Redis implements structural resource pool and performance testing of distributed data storage

Redis implements structural resource pool and performance testing of distributed data storage

Redis is an open source, high-performance key-value pair storage database, which is fast and reliable. It supports a variety of data structures, including strings, hash tables, lists, sets and ordered sets, etc., to meet the needs of different application scenarios. In a distributed system, data storage and access are often scattered among different nodes. A distributed data storage solution is needed. Redis provides an implementation solution for a distributed structure resource pool to support the distributed environment. data storage and access.

1. Implementation principle of Redis distributed structure resource pool

Redis' distributed structure resource pool is implemented through Redis Sentinel or Redis Cluster. Redis Sentinel is a high-availability solution that can monitor the status of Redis master and slave nodes and elect a new master node to achieve automatic failover when the master node goes down. Redis Cluster is a distributed solution that can distribute data to multiple nodes. Each node is responsible for the storage and management of a part of the data, enabling distributed storage and access of data.

The core of Redis' distributed structure resource pool implementation is to use data synchronization between master-slave nodes or cluster nodes to achieve distributed storage and access of data. When the client sends a write request to the master node or cluster node, the node will first write the data into its own database, and then synchronize the data to the corresponding slave node or cluster node. When the client sends a read request to the master node or cluster node, the node will first read the data from its own database. If it does not have one, it will read the data from the corresponding slave node or cluster node. This method can ensure real-time synchronization and high availability of data.

2. Performance test of Redis distributed structure resource pool

In order to verify the performance and reliability of Redis distributed structure resource pool, we conducted a series of performance tests and fault simulation tests.

1. Test environment

The test environment is a set of three Alibaba Cloud CentOS 7.4 64-bit servers. Each server is configured with 4 cores, 16G memory, and 100G cloud disk.

2. Test data

The test data is 10,000 randomly generated strings, each string is 100 characters long.

3. Testing Tool

We use the redis-benchmark tool for testing, simulating concurrent access by multiple users by creating 10 clients that send requests to the server at the same time. The test command is:

redis-benchmark -h host -p port -c 10 -n 10000 -d 100 -t set,get

Among them, -h specifies the server address and -p specifies Port number, -c specifies the number of concurrent connections, -n specifies the number of requests, -d specifies the data length, and -t specifies the operation type.

4. Test results

The test results are shown in the following table:

Test items	Result
SET	15206.92 requests per second
GET	23165.93 requests per second

#The test results show that the Redis distributed structure resource pool can maintain efficient data access and query in a high-concurrency environment.

5. Fault Simulation Test

We simulate node downtime by shutting down the master node or cluster node. The test results show that the slave node or other cluster nodes can automatically take over. Data storage and access realize automatic failover and high data availability.

3. Summary

Redis’ distributed structure resource pool is a reliable solution for distributed data storage and access, which is guaranteed through data synchronization between master-slave nodes or cluster nodes. High availability of data. In a high-concurrency environment, it can maintain efficient data access and query. At the same time, it also has features such as automatic failover and fault tolerance, achieving high availability and stability of data. It is a very suitable method for use in distributed environments. storage solution.

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