Elastic Logs 7.8.0 released with quick and easy alerting, viewing contextual logs, the ability to filter datasets at machine learning job configuration, and more

<p>We are pleased to announce the release of Elastic Logs 7.8.0 — available on&nbsp;<a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a>, or when you <a href="https://www.elastic.co/downloads/">download</a> the Elastic Stack. This release brings you quick and easy alerting, the ability to view&nbsp;contextual logs, and filtering for datasets during machine learning job configuration.</p><h2><strong>Viewing logs in context&nbsp;</strong></h2><p>A frequent use case in log exploration is filtering logs with terms that carry context from outside of logs, such as API route, customer ID, or error text from a triggered alert. Once the logs are filtered and an interesting log line is identified, the next step is to learn what happened before and after this log line. That’s exactly what we are solving for with this feature. Keep in mind that simply launching another logs view with the filter term removed will fetch logs from <em>every</em> other log&nbsp;data source, assuming you have a centralized logging system. The real context you are looking for is what happened before and after that log line <em>within </em>that single log&nbsp;data source.</p><p>For example, imagine you are running containerized applications on a Kubernetes&nbsp;cluster.&nbsp;You filter&nbsp;the logs for the term “error,”&nbsp;and you find an interesting error log line. The context you are looking for is what happened before and after this error line within the logs of <em>this specific container and application</em>. So this context is preserved when you click on&nbsp;“view in context”&nbsp;to help you find the root cause as soon as possible.&nbsp;</p><p>This feature is free in the Elastic Stack for everyone. Please use it and let us know your feedback in our&nbsp;<a href="https://discuss.elastic.co/">forums</a>.</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt29e4fb969f0d546d/5ee11a5b0f079e4334fd64c7/logs-in-context.gif" data-sys-asset-uid="blt29e4fb969f0d546d" alt="logs-in-context.gif"/></p><h2><strong>Alerting-based log patterns</strong></h2><p>We are very happy to announce that we are supporting threshold-based alerting in the Logs app. Alerts for all observability products can be created and managed from a central place in Kibana. However, from a usability perspective, we created a smoother experience by allowing you to create alerts right in the Logs app:</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltd7142b1bd8bcec7f/5ee11a78a1cefa3b5ed1d2d6/logs-alerting.gif" data-sys-asset-uid="bltd7142b1bd8bcec7f" alt="logs-alerting.gif"/></p><p>We support several key alerting use cases as shown below.</p><h4>Alerts based on fields containing or matching a text pattern:</h4><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltb8558eff7c479689/5ee11a8f395b50323f2628cd/logs-alerting-matching-phrase.png" data-sys-asset-uid="bltb8558eff7c479689" alt="logs-alerting-matching-phrase.png"/></p><h4>Alerts based on numeric field and arithmetic operator:</h4><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt6aa17093be1220af/5ee11aa05cffd6317204ad9e/logs-alerting-threshold.png" data-sys-asset-uid="blt6aa17093be1220af" alt="logs-alerting-threshold.png"/></p><h4>Single alert with multiple conditions:</h4><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt92f811251c36618e/5ee11aab1579a83a8b80ba41/logs-alerting-multiple-conditions.png" data-sys-asset-uid="blt92f811251c36618e" alt="logs-alerting-multiple-conditions.png"/></p><h3><strong>Alert connectors</strong></h3><p>We support many common enterprise connectors as built-in integrations shown below, as well as document indexing and Kibana server logging.</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltef94e485494edeba/5ee11abd26fd9f3b64a5ac6d/logs-alerting-action-types.png" data-sys-asset-uid="bltef94e485494edeba" alt="logs-alerting-action-types.png"/></p><p>And, of course, you can connect and notify any other system with the use of webhooks.&nbsp;</p><p>Did we mention that it’s free? That’s right —&nbsp;the new alerting for logs is free for everyone to use, including the server log and index notification actions! So give it a try:&nbsp;download the latest version of the Elastic Stack or use it on Elastic Cloud and let us know what you think! Ask a question or leave feedback in our <a href="https://discuss.elastic.co/">forums</a>.</p><h2><strong>Allowing users to filter datasets in machine learning job configuration</strong></h2><p>Now we are extending the machine learning job configuration to allow you to filter datasets. This helps to have granular control over&nbsp;machine learning jobs, which is useful when you are trying to reserve machine learning capacity on the datasets that matter the most as well as filter out datasets not suitable for categorization.</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt6ed2023be266a5e0/5ee11ad50f079e4334fd64dd/logs-ml-dataset-config.png" data-sys-asset-uid="blt6ed2023be266a5e0" alt="logs-ml-dataset-config.png"/></p><p><br /></p><h2><strong>See it in action!</strong></h2><p>You can access the latest version of the Elastic Logs app&nbsp;on&nbsp;<a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a> on Elastic Cloud by creating a new cluster, upgrading an existing cluster the day of release, or by <a href="https://www.elastic.co/downloads/">downloading</a> the Elastic Stack.</p><h2><strong>Related blogs</strong></h2><ul><li><a href="/blog/elastic-observability-7-8-0-released">Elastic Observability 7.8 released</a></li><li><a href="/blog/elastic-metrics-7-8-0-released">Elastic Metrics 7.8 released</a></li><li><a href="/blog/elastic-apm-7-8-0-released">Elastic APM 7.8 released</a></li><li><a href="/blog/elastic-uptime-monitoring-7-8-0-released">Elastic Uptime 7.8 released</a></li></ul>

