Learn about RedisBloom caching technology

With the vigorous development of Internet businesses such as social networking, e-commerce, and games, the amount of data and concurrency are also increasing. In order to better improve system performance and throughput, caching technology has been widely used. As a module of Redis, RedisBloom not only provides common caching functions, but also optimizes the high performance and space occupation of Bloom filters. This article will introduce the principles, application scenarios, advantages and disadvantages of RedisBloom caching technology.

1. RedisBloom Principle

The core technology of RedisBloom is Bloom Filter, which is used to quickly determine whether an element exists in a certain set. The Bloom filter is a data structure based on a hash function. It can quickly determine whether an element is in a set, but there is a possibility of misjudgment (that is, determining that a non-existent element is present in the set). The misjudgment rate is the same as The number of hash functions is related to the choice of mapping function. Compared with traditional caching technology, Bloom filters have higher space utilization and query efficiency. RedisBloom provides a variety of collection types based on Bloom filters, including Bloom Filter, Count-Min Sketch, and Top-K algorithm. These collection types can meet different needs. needs in the scenario.

2. RedisBloom application scenario

1. Message queue deduplication

In the message queue, there may be duplicate messages, which will cause consumers to Process the same business logic repeatedly, such as double sending text messages, repeated deductions, etc. Bloom filters can be used to effectively remove duplicates and determine whether the message has been processed, thereby avoiding problems caused by repeated processing.

2. URL deduplication

In businesses such as crawlers and search engines, it is often necessary to deduplicate URLs to avoid crawling the same web page repeatedly. Bloom filters can be used to quickly determine whether a URL has been crawled, thereby avoiding repeated requests and improving crawler efficiency.

3. Cache penetration

Cache penetration means that querying data that does not exist in the cache causes the database to be requested every time, resulting in increased database pressure. Bloom filters can be used to determine whether the data exists in the cache. If it does not exist, there is no need to request the database, thereby reducing database pressure.

4. Recommendation system deduplication

In the recommendation system, the recommendation results need to be deduplicated to avoid repeatedly recommending the same product or article. Bloom filters can be used to quickly determine whether a product or article has been recommended before, thereby avoiding repeated recommendations.

3. Advantages of RedisBloom

1. Small space occupation

Traditional caching technology needs to store all data in memory, which takes up a lot of space. The Bloom filter only needs to store the hash value corresponding to each element, which takes up less space.

2. High query efficiency

The query efficiency of Bloom filter is very high. Each element only needs to be hashed once and queried once to determine whether it exists in the set. , the time complexity is O(k), where k is the number of hash functions.

3. Fault tolerance rate is controllable

The Bloom filter fault tolerance rate can be adjusted through the number of hash functions and the selection of mapping functions, and can be optimized according to actual scene requirements .

4. Disadvantages of RedisBloom

1. High misjudgment rate

Due to the hash conflict problem of the Bloom filter, it may lead to misjudgment. Elements that do not exist are judged to be present. The false positive rate depends on the number of hash functions and the choice of mapping function.

2. Unable to delete elements

Since the Bloom filter does not have a delete operation, deleting elements can only be achieved by rebuilding the Bloom filter. This may cause problems in some scenarios.

5. Summary

With the rapid development of Internet business, caching technology has received more and more attention. As a module of Redis, RedisBloom provides a variety of collection types to meet the needs of different scenarios by taking advantage of the high performance and space optimization of Bloom filters on the basis of providing common caching functions. However, since Bloom filters have a certain false positive rate and cannot delete elements, they need to be carefully selected and optimized when used.

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