Learn about Cassandra caching technology

Cassandra is a high-performance, distributed NoSQL database that is widely used in large-scale data management. Cassandra's caching technology is one of the keys to its high performance. This article will introduce the basic principles, cache types and optimization methods of Cassandra caching technology.

1. Principle of Cassandra caching technology

Cassandra's cache is a technology that stores frequently accessed data in memory to improve read performance. There are two main types of caches in Cassandra: key cache and row cache.

1. Key cache

Key cache is a caching mechanism for managing SSTables (Sorted String Table) data files. SSTable is a file format for saving data in Cassandra. Each SSTable file contains row data within a certain range. Cassandra uses Bloom filters to determine whether an SSTable contains the required rows. The Bloom filter in Cassandra is an efficient data structure that can quickly determine whether an element is in a set.

Key caching in Cassandra reduces the number of SSTable files loaded from disk by caching Bloom filters and SSTable indexes in memory. When an SSTable file is cached in memory, Cassandra can quickly access the data in it, thereby accelerating data reading. In Cassandra's caching strategy, key caching is a lower priority cache type.

2. Row cache

Row cache caches frequently accessed row data into memory to speed up reading. Unlike key caching, row caching does not use Bloom filters. When data is cached in memory, Cassandra can quickly read its data, thereby reducing read latency and improving read performance.

Row caching is a more commonly used cache type because it can speed up common query operations. However, it should be noted that since row caching consumes more memory space, the memory usage needs to be fully evaluated and planned when caching data.

2. Cassandra cache type

Cassandra cache is generally divided into two types: local cache and remote cache.

1. Local cache

The local cache refers to the cache running on each Cassandra node. Since each node stores the same data, when the data on a node is cached in the local cache, other nodes are also able to obtain the cached data from that node, thereby improving the read performance of the entire cluster.

2. Remote cache

Remote cache refers to a cache shared between multiple Cassandra nodes. Remote caching is generally implemented using distributed caching systems such as Redis or Memcached. When a node needs to cache some data, it saves the data in the remote cache. Other nodes can also obtain cached data from the remote cache, thereby improving read performance across the cluster.

3. Cassandra cache optimization method

In order to further improve the read performance of Cassandra, we can also adopt some optimization methods, including:

1. Increase the cache size appropriately

Appropriately increasing the cache size can improve the reading speed of data that is accessed more frequently.

2. Reasonable use of caching strategies

Cassandra provides a variety of caching strategies, including Auto, KeysOnly, RowsOnly and All. For different business scenarios, you can improve read performance by properly setting caching strategies.

3. Use local cache

Using local cache can reduce data transmission between nodes, thereby improving read performance.

4. Reasonably set the false positive rate of the Bloom filter

The false positive rate of the Bloom filter refers to the probability of judging that an element is not in the set. The lower the false positive rate, the fewer SSTables files are loaded from disk, thus improving read performance.

Summary

Cassandra’s caching technology is an important means to improve read performance. This article introduces Cassandra's caching technology principles, cache types, and optimization methods. In actual applications, cache settings and optimization need to be performed according to specific business scenarios to maximize Cassandra's read performance.

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