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Elastic Logs 7.8.0 released

Elastic Logs 7.7 Released with PCF support, MQTT support, and more

<p>We are pleased to announce the release of Elastic Logs 7.7.0 — available on the <a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a>, or when you <a href="https://www.elastic.co/downloads/">download</a> the Elastic Stack. This release introduces support for PCF observability,&nbsp;new date/time picker in the Logs app, support for MQTT protocol input in Filebeat, and an enhancement to log categorization
</p><h2>Observability for Pivotal Cloud Foundry (PCF) operators and application developers</h2><p>We are announcing beta release for PCF observability in this release. For PCF monitoring, we are releasing <a href="https://www.elastic.co/guide/en/beats/metricbeat/7.7/metricbeat-module-cloudfoundry.html">Metricbeat</a> module and PCF application and relevant component logs are available to consume via <a href="https://www.elastic.co/guide/en/beats/filebeat/7.7/filebeat-input-cloudfoundry.html">Filebeat input</a>.&nbsp;
</p><h3>PCF operator observability</h3><p>Monitoring PCF is no joke — it's a complicated, distributed black box that just works! Except when it doesn't. When something goes wrong it is an operational nightmare to figure out the root cause. Thankfully, the <a href="https://docs.pivotal.io/platform/healthwatch/1-8/index.html">PCF healthwatch</a> team built <a href="https://docs.pivotal.io/platform/healthwatch/2-0/metrics.html">super-metrics</a>&nbsp;to measure uptime and performance KPIs for PCF developer experience, and&nbsp;<a href="https://docs.pivotal.io/pivotalcf/2-6/monitoring/kpi.html">PCF KPIs</a> which can help PCF operators maintain PCF SLAs with the help of dashboards, shown below.&nbsp;
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt7a347e393699b501/5e98b6315ce5f45edfe36395/pcf-support.gif" data-sys-asset-uid="blt7a347e393699b501" alt="pcf-support.gif"></p><p>Another common problem that many PCF operators struggle with is identifying noisy apps that put a sudden load on the PCF logging system which can be surfaced with dashboards like this:
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt9f2025a9f5995c56/5e98a176461e697539f23fd9/image1.png" data-sys-asset-uid="blt9f2025a9f5995c56" alt="image1.png"></p><h3>PCF developer observability</h3><p>I had the privilege to build <a href="https://docs.pivotal.io/app-metrics/1-6/using.html">PCF Metrics</a> at Pivotal which integrated app logs, metrics, and traces in a contextual view that became a very attractive app dev observability product for pivotal customers. With this release, PCF app developers will have a similar observability experience as shown in the dashboard below. Contextual view of application logs and metrics, with a simple time toggle at top.&nbsp;
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltff1a32558b6d04ac/5e98a1b2a1d1a740f83c2030/image4.png" data-sys-asset-uid="bltff1a32558b6d04ac" alt="image4.png"></p><h3>Easy to maintain and scale deployment model&nbsp;</h3><p>A perpetual problem with building and maintaining product integrations with PCF is the painful tile lifecycle management. In addition, observing and scaling these integrations in the event of of a sudden firehose burst of events was non-trivial. That’s why our integration is developed as a PCF app that is easy to install, configure, upgrade, and scale, right at home in the PCF ecosystem.
