Below is the intro to a
great article by
Lan Vuong posted on
InfoQ about extreme transaction processing patterns and write behind database caching.
Applications typically use a data cache to increase performance, especially where the application predominantly uses read-only transactions. These applications directly update the database for changes in the data. The issue here is that as the load increases then the response time on these updates grows. Databases are not good at executing lots of concurrent transactions with a small number of records per transaction. Databases are much better at executing batched transactions.
Eventually, the database will saturate the CPU or disks and at that point the response time will rise as additional load is added. Conventional in-memory caches are also limited to only storing what can fit in the free memory of a JVM. Once we need to cache more than this amount of data then thrashing occurs where the cache continuously evicts data to make room for other data. The required record must then be read continually thereby making the cache useless and exposing the database to the full read load.
There are several products currently available, including IBM® WebSphere® eXtreme Scale, Oracle Coherence, and Gigaspaces that allow all of the free memory of a cluster of JVMs to be used as a cache rather than just the free memory of a single JVM. This allows the capacity of the cache to scale as more JVMs are incorporated. If these JVMs are on additional physical servers with CPU, memory and network, then this allows scalable servicing of read requests. They can also provide scalable servicing of update requests by leveraging their write-behind technology. The scalability of write-behind caching makes it ideal to handle extreme transaction processing (XTP) scenarios. XTP is defined by Gartner as "an application style aimed at supporting the design, development, deployment, management and maintenance of distributed TP applications characterized by exceptionally demanding performance, scalability, availability, security, manageability and dependability requirements.”
In this paper, we will illustrate how to optimize the performance of an application by leveraging the write-behind pattern with examples using IBM WebSphere eXtreme Scale. The write-behind function batches updates to the back-end database asynchronously within a user configurable interval of time. The obvious advantage of this scenario is reduced database calls and therefore reduced transaction load and faster access to objects in the grid. This scenario also has faster response times than the write-through caching scenario where an update to the cache results in an immediate update to the database. In the write-behind case, transactions no longer have to wait for the database write operation to finish. Additionally, it protects the application from database failure as the write-behind buffer will hold changes through memory replication until it can propagate them to the database.
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