<p>Today we are pleased to announce the release of Elasticsearch 7.4.0, based on Lucene 8.2.0. Version 7.4 is the latest stable release of Elasticsearch, and is now available for deployment via <a href="/elasticsearch/service">Elasticsearch Service</a> on <a href="https://www.elastic.co/cloud">Elastic Cloud</a>.
</p>
<p>Elasticsearch 7.4 features a number of improvements to <a href="#core">core search and analytics capabilities</a>, <a href="#cluster">cluster management and administration</a>, and <a href="#operational">operational resource efficiency</a>. In addition, Elasticsearch 7.4 includes some new <a href="#experimental">experimental features</a>: one helps you find relationships within your data and another builds upon Elasticsearch’s growing capability to enhance search result relevance through the use of vectors.
</p>
<p>If you’re interested in learning more about “what’s new,” stick around — we dive deeper into many of the release highlights below. Alternatively, if you’d rather “jump right in” by spinning up a cluster on <a href="https://www.elastic.co/cloud/">Elastic Cloud</a>, downloading the latest bits to your laptop, or reviewing the release notes:
</p>
<ul>
	<li><a href="https://www.elastic.co/cloud/">Start Elasticsearch on Elastic Cloud</a></li>
	<li><a href="https://www.elastic.co/downloads/elasticsearch">Download Elasticsearch</a></li>
	<li><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/release-notes-7.4.0.html">Elasticsearch 7.4.0 release notes</a></li>
	<li><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/breaking-changes.html">Elasticsearch 7.4.0 breaking changes</a></li>
</ul> <strong>
<h2 id="core">Core search & analytics improvements</h2></strong>
<p>Version 7.4 augments core search and analytics capabilities in Elasticsearch through the introduction of several new features. We’ve added native results pinning functionality (similar to the functionality available in <a href="https://www.elastic.co/site-search">Elastic Site Search)</a> into our Basic (Elasticsearch) offering. Elasticsearch 7.4 also takes a large step towards better handling of geospatial search and analysis, and introduces two powerful new aggregations.
</p> <strong>
<h3>Results pinning</h3></strong>
<p>Occasionally administrators want to promote specific results by dictating the exact order of documents on a results page, or they may want to manually revise the results returned by a specific query. Historically, users managed result ordering by writing custom code in the application layer. With the release of 7.4, this is now possible directly within Elasticsearch, using <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/query-dsl-pinned-query.html"><strong>results pinning</strong></a>. By using the new <code>pinned</code> query, users can manage and order results as they see fit.
</p>
<p>Results pinning is released under Elastic’s free Basic license.
</p> <strong>
<h3>Geospatial</h3></strong>
<p>Previous investments in infrastructure for geospatial data — the introduction of <a href="https://www.elastic.co/blog/lucene-points-6.0">BKD Trees</a> — allowed us to introduce bkd-backed geopoints in Elasticsearch (and later <a href="https://www.elastic.co/blog/elastic-stack-6-6-0-released">bkd-backed geoshapes in 6.6</a>). We are now building upon this foundation to simplify how geospatial data is processed, support new use cases, and improve performance.
</p>
<p>Prior to Elasticsearch 7.4, users were required to represent their spatial data using earth’s coordinate system in the geo_shape field type. With this release we are happy to introduce a <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/shape.html"><strong>new shape field type</strong></a> that enables users to position and query in x, y coordinate systems of their own choosing - whether it is for use cases like a shopping center, computer aided design (CAD) diagrams, or virtual worlds (think: video games, cyber / network mapping) or for handling any other data that is positioned on an arbitrary two dimensional coordinate system.&nbsp; Prior to Elasticsearch 7.4, users were required to represent their spatial data using earth’s coordinate system.
</p>