</p><h2>Logs app&nbsp;improvements</h2><h3>Improved date and time selection control</h3><p>The new and improved date and time picker is tuned to how users think about navigating to different timeframes in their troubleshooting process.&nbsp;Straightforward time selection using minutes, days, and weeks as units, and shuffling between next and previous time windows, as opposed to selecting calendar dates the way you do in travel booking portals. This will help you reduce the cognitive load so that you focus on doing what matters — reduce the mean time to detect (MTTD) and mean time to repair (MTTR) and thus maintain the application and infrastructure SLAs that your customers expect.&nbsp;
</p><h4>Before</h4><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt841f2687f6aa3e16/5e98a21aeef14c5fa2b69df3/image3.png" data-sys-asset-uid="blt841f2687f6aa3e16" alt="image3.png">
</p><h4>After</h4><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltf521f874b3c4b33f/5e98a228271d775edb04b8b2/image5.png" data-sys-asset-uid="bltf521f874b3c4b33f" alt="image5.png">
</p><h3>Log categorization and contextual examples</h3><p>In the last release, we released <a href="https://www.elastic.co/blog/elastic-logs-7-6-0-released">log categorization</a> as an effective way to reduce the human effort required to analyze logs by reducing the amount of logs to a handful of representative categories, surfacing the anomalous to the top. We have enhanced the categorization to include sample log lines for each category, allowing you to inspect them, just like any other log line, while allowing you to dive deeper into the machine learning UI for further investigation.&nbsp;
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt07e0abff1dfc051b/5e98a23a271d775edb04b8b8/image6.png" data-sys-asset-uid="blt07e0abff1dfc051b" alt="image6.png">
</p><p><br>
</p><h2>Support for IoT messaging framework MQTT</h2><p>We are announcing <a href="https://www.elastic.co/guide/en/beats/filebeat/master/filebeat-input-mqtt.html">general availability</a> for retrieving logs from devices that support the MQTT protocol.
</p><p>Use the MQTT input to read data transmitted using lightweight messaging protocol for small and mobile devices optimized for high-latency or unreliable networks. The input connects to the MQTT broker, subscribes to selected topics, and parses data into common message lines. It can be combined with other settings like line filtering, multiline, and JSON decoding, allowing you to inspect your MQTT data right alongside your logs, metrics, and application traces from your applications and your entire ecosystem.
</p><h2>What’s coming next</h2><p>The most exciting feature that we want to ship next is alerting in the Logs app. Please find more details <a href="https://github.com/elastic/kibana/issues/61493">here</a>.
</p><h2>See it in action!</h2><p>You can access the latest version of the Elastic Logs app on the <a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a> on Elastic Cloud by creating a new cluster, or upgrading an existing cluster the day of release, or you can <a href="https://www.elastic.co/downloads/">download</a> the Elastic Stack.
</p><h2>Related blogs</h2><ul>
<li><a href="/blog/elastic-observability-7-7-0-released">Elastic Observability 7.7 released</a></li>
<li><a href="/blog/elastic-metrics-7-7-0-released">Elastic Metrics 7.7 released</a></li>
<li><a href="/blog/elastic-apm-7-7-0-released">Elastic APM 7.7 released</a></li>
<li><a href="/blog/elastic-uptime-monitoring-7-7-0-released">Elastic Uptime 7.7 released</a></li>
</ul>

