Read about the updates and bug fixes that have been included in the 8.6.1 release.

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Read about the updates and bug fixes that have been included in this release.

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Logstash 7.0.0 is now out with a new pipeline execution engine written in Java, faster and more memory efficient, along with many plugin improvements.

<html><head></head><body>The new major version of Logstash is now available to download <a href="https://www.elastic.co/downloads/logstash">here</a>! This&#xA0;7.0.0 milestone brings a better out of the box experience to our users through:
<ul>
	<li><a href="#java">Default Java pipeline execution</a></li>
	<li><a href="#ilm">Automatic index lifecycle management </a></li>
	<li><a href="#plugins">Plugins, plugins, plugins</a></li>
</ul>Refer to the Logstash&#xA0;<a href="https://www.elastic.co/guide/en/logstash/7.0/breaking-changes.html">breaking changes</a> and <a href="https://www.elastic.co/guide/en/logstash/7.0/logstash-7-0-0.html">release notes</a> for a list of bug fixes and other changes.
<h2 id="java">Default Java pipeline execution</h2>Logstash was created by Jordan Sissel some time ago as a tool &#x2014; written in Ruby &#x2014; to get data from point A to point&#xA0;B. Logstash&apos;s accessible text DSL and&#xA0;simple&#xA0;three stage &#x2014;&#xA0;input/filter/output &#x2014;&#xA0;execution model&#xA0;proved to be a friendly&#xA0;way for users to move data from any source to any destination.
Originally, Logstash allowed the users to decide which of the Ruby implementations to use: CRuby, JRuby, Rubinius or any other.&#xA0;As the project&#xA0;evolved,&#xA0;JRuby became the de facto runtime&#xA0;due to its performance characteristics and its ability to leverage&#xA0;a vast set&#xA0;of mature and diverse&#xA0;Java libraries.&#xA0;
JRuby is a marvelous piece of technology in its ability to closely implement the Ruby Spec while still squeezing&#xA0;a lot of performance from the JVM. But as we leaned more and more on JRuby, we also identified some concerns.&#xA0;Notably,&#xA0;growing use of Java code in Logstash meant&#xA0;a computational and memory&#xA0;overhead when crossing the&#xA0;barrier between the two languages. This and the increasing <a href="https://github.com/logstash-plugins/logstash-input-tcp/tree/master/src/main/java/org/logstash/tcp">number</a> <a href="https://github.com/logstash-plugins/logstash-input-http/tree/master/src/main/java/org/logstash/plugins/inputs/http">of</a> <a href="https://github.com/logstash-plugins/logstash-filter-date/tree/master/src/main/java/org/logstash/filters">Java</a> <a href="https://github.com/logstash-plugins/logstash-filter-dissect/tree/master/src/main/java">based</a> <a href="https://github.com/logstash-plugins/logstash-input-beats/tree/master/src/main/java/org/logstash/beats">plugins</a>&#xA0;led us into a path of moving&#xA0;pieces of Logstash&apos;s internals to Java. The rewrite has opened up the <a href="https://www.elastic.co/blog/previewing-native-support-for-java-plugins-in-logstash">possibility&#xA0;for&#xA0;Java based plugins</a> to interact directly&#xA0;with a Java based pipeline while also providing <a href="https://www.elastic.co/blog/meet-the-new-logstash-java-execution-engine">better performance and lower memory footprint</a>.
But that&apos;s not all! The original Ruby execution engine reads&#xA0;the Logstash config files using Treetop to parse and convert them to executable Ruby source code which is then interpreted and executed at runtime by the JRuby runtime. The new Java execution engine replaces the Ruby source code generation phase with compilation directly to JVM bytecode using Janino. Some execution optimizations can be applied at this stage and the startup and reload times for some large configurations have been reduced by as much as 20x.
<h2 id="ilm">Index lifecycle management</h2>Version 6.6.0 the Elastic stack <a href="https://www.elastic.co/guide/en/elasticsearch/reference/6.6/ilm-start.html">introduced ILM</a> to give users a more flexible control over&#xA0;management actions on your indices, based on criteria&#xA0;such as shard size and performance requirements.&#xA0;A policy is added to the index template in order to drive these actions, which can be changed per index after it is created. In version 6.7.0 Logstash&apos;s Elasticsearch output plugin offered&#xA0;a&#xA0;<a href="https://www.elastic.co/guide/en/logstash/6.7/plugins-outputs-elasticsearch.html#plugins-outputs-elasticsearch-ilm">capability</a>&#xA0;&#x2014;&#xA0;disabled by default &#x2014;&#xA0;to deploy an ILM policy in the same way it has been doing for years regarding index templates.
Now in version 7.0.0, this Elasticsearch output plugin&#xA0;will <a href="https://www.elastic.co/guide/en/logstash/7.0/plugins-outputs-elasticsearch.html#plugins-outputs-elasticsearch-ilm_enabled">automatically detect</a> if the Elasticsearch cluster supports ILM and use deploy the ILM policy if it does. This behaviour can be changed by changing your `elasticsearch { ilm_enabled =&gt; auto }` to `true` or `false`.
<h2 id="plugins">Plugins, plugins, plugins</h2>A major release is a rare opportunity to introduce desirable breaking changes that have been held off. To this effect we have deprecated settings, made deprecated settings obsolete, and removed existing obsolete settings in many of our plugins. For a complete list please check the&#xA0;<a href="https://www.elastic.co/guide/en/logstash/7.0/breaking-changes.html">breaking changes</a>&#xA0;page.
<h2>Wrapping up</h2>We hope this set of improvements in&#xA0;Logstash 7.0.0 is useful to you. Give&#xA0;it&#xA0;a try by downloading Logstash <a href="https://www.elastic.co/downloads/logstash">here</a>, and please reach out on our <a href="https://discuss.elastic.co/c/logstash">Discuss forum</a> if you have any questions or feedback. Cheers!
</body></html>

