Announcing InfluxDB 1.0 Beta - A Big Step Forward for Time Series
By Paul Dix / Jun 07, 2016 / Chronograf, InfluxData, InfluxDB, Community, Kapacitor, Telegraf, Newsroom, Press Releases
The team at InfluxData is excited to announce the immediate availability of InfluxDB 1.0 Beta plus the rest of the components in the TICK stack: Telegraf, Chronograf, and Kapacitor. While there are many new features like exponential smoothing via Holt Winters queries, templates for Kapacitor TICKscripts, Telegraf Cloudwatch integration, and dozens of bug fixes, this release marks a significant point in the development of these projects.
We’ve had customers and community members running the TICK-stack in production at significant scale for months and we are therefore comfortable that the quality of the codebase is worthy of the 1.0 moniker. Second, we’re ready to lock down the API and make a commitment to zero breaking changes for a significant length of time. This is especially important for organizations building products and services on top of the InfluxData stack whose products may have longer development cycles or require a higher degree of stability from the code base to ensure continuity for their customers and users.
Getting to 1.0 GA
This release is the first Beta of the upcoming 1.0 GA release. We still have some known bugs to fix, but from here until 1.0 we’ll be focused on testing, benchmarking and bug fixes. What about new features? They’ll come in the point releases after 1.0. For community members, this Beta is what you should be testing against. For some users, the Beta may even be suitable for production use. Many fixes have gone into all the projects since the 0.13 release nearly 4 weeks ago.
What's after 1.0 GA?
While we’re very excited about nearing the goal of a 1.0 GA release, we still have many great features and enhancements planned for the point releases coming after 1.0. For example, we have plans to improve rollups and aggregations in InfluxDB, along with integration into the query engine to automatically scale to the proper rollups based on the length of time being queried. We’ll also be updating the indexing scheme for measurements and tags to be both on-disk and in-memory, which will make very high series cardinality sets possible on a single InfluxDB server.
We’ll continue to push performance improvements and other significant features in the open source projects. Following the release of 1.0 we plan on having regularly scheduled point releases (most likely every 2-3 months) which will be the vehicles for introducing new features and performance enhancements. These will be drop in replacements for previous 1.x releases and will not require data migrations of any kind. We may iterate on the data storage format, but these improvements will have to run while still supporting previous versions.
Why InfluxDB 1.0 matters
Time-series data has historically been associated with applications in finance. However, as developers and businesses move to instrument more of their servers, applications, architecture and the physical world, time-series is becoming the defacto standard for how to think about storing, retrieving,and mining this data for real-time and historical insight. At InfluxData, we are confident that time-series data will become more and more relevant across a broader spectrum of use cases. Right now, it is without a doubt a key ingredient for custom DevOps monitoring and metrics, real-time analytics, and Internet of Things/sensor data. InfluxData is at the forefront of what is sure to be the next wave of data platforms after NoSQL and we are excited to have you along for the ride! If you are interested in learning more about how time-series is disrupting the metrics and instrumentation space, download Paul Dix's technical paper on the subject.
An important feature in 1.0 for Grafana users
One last special call out worth mentioning in the 1.0 beta that will be of great interest to Grafana users, is the merging of PR #6792.
This commit optimizes
SHOW TAG VALUES so that it avoids the
SELECT query engine execution and iterator creation. There are also optimizations to reduce individual memory allocations and to reduce in-memory heap size by only operating on one measurement at a time. Execution time has been reduced to approximately 900ms for 500,000 rows. This is about 2µs per row. Of this time, approximately 1µs is spent retrieving and sorting the row and 1µs is spent encoding into JSON and writing to the response body.
This execution time makes this version of InfluxDB work well for Grafana users with a large number of series that are building new graphs or using template variables.
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