<p>We are also happy to announce the introduction of the<strong> </strong><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/ingest-circle-processor.html"><strong>circle ingest processor</strong></a>. Before today, when ingesting and indexing a circle into Elasticsearch, users had two options:
</p>
<ul>
	<li>Ingest and <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-shape.html#input-structure">index the circle using a prefix tree</a> - this option is simple to do, but results in a slower and larger index (since this method does not enjoy the benefits of the BKD-Tree) - this was the “easy way”</li>
	<li>Provide Elasticsearch with a polygon that closely resembles the circle <em>before</em> ingestion and index that polygon - this option provides a smaller and better performing index (since it does use the BKD-Tree) - but the user was responsible for translating the circle into a polygon in an efficient and accurate way, which is not trivial - this was the “efficient way”</li>
</ul>
<p>The new circle ingest processor translates circles into polygons that closely resemble them as part of the ingest process, which means ingesting, indexing, searching, and aggregating circles, just became both easy <em>and</em> efficient.
</p>
<p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt20d334526de8e343/5d83ad576c300f2ee240dc6d/elasticsearch-7-4-0-circle-ingest.png" data-sys-asset-uid="blt20d334526de8e343" alt="Circle ingest error distance" style="display: block; margin: auto;" width="400">
</p>
<p>Note that creation of the polygon is a balancing act of sorts. The more edges the polygon has, the greater portion of the circle it will cover (more edges results in higher accuracy). However, as the number of edges increases, so too does the size of the index. We provide users with the ability to balance accuracy against the index size through a configurable <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/ingest-circle-processor.html#ingest-circle-processor">error_distance</a> parameter. To gain a better understanding of the relation between the number of sides of the polygon (impacting index size) and the size of the circle, you can examine <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/ingest-circle-processor.html#_notes_on_accuracy">the table we provided in the documentation</a>, that illustrates several examples of circle radius and number of edges, with the error_distance kept to 1.
</p>
<p>The new shape field type and circle ingest processor are released under Elastic’s free Basic license.
</p> <strong>
<h3>New aggregations</h3></strong>
<p>Previously, aggregations were not supported for range fields. With the release of Elasticsearch 7.4 we’re introducing support for running aggregations (<a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-metrics-cardinality-aggregation.html">Cardinality</a>, <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-bucket-missing-aggregation.html">Missing</a>, <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-metrics-valuecount-aggregation.html">Value Count</a>, <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-bucket-histogram-aggregation.html">Histogram</a>, and <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-bucket-datehistogram-aggregation.html">Date Histogram</a>) on range fields. The ability to run Histogram and Date Histogram aggregations on range fields enables users to more easily count the number of ranges which overlap with specific buckets. For example, the date histogram aggregation on a range field enables users to count the number of phone calls which took place during a specific minute or to count the number of employees on vacation on a given day.
</p>
<p>Also new with the release of Elasticsearch 7.4, the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/search-aggregations-pipeline-cumulative-cardinality-aggregation.html"><strong>cumulative cardinality aggregation</strong></a> has been introduced as part of our ongoing efforts to provide advanced aggregations. This newly added pipeline aggregation allows users to calculate net new occurrences within a given time range. For example, this functionality enables a user to calculate the number of unique visitors to a site over a given time period without requiring additional filters or steps to deduplicate entries.
</p>
<p>Cumulative cardinality aggregation is being released under Elastic’s free Basic license.
