What kind of data does redis generally cache?

Redis supports multiple cache data types, including strings, hash tables, lists, sets, sorted sets, geospatial data, HyperLogLog, and bitmaps. These types can be selected based on data access frequency, change frequency, size, and eviction policy to optimize cache performance.

Redis commonly cached data types

Redis is a powerful key-value storage system that is widely used in Cache various types of data to improve application performance. Generally, data types suitable for caching into Redis include:

1. String

• is used to store simple text, JSON string or XML data.
• Advantages: Small footprint, efficient reading and writing.

2. Hash table (Hash)

• is used to store key-value pairs, where the key is a string and the value is another type (character string, set, ordered set, etc.).
• Advantages: Fast search and retrieval, suitable for storing object data.

3. List

• is used to store an ordered collection of elements, supporting adding/removing elements from the beginning or the end.
• Advantages: Fast reading and writing, suitable for queue or stack operations.

4. Collection

• is used to store unordered collections of elements and supports adding, deleting and searching elements.
• Advantages: High space efficiency, suitable for storing unique elements.

5. Sorted Set

• is used to store a collection of elements with scores, and supports sorting and searching by scores.
• Advantages: Efficient search and sorting, suitable for rankings or priority queues.

6. Geospatial data

• is used to store geospatial data, such as latitude and longitude coordinates.
• Advantages: Fast range query and nearest neighbor search.

7. HyperLogLog

• is used to estimate the number of unique elements in large data sets.
• Advantages: Saves space, suitable for calculating base numbers (distinct count).

8. Bitmap

• is used to store and manipulate binary bits efficiently.
• Advantages: Space saving, suitable for tracking a large number of statuses or counts.

Notes on selecting cached data:

• Data access frequency: Frequently accessed data is suitable for caching.
• Data change frequency: Frequently changing data is not suitable for caching.
• Data size: Large data may cause the cache hit rate to decrease.
• Cache elimination strategy: You need to consider how to manage cache space to avoid cache overload.

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