Real-Time Performance Monitoring for ClickHouse Cluster Using Python Script

Introduction

There are several ways to monitor the performance of a ClickHouse cluster:

1. Using built-in system tables: ClickHouse has several built-in system tables, such as system.metrics, system.metrics_events, and system.query_log, that provide detailed information about the performance of the cluster. These tables can be queried to retrieve information about the CPU usage, memory usage, disk I/O, and query performance.
2. Using the clickhouse-client tool: The clickhouse-client tool provides several commands that can be used to monitor the performance of a ClickHouse cluster. For example, the ‘SHOW PROCESSLIST’ command can be used to retrieve information about the currently running queries and their status.
3. Using external monitoring tools: There are several external monitoring tools that can be used to monitor the performance of a ClickHouse cluster. For example, Grafana can be used to create visualizations of the performance metrics collected from ClickHouse.
4. Using ClickHouse performance monitoring plugins: ClickHouse has a set of performance monitoring plugins that are easy to install and use, like Prometheus and Graphite. These plugins can be used to collect performance metrics and export them to external monitoring systems.
5. Using ClickHouse performance monitoring API: ClickHouse also provides a performance monitoring API that allows to get the performance metrics of the cluster in real-time. These metrics include system usage, query execution times, and user-defined metrics.
6. Using ClickHouse performance monitoring scripts: You can also use python or shell scripts to monitor the performance of ClickHouse. These scripts can be scheduled to run periodically and collect the performance metrics, like CPU and memory usage, query execution time, etc, and store them in a database or send an alert if they exceed a certain threshold.

Python script for real-time performance monitoring for ClickHouse

```
#!/usr/bin/env python

import time
import clickhouse_driver

# Connection to ClickHouse
conn = clickhouse_driver.connect(host='127.0.0.1', port=9000)

# Check Queries Per Second
query = "SELECT count(*) FROM system.queries WHERE query_start>now()-interval 1 second"
while True:
     cur = conn.cursor()
     cur.execute(query)
     for row in cur:
          qps = row[0]
          print("Queries Per Second (QPS): {}".format(qps))
     cur.close()
     time.sleep(1)

# Check Memory Usage
query = "SELECT sum(memory_usage) FROM system.queries WHERE query_start>now()-interval 1 second"
while True:
     cur = conn.cursor()
     cur.execute(query)
     for row in cur:
          memory_usage = row[0]
          print("Memory Usage: {}".format(memory_usage))
     cur.close()
     time.sleep(1)

# Check Read/Write Speed
query = "SELECT sum(read_rows), sum(read_bytes), sum(written_rows), sum(written_bytes) FROM system.queries WHERE query_start>now()-interval 1 second"
while True:
     cur = conn.cursor()
     cur.execute(query)
     for row in cur:
          read_rows = row[0]
          read_bytes = row[1]
          written_rows = row[2]
          written_bytes = row[3]
          print("Read Rows: {}   Read Bytes: {}   Written Rows: {}   Written Bytes: {}".format(read_rows, read_bytes, written_rows, written_bytes))
     cur.close()
     time.sleep(1)
``` 

This code will print out the real–time performance of the ClickHouse server. It will print out the number of queries per second, memory usage, read/write speed in terms of rows and bytes. This code will keep running until the user stops it. It can be used to monitor the performance of the ClickHouse server in real–time and detect any performance issues as soon as they arise. It can also be used to detect any sudden spikes in queries, memory usage or read/write speed which could indicate a potential issue. This code can also be used for benchmarking and troubleshooting purposes. It can also be used to monitor the performance of the ClickHouse server over time to ensure that it is performing as expected. This code can be easily modified to add additional metrics or to customize the output. This code is a great way to make sure that the ClickHouse server is performing optimally and can be used in production environments.

Conclusion

There are several ways to monitor the performance of a ClickHouse cluster, including using built-in system tables, the clickhouse-client tool, external monitoring tools, ClickHouse performance monitoring plugins, performance monitoring API and performance monitoring scripts. It’s important to monitor the performance of your cluster regularly to ensure that it is running efficiently and to identify and resolve any potential performance issues as soon as they arise.