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Logstash 7.0.0 released

If you're a user of Logstash it is very likely that you have found yourself in a situation where none of the existing&nbsp;<a href="https://www.elastic.co/guide/en/logstash/current/filter-plugins.html" target="_self">filter plugins</a>&nbsp;are&nbsp;capable of performing the transformations or validations you need for your data pipeline.&nbsp;In these situations Logstash provides you with&nbsp;two options:&nbsp;<a href="https://www.elastic.co/guide/en/logstash/6.1/_how_to_write_a_logstash_filter_plugin.html" target="_self">writing your own filter plugin</a>&nbsp;or resorting to the&nbsp;<a href="https://www.elastic.co/guide/en/logstash/current/plugins-filters-ruby.html" target="_self">ruby filter</a>.Writing a&nbsp;plugin has multiple benefits: it encapsulates the logic into a <a href="https://rubygems.org/" target="_self">ruby gem</a>&nbsp;which can be easily distributed to others, it can be installed, updated and removed&nbsp;through the Logstash plugin manager, it allows you to use&nbsp;additional third-party dependencies and it can be tested in isolation. On the other hand, many aren't familiar with the Ruby ecosystem and&nbsp;make a (perfectly valid)&nbsp;decision to not invest their time on this option.The alternative&nbsp;of using the ruby filter&nbsp;consists of placing&nbsp;a block of Ruby code wrapped in quotes in the "filter stage" of your&nbsp;pipeline configuration file that will&nbsp;be executed for each event. For example, to enrich each event with the size of one of its&nbsp;fields, you can do:<pre>input { # ... } filter { ruby { code =&gt; 'size = event.get("description").size; event.set("description_size", size)' } } output { # ... }</pre>This is simple, doesn't require knowledge about the ruby ecosystem and the code is shipped with the pipeline configuration.&nbsp;While&nbsp;this&nbsp;solution isn't wrong, we can foresee&nbsp;two major ways in which a small ruby filter like this can&nbsp;evolve: the need to reuse this code in multiple places and the growing complexity on the script itself.<h4>Reusing the code of a&nbsp;ruby filter</h4>Imagine&nbsp;that you need to enrich an event with the size of the "description" field and also&nbsp;the "extra" field. Copy/Paste to the rescue: <pre style="font-size: 12.6px;">input { # ... } filter { ruby { code =&gt; 'size = event.get("description").size; event.set("description_size", size)' } ruby { code =&gt; 'size = event.get("extra").size; event.set("extra_size", size)' } } output { # ... }</pre>You might be thinking that you could write that in a single block of ruby by having&nbsp;a list of fields to measure the size of, but these ruby filters can occur&nbsp;in different conditional branches for different kinds of data going through your pipeline. Once you&nbsp;need to reuse this functionality,&nbsp;you'll have multiple nearly identical pieces of ruby code repeated in the filter section&nbsp;and if there's&nbsp;a bug in the logic you'll have to fix the same thing in multiple places (and hopefully not forget one..or two).<h4>Growing complexity&nbsp;of the Ruby&nbsp;code</h4>Suppose now that you&nbsp;need a bit more than just the size of the field. To protect yourself from bad data that is being ingested, you want to ensure the field is there, and tag the event if it's missing. Oh and by the way, would be cool to add a field with a qualitative notion of how big the string is. Oh and also drop events of a certain size class. Our two line script has grown to something like this:<pre>input { # ... } filter { ruby { code =&gt; 'if event.get("description").nil? event.tag("description_not_found") else size = event.get("description").size event.set("description_size", size) size_class = if size &gt;= 50000 "tremendous"] elsif size &gt;= 200 "sizeable" elsif size &gt;= 1 "tiny" end event.set("description_size_class", size_class) event.cancel if size_class == "tremendous" end' } } output { # ... }</pre>Looking at the code, one must wonder if it code actually works. It probably does. Probably.