Data storage efficiency showdown: Redis String vs. Hash performance competition, which one is more suitable for you?

1. Redis data types

1. Conventional type

1) String: The most basic data structure, which can store any type of string, number or binary data.

2) Hash (hash table): Similar to an associative array or dictionary, it can store multiple fields and corresponding values, and is often used to store object attributes or configuration information.

3) List: An ordered list of strings that supports insertion and deletion operations at both ends of the list. It also provides a variety of operations, such as search, cropping, sorting, etc.

4) Set: An unordered collection of strings that supports operations such as addition, deletion, search, intersection, union, and difference. It also provides a variety of operations, such as finding random elements, determining whether elements exist, etc.

5) ZSet (ordered set): Similar to the Set type, but each element has a score. It is sorted according to the score and can support search, insertion and deletion operations according to the score range.

2. The most complete type of official website

Overview of data types supported by Redis

Redis is a data structure server. At its core, Redis provides a range of native data types to help you solve problems ranging from caching to queuing to event handling. Below is a brief description of each data type, along with links to a broader overview and command reference.

If you'd like to try a comprehensive tutorial on each data structure, see the overview page below.

String

Redis String is the most basic Redis data type, representing a sequence of bytes. For more information, see:

• Redis string overview
• Redis string command reference

List

Redis List is a list of strings sorted in insertion order. For more information, see:

• Redis list overview
• Redis list command reference

Sets

A Redis set is an unordered collection of unique strings that acts like a set in your favorite programming language (e.g., Java HashSets, Python Sets, etc.). Using Redis collections, you can add, remove, and test for existence in O(1) time (in other words, regardless of the number of collection elements). For more information, see:

• Redis set overview
• Redis set command reference

Hash

A Redis hash is a record type modeled as a collection of field value pairs. Therefore, Redis hashes are similar to Python dictionaries, Java HashMap, and Ruby hashes. For more information, see:

• Redis hashing overview
• Redis hash command reference

Sorted sets

Redis sorted sets are collections of unique strings that maintain order by the correlation score of each string. For more information, see:

• Redis sorted set overview
• Redis ordered set command reference

Streams

Redis Streams are a data structure that act like append-only logs. Streams help record events in the order in which they occur and then join them together for processing. For more information, see:

• Redis streams overview
• Redis Streams command reference

Geospatial indexes

Redis Geospatial indexes are useful for finding locations within a given geographic radius or bounding box. For more information, see:

• Redis geospatial index overview
• Redis geospatial index command reference

Bitmaps

Redis Bitmaps allow you to perform bitwise operations on strings. For more information, see:

• Redis bitmap overview
• Redis bitmap command reference

Bitfields

Redis Bitfields efficiently encode multiple counters in a string value. Bitfields provide atomic get, set, and increment operations, and support different overflow strategies. For more information, see:

• Redis bitfield overview
• Command BITFIELD .

HyperLogLog

The Redis HyperLogLog data structure provides probabilistic estimates of the cardinality (i.e., number of elements) of large collections. For more information, see:

• Redis HyperLogLog overview
• Redis HyperLogLog command reference

Extensions

To extend the functionality provided by the included data types, use one of the following options:

1. Write your own custom server-side functions in Lua.
2. Use the module API to write your own Redis module or check out community-supported modules .
3. Use JSON, queries, time series, and other features provided by the Redis Stack.

3. Redis internal encoding

In Redis, "internal encoding" refers to the way data is encoded and stored in memory so that it can be efficiently managed and manipulated at runtime. Redis chooses a different set of encodings for each data type to minimize memory footprint and improve performance based on the characteristics and usage of the data.

Each data type may have multiple internal encoding methods, and these encoding methods correspond to different data structures to adapt to different data scenarios. For example, for string types, Redis may choose different internal encodings based on the content and length of the string. For hash types, Redis may choose an internal encoding method based on the number of fields and the length of the field values.

The internal encoding is selected automatically and is dynamically switched by Redis at runtime based on the characteristics of the data and storage requirements. This dynamic switching enables Redis to optimize memory usage and performance under different circumstances to better meet the needs of different application scenarios.

Understanding the internal coding can help you better understand how Redis manages data and make more informed decisions when designing and optimizing your applications.

