Elastic, the company behind Elasticsearch and the Elastic Stack, announced Elastic APM.

This first production-ready release of Elastic APM is an extension of Elastic's product stack into application performance. It allows application developers and devops engineers to monitor and analyze the impact of individual lines of code on system and business performance. This not only speeds, but also extends the debugging process, incorporating code performance into a holistic view of operational efficiency.

Elastic APM stores data into an Elasticsearch index, allows for APM data to be correlated with logs and metrics collected via Logstash and Beats, includes a server-side component and agents for Node.js, Python, Ruby and JavaScript; and an APM app tailored for a typical APM workflow.

Elastic APM is now available as part of the Elastic 6.2 release.

In addition, Elastic announced the following new and upcoming features:

- Swiftype App Search: Built for developers to add more powerful search functionality to their applications, Swiftype App Search delivers a robust set of APIs and additional search-specific features such as result positioning, synonyms, and typo-tolerance. Swiftype App Search is a turnkey SaaS solution requiring no infrastructure, management and maintenance, and offers an easy getting-started experience. Swiftype App Search is now available as a public beta.

- Machine Learning Forecasting: The first major extension of Elastic's machine learning capabilities extends functionality into the realm of predictive analytics. Users can model time series data and use sophisticated, ready-made, machine learning algorithms to forecast outcomes several time intervals into the future. With on-demand forecasting, users can take an existing machine learning job and, using the predictive model built into machine learning, gain accurate predictions on where that model is expected to grow over the forecast period. The forecast results are written to an Elasticsearch index allowing users to compare actual results to forecast models. Elastic's machine learning forecasting capabilities are now available as part of the 6.2 release.

- SQL for Elasticsearch: This new feature opens up the power of the Elastic Stack to the world's most established database community of SQL developers, allowing users to query Elasticsearch data in familiar SQL Syntax. It also dramatically simplifies the (re)export of data from Elasticsearch back into external SQL environments with out-of-the-box JDBC support. By allowing Elasticsearch to understand SQL through a RESTful interface, SQL for Elasticsearch lets you query your Elasticsearch data using SQL syntax, returns results to those queries in a tabular form consistent with traditional SQL engines and provides a user interface to explore the data. SQL for Elasticsearch was introduced last year as a concept and will soon be available in an alpha and beta release.

- Rollups: Commonly associated with metrics and logging use cases when storing data for long periods of time is required, rollups enable users to store a limited set of data, reducing the disk usage of historical data. An Elasticsearch rollup job allows users to configure periodic jobs that "rollup" or pre-aggregate data, and store the rollup in an index. One example is a metric like "average load time returned by the web server per hour," of which, the average data is rolled up and stored, but other raw data attributes like the specific user, page, and IP information are not. This will be available soon in a beta for Elasticsearch and later with Kibana support.

- Flexible Deployment Configurations: As customers put more and more data into Elasticsearch and expand their use cases, Elastic introduces the concept of "sliders" to give users the ability to customize their cluster configurations. Available for Elastic Cloud and Elastic Cloud Enterprise (ECE) customers, some of the new capabilities include: support for multiple classes of hardware; support for cluster templates and hot/warm clusters; and the ability to add machine learning, dedicated master nodes, and APM nodes to existing cluster configurations. These new features will be available soon in both Elastic Cloud and Elastic Cloud Enterprise.

- Logstash Azure Monitoring Module: Built in collaboration with Microsoft, the Logstash Azure Monitoring module is the easiest way to monitor your Azure infrastructure and services with the Elastic Stack. This new module integrates with Azure's centralized logging service to normalize Azure logs and metrics into JSON; uses Logstash to consume the data into Elasticsearch; and with Kibana, users can analyze infrastructure changes and authorization failures; identify suspicious activity and potential malicious actors; perform root-cause analysis by investigating user activity; and monitor and optimize SQL DB deployments. This will be available soon as a beta release.

- Elastic certification program. Fueled by user demand to have professional accreditation, Elastic will be offering new training curriculum designed for users to become experts and be certified by Elastic. New courses, Elasticsearch Engineer I and Elasticsearch Engineer II, will give users first-hand knowledge of installing, managing and optimizing Elasticsearch clusters, as well as, developing new solutions for analyzing their data. These courses are the foundation to becoming an Elastic Certified Engineer, which includes a hands-ons, technical and performance-based certification exam and an official digital Elastic certification badge for users who pass the exam.