly access relevant data is crucial. Mark cache reduces the time needed to locate data granules, ensuring that real-time queries are executed with minimal delay, which is essential for timely decision-making.
4. Reducing disk I/O
   By optimizing data retrieval, mark cache helps reduce the number of random disk accesses and makes better use of memory resources.

How to monitor mark cache?

Effective monitoring of mark cache is crucial for maintaining optimal performance. ClickHouse provides system metrics to check mark cache statistics. Here are some key queries to help you monitor mark cache:

• Querying mark cache usage
  You can query the system.asynchronous_metrics table to get information about the usage of the mark cache. For example:
  
  SELECT metric, value FROM system.asynchronous_metrics WHERE metric LIKE 'Mark%'; OR SELECT formateReadableSize(value) AS mark_cache_usage FROM system.asynchronous_metrics WHERE metric = 'MarkCacheBytes';

  1 2 3 4 5 6 7

  SELECT metric, value FROM system.asynchronous_metrics WHERE metric LIKE 'Mark%';   OR   SELECT formateReadableSize(value) AS mark_cache_usage FROM system.asynchronous_metrics WHERE metric = 'MarkCacheBytes';

• Checking mark cache hits and misses:
  
  
  SELECT event, value FROM system.events WHERE event LIKE 'Mark%';

  1	SELECT event, value FROM system.events WHERE event LIKE 'Mark%';

  
  This will return the values of MarkCacheHits and MarkCacheMisses
  • MarkCacheHits: Counts the number of times a query successfully retrieves the required marks from the mark cache.
  • MarkCacheMisses: Counts the number of times a query fails to find the required marks in the mark cache and had to read them from disk.
• Checking mark cache hits percentage:
  
  SELECT (value/(value + (SELECT value FROM system.events WHERE event = 'MarkCacheMisses'))) * 100 AS MarkCacheHits_percentage FROM system.events WHERE event = 'MarkCacheHits';

  1	SELECT (value/(value + (SELECT value FROM system.events WHERE event = 'MarkCacheMisses'))) * 100 AS MarkCacheHits_percentage FROM system.events WHERE event = 'MarkCacheHits';

How to calculate mark cache size?

Determining the appropriate size for the mark cache in ClickHouse is crucial for optimizing query performance and ensuring efficient use of memory resources.

The ideal mark cache size depends on various factors:

• Data volume and parts: The more data parts you have, the more marks ClickHouse needs to store. Large tables with numerous parts will require a larger mark cache.
• Query patterns: The nature and frequency of queries, especially those that repeatedly access the same data.
• Available memory: The total amount of RAM available on the server. Reserve sufficient RAM for the operating system and other ClickHouse processes and Cache.

Best practices for mark cache in ClickHouse

• Estimate properly: Calculate the appropriate size for your mark cache based on data volume, query patterns, and available memory.
• Avoid over-allocation: Over allocating memory can lead to resource contention and impact other processes, so target for a balanced allocation.
• Monitor and adjust: Continuously monitor key metrics such as MarkCacheHits and MarkCacheMisses to understand the cache’s effectiveness. If you observe a low hit rate or frequent cache misses, consider adjusting the mark cache size. Regular audits and adjustments ensure that the cache remains optimally configured.
• Fine-tune alongside other caches: Balance mark cache with uncompressed cache and other internal caches for optimal performance.
• Understand workload characteristics: Different workloads have different caching needs. Analyze your workload to understand the specific requirements and configure the mark cache accordingly.
• Continuous monitoring: Implement monitoring solutions, such as Grafana and Prometheus, to continuously observe the mark cache hit ratio and memory utilization. Configure alerts to notify administrators of suboptimal hit rates or excessive memory consumption.

Improve ClickHouse performance

The mark cache is an effective way to make your ClickHouse queries much faster. By keeping an eye on it, setting it to the right size, and tweaking it as needed, you can keep your ClickHouse running smoothly. Don’t forget to regularly check how it’s being used and make changes based on your workload to keep things running at their best.

For those looking for a scalable ClickHouse solution, consider using NetApp Instaclustr for ClickHouse. NetApp provides enhanced performance, reliability, and scalability, making it an excellent choice for managing large-scale data analytics. With NetApp managing your ClickHouse deployments, you can leverage advanced features and comprehensive support to further optimize your data processing and analytics workflows.

Furthermore, integrating advanced monitoring tools like Grafana with NetApp Instaclustr can provide deeper insights into mark cache performance. This will allow you to visualize key metrics and gain a comprehensive understanding of your system’s health.