August 2016 Summaries

A new version of Simba ODBC driver with CQL connector for DataStax Enterprise 5.0 has been released, developed in collaboration with Simba. This updated driver is built on the latest DataStax C/C++ driver (version 2.4.1) and supports DataStax Enterprise 5.0 and Apache Cassandra 3.0+. The new version introduces support for additional data types such as smallint, tinyint, time, and date, and allows users to configure whitelists or blacklists when establishing connections. Detailed release notes can be found in a PDF document, while the driver itself (version 2.4.1.1001) is available for download from the DataStax drivers page. An installation and configuration guide is also provided in PDF format.

Containers are efficient for managing stateless applications and can be easily scaled and replaced in a containerized environment, with tools like Kubernetes automating the process. Google Container Engine (GKE) further simplifies this by integrating with top-tier cloud providers like Google Cloud Platform (GCP). Managing stateful workloads is more complex due to potential data loss when replacing failed nodes. This blog post focuses on DataStax Enterprise (DSE), a highly scalable distributed database built on Apache Cassandra™, and its integration with GKE for deploying entire applications, including the data store in GKE and managing it using Kubernetes. The integration simplifies administration, improves reliability, and provides a more holistic framework for building applications, leading to lower cost of ownership and improved time to market. DataStax and Google have been working closely on this integration, which is available on GitHub at https://github.com/DSPN/google-container-engine-dse. The deployment process is straightforward, with detailed instructions provided. While the current integration is more demo grade than production-ready, improvements are actively being worked on, and feedback and contributions are welcome.

Apache Cassandra introduced User-Defined Functions (UDF) and User-Defined Aggregates (UDA) in version 2.2, allowing users to write their own scalar functions and build custom aggregations. UDFs are executed on the coordinator node and can be used for partition-level aggregations. They should be written in Java and can be applied to one or more columns. Aggregations work on multiple rows and produce a single row result, requiring an initial state and optional final function. The sandbox introduced in DSE 5.0 ensures that UDFs do not perform malicious actions. It uses Java bytecode inspection and restricted class loading for protection. Analytics in Apache Spark uses resilient distributed datasets (RDD) with various operations, including map-reduce, grouping, aggregation, re-partitioning, and more. UDF and UDA are building blocks for something bigger and can be used from analytics code like Spark.