Measuring load and queue length of ClickHouse Server is important for troubleshooting performance bottleneck. The load is a measure of the amount of work that ClickHouse is currently doing or is capable of doing. It is typically measured as a combination of the total number of ClickHouse processes running, the number of threads running, and the CPU utilization. The queue length is a measure of the number of tasks waiting to be executed by the system. This can be affected by the number of ClickHouse processes waiting to be run, the number of threads waiting to be executed, and the number of I/O requests waiting to be serviced.
To measure load and queue length in a system, you can use various tools and commands such as:
- top command: it provides a real-time view of the system’s load and queue length
- sar command: it provides a historical view of system performance metrics
- uptime command: it provides a quick summary of system load and queue length
- vmstat command: it provides detailed information about system resources such as CPU, memory, and I/O
- lsof command: it shows the number of open files and the number of processes that are accessing them
In addition, there are also several Python libraries available for monitoring system performance such as psutil, os, and subprocess. These libraries can be used to write custom scripts for monitoring load and queue length in a system.
There are several ways to monitor the load on a ClickHouse server. One way is to use the built-in system tables and queries in ClickHouse that provide information on the current load and performance of the server. For example, the system.query_log table contains information on all queries that have been executed on the server, including the query text, the time it was executed, and the amount of time it took to execute.
Another way to monitor the load on a ClickHouse server is to use a monitoring tool such as Grafana, which can be configured to collect and display metrics from ClickHouse in real-time. This can provide a visual representation of the load on the server, including information on CPU and memory usage, disk I/O, and the number of queries being executed.
You can also use a Python script to monitor the load on a ClickHouse server by executing queries to the system tables and gathering metrics from the results. This can be done using the ClickHouse-driver library for Python, which allows you to connect to a ClickHouse server and execute queries.
In addition to monitoring the load, it’s also important to monitor the queue length, which is the number of queries that are waiting to be executed. You can monitor this by checking the system.query_thread_log table, which contains information on all queries that are waiting to be executed, including the time they were queued, and the number of queries in the queue.
It’s also important to monitor the service time, CPU utilization, RAM utilization, disk I/O, residence time and queue length of ClickHouse in real-time. You can do this by using the appropriate system tables and queries or by using monitoring tools like Grafana.
Here is an example of a Python script that can be used to monitor various performance metrics of a ClickHouse server in real-time:
import time import requests CLICKHOUSE_URL = "http://localhost:8123" while True: try: # Fetch system.metrics table from ClickHouse r = requests.get(CLICKHOUSE_URL + "/?query=SELECT%20*%20FROM%20system.metrics") metrics = r.json()["data"] # Extract the relevant metrics query_arrival_time = metrics service_time = metrics cpu_utilization = metrics ram_utilization = metrics disk_io = metrics residence_time = metrics queue_length = metrics # Print the metrics print("Query Arrival Time: ", query_arrival_time) print("Service Time: ", service_time) print("CPU Utilization: ", cpu_utilization) print("RAM Utilization: ", ram_utilization) print("Disk I/O: ", disk_io) print("Residence Time: ", residence_time) print("Queue Length: ", queue_length) except: print("Error fetching metrics") # Wait for 1 second before fetching metrics again time.sleep(1)
This script uses the requests library to fetch data from the system.metrics table in ClickHouse, which contains various performance metrics. The script then extracts the relevant metrics and prints them to the console. The script runs in a loop, fetching metrics every second.
You will need to modify the CLICKHOUSE_URL variable to point to the appropriate ClickHouse server, and also make sure that the clickhouse-client package is installed.