Elastic Logs 7.7.0 released with PCF support, MQTT support, and more

Elastic Logs 7.7 released with PCF support, MQTT support, and more

Elastic Logs 7.6.0 expands log anomaly detection with the intro of logs categorization, improved support for Dockerized apps, and more cloud + messaging support

<p>We are pleased to announce the release of Elastic Logs 7.6.0 — available on the <a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a>, or as part of the <a href="https://www.elastic.co/downloads/">default distribution</a> of Elastic Stack. This release moves logs anomaly detection one step further through introduction of logs categorization. In addition, we have added support for ingesting from Dockerized applications, <a href="https://docs.aws.amazon.com/vpc/latest/userguide/flow-logs.html">AWS VPC flow</a>, and <a href="https://activemq.apache.org/">ActiveMQ</a>.&nbsp;
</p><h2>Log categorization led anomaly detection</h2><h3>What is log categorization?</h3><p>Most software application and infrastructure log message content is constrained to a finite set of patterns, unlike human written content from blogs, reviews, and social media. Let’s take a simplified example of application logs which are written during an application lifecycle.
</p><pre class="prettyprint">Reading text ‘foo’ from file ‘bar’ on host ‘host1’ 
Reading text ‘foo1’ from file ‘bar1’ on ‘host2’ 
Error: File ‘bar’ on ‘host2’ can not be read 
Error: File ‘bar1’ on ‘host2’ can not be read 
Database connection ‘conn1’ failed
</pre><p>In this case, log categorization will cluster similar looking messages into a single category even when there is variable content in between. For example, here we have three categories of messages.
</p><pre class="prettyprint">Category 1 - Reading*text*from*file*on*host* 
Category 2 - Error*File*on*can*not*be*read* 
Category 3 - Database*connection*failed
</pre><p>So we reduced the number of messages from five to three, which is the essence of categorization, reducing vast amounts of logs into a smaller number of categories.
</p><h3>Where does anomaly detection fit in categorization?</h3><p>Our log categorization builds a statistical model by analyzing past logs, and then uses that model to compute expected values of statistical parameters for each category for a future dataset. When the statistical parameter value of an existing category within a new set of logs diverge significantly from expected value, an ML algorithm flags that category as anomalous.&nbsp;
</p><h3>Why is this important?</h3><h4>Estimating expected behavior</h4><p>In the above example, you may have a built-in retry logic for fault tolerance to account for random infrastructure failures of file readers and database connectors, so you may not care about occasional failures as much. However, you’d be alarmed if the failure rate is higher than normal as it may be a leading indicator of a systemic failure. This distinction between normal and anomalous behavior&nbsp;is where the ML algorithm plays a critical role by predicting expected behavior based on statistical analysis of past dataset.&nbsp;&nbsp;&nbsp;
</p><h4>Finding anomalies without needing to specify log patterns</h4><p>The second and more important advantage of log categorization is to free users from worrying about needing to know the exact log patterns or maintaining complex regex math to catch these errors and run analytics on them. Log parsing processes are prone to log changes and also require deep knowledge of log message content. Often the folks who write application software and the ones who create analytics dashboards are different, so the process of creating and maintaining dashboards becomes cumbersome.&nbsp;
</p><h4>No ML expertise required</h4><p>The only inputs that you’d need to provide are the index to search for anomalies and the time window to train the model for. Here is how simple categorization is to set up:&nbsp;
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltac258e37e4a4a093/5e3c3ff8e029072f7b62a2b8/image5.png" data-sys-asset-uid="bltac258e37e4a4a093" alt="image5.png">
</p><p>We take care of the required plumbing to integrate with Elastic’s machine learning capabilities to start the machine learning jobs, create detectors, and to find and visualize the anomalies. And we will do much more in future, so that you don’t have to learn yet another thing to do your job better.&nbsp;<br>
