Two-Phase-Commit for In-Memory Caches - Part II
What happens when an in-memory cache serves as a layer on top of a persistent database? You need data consistency via read- or write-throughs.
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Join For FreeGenerally, persistent disk-oriented systems will require the additional 3rd phase in commit protocol in order to ensure data consistency in case of failures. In my previous blog I covered why the 2-Phase-Commit protocol (without 3rd phase) is sufficient to handle failures for distributed in-memory caches. The explanation was based on the open source GridGain architecture, however it can be applied to any in-memory distributed system.
In this blog we will cover a case when an in-memory cache serves as a layer on top of a persistent database. In this case the database serves as a primary system of records, and distributed in-memory cache is added for performance and scalability reasons to accelerate reads and (sometimes) writes to the data. Cache must be kept consistent with database which means that a cache transaction must merge with the database transaction.
When we add a persistent store to an in-memory cache, our primary goal is to make sure that the cache will remain consistent with on-disk database at all times.
In order to keep the data consistent between memory and database, data is automatically loaded on demand whenever a read happens and the data cannot be found in cache. This behavior is called read-through. Alternatively, whenever a write operation happens, data is stored in cache and is automatically persisted to the database. This behavior is called write-through. Additionally, there is also a mode called write-behind which batches up the writes in memory and flushes them to the database in one bulk operation (we will not be covering this mode here).
Conclusion
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