Learn about Scylla caching technology

With the continuous growth of data volume and the acceleration of read and write operations, the performance requirements for the database are becoming higher and higher. In order to deal with this problem, database manufacturers have continuously developed new technologies in the past few decades, among which caching technology is one of them. Scylla is a high-performance distributed NoSQL database. It not only has excellent data reading and writing performance, but also uses some advanced caching technologies to improve its overall performance and stability. Today, let’s take a look at how Scylla caching technology works and how it is implemented.

How Scylla caching technology works

Scylla is a distributed NoSQL database based on Apache Cassandra that enables scalability by distributing data across a large number of servers. In order to improve the performance of the database, Scylla uses a variety of caching technologies based on Cassandra. Scylla's caching technology mainly includes the following types:

1. Second Level Cache:

While sharing data between multiple Scylla nodes, Each node can also maintain a cache pool (Cache Pool) locally to save the most commonly used data. In this way, when querying data, the data is first searched for in the local cache pool. If found, it is returned directly to the user. Otherwise, the query request is sent to other nodes. This caching mechanism can effectively reduce network transmission overhead and query latency.

2. Bloom Filter:

Bloom Filter is a simple and useful data structure that can quickly determine whether an element is in a set. Scylla uses Bloom Filter technology to speed up data queries. When a user sends a query request, Scylla can quickly find whether the data exists in the Bloom Filter. If it exists, the results can be returned immediately, otherwise detailed query operations will be performed. Bloom Filter technology can significantly increase query speed and reduce query latency.

3. Local disk cache:

In addition to using local cache pools and Bloom Filters, Scylla can also use local disk cache to store the most commonly used data. This method can automatically load data when the node starts, and can automatically switch to the backup node when a node fails, ensuring data reliability and high availability.

4. Compression Cache:

Scylla can also use compression caching technology to reduce the space occupied by database storage. In the compressed cache, Scylla compresses and stores frequently used data blocks and stores the compressed data in the cache. This can significantly reduce disk space usage and increase the speed of data reading.

How to implement Scylla caching technology

Scylla’s caching technology can be implemented by configuring Scylla’s relevant parameters. The following are some common configuration parameters:

1. cache_size_in_mb:

Specifies the memory size allocated in the local cache pool for each node, in MB. The default value of this parameter is 256 MB and can be adjusted appropriately based on the node's hardware configuration.

2. bloom_filter_fp_chance:

Specify the threshold of false positive rate in Bloom Filter technology. The default value of this parameter is 0.01 and usually does not need to be modified.

3. disk_cache_size_in_mb:

Specify the cache size that each node maintains on the local disk, in MB. The default value of this parameter is 4096 MB. If the node hard disk capacity is small, this parameter can be adjusted appropriately.

4. compression:

Specify whether to use data compression technology in the cache. If this parameter is set to true, the node attempts to use the compression algorithm to compress frequently used data blocks. The default value of this parameter is false.

Summary

Scylla caching technology is one of the important means to improve Scylla performance and stability. It shares data between multiple nodes and uses a variety of caching technologies to optimize data queries and storage, which can significantly improve the performance and availability of Scylla database. By understanding and mastering the working principle and implementation of Scylla caching technology, we can better use Scylla to meet different data storage needs and improve work efficiency in actual application scenarios.

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