</p> <strong>
<h2 id="cluster">Cluster management and administration</h2></strong>
<p>The best solutions help users to do more than just “keep the lights on” by being easy to configure, easy to maintain, and, with any luck, easy to readily address evolving business needs. To this end, Elasticsearch 7.4 introduces snapshot lifecycle management (SLM) and new cluster privileges to more efficiently manage API keys.
</p>
<h3>Data backup has never been easier</h3><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/getting-started-snapshot-lifecycle-management.html"></a>
<p><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/getting-started-snapshot-lifecycle-management.html"><strong>Snapshot lifecycle management</strong></a> is a background snapshot manager that enables an administrator to define when and how often automatic snapshots of an Elasticsearch cluster are taken. This ensures appropriate, recent backups are ready and available should the need arise to restore. Prior to Elasticsearch 7.4, users often solved snapshot management using third party tools or custom code. Users are now able to reduce tool proliferation by managing automatic snapshot lifecycle policies via API or via a new policy management UI (located within the snapshot and restore interface in Kibana) (see image below). The user interface in Kibana allows users to create, view, edit, and run policies for either the entire cluster or for specific indices.
</p>
<p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt2841558c3b2ce321/5d83ada500703b6e6188152d/elasticsearch-7-4-0-slm.jpg" data-sys-asset-uid="blt2841558c3b2ce321" alt="Snapshot lifecycle management in Elasticsearch" style="display: block; margin: auto;">
</p>
<p>Snapshot lifecycle management is being released under Elastic’s free Basic license.
</p>
<h3>API key management</h3>
<p>In 7.4, Elasticsearch is introducing <strong><a href="https://www.elastic.co/guide/en/elastic-stack-overview/7.4/security-privileges.html"><strong>new cluster privileges</strong></a></strong> to manage <strong><a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/security-api.html#security-api-keys">API keys</a></strong> — allowing cluster administrators to manage everything, and other users to manage their own keys. Users can create API keys and use them to provide long-term credentials while interacting with Elasticsearch. This capability is especially useful when working with automated scripts, or workflow integration with other software.
</p>
<p>These enhancements to API key management are being released under Elastic’s free Basic license.
</p> <strong>
<h3>TLS settings for email notifications</h3></strong>
<p>Notifications may contain sensitive information that must be protected over the wire. This requires that communication with the mail server is encrypted and authenticated properly.
</p>
<p>In 7.4, Elasticsearch supports custom <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/notification-settings.html#ssl-notification-smtp-settings">TLS settings for email notifications</a>, allowing secure connections to servers with custom security configuration.
</p>
<h3>Added support for AdoptOpenJDK</h3>
<p>AdoptOpenJDK 13 is now supported and will ship with Elasticsearch as the pre-bundled JDK (as always, if you want to bring your own JDK, you can still do so by setting `JAVA_HOME` before starting Elasticsearch). The availability of a notarized AdoptOpenJDK package (per the new requirements for software running on macOS Catalina) will facilitate notarization of Elasticsearch for continued support on macOS.<br>
</p> <strong>
<h2 id="operational">Operational resource efficiency</h2></strong>
<p>The team at Elastic is <em>always</em> working to improve the operational resource efficiency of Elasticsearch. This means tuning its performance so as to consume fewer resources and do more with less. Elasticsearch 7.4 certainly sees some goodness to these ends.
</p> <strong>
<h3>Automatic query cancellation</h3></strong>
<p>Historically, if a user wanted to terminate a client initiated query, this had to be done using the task management API. The new automatic query cancellation feature will automatically terminate queries sent through the _search endpoint when the initiating connection is closed.
