Optimizing ClickHouse Performance: Strategies for Identifying and Reducing Over-fetching in SELECT Queries

#clickhouse #sql #dba #olap

When ClickHouse, a columnar database optimized for read performance and designed for real-time analytical processing, handles SELECT queries that fetch more records than necessary, it can significantly hamper system performance. This usually results in excessive CPU utilization, disk I/O, and memory consumption, leading to slower query responses and a higher load on the server, potentially affecting other operations.

Why Over-fetching Affects Performance:

Increased Disk I/O: ClickHouse stores data in a columnar format, so unnecessary data retrieval means reading more data from disk than required.
Higher Memory Usage: Over-fetching data consumes more RAM, especially when large datasets are loaded into memory for processing.
CPU Overhead: Processing unnecessary data requires additional CPU cycles for decompression, filtering, and aggregation, which could otherwise be used for executing other queries.

Identifying Queries Fetching More Records Than Required

To pinpoint and optimize such queries, you can follow these steps:

1. Monitor Query Performance

Query Log Analysis: Enable and utilize the system.query_log table in ClickHouse, which logs various statistics about query execution, including execution time, number of rows read, and number of bytes read.



  CREATE TABLE IF NOT EXISTS system.query_log (
      event_date Date,
      event_time DateTime,
      query_start_time DateTime,
      query_duration_ms UInt64,
      read_rows UInt64,
      read_bytes UInt64,
      query String,
      user String
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(event_date)
  ORDER BY (event_time);

Identifying High-Load Queries: Use SQL queries to analyze patterns and find queries with disproportionately high read_rows or read_bytes relative to their output. For example:



  SELECT 
      query, 
      read_rows, 
      result_rows, 
      read_bytes, 
      query_duration_ms
  FROM system.query_log
  WHERE event_date = today() AND read_rows > 1000000
  ORDER BY read_bytes DESC
  LIMIT 10;

2. Use EXPLAIN Statements

Understanding Query Execution Plans: Analyze how ClickHouse executes a query using the EXPLAIN statement. This can help you see if unnecessary table scans or inefficient joins are causing extra data fetching.



  EXPLAIN SYNTAX
  SELECT * FROM your_table WHERE some_conditions;

3. Rational Statistical Analysis for Forecasting

Performance Forecasting:
Employ statistical methods like linear regression on historical query performance data to predict future trends. For instance, regress query_duration_ms against read_bytes and read_rows to forecast performance based on input size.
Moving Average and Standard Deviation:
Use a moving average to smooth out normal fluctuations in performance metrics, and calculate the standard deviation to identify outliers in data fetching. Queries that fetch amounts of data beyond the mean plus two standard deviations might be considered excessive and warrant further investigation.



  SELECT 
      AVG(read_rows) AS avg_read_rows,
      STDDEVPop(read_rows) AS stddev_read_rows
  FROM system.query_log
  WHERE event_date BETWEEN today() - 7 AND today();

Best Practices for Query Optimization

Limit Clauses: Use LIMIT clauses whenever possible to restrict the amount of data processed.
Precise Filtering: Ensure that WHERE clauses are specific and leverage indexed columns to minimize unnecessary data retrieval.
Index Optimization: Regularly review and optimize indexes based on the most frequent and heavy queries.
Regular Maintenance: Run OPTIMIZE commands during low-traffic periods to improve the physical layout of the data on disk, reducing read overhead.

By following these steps, you can identify and rectify queries fetching more data than needed in ClickHouse, improving both individual query performance and overall system efficiency.