Cache data sharding in Java caching technology

Cache data sharding in Java caching technology

With the rapid development of the Internet and the advent of the big data era, the rapid growth in data volume has brought huge challenges to our data storage and processing. In order to solve this problem, caching technology came into being. Caching refers to storing data in faster storage devices in order to speed up data access and read and write operations. In Java caching technology, caching data sharding is a common technical means.

What is cache data sharding

In high concurrency scenarios, caching often becomes a bottleneck. At this time, we can solve the problem by caching data shards. The so-called cache data sharding is to divide cache data into multiple fragments according to certain rules, and each fragment is responsible for different cache nodes. This has two benefits:

First, it can reduce the load pressure on a single cache node and improve the concurrency performance of the cache. If a single cache node stores a large amount of data, problems such as cache breakdown or cache avalanche may easily occur during highly concurrent accesses, resulting in reduced system performance. After using cache data sharding, the amount of data stored in each cache node becomes smaller, which can effectively avoid these problems.

Second, shorten the cache search time and improve the cache hit rate. It takes a certain amount of time to search for data in the cache. If the amount of cached data is large, the search time will become longer, thereby reducing the cache hit rate. After using cache data sharding, each cache node stores less data, shortening the search time and improving the cache hit rate.

How to implement cache data sharding

There are two ways to implement cache data sharding: horizontal sharding and vertical sharding.

Horizontal sharding refers to sharding data according to data ID or storage time. Each data ID or time period corresponds to a cache node. For example, the user ID is hashed according to certain rules to obtain a hash value, and then the hash value is modulo the number of nodes to obtain which node the data should be stored on. In this way, the amount of data stored in each node is relatively uniform, which can effectively avoid the situation of excessive load on a single node.

Vertical sharding refers to sharding data according to business type, data type or data source. Each business type, data type or data source corresponds to a cache node. For example, the data of different businesses are stored in different cache nodes, such as product information is stored in one node and user information is stored in another node. In this way, different services do not interfere with each other, which can improve the security and stability of the cache.

Notes on cached data sharding

When using cached data sharding, you need to pay attention to the following issues:

First of all, cached data sharding should ensure that the data is consistency. After sharding, different attributes of the same object may be assigned to different cache nodes, and a certain mechanism needs to be used to ensure the consistency of the object on each node.

Secondly, cache data sharding needs to consider the expansion and contraction of cache nodes. The expansion of cache nodes can be achieved by adding new nodes, while shrinking requires migrating cache data to other nodes. At this time, it is necessary to ensure the consistency and availability of data to avoid problems such as data loss and access delay.

Finally, cache data sharding should also consider the popularity of the data. Hotspot data may be allocated to different nodes in different time periods. Hotspot data preheating, data movement and other technical means need to be used to ensure efficient access to hotspot data.

Summary

Cache data sharding is a common technical means in Java caching technology, which can effectively improve the concurrency performance and hit rate of the cache. To implement cache data sharding, you need to consider data consistency, node expansion and contraction, and data popularity. For large-scale caching systems, the use of cache data sharding technology is essential.

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