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Tuning Consistency With Apache Cassandra

One of the challenges faced by distributed systems is how to keep the replicas consistent with each other. Maintaining consistency requires balancing availability and partitioning. Fortunately, Apache Cassandra lets us tune this balancing according to our needs. In this blog, we are going to see how we can tune consistency levels during reads and writes to achieve faster reads and writes.

Before digging more about consistency, let me first discuss CAP Theorem. CAP Theorem describes the tradeoffs in distributed systems; it states that any networked shared-data system can have at most two of three desirable properties:

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How to Tune Garbage Collection in Java

Garbage collection is the mechanism by which the JVM reclaims memory on behalf of the application when it's no longer needed. At a high level, it consists of finding objects that are no longer in use, freeing the memory associated with those objects, and occasionally compacting the heap to prevent memory fragmentation.

The garbage collector performs it's work using one or more threads. But in order to do the job of tracking down object references and moving objects around in memory, it needs to make sure that the application threads are not currently using those objects because if, for example, an application thread is using an object and then the memory location of the object changes due to GC, then bad and unpredictable things could happen. This is why garbage collectors must pause all application threads when performing certain tasks. These pauses are sometimes called Stop-The-World pauses, and the minimization of them is the primary concern of GC tuning, as they can have a huge impact on the performance of a Java application.