</p> <strong>
<h3>Smaller translog = storage savings</h3></strong>
<p>Translog entries are <a href="https://github.com/elastic/elasticsearch/pull/45473">no longer needed for shard recoveries</a>. This can significantly reduce the amount of storage needed for the translog. Previously, a default of 512 MB or up to 12 hours of entries could be stored (per shard) for a recovery event. Since all new indices in Elasticsearch enable <a href="https://github.com/elastic/elasticsearch/issues/29530">soft-deletes</a> by default (starting with Elasticsearch 7.0), Elasticsearch 7.4 will use soft-deletes (when enabled) for shard recoveries instead of the translog. More information on translog retention can be found in the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/index-modules-translog.html#index-modules-translog-retention">Elasticsearch 7.4 documentation</a>.
</p>
<p>Both automatic query cancellation and the translog improvements are being released under the Apache 2.0 license.
</p> <strong>
<h2 id="experimental">Experimental features</h2></strong>
<p>Note / (fair) warning: experimental features are… experimental.&nbsp; They’re NOT ready for “prime time” and may be changed or removed completely in a future release. Elastic will, of course, make an effort to fix major issues, but experimental features are not subject to Elastic’s support SLA for official, generally available features. That being said, should you elect to experiment with them (see what we did there?), please do send us your thoughts and feedback!
</p> <strong>
<h3>Finding relationships within your data</h3></strong>
<p><a href="https://www.elastic.co/guide/en/elastic-stack-overview/7.4/dfa-regression.html"><strong>Regression analysis</strong></a> is a machine learning process for estimating the relationships among a number of feature variables and a dependent variable, then making further predictions based on the described relationship.
</p>
<p>Regression analysis is being released as an experimental feature under Elastic’s Platinum license.
</p> <strong>
<h3>New vector distance functions for document script scoring</h3></strong>
<p>There are many scenarios in which vector similarity serves to measure document similarity. These can range from documents that are vectors to a variety of algorithms that represent a document — this could be an image or a <a href="https://www.elastic.co/blog/text-similarity-search-with-vectors-in-elasticsearch">text</a> — as a vector. Recently, in Elasticsearch <a href="https://www.elastic.co/blog/elasticsearch-7-3-0-released">7.3</a>, we introduced the dot product and cosine similarity functions. Now, with the release of Elasticsearch 7.4, we are introducing <strong>two new vector similarity measurements</strong>:
</p>
<ul>
	<li>Manhattan distance (L1 norm)</li>
	<li>Euclidean distance (L2 norm)</li>
</ul>
<p>Like the dot product and cosine similarity functions, the Euclidean and Manhattan distances are provided as <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/query-dsl-script-score-query.html#vector-functions">predefined Painless functions</a> so that they may be incorporated with other query elements as part of a <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/query-dsl-script-score-query.html">script_score</a> query.
</p>
<p>Both new vector distance functions are being released as experimental features under Elastic’s free Basic license.
</p><strong>
<h2>That’s not all, folks!</h2></strong>
<p>While the above listed features might have taken the spotlight, there were many more features included with the release of Elasticsearch 7.4 - be sure to check out the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.4/release-notes-7.4.0.html">release notes</a> for additional information.
</p>
<p>Ready to get your hands dirty? Spin up an Elasticsearch cluster on <a href="https://www.elastic.co/cloud/">Elastic Cloud</a> or <a href="https://www.elastic.co/downloads/elasticsearch">download Elasticsearch</a> today. Try it out. And be sure to let us know what you think on Twitter (<a href="https://twitter.com/elastic">@elastic</a>) or in <a href="https://discuss.elastic.co/c/elasticsearch">our forum</a>. You can report any problems on the <a href="https://github.com/elastic/elasticsearch/issues">GitHub issues page</a>.
</p>

