Introduction
The Library Cache in ClickHouse is a cache of the compiled and optimized query plans used to execute queries. The purpose is to store frequently used query plans to reduce the overhead of parsing, compiling, and optimizing new queries, and to allow for faster execution of repeat queries.
Monitoring Query Plans in Library Cache
When a query is executed in ClickHouse, it is first parsed, compiled, and optimized into a query plan. The query plan is then stored in the Library Cache, so that it can be reused for subsequent executions of the same query. This helps to reduce the overhead of parsing, compiling, and optimizing new queries, and speeds up the execution of repeat queries.
In ClickHouse, you can monitor the Library Cache to see information about the query plans stored in the cache, including the number of queries stored, the size of the cache, and the average age of the queries in the cache. To monitor you can use the system.query_log table and the system.query_log_optimizing table.
For example, you can use the following query to see the number of queries stored in the Library Cache:
SELECT count(*) FROM system.query_log;
You can also use the system.query_log_optimizing table to see the status of queries that are being compiled and optimized, including the time it takes to compile and optimize each query.
Conclusion
By monitoring the Library Cache, you can gain insights into the performance and efficiency of your ClickHouse database, and identify any potential performance bottlenecks that may be related to the Library Cache.
If you would like to read more about Caches and ClickHouses, do consider reading the below articles:
- ClickHouse Troubleshooting: Impact of External Cache on Internal Cache Efficiency
- Deep Dive into ClickHouse Query Cache Implementation
- Configuring Data Caches in ClickHouse for High Performance
- ClickHouse Caches: Configuring Buffer Cache for High Performance
You might also like:
- ClickHouse vs Hadoop for Real-time Analytics
- ClickHouse Materialized Views: Using Live Views to Compute Real-time Moving Averages
- Integrating Parquet File Ingestion into ClickHouse Using Kafka: A Step-by-Step Guide
- ClickHouse Proxy Server for Read-Write Splitting with Connection Pooling & Query Cache
- ClickHouse Performance: Choosing the Right CPU Infrastructure
