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Redis is an open source, in-memory data store and an advanced key-value store. It can be used as a database, cache and message broker. Redis supports data structures including hashes, strings, lists, sets, sorted sets, and bitmaps. Redis tables can store data with a variety of types, including strings, hashes, lists and sets. When data is stored in Redis, it is organized into Redis tables, which can be thought of as abstract “contners” for storing data. Redis tables can also be used to store different types of data.
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Redis tables can be thought of as a hash table or a sorted set of values and associated keys. they are composed of both keys and values, and provide a data structure that can look up specific values based on a given key. Redis tables also support atomic commands, so if multiple operations are being performed on the same key, they are guaranteed to be performed atomically. A popular use case for Redis tables is caching data, as the data can be quickly looked up by a given key.
To understand how data is stored in Redis tables, let’s look at an example of a Redis hash table. A hash table is a data structure that associates data items with string keys. The data items in the hash table are stored and retrieved using their unique keys. In a Redis hash, the hash key is used to identify the data items stored in the hash table. The value associated with the hash key can consist of strings, lists, sets, hashes or any other type of data for that matter.
Let’s look at an example, where a hash table is used to store user data. We can store a user’s name and age, as shown below:
`127.0.0.1:6379> HSET user “name” “John”`
`127.0.0.1:6379> HSET user “age” “32”`
In this example, the hash key is user and the values stored are name and age. We can later look up this data by passing the hash key.
`127.0.0.1:6379> HGET user “name”`
->”John”
`127.0.0.1:6379> HGET user “age”`
->”32″
By using Redis tables, we can easily look up data stored in them by using the associated key. Redis tables also offer scalability, avlability and speed, making them an ideal choice for caching data.
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文章題目：深入分析Redis表中的數(shù)據(jù)（查看redis表數(shù)據(jù)）
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