Redis data analysis with pictures and texts

With the increase in modern data processing needs, caching technology is also constantly developing and improving. Among them, Redis, as a high-performance non-relational database, is widely used in various data processing scenarios. In the field of data analysis, Redis can exert its unique advantages.

This article will introduce the application of Redis in data analysis and related technologies, including the data structure and operation method of Redis, the application scenarios of Redis in data analysis, how to implement Redis multi-machine cluster and how to Use Redis to persist data, etc.

1. Redis data structure and operation method

Redis supports five data structures, namely string, hash, list, and set ) and ordered sets. These data structures and their respective related operations are shown in the following table:

Data structure	Related operations
String	SET key value;GET key
Hash	HSET key field value;HGET key field
List	LPUSH key value;LRANGE key 0 -1
Collection	SADD key member; SMEMBERS key
Ordered set	ZADD key score member；ZRANGE key 0 -1 WITHSCORES

二, Redis application scenarios in data analysis

1. Caching

Redis can be used as an efficient caching tool. By storing commonly used data in Redis, you can avoid each Access the database for every request, thereby improving the data access speed. There are two commonly used caching strategies, caching the entire page and caching data.

2. Data statistics

Redis can quickly count, sum, sort and other operations on data, with extremely high concurrency and scalability. For example, Redis can be used to implement functions such as counters and TOP N rankings.

3. Real-time data processing

Because Redis has very high performance and low latency, real-time data can be quickly obtained, stored and calculated. For example, in the recommendation system, Redis can update user, product, tag and other information in real time and provide real-time recommendation results.

4. Time series data processing

Redis's ordered set (sorted set) is very suitable for storing time series data, including real-time data and historical data. Redis can be used to store and query time series data, as well as calculate various statistical indicators including sliding windows, averages, etc. in real time.

3. Multi-machine cluster of Redis

In order to ensure the reliability and scalability of data, when Redis needs to store a large amount of data or handle high concurrent requests, it needs to use a multi-machine cluster. Redis's multi-machine cluster has two modes, namely master-slave replication and sharding.

1. Master-slave replication

Master-slave replication refers to synchronizing all write operations of one Redis instance (i.e. "master node") to one or more other Redis Instance (i.e. "slave node") is backed up. In this way, when the master node goes down or crashes, the slave node can be quickly upgraded to the master node to ensure service availability.

2. Sharding

Sharding refers to dividing a large Redis data set into multiple small data sets and storing them in different Redis instances. Thus achieving the purpose of distributed storage and processing. Each Redis instance only stores part of the data. When users need to read and write data, they need to route the request to the corresponding Redis instance for processing.

4. Redis data persistence

Since Redis is an in-memory database, all data will be lost when the Redis instance crashes or restarts. In order to avoid data loss, Redis provides two methods for data persistence, namely RDB and AOF.

1. RDB

RDB refers to storing snapshots of Redis data collections on disk to ensure that data can be quickly restored when the Redis instance goes down. RDB copies the data set through the fork process, and then writes the snapshot to the disk file to achieve data persistence. The disadvantage of the RDB method is that the last updated data may be lost.

2. AOF

AOF means that when the Redis data collection is modified, the modification operation is appended to the log file in the form of text, thereby achieving data persistence. The AOF method can reduce the possibility of data loss, but it will cause performance degradation in read and write operations.

To sum up, Redis, as a high-performance non-relational database, has a wide range of applications in data analysis. By becoming familiar with the data structure and operation methods of Redis, and mastering its related technologies in data analysis, you can better use Redis to achieve data processing and analysis purposes. At the same time, when actually using Redis, you also need to pay attention to issues such as data security and performance optimization to ensure the stability and reliability of the system.

The above is the detailed content of Redis data analysis with pictures and texts. For more information, please follow other related articles on the PHP Chinese website!