We’re very pleased to announce the availability of DataStax DevCenter 1.5, which can be downloaded here.
This new version is compatible with Apache Cassandra™ 3.0 supporting Materialized Views (blog / docs) and Multiple Indexes (docs) with content assist, quick fix suggestions, validations, and wizards!
Introduction to repairs and the Repair Service
Cassandra repairs consist of comparing data from between replica nodes, identifying inconsistencies, and streaming the latest value for mismatched data.
Today we are happy to announce the release of the DataStax Python Driver 3.0.0 for Apache Cassandra. The main focus of this release was to add support for the updated schema metadata introduced in Cassandra 3.0, while maintaining compatibility with earlier server versions.
This article is about setting up a DataStax Enterprise cluster running in a single host.
There are a variety of reasons why you might want to run a DataStax Enterprise cluster inside a single host. For instance, your server vendor talked you into buying this vertical-scale machine but Cassandra can't effectively use all the resources available. Or your developers need to test your app as they develop it, and they'd rather test it locally.
Whatever the reason, you'll learn how to set the cluster up from the ground up.
Here’s the link to the Data Modeler that is discussed in this post. The main drivers behind Cassandra performance are:
Application specific design and configuration
For many early stage projects that are trying to make hardware and data modeling decisions to maximize performance, it is often beneficial to take the app specific questions out of the equation and design and test a table that will scale on a given hardware configuration.
We are pleased to announce the 2.2 GA release of the C/C++ driver for Apache Cassandra. This release includes all the features necessary to take full advantage of Apache Cassandra 2.2 including support for new data types (‘tinyint’, ‘smallint’, ‘time’, and ‘date’) and support for user defined function/aggregate (UDF/UDA) schema metadata.
Using map collections in DSE Search takes advantage of dynamic fields in Solr for indexing. For this to work, every key in your map has to be prefixed with the name of the collection.
Tuple and UDTs are convenient ways to handle certain data structures which usually go together (check the Cassandra on-line documentation for an updated explanation on them). DSE Search starting version 4.8 is supporting them and we intend to explain in this post how to use them best.
Gremlin is the popular graph query language that comes from the Apache TinkerPop graph framework project. As part of the DataStax commitment to the Gremlin and TinkerPop graph community, I'm going to introduce you to a great way to get started with Gremlin graph queries.