Editor's Note (September 7, 2018): This post refers to X-Pack. Starting with the 6.3 release, the X-Pack code is now open and fully integrated as features into the Elastic Stack.
6.1.0 is here.
Fresh on the heels of the 6.0.0 GA release, we are pleased to introduce you to the capabilities of 6.1.0. You should download it now or use it on Elastic Cloud (your favourite hosted Elasticsearch and Kibana provider.
The line between products and features is often a blurry one. And we feel that pain -- or experience that delight -- as keenly as many. When there are so many features to highlight in a release, where do you even start? Either you craft the next great novel or you choose to provide links to details. Happy reading and…more importantly…happy searching, analyzing, and visualizing.
Let’s start with some features that are, without question, worth mentioning:
After announcing that we joined forces with Opbeat, and an alpha (in the 6.0 timeframe), we are super pleased to share that Elastic APM is now in beta. This includes not only all of the goodness we have described in the past, but also features a brand new UI. It is available in X-Pack Basic (free!) and you can read more information in the APM 6.1 blog post.
Unsupervised, Automated, Expedited…adjectives abound when describing machine learning solutions. But, as our team has said, Elastic Machine Learning ‘catches what you might miss, all by itself.’ In 6.1.0 this expanded to include a series of substantive new features including On Demand Forecasting (based on the past, what values would you expect in the future), smarter allocation for efficiently assigning jobs to ML nodes, and automatic job creation for known data types. The fun doesn’t stop there, and all the features are described in the Machine Learning 6.1.0 Released post.
The summary, or aggregation, of features is in the detail post.
- As a companion to the Shrink Index API, we now have a Split Index Api. Each original primary shard is split into two, or more, primary shards in the new index
compositeaggregation is designed to return ALL terms and sorted in ‘natural order’
- A 15% increase in indexing throughput by refactoring the
existsquery? Yes please.
Visualize the future of interacting with your data in the detail post because there is MUCH more than can be listed here.
- Input control visualization components allow users to select particular values and guide to important filtering values for a dashboard.
- Kibana has a homepage. That logo in the upper left is actually useful now.
- The Monitoring UI will automatically use Cross Cluster Search to load data from remote clusters…super helpful when using a dedicated monitoring cluster.
Grok the details in the detail post.
- Complex modification of events in Logstash is now possible via the Logstash Ruby filter
- Disabled by default, Logstash’s core execution logic can be moved from JRuby to Java/JVM Bytecode3
If you want all the details, ‘Go’ read the detail post.
- You asked. We listened. We begin our journey into support of autodiscovery with Docker Autodiscovery in 6.1.
- Moar Metricbeat modules? Awesome. New Filebeat modules too? Even better. Many of these contributed by the community? Thank you!
- Packetbeat 6.1 adds support for the TLS protocol (one of the most requested Packetbeat features).