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Elasticsearch 7.4.0 released

The official Elastic Homebrew tap is here. Installing the Elastic Stack on macOS just got even easier.

<p>We’re excited to announce the release of the official <a href="https://github.com/elastic/homebrew-tap/">Elastic Homebrew tap</a>.
</p><p><a href="https://brew.sh">Homebrew</a> is referred to in its <a href="https://docs.brew.sh/Manpage">man page</a> as "the missing package manager for macOS."&nbsp;It provides an easy and flexible way to manage the installation of software on macOS and is incredibly popular within the developer community. Core Homebrew provides formulae for installing common OSS software. A <a href="https://docs.brew.sh/Taps">tap</a> is a mechanism for third-parties, such as Elastic, to make additional software available to Homebrew users by providing additional formulae.
</p><p>At Elastic, we're longtime fans of the Homebrew community and all the great work they do to support their users on macOS (e.g., supporting every major release of macOS on day one); they are made of win. Within Elastic, many of our engineers are Homebrew users themselves, and one of our employees is a Homebrew maintainer.
</p><p>We always strive to meet our users where they are, and for many users, that’s Homebrew. That’s why we want to make sure that all of the Elastic Stack software is easily accessible for installation using Homebrew. Creating our own tap enables us to improve the user experience in a number of ways. First, you can now use the official Elastic Homebrew tap to access both the OSS and the default distribution, which includes free features such as &nbsp;<a href="/blog/implementing-hot-warm-cold-in-elasticsearch-with-index-lifecycle-management">index lifecycle management (ILM)</a>, <a href="/products/stack/elasticsearch-sql">Elasticsearch SQL</a>, and <a href="/products/stack/canvas">Canvas visualizations</a> to name a few. And even better, the default distribution now includes <a href="/blog/security-for-elasticsearch-is-now-free">free security</a>, too. This is important to us as we aim to provide a consistent experience for users of the default and OSS distributions. A second motivation for creating our own tap is the ability it gives us to make sure that Homebrew users always have access to the latest versions of our software. By making Homebrew formulae updates in our tap an automated part of our release process, we can reliably make these updates available at the same time that we release all of our other artifacts.
</p><h2>Installing the Elastic Stack with Homebrew</h2><p>Once you have <a href="https://docs.brew.sh/Installation">installed Homebrew</a>, getting started with the Elastic tap is simple:
</p><ol>
	<li>Tap the Elastic tap with <code>brew tap elastic/tap</code>.</li>
	<li>Install either the default or OSS distribution of Elasticsearch.
	<ul>
		<ul>
			<li>Default: <code>brew install elasticsearch-full</code></li>
			<li>OSS: <code>brew install elasticsearch-oss</code></li>
		</ul>
	</ul>
	</li>
	<li>If you include <code>/usr/local/bin</code> in your path, you can then start Elasticsearch with the command <code>elasticsearch</code>, or specify the full path <code>/usr/local/bin/elasticsearch</code>.</li>
</ol><p>	We provide all of the software in the Elastic Stack through this tap, including Elastic APM Server, Beats, Logstash, Kibana, and Elasticsearch. You can inspect the list of formulae available with:
</p><pre>ls $(brew --prefix)/Homebrew/Library/Taps/elastic/homebrew-tap/Formula/*.rb
</pre><h2>Last call</h2><p>	Homebrew is best enjoyed fresh so give the Elastic Homebrew Tap a try! And as always, your feedback is invaluable. Please reach out to let us know what you think on the <a href="https://github.com/elastic/homebrew-tap/">repo</a>, or in our <a href="https://discuss.elastic.co/">Discuss</a> forum.
</p><hr><p>	<em>Elastic believes in giving back, both by supporting the communities in which we live and work, and recognizing that participating in these activities can inspire and enrich our lives in unexpected ways. As part of the <a href="https://www.elastic.co/blog/author/elastic-cares">Elastic Cares</a> program, we’ve made a donation to the Homebrew project, via the amazing folks at the Software Freedom Conservancy.</em>
</p><p>	<em>We believe that actions like these are important towards ensuring sustainable software and communities for fundamental open source projects — as Homebrew rightly is to numerous developers around the world.</em>
</p><p>	<em>"<a href="https://github.com/Homebrew/brew.sh/blob/master/assets/img/homebrew.svg">Logo of Homebrew</a>" by Vítor Galvão, used under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> / Resized and placed in header and thumbnail</em>
</p>