</p><h4>The bottom line:</h4><p>Our log categorization builds categories for your logs and finds anomalous categories, without you needing to tell us what you are looking for, the case of unknown unknowns, and any prerequisite ML experience. That’s quite powerful, isn’t it?
</p><h2>Logs categorization visualization</h2><p>Alright, now that we got the basics out of the way, let’s look at a real example.</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt70b222d7eb5899cd/5e3c402a0ee5056ab4d86f0a/log-categories.gif" data-sys-asset-uid="blt70b222d7eb5899cd" alt="log-categories.gif">
</p><p>Here we see the new categorization tab in the Logs UI, which shows various log categories from <code>elasticsearch.server</code> logs, along with the anomaly score on the right. The one we've highlighted has a very high anomaly score, and looks like this:&nbsp;
</p><pre class="prettyprint">⁕type⁕server⁕timestamp⁕level⁕component⁕o.e.m.j.JvmGcMonitorService⁕cluster.name⁕elasticsearch⁕node.name⁕message⁕gc⁕overhead⁕spent⁕collecting⁕in⁕the⁕last⁕cluster.uuid⁕jx_Bw17eT_OQshT2IrDuzg⁕node.id⁕
</pre><p>And an example of a log line in this category is as follows:
</p><pre class="prettyprint">[elasticsearch.server][INFO] {"type": "server", "timestamp": "2020-01-31T22:51:03,633Z", "level": "INFO", "component": "o.e.m.j.JvmGcMonitorService", "cluster.name": "elasticsearch", "node.name": "elasticsearch-master-0", "message": "[gc][411617] overhead, spent [316ms] collecting in the last [1s]", "cluster.uuid": "jx_Bw17eT_OQshT2IrDuzg", "node.id": "JwIW6wI2T6WsgpoaUB3eaA" }
</pre><p>Our log categorization algorithm bubbled up an anomaly indicating an unexpected slow down of garbage collection runs, which may be a leading indicator of low JVM memory problems impacting Elasticsearch performance. Likewise, if you were already alerted about low JVM memory through the monitoring setup, logs categorization will make it trivial to find the root cause behind it by bubbling up the above category of “stopped garbage collection”. So it’s a great tool for both&nbsp;anomaly detection and recovery.&nbsp;
</p><h3>A few things to remember</h3><h4>Good datasets train the models for better categorization results</h4><p>The categorization algorithms use past datasets to train statistical models and predict expected behavior. Statistically, these models are best trained with two&nbsp;or more weeks of data. But if you know that your logs pattern lifecycle without anomalies lasts more than four weeks, you can choose to go as far as back as you want in the logs categorization set up screen.&nbsp;
</p><h4>Category avalanche!</h4><p>By design, categorization works well with repeated data patterns in logs. If your logs are free form (think of tweets or comments) then the number of categories will explode. Similarly, if logs consist mostly of dynamic values with a lot of randomness then they are probably not good candidates for&nbsp;categorization.&nbsp;
</p><h3>How to use it&nbsp;</h3><p>If you are running on a platinum license, you are all set. If not, just start a trial to unleash the power of machine learning on all of your elasticsearch data.&nbsp;
</p><h3>What's next?</h3><p>We will be working on improving UX of logs rate and categorization to make your anomaly detection and troubleshooting experience more pleasant. Stay tuned!
</p><h2>Expansion of log sources</h2><p>In addition to log categorization, we have also added support for AWS VPC flow, AWS ELB,&nbsp; ActiveMQ, and easy UX for Dockerized apps to send logs to Elasticsearch.&nbsp;
</p><h3>AWS VPC Flow&nbsp;</h3><h4>What are VPC flow logs?</h4><p>VPC flow logs enable you to capture information about the IP traffic going to and from network interfaces in your AWS virtual private cloud. Flow logs can be published to an Amazon S3 bucket and then shipped to Elasticsearch using SQS and S3 Filebeat.&nbsp;
</p><h4>Why are flow logs important?&nbsp;</h4><p>They can help you with a number of troubleshooting tasks, such as:
</p><ul>
	<li>Diagnosing overly restrictive security group rules</li>
	<li>Monitoring the traffic that is reaching your instance</li>
