April 2015 Summaries

The DataStax Test Engineering group is developing new tools to improve testing and developer enablement for the Cassandra project. They aim to support a "tick-tock" release cycle where new features and bug fixes will be released on a monthly basis. To achieve this, they are introducing functional test parallelization and self-serve branch testing for developers. These tools provide faster feedback and earlier intervention in case of issues, leading to more reliable releases. Developers can now set up continuous integration systems to automatically test their branches after committing changes, increasing confidence before code review and merging into the source.

Data security is crucial in today's world, and one of the mechanisms provided by Cassandra for this purpose is SSL encryption and authentication. This post outlines the steps to connect DataStax DevCenter to an SSL-enabled Cassandra cluster. To verify server identity, the client needs a public key certificate for each Cassandra node stored in a local truststore file. The truststore file and password are entered into the DevCenter connection manager dialog box. If client verification is required, additional steps include storing the public certificate in an ikapl.cer file. An error message will be displayed if the path to the file or password is incorrect. Once these steps are completed, a new connection can be created and enabled for communication with the SSL-enabled Cassandra cluster.

Microsoft's new Premium Storage offering combines network attached persistent SSD with locally attached SSD cache, making it an interesting storage option for DSE and Cassandra deployments in Azure that need to rely on persistent rather than ephemeral storage. The starter config includes a single P30 disk and a P10 for commitlog IO performance and capacity requirements. Premium Storage extends the cache capability with an additional layer backed by SSD, which can be incredibly powerful when combined with technologies like DTCS. It's important to make some config changes to ensure optimal operation of Azure Premium Storage.

The DataStax C/C++ driver for Apache Cassandra and DataStax Enterprise has released its 2.0 version, featuring new major enhancements such as a latency-aware routing policy and performance metrics. Additionally, the API has been improved with the removal of wrapper types in favor of plain C types, and the addition of string conversion functions for CassInet. The internal dependency on Boost has also been removed. This release aims to improve application performance and debugging capabilities while maintaining compatibility with existing codebases. Future releases will focus on supporting newer versions of Cassandra and incorporating user feedback.

DevCenter 1.3.1 has been released for download, offering support for Cassandra 2.1.3 CQL features, DateTieredCompactionStrategy, and extra table options in the Table DDL Wizard. For more information on updates and changes, refer to the release notes.

DateTieredCompactionStrategy (DTCS) is a new compaction strategy in Apache Cassandra designed for time-series data and access patterns. It simplifies the stored data to more closely match the logical view by grouping the stored data for optimal access, which is less intensive than traditional compaction methods. DTCS is best suited for scenarios where data models and access patterns utilize time-ordering for clustering. The strategy can be tuned with parameters such as min_threshold, max_sstable_age_days, and others to balance the compaction workload with the need to optimize read requests to the storage layer. A test showed that DTCS performed well in a time-series workload scenario, maintaining stable latencies and throughput while increasing data density. However, it is crucial to consider operational headroom when using DTCS, as high data density can affect bootstrapping or repairing nodes.

The seamless integration between Apache Cassandra and Apache Solr provided by DataStax Enterprise (DSE) allows automatic indexing of Cassandra columns through its secondary index API. This enables developers to map a single Cassandra column to multiple Solr document fields using pre-processing hooks like FieldInputTransformer and FieldOutputTransformer classes. These classes can be extended to customize the Solr document just before indexing, allowing for flexible parsing of data types such as JSON objects or binary files. The integration also supports searching individual fields from both Solr HTTP interface and CQL, providing a powerful tool for managing and querying large datasets stored in Cassandra.

Erich Ess, CTO of SimpleRelevance, discusses using Apache Spark with Cassandra for Extract, Transform, and Load (ETL) tasks. The text provides a detailed walkthrough on how to parse and load the MovieLens dataset into Cassandra, then perform simple analytics. It also highlights the benefits of caching in Spark for improved performance during ETL processes.