How redis determines hot data

Redis determines hot data through slow query logs, memory sampling, modules and third-party tools. Once hotspot data is identified, steps can be taken to mitigate its impact, such as using caching, data sharding, and optimizing data structures.

How Redis determines hot data

Hot data in Redis refers to data that is frequently read or written data, it will affect the performance of Redis. In order to accurately judge hot data, Redis provides the following methods:

1. Slow query log

Redis provides a slow query log function, which can record the execution time exceeding Queries specifying a threshold (usually 1 millisecond). By analyzing slow query logs, we can discover hot queries that are executed frequently.

2. Memory Sampling

The INFO command of Redis provides the "Used Memory Histogram" section, which contains information about memory usage. You can identify potential hot data by analyzing this section to see which key-value pairs take up the most memory.

3. Redis module

Redis provides several third-party modules specifically for identifying hotspot data, for example:

• redis-hotkey: A module that monitors the access frequency of key-value pairs and identifies hot data.
• redis-topkey: A module that tracks the most visited key-value pairs within a specified time range.

4. Use tools

In addition to using the tools provided by Redis itself, you can also use third-party tools to determine hot data, such as:

• RedisInsight: A graphical management tool that provides insights into Redis performance and data conditions, including identification of hotspot data.
• RedisGears: An open source framework that adds additional functionality to Redis, including hotspot data analysis.

What are the next steps after identifying hotspot data

Once hotspot data is identified, steps can be taken to mitigate its impact on Redis performance:

• Use cache: Create a local cache for hotspot data to reduce read requests to Redis.
• Adopt data sharding: Split hotspot data into multiple Redis instances to distribute the load.
• Optimize data structures: Use memory-saving data structures such as HyperLogLog or TDigest to store hotspot data.

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