	<li>Determining the direction of the traffic to and from the network interfaces</li>
</ul><p>We include a built-in Kibana dashboard to help you diagnose some of these problems:&nbsp;
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blte3fb4cda6ad18a53/5e3c40475819152bd99cddc2/image2.png" data-sys-asset-uid="blte3fb4cda6ad18a53" alt="image2.png">
</p><h3>AWS Elastic Load Balancer (ELB)</h3><p>We released beta support for AWS Elastic Load Balancer in <a href="https://www.elastic.co/blog/elastic-logs-7-5-0-released">7.5</a> and today we are happy to announce that it has been promoted to generally available!
</p><p>It supports three kinds of load balancers offered by AWS: Classic Load Balancer, Application Load Balancer, and Network Load Balancer. We've also included a built-in dashboard for it so that you don’t have to build one.&nbsp;&nbsp;</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blta98fbe9464746737/5e3c40f53f60d62ef4edbd53/image4.png" data-sys-asset-uid="blta98fbe9464746737" alt="image4.png">
</p><h3>ActiveMQ&nbsp;</h3><p>We are releasing a beta Apache ActiveMQ Filebeat module to ingest ActiveMQ application and audit logs in Elasticsearch. To help you get up and running in terms of how and what to use them for, we have created built-in ActiveMQ dashboards for application and audit logs, respectively.
</p><p>Use the application logs dashboard to quickly identify and troubleshoot issues with the messaging stack, identify most problematic platform components, or review recents events presented on a time series graph.
</p><p>Apart from collecting and watching application events, it’s highly recommended to also observe audit logs. To properly secure an environment, there’s a necessity to record every user’s management action, no matter if it’s made with JMX or WebConsole interface. The predefined audit log dashboard shows all kinds of activity, and&nbsp;includes the&nbsp;username, severity, and a detailed message.
</p><p><img src="https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/bltd3a59fff9e4133a9/5e3c45688458282f81ea79a6/activeMQ-Integration.gif" data-sys-asset-uid="bltd3a59fff9e4133a9" alt="activeMQ-Integration.gif">
</p><h2>Docker logs&nbsp;&nbsp;</h2><p>This release also includes&nbsp;an experimental release of the Elastic Logging Plugin on Docker hub to enable an easy logging experience for Docker apps. On any Mac or Linux Docker installation, the driver can be installed with a single command:&nbsp;<code>docker plugin install store/elastic/elastic-logging-plugin:7.6.0</code>. The&nbsp;Elastic Logging Plugin for Docker can&nbsp;be used to forward logs to Elasticsearch, Logstash, Kafka, or Redis, for all Docker containers or on a per-container basis. Unlike other Beats, the Elastic Logging Plugin requires no elevated permissions to read container logs, and installation is done entirely within the Docker CLI.
</p><h2 style="background-color: rgb(255, 255, 255);">Functionbeat adds support for GCP&nbsp;&nbsp;</h2><p><a href="https://www.elastic.co/beats/functionbeat">Functionbeat</a>&nbsp;is a project that allows you to run Beats data ingestion code as a function in major cloud providers. Starting with&nbsp;the 7.6 release, Functionbeat can&nbsp;be deployed as a&nbsp;Google Function&nbsp;via&nbsp;Google Cloud Deployment Manager and pull log events from Google PubSub and&nbsp;Google Cloud Storage.&nbsp;
</p><h2>See it in action</h2><p>You can access the latest version of the Elastic Logs application on the <a href="https://www.elastic.co/cloud/elasticsearch-service">Elasticsearch Service</a> on Elastic Cloud by creating a new cluster or upgrading an existing cluster on the day of release.&nbsp;Or you can download it as part of the <a href="https://www.elastic.co/downloads/">default distribution</a> of the Elastic Stack.
</p><h2>Related blogs</h2><ul>
	<li><a href="/blog/elastic-observability-7-6-0-released">Elastic Observability 7.6 released</a></li>
	<li><a href="/blog/elastic-metrics-7-6-0-released">Elastic Metrics 7.6 released</a></li>
	<li><a href="/blog/elastic-apm-7-6-0-released">Elastic APM 7.6 released</a></li>
	<li><a href="/blog/elastic-uptime-monitoring-7-6-0-released">Elastic Uptime 7.6 released</a></li>
</ul>

