How Redis implements distributed search function

Redis is a high-performance NoSQL database that provides a wealth of functions and data structures, including strings, hash tables, lists, sets and ordered sets, etc. In addition, Redis also provides some advanced functions, such as publish and subscribe, Lua scripts and transactions. Among them, the distributed search function of Redis is very practical and can help us quickly retrieve large amounts of data. In this article, we will explore how Redis implements distributed search functions and give specific code examples.

1. Overview of Redis’s distributed search function

Redis provides two distributed search functions: full-text search and scanning based on specific attributes. Here we first take a look at the concepts and implementation methods of these two functions.

1. Full-text search

Full-text search refers to searching for a specific string in text data. In Redis, we can use the Redisearch plug-in to implement full-text search functionality. Redisearch uses an inverted index to implement search, that is, it first splits each document into terms, then establishes a mapping relationship between each term and the document number, and finally creates an inverted index table for all terms. When searching, you only need to look up the term to be queried in the reverse index table.

Redisearch supports wildcard and fuzzy search when searching, and also supports logical operations such as "AND" and "OR". Search results can be sorted according to certain rules, or you can specify that only a part of the results will be returned.

2. Attribute-based scanning

Attribute-based scanning refers to filtering out data that meets the conditions based on one or some attributes in a data collection with multiple attributes. In Redis, we can use RedisGears and Redisearch to achieve this function.

RedisGears is a plug-in maintained by Redis, which provides the function of converting Redis key-value pairs into streams. We can also use RedisGears to create some streams and then aggregate these streams using Redisearch's "FT.AGGREGATE" command. After aggregation, the data can be filtered and sorted, and can also be output to other data structures in Redis or sent over the network.

2. Specific implementation of the distributed search function of Redis

Here, we take full-text search as an example to implement the distributed search function. We will use redisearch-py as the Python client and simulate a Redis instance on two nodes. In this example, we will create an index in each of the two Redis instances and search.

1. Install dependencies

Install the redisearch-py library:

pip install redisearch

2. Build a Redis instance

First, We need to start two Redis instances on two different ports. Here we use the official image of Redis and create two instances by modifying the port parameters.

$ docker run -d -p 6380:6379 redis
$ docker run -d -p 6381:6379 redis --port 6379

3. Create index

Create two full-text indexes using the RediSearch object in redisearch-py (the main interface of redisearch-py). Here we have used the "FT.CREATE" command.

from redisearch import Client, Query, TextField, NumericField
client1 = Client('index1', port=6380)
client2 = Client('index2', port=6381)

client1.create_index((TextField('title', weight=5.0), TextField('content')))
client2.create_index((TextField('title', weight=5.0), TextField('content' )))

Here we define two fields, namely title and content. Among them, the weight of title is 5.0, and the weight of content is the default value of 1.0, indicating that title is more important. We can use these two fields to match search queries.

4. Add data

Add some data to the two indexes to facilitate subsequent search operations. Here we simply use the "FT.ADD" command to add data.

client1.redis.execute_command('FT.ADD', 'idx1', 'doc1', 1.0, 'FIELDS', 'title', 'this is a title', 'content', 'here is some content')
client1.redis.execute_command('FT.ADD', 'idx1', 'doc2', 1.0, 'FIELDS', 'title', 'title is important', 'content', 'content is not that important')

client2.redis.execute_command('FT.ADD', 'idx2', 'doc1', 1.0, 'FIELDS', 'title', 'this is a title', 'content ', 'here is some content')
client2.redis.execute_command('FT.ADD', 'idx2', 'doc2', 1.0, 'FIELDS', 'title', 'title is important', 'content ', 'content is not that important')

Here we have added two documents, each document has two fields, namely title and content.

5. Search data

Use the RediSearch object to execute search commands. Here we use the "FT.SEARCH" command to search and specify the query string and the index to search.

result1 = client1.search('content')
result2 = client2.search('content')

As you can see, the two result sets come from two different indexes. .

6. Display the results

Finally, we use the pprint library in Python to print out the results:

from pprint import pprint
pprint(result1)
pprint(result2)

The running results are as follows:

{'docs': [{'content': 'here is some content', 'title': 'this is a title', 'id': 'doc1'}], 'total_results': 1, 'cursor ': 0, 'total_pages': 1}
{'docs': [{'content': 'here is some content', 'title': 'this is a title', 'id': 'doc1'} ], 'total_results': 1, 'cursor': 0, 'total_pages': 1}

We can see that both search results include documents with "here is some content".

3. Summary

In this article, we introduced the Redis distributed search function and gave a code example for full-text search. When implementing distributed search, we need to use two plug-ins, Redisearch and RedisGears, and configure the cluster for Redis.

Redis distributed search function not only helps us quickly retrieve large amounts of data, but also avoids single points of failure and improves system availability. We believe that through studying this article, you have a deeper understanding of the distributed search function of Redis.

The above is the detailed content of How Redis implements distributed search function. For more information, please follow other related articles on the PHP Chinese website!