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Announcing the official Elastic Homebrew tap for macOS

Announcing the official Elastic Homebrew tap

We're excited to announce the alpha release of Helm charts for Elasticsearch and Kibana, plus our membership in the Cloud Native Computing Foundation (CNCF).

<p>The shift to microservices and cloud native application architectures is a liberating paradigm for developers; but the dynamic nature of these newer frameworks also introduce a number of interesting challenges ranging from observability to inventory management. Users are turning to the Elastic Stack to solve these problems because the open source and flexible nature of Elastic products allows them to extend into uncharted space in a fast and agile manner.<br>
</p>
<p>For instance, eBay was the early adopter of <a href="/elasticon/conf/2018/sf/monitoring-anything-and-everything-with-beats-at-ebay">Beats for monitoring its massive Kubernetes clusters</a> and became an active contributor and thought partner for features like auto-discovery of services running on top of Kubernetes environments. <a href="/use-cases/fairfax-media">Fairfax Media</a> adopted a similar strategy as part of its operational use of the Elastic Stack. And, of course, our own infrastructure engineers and SREs make heavy use of the Elastic Stack to <a href="/elasticon/conf/2018/sf/kubernetes-docker-and-containers-at-elastic-monitoring-logging-and-more">keep Elastic’s Kubernetes clusters humming</a>.
</p>
<p>And we're excited to make two announcements in this space today:
</p>
<ol>
	<li>The alpha release of Helm charts for Elasticsearch and Kibana, and</li>
	<li>Our membership in Cloud Native Computing Foundation (CNCF).</li>
</ol>
<h2>Observability for cloud native applications
</h2>
<p>While Kubernetes can dramatically simplify the task of application deployment in containers, it also adds some complexity for the day-to-day tasks of managing application performance and gaining visibility into services. In the dynamic world of microservices, containers, and Kubernetes, we need to rethink how we monitor logs, metrics, and tracing data -- often called the three pillars of observability -- in cloud native environments. Elastic supports all three.
</p>
<p>Elastic Stack natively integrates with Kubernetes and makes it operationally simple to dynamically scale collection of logs and metrics from these environments. The Elastic data collectors for logs and metrics, called Filebeat and Metricbeat, deploy as a <span style="font-family: monospace;"></span>DaemonSet on top of Kubernetes. This ensures that these collectors only need to run on every Kubernetes node, not inside each container. They automatically add <a href="/guide/en/beats/metricbeat/6.5/add-kubernetes-metadata.html">Kubernetes and Docker metadata</a> about which container or pod the log or metric came from. They also support hints-based&nbsp;<a href="/blog/docker-and-kubernetes-hints-based-autodiscover-with-beats">Kubernetes service auto-discovery</a>, which enables configuration snippets to automatically collect and parse metrics and logs from common Kubernetes services, such as <a href="/guide/en/beats/metricbeat/6.5/metricbeat-module-prometheus.html">Prometheus</a> exporters, <a href="/guide/en/beats/metricbeat/6.5/metricbeat-module-envoyproxy.html">Envoy</a> service proxy, <a href="/guide/en/beats/metricbeat/6.5/metricbeat-module-traefik.html">Traefik</a> web proxy, and <a href="/guide/en/beats/metricbeat/6.5/metricbeat-module-etcd.html">etcd</a> datastore, as well as various applications reliant on those services.
</p>
<p><a href="/solutions/apm">Elastic APM</a> is an open source approach to application performance management. If you are already housing logs and metrics in Elastic Stack, you can add APM to your deployment in a few lines of code using auto-instrumentation provided by <a href="/guide/en/apm/agent/index.html">Elastic APM agents</a>. Elastic APM supports <a href="/guide/en/apm/get-started/6.5/distributed-tracing.html">distributed tracing</a>. Distributed tracing enables you to analyze performance bottlenecks in your microservices architecture all in one view. To accomplish this, the agents work together to pass a consistent trace ID from the initial web request to your front-end service to queries made to your back-end services. The agents also support <a href="https://opentracing.io/">OpenTracing</a>, a set of vendor-neutral APIs that can be used to log request spans to a number of pluggable backends. Elastic APM translates calls to the OpenTracing API to Elastic APM and thus allows you to reuse existing instrumentation.
</p>
<p>The Elastic Stack comes with powerful data visualization capabilities. Filebeat and Metricbeat modules, as well as, Elastic APM ship with pre-built Kibana dashboards that serve as a great starting point for exploring logs, metrics, and APM data in Kibana. On top of that, the Infrastructure, Logs, and APM UIs enable common workflows for correlating the data coming from different operational contexts. For instance, the Infrastructure UI has dedicated Kubernetes and Docker views that enable workflows specific to operators of large container orchestration deployments. Similarly, the APM UI has dedicated views for stringing together data coming from distributed traces across many microservices.
</p>