We can view the internal encoding of the object through the command object encoding:

> SET mystring "123"
OK
> OBJECT ENCODING mystring
int

The following are common object types and corresponding internal encodings:

1. String type (string):

• Internal encoding: Depending on the content and length of the string, three encoding methods, int, embstr, and raw, may be used.
• Note: int encoding is used to store strings that can be represented as integers, embstr encoding is used for short strings, and raw encoding is used for general strings.

1. Hash type (hash):

• Internal encoding: Depending on the number of fields in the hash table and the length of the field values, two encoding methods, ziplist and hashtable, may be used.
• Note: ziplist encoding is used when there are fewer fields or shorter field values, and hashtable encoding is used when there are more fields or longer field values.

1. List type (list):

• Internal encoding: Depending on the length of the list and the length of the elements, two encoding methods, ziplist and linkedlist, may be used.
• Note: ziplist encoding is used for short lists, and linkedlist encoding is used for longer lists.

1. Collection type (set):

• Internal encoding: Depending on the number of elements in the set and the length of the elements, two encoding methods, intset and hashtable, may be used.
• Note: intset encoding is used to store a collection of integer members, and hashtable encoding is used to store a collection of string members.

1. Ordered set type (zset or sortedset):

• Internal encoding: Depending on the number of elements and the length of the elements in the ordered set, two encoding methods, ziplist and skiplist, may be used.
• Note: ziplist encoding is used for short ordered collections, and skiplist encoding is used for longer ordered collections.

2. How to choose between String and Hash?

As a popular in-memory data store, Redis provides a variety of data structures to suit different use cases. Among them, String and Hash are the two most basic data structures in Redis. Although they are both important components of Redis, they each have different purposes and performance characteristics. In this article, we will take a deep dive into how to choose between String and Hash given your application needs.

1. String data structure

String is the simplest data type in Redis and can store text, integers and floating point numbers. When choosing to use the String data structure, you should consider the following scenarios:

1. Single value storage: If you only need to store a single value for a given key, such as caching calculation results or storing user preferences, then String is the preferred option.
2. Atomic operations: Redis provides atomic operations on String, allowing you to increment, decrement, and operate on the value of the key. This is useful for implementing counters, locks, and simple analysis.
3. Caching simple data: If you want to cache data that does not require complex structures, such as HTML fragments or serialized objects, String can provide efficient storage and retrieval.

2. Hash data structure

Relative to String, Hash is a more complex data structure that allows you to store field-value pairs under a single key. Consider using the Hash data structure in the following situations:

1. Structured data: Hash is great for storing structured data, such as user profiles, where each field corresponds to a specific attribute (such as username, email, age).
2. Partial update: Hash excels when specific fields of an object need to be updated without affecting the entire data structure. This is more efficient than replacing the entire value with a String.
3. Reduce key space clutter: Using Hash, you can group related fields under one key instead of using separate String keys for each field of an entity, making the key space more orderly.

• scenes to be used:

1. String data type:

• • Suitable for storing a single value, such as user session data, counters, configuration information, etc.
  • No complex structured queries or data processing required.
  • Suitable for storing simple string data, such as cache, verification codes, etc.

1. Hash data type:

• • Suitable for storing data of multiple fields, similar to associative arrays or objects.
  • Complex structured queries and data processing are required, such as user information, product information, article content, etc.
  • Single fields can be read and written efficiently, avoiding the serialization and deserialization overhead of the entire object.

• Performance analysis:

1. String data type performance:

• • Read and write operations are very efficient and can be completed in constant time.
  • Suitable for simple GET and SET operations, especially if the key is updated less frequently.
  • Excellent performance when storing large amounts of short-term data, such as cached data.

1. Hash data type performance:

• • It is suitable for data that needs to store and operate multiple fields without reading and writing the entire object.
  • Hash can save memory when storing a large number of fields, because Redis stores each field in a dictionary-like structure.
  • For large-scale query and update operations, the Hash data type can be performed more efficiently.
  • Note that when the number of internal fields in Hash is small, memory waste may occur, because Hash requires a certain amount of extra space to store field information.

In practical applications, different Redis data types are often used in combination to meet different needs. For example, for the user's basic information, you can use the Hash data type to store the mapping relationship between user ID and detailed information; and for the user's session data, you can use the String data type to store the session information of a single user.

When making a choice, factors such as storage structure, frequency of queries and updates, and data volume should be weighed based on actual needs. It's important to emphasize that Redis's performance is excellent in most cases, but the best choice will depend on your specific application needs. The best way is to test and benchmark in real-world scenarios to determine which data types work best for your application.