<h4>Mixing reuse of code and growing complexity</h4>Combining the complexity increase and reuse, it's easy to see how one can end up with a block of 30 lines of Ruby code repeated 7 times in a Logstash pipeline.&nbsp;Thankfully, there's a solution to both of these problems in version <a href="https://www.elastic.co/guide/en/logstash/6.1/plugins-filters-ruby.html#_using_a_ruby_script_file" target="_self">3.1.0 of the ruby filter plugin</a>: file-based scripting.<h2>File-Based Scripting</h2>This&nbsp;new feature of the ruby filter allows you to take the large&nbsp;blocks of inline ruby code shown above to the following:<pre style="font-size: 12.6px;">filter { ruby { path =&gt; "/etc/logstash/compute_string_size.rb" script_params =&gt; { "source_field" =&gt; "description" "sizes" =&gt; { 50000 =&gt; "tremendous" 200 =&gt; "sizeable" 1 =&gt; "tiny" } "drop_size" =&gt; "tremendous" } } }</pre>So what's in the "compute_string_size.rb" file? The inline logic we had before, but with a few tweaks to make it more generic and actual testing:<pre># register accepts the hashmap passed to "script_params" # it runs once at startup def register(params) @source_field = params["source_field"] @sizes = params["sizes"] @drop_size = params["drop_size"] end # filter runs for every event # return the list of events to be passed forward # returning empty list is equivalent to event.cancel def filter(event) # tag if field isn't present if event.get(@source_field).nil? event.tag("#{@source_field}_not_found") return [event] end # set string size size = event.get(@source_field).size event.set("#{@source_field}_size", size) # calculate and tag size class return [event] unless @sizes size_class = size_class(size) event.set("#{@source_field}_size_class", size_class) # drop if it's the right size class size_class == @drop_size ? [] : [event] end # it's possible to have auxiliary methods def size_class(size) @sizes.each do |lower_bound, size_class| return size_class if size &gt;= lower_bound.to_i end end # testing!! test "when field exists" do parameters { { "source_field" =&gt; "field_A" } } in_event { { "field_A" =&gt; "hello" } } expect("the size is computed") {|events| events.first.get("field_A_size") == 5 } end test "when field doesn't exist" do parameters { { "source_field" =&gt; "field_A" } } in_event { { "field_B" =&gt; "hello" } } expect("tags as not found") {|events| events.first.get("tags").include?("field_A_not_found") } end test "when drop size is set" do parameters do { "source_field" =&gt; "field_A", "sizes" =&gt; { 50 =&gt; "big", 5 =&gt; "medium", 1 =&gt; "small" }, "drop_size" =&gt; "medium" } end in_event { { "field_A" =&gt; "a kind of medium sized string" } } expect("drops events of a certain size class") {|events| events.empty? } end</pre>You can find more information about&nbsp;building these&nbsp;scripts&nbsp;<a href="https://www.elastic.co/guide/en/logstash/6.1/plugins-filters-ruby.html#_using_a_ruby_script_file" target="_self">in the documentation</a>&nbsp;but even though the code&nbsp;is lengthier than its inline counterpart, it's easy to see&nbsp;the benefits of this approach:<h4>Reusability</h4>Thinking back to the previous example of computing the string size of multiple fields, the source field, list of size&nbsp;classes and which class to drop are now configurable. This means we&nbsp;can reuse this script in multiple places, rewriting the dual usage&nbsp;example as:<pre>input { # ... } filter { ruby { path