How to identify blocks causing latch contention in ClickHouse?

Latch contention in ClickHouse can have a significant impact on performance. A latch is a synchronization mechanism that allows multiple threads to access a shared resource, such as a data block, in a controlled manner. When multiple threads try to access the same data block simultaneously, latch contention can occur. This can result in a bottleneck in performance as the threads must wait for the latch to be released before they can access the block.

The following are some of the ways latch contention can impact ClickHouse:

  1. Increased latency: Latch contention can increase query latency as threads must wait for the latch to be released before they can access the data block they need. This can result in slower query times and reduced overall system performance.
  2. Decreased throughput: Latch contention can reduce the number of queries that can be executed simultaneously, reducing the overall throughput of the system.
  3. Increased CPU usage: Threads waiting for a latch to be released will consume CPU resources, which can result in increased CPU utilization and reduced system efficiency.
  4. Decreased query parallelism: Latch contention can reduce the ability of queries to run in parallel, further reducing system performance.

To minimize the impact of latch contention on ClickHouse, it is important to monitor and address the causes of contention, such as optimizing the buffer pool size, adjusting memory settings, improving the query or table structure, and increasing the number of replicas.

Identifying blocks causing latch contention in ClickHouse for performance troubleshooting 

In ClickHouse, latch contention can occur when multiple threads are trying to access the same data block at the same time, causing a bottleneck in performance. To identify the blocks causing latch contention, you can use the following steps:

  1. Enable query logging: To log all queries, set the “log_queries” configuration option to “1” in the ClickHouse server configuration file.
  2. Monitor the system log: In the system log, look for messages indicating latch contention, such as “Too many waiters for latch”. These messages will contain information about the data block causing the contention.
  3. Use performance metrics: The ClickHouse performance_counters table provides information about various performance metrics, including latch contention. You can use a query like “SELECT * FROM system.performance_counters WHERE counter = ‘LatchContention'” to retrieve information about latch contention.
  4. Use profiling tools: Tools like perf or OProfile can be used to profile the ClickHouse process and identify the functions that are spending the most time waiting for latches.

By using the above steps, you can determine the blocks causing latch contention and take steps to resolve the issue, such as increasing the buffer pool size, adjusting the memory settings, or improving the query or table structure.

How to identify blocks causing latch contention in ClickHouse in real time?

Here’s a simple Python script that you can use to identify blocks causing latch contention in ClickHouse in real-time:

import os
import time

def find_latch_contention():
    while True:
        result = os.popen("grep 'Too many waiters for latch' /var/log/clickhouse-server/clickhouse-server.log").read()
        if result:
            print(result)
        time.sleep(1)

if __name__ == '__main__':
    find_latch_contention()

This script uses the os and time modules to run a grep command on the ClickHouse log file and search for messages indicating latch contention. If any messages are found, they are printed to the console. The script runs in an infinite loop and checks the log file every second.

Note: This script assumes that the ClickHouse log file is located at “/var/log/clickhouse-server/clickhouse-server.log”. If your log file is located elsewhere, you’ll need to adjust the path in the script accordingly.

About Shiv Iyer 90 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.