Caching limited data sets in Java caching technology

As the complexity of modern applications continues to increase, the demand for data throughput and availability is also increasing. In order to solve these problems, the application of caching technology has gradually been widely used.

In Java caching technology, caching limited data sets is a particularly common scenario. Caching limited data sets usually means that some data sets (such as database query result sets) are cached in memory to improve data access speed and responsiveness, and the size of the cached data set is also limited to a certain range. When the cache size is reached At the limit, some cached data sets will be eliminated according to certain strategies to make room for new data sets.

Let’s discuss how to implement caching of limited data sets in Java caching technology.

1. Cache data structure selection

In Java cache technology, there are two main cache data structures: hash table and red-black tree.

The characteristic of the hash table is to disperse the stored data through the hash function, so as to achieve the purpose of quickly finding and accessing the data. Since the lookup speed of hash table is very fast, it is widely used in caching data set scenarios.

In contrast, the characteristic of the red-black tree is to continuously sort and balance the data to ensure that it can maintain a fast search speed in the worst case. Although the red-black tree is not as fast as the hash table, it has better universality and stability, and is more flexible to use.

According to different needs, we can choose an appropriate data structure as the storage structure for cached data. If we need to find data quickly, it is more suitable to choose a hash table; if we need to support range search, sorting and other operations, it is more suitable to choose a red-black tree.

2. Cache strategy selection

The cache strategy refers to how to eliminate some cached data sets after the cache reaches a certain size limit to leave enough space to store new data. data.

There are three common caching strategies: first-in-first-out (FIFO), least recently used (LRU) and least recently used (LFU).

• The first-in, first-out (FIFO) strategy is a relatively simple strategy, which is to eliminate the earliest data set that enters the cache first. However, this strategy is prone to situations where new data entries overwhelm old data entries.
• The least recently used (LRU) strategy is a commonly used strategy. This strategy selects the least recently used data sets for elimination. This ensures that the data sets in the cache are frequently used, rather than some rarely used data sets.
• The least used (LFU) strategy is a strategy for elimination based on the number of times the data set is used. This strategy selects the least frequently used data sets for elimination. This strategy usually requires recording the number of times each data set is used, so it is relatively complex to implement.

According to different application scenarios and requirements, you can choose an appropriate caching strategy for implementation.

3. Auto-loading mechanism

When the data set to be queried does not exist in the cache, how should the data set be loaded and stored? This requires the implementation of the automatic loading mechanism.

The automatic loading mechanism can automatically load data sets by pre-setting parameters, asynchronous loading and cache storage. In this way, when the data set is needed next time, it can be obtained directly from the cache to speed up data access.

It should be noted that when performing automatic loading, it is necessary to master the balance between loading parameters and cache size to avoid loading too many data sets, resulting in too large cache sizes, or loading too few data sets, resulting in excessive hit rates. Low.

4. Concurrency control

Concurrency control is also one of the important issues in caching technology. If multiple threads operate the cache at the same time, concurrent read and write problems may occur, resulting in data inconsistency.

In order to solve concurrency problems, various methods can be used, such as lock mechanisms, CAS (Compare And Swap) mechanisms, etc.

The lock mechanism is a relatively common method, and you can use read-write locks, pessimistic locks, optimistic locks, etc. The characteristic of read-write lock is that it supports concurrent reading, but can only write alone; the characteristic of pessimistic lock is that by default, it is believed that there is a problem with concurrency and needs to be locked; the characteristic of optimistic lock is that by default, it is believed that there is no problem with concurrency. Not locked.

According to the actual concurrency situation and application scenarios, you can choose an appropriate concurrency control method to ensure the correctness and availability of the cache.

To summarize, caching limited data sets in Java caching technology requires consideration of multiple aspects such as cache data structure selection, caching strategy selection, automatic loading mechanism, and concurrency control. Only by adopting a suitable implementation method based on actual needs can the cache play its maximum role and improve the overall performance and availability of the application.

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