With today’s release of DataStax of the next version of its enterprise platform, it fulfills a promise made last year to fully integrate the graph engine into the core platform. DataStax Enterprise 6.8 as well adds support from the Cassandra Kubernetes operator announced last week and a number of features designed to facilitate operations.
ace we declared almost a year ago, one of DataStax’s goals was to make graph a first-class citizen in its core platform. The new release accomplished this by allowing the use of the same API and query data graph from CQL, the original query language on the Cassandra platform. Computer scientists and other practitioners are knowledgeable on graph databases will also still be able to use Gremlin API.
Before this DataStax Enterprise was actually a dual-model database that lets you work in Cassandra or in graph, but not both on the same core engine. In that sense, it was similar Microsoft Azure Cosmos DB, a multi-model database that allowed you to choose which data model you want to work in, and then use that API exclusively for that dataset.
With graph fully integrated, this means data can be captured and modeled as it would normally be for Cassandra, with graph views based on the partition keys that are key to the Cassandra data model. So you don’t have to load data somehow or model it differently, and the graph view can now benefit from the same multi-master, scaling capacity on the underlying Cassandra platform.
Add on Kubernetes support is part of DataStax’s roadmap to both make Cassandra suitable for cloud-native operations via containers and microservices, and provide another means to get back in line with Apache Cassandra open source community. In this case, DataStax contributed their Kubernetes operator to the Apache project, where it could be converged with operators developed by other members of the open source community.
Operational simplification is another key one in the 6.8 release – and here the Kubernetes operator plays directly into it by allowing Cassandra to deploy and resize scales more easily.
Other operational features include new protection frameworks that codify best practices when deploying Cassandra. These safeguards can give alerts when you release code into operation, trigger alerts when implementation specifications such as column size or number of indexes can compromise operations; they allow operators or developers to reconfigure before problems hit the fan.
Incremental node synchronization is another new 6.8 feature that aims to streamline operations. The guiding idea was to reduce the cost of synchronizing data when a node or network connection goes down. In the past, you would have had to sync the entire table and remove a node, but with the new incremental function, only the specific data affected must be synchronized. And a new feature, Zero Copy Streaming, speeds up add or remove nodes for business continuity tasks.
Another of last year’s promises that simplify the developer experience is waiting for another release cycle. For now, the emphasis is on completion Astra, the managed Apache Cassandra cloud service promised by DataStax for over a year. Currently in beta on Google Cloud is pressed now that AWS is currently previewing its own Managed Cassandra Service Counterpart.
The other major goal of DataStax is to tie the loose ends together to reconnect with the Apache Cassandra community and restructure its core platform accordingly. An important milestone will be the release of Apache Cassandra 4.0 that will focus more on fine tuning and updating (such as Java 11 support) rather than dramatic extensions of the platform. Getting releases adjusted so that the enterprise platform is a modular super-set is a work in progress.