Elastic Logs 7.6 released with log categorization and more

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Mukesh Gadiya

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<p>제출하면 귀하가 <a href="/kr/legal/elastic-cloud-account-terms" target="_self">Elastic 서비스 약관</a>을 읽고 이에 동의하며, 위에 제공된 연락처로 <a href="/kr/legal/privacy-statement#how-we-use-the-information" target="_self">선택한 커뮤니케이션</a>을 수신하는 데 동의한 것으로 간주됩니다. 언제든지 수신을 거부할 수 있으며 자세한 내용은 <a href="/kr/legal/privacy-statement/" target="_self">Elastic 개인정보 취급방침</a>을 참조하시기 바랍니다.</p>

<p>By submitting you acknowledge that you've read and agree to our <a href="/legal/serverless-preview-terms" target="_blank">Terms of Service</a>, and that Elastic may <a href="/legal/privacy-statement#how-we-use-the-information" target="_blank">contact you</a> about our related products and services, using the details you provide above. See <a href="/legal/privacy-statement/" target="_blank">Elastic’s Privacy Statement</a> for more details or to opt-out at any time.</p>

Serverless GDPR Text

<p>제출하면 귀하가 Elastic <a href="/kr/legal/serverless-preview-terms" target="_self">서비스 약관</a>을 읽고 이에 동의하며, 위에 제공된 세부 정보를 사용하여 Elastic이 관련 제품 및 서비스에 대해 귀하에게 <a href="/kr/legal/privacy-statement#how-we-use-the-information" target="_self">연락할 수 있음</a>을 이해한 것으로 간주됩니다. 언제든지 수신을 거부할 수 있으며 자세한 내용은 <a href="/kr/legal/privacy-statement/" target="_self">Elastic 개인정보 취급방침</a>을 참조하시기 바랍니다.</p>

<p>By submitting you agree to <a href="/agreements/global/cloud-services-monthly" rel="nofollow">Elastic Cloud Terms of Service</a>. Your personal data will be processed in accordance with <a href="/legal/privacy-statement">Elastic's Privacy Statement</a>.</p>

Cloud Trial GDPR Text

<p>제출하면 <a href="/kr/agreements/global/cloud-services-monthly" rel="nofollow">Elastic Cloud 서비스 약관</a>에 동의하시게 됩니다. 개인 데이터는 <a href="/kr/legal/privacy-statement">Elastic의 개인정보 취급방침</a>에 따라 처리됩니다.</p>

<p>By submitting you acknowledge that you've read and agree to our <a href="/legal/terms-of-use" target="_blank">Terms of Service</a>, and that Elastic may <a href="/legal/privacy-statement#how-we-use-the-information" target="_blank">contact you</a> about our related products and services, using the details you provide above. See <a href="/legal/privacy-statement/" target="_blank">Elastic’s Privacy Statement</a> for more details or to opt-out at any time.</p>

Newsletter GDPR Text

<p>제출하면 귀하가 Elastic <a href="/kr/legal/terms-of-use" target="_self">서비스 약관</a>을 읽고 이에 동의하며, 위에 제공된 세부 정보를 사용하여 Elastic이 관련 제품 및 서비스에 대해 귀하에게 <a href="/kr/legal/privacy-statement#how-we-use-the-information" target="_self">연락할 수 있음</a>을 이해한 것으로 간주됩니다. 언제든지 수신을 거부할 수 있으며 자세한 내용은 <a href="/kr/legal/privacy-statement/" target="_self">Elastic 개인정보 취급방침</a>을 참조하시기 바랍니다.</p>

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