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With the popularity of cloud computing, more and more people are beginning to use Docker to deploy applications and development environments. However, before using Docker, many people will ask a question: How many Docker containers do I need to run to meet my needs? This article will show you how to evaluate how many Docker containers you need to run.
First, the load on the system and application needs to be assessed. For systems, there are CPU, memory, disk IO, and network IO to consider. For applications, consider request concurrency, request processing time, memory usage, and database load.
Next, you need to use the benchmark tool to simulate the actual load and measure the performance of the system. For example, you can use tools such as ApacheBench, Gatling, or Loadrunner to simulate HTTP requests. For database load, you can use sysbench or self-developed testing tools to simulate database load. Test results should include usage of various hardware metrics, such as CPU usage, memory usage, disk IO, and network IO.
Then, the required number of Docker containers needs to be calculated based on the test results. Resource isolation between containers needs to be considered, and each container should have certain CPU and memory limits. For applications with high CPU computing requirements, you can set the CPU weight parameters of each container to ensure fairness in resource allocation; for applications with high memory requirements, you can set the memory limit parameters of each container to ensure system stability. sex.
Finally, you need to monitor the running status of the Docker container to discover and solve problems in time. For this, you can use Docker's own monitoring tools, or you can use third-party tools such as cAdvisor for monitoring.
To sum up, when evaluating how many Docker containers need to be run, many factors need to be considered, including system and application load, benchmark test results, etc. Appropriate control of the number of Docker containers and hardware resource allocation can improve system performance and stability.
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