Azul Systems, Inc., has just released a new tool to help enterprises optimize their Apache Cassandra deployments on the fly.

Azul is a developer of runtime platforms for executing Java-based applications, and Cassandra HealthCheck (Cassandra HC) has been designed to help eliminate Java-related barriers and improve Cassandra’s runtime consistency, reliability and up-time.

Apache Cassandra is a highly performant, scalable open-source distributed database management system written in Java. It’s designed to handle large amounts of data across multiple commodity servers, can support clusters spanning multiple data centers, and provides high availability with no single point of failure. But even so, Cassandra’s performance is often impacted by the capabilities of the underlying Java runtime platform, Azul said. For example, while Legacy Java Virtual Machines (JVMs) may suffice for some use cases, many enterprises report being limited by the JVM, especially the detrimental impact of garbage collection. This process can often freeze Cassandra while memory is being defragmented and compacted, and that leads to pauses in Cassandra that cause response time inconsistencies, increased time to data consistency, and can even trigger serious conditions like cascading node failures.

“Many users do not understand the negative impact a Java runtime can have on Cassandra response time, reliability and throughput,” said Scott Sellers, CEO and president of Azul Systems, in a statement. “With Azul’s Cassandra HC service offering, our specialists can quickly show enterprises how to maximize the value of their Cassandra investment and eliminate many of the performance and reliability challenges facing Cassandra deployments today. Better Cassandra deployments ultimately mean better business performance.”

Cassandra HC leverages both open-source and proprietary components, and delivers a combination of on-premise and cloud-based runtime analytics services that can help IT and DevOps teams to optimize Java runtimes and system and application configurations to boost Cassandra. This includes boosting components that leverage Cassandra data stores like Apache Spark, Lucene or Solr.

In a test case involving a financial services company, Azul said Cassandra HC resulted in an improvement of more than 56 times in worst-case latency, and a 25 percent improvement in throughput. It also led to a clear reduction in disconnects due to node timeouts.

Garbage collection issues can hit any Java deployment, but are particularly significant where low-latency performance is a requirement,” said Matt Aslett, research director for data platforms and analytics at 451 Research. “With Azul’s new Cassandra HealthCheck offering, Cassandra users can leverage the company’s JVM and performance tuning expertise, allowing for more consistent and reliable deployments while focusing their attention on the application itself, rather than spending time trying to tune away JVM issues.”

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