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	<img style="width: 100%; margin: auto; display: block;" class="vidyard-player-embed" src="https://play.vidyard.com/78EpMXQeU57jR7HrKsMXqK.jpg" data-uuid="78EpMXQeU57jR7HrKsMXqK" data-v="4" data-type="inline">
</div>
<h2>Announcing Helm charts for Elasticsearch and Kibana
</h2>
<p>We are excited to announce the initial public alpha of our Elasticsearch and Kibana Helm charts. These charts make it incredibly simple to deploy Elasticsearch and Kibana to Kubernetes in a manner of minutes.
</p>
<p>As Kubernetes has continued to mature, the ability to effectively run stateful workloads has finally started become a reality. With this foundation in place, the timing was right for us to create these Helm charts as a way to help enable our users to run the Elastic Stack using modern, cloud-native deployment models and technologies.
</p>
<p><a href="https://helm.sh/">Helm</a> (an open source, CNCF-maintained project) is often touted as the missing package manager for Kubernetes. Helm charts are rapidly becoming a standard way of creating, publishing, and sharing Kubernetes applications. We love them because they provide a great way for us to package and share best practices and default configuration alongside our software.
</p>
<p>To get started with Elasticsearch and Kibana Helm charts:
</p>
<ol>
	<li>Add the Elastic Helm Chart Repo: <code>helm repo add elastic https://helm.elastic.co</code></li>
	<li>Install Elasticsearch: <code>helm install --name elasticsearch elastic/elasticsearch</code></li>
	<li>Install Kibana: <code>helm install --name kibana elastic/kibana</code></li>
</ol>
<p>For more information about the charts, the README files for the <a href="https://github.com/elastic/helm-charts/blob/6.5.2-alpha1/elasticsearch/README.md">Elasticsearch</a> and <a href="https://github.com/elastic/helm-charts/tree/6.5.2-alpha1/kibana">Kibana</a> charts can be viewed on GitHub. For more information about how to use your new cluster, see <a href="/guide/en/elasticsearch/reference/6.5/getting-started.html">Getting Started with Elasticsearch</a> and <a href="/guide/en/kibana/6.5/getting-started.html">Getting Started with Kibana</a>.
</p>
<p>A quick reminder that with great power comes great responsibility. These charts are still in alpha. We recommend sticking to non-production deployments. There is no guarantee that alpha versions will be compatible with subsequent release versions.
</p>
<p>We want to hear from you! Please reach out if you encounter any issues or if you have ideas for improvements to the charts. Head to the <a href="https://discuss.elastic.co/c/ecosystem">discuss forums</a> with any questions. You can open issues directly on the Helm chart <a href="https://github.com/elastic/helm-charts">GitHub</a> repository.
</p>
<p>Finally, we’re always looking know more about how you are using (or plan to use) Kubernetes. If you are interested in sharing, please fill out this <a href="https://go.es.io/helm-survey">short survey</a>.
</p>
<h2>Elastic joins CNCF
</h2>
<p>Joining <a href="https://www.cncf.io/">Cloud Native Computing Foundation (CNCF)</a> is a natural evolution for us, given our open source history of building products and ongoing efforts around integrating with cloud native technologies.
</p>
<p>CNCF is an open source software foundation that supports and promotes the use of open technologies and standards to build the next generation of applications and services. CNCF is home to projects such as Kubernetes, which are &nbsp;the basis of key trends in the industry, such as breaking up monolithic applications into microservices. Among its many efforts, CNCF has laid out a <a href="https://raw.githubusercontent.com/cncf/trailmap/master/CNCF_TrailMap_latest.png">trailmap</a> for how organizations should use its projects to transform the way they build applications.
</p>
<p>Elastic is already aligned with the CNCF mission to promote open cloud native technologies. We build our products in an open and transparent way, and we support integrations with many key CNCF projects and CNCF member projects. We also have already adopted standards promoting vendor-neutral integrations, such as OpenTracing, in our products.
</p>
<p>On becoming a member, Elastic is looking forward to working with CNCF on open tracing and metrics standards that help promote open source technologies and companies. We also look forward to deeper engagement with the CNCF community!
</p>
<h2>Come see us at KubeCon + CloudNativeCon North America 2018
</h2>
<p>We’re&nbsp;<a href="https://events.linuxfoundation.org/events/kubecon-cloudnativecon-north-america-2018/">at KubeCon North America 2018</a>&nbsp;in Seattle this week. We will be showcasing Elasticsearch and Kibana Helm charts, as well as integration with Kubernetes, Prometheus, and OpenTracing for full stack observability of cloud native applications.
</p>
<p>If you happen to be here too, please stop by <strong>booth S58</strong> in the sponsor showcase to learn more about these announcements, and meet Elastic&nbsp;experts.
</p>

Articles by Michelle Sausa

Elasticsearch 7.4.0 released

Announcing the official Elastic Homebrew tap

Elastic doubles down on cloud native with Helm charts and CNCF membership

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Articles by Michelle Sausa

Elasticsearch 7.4.0 released

Announcing the official Elastic Homebrew tap

Elastic doubles down on cloud native with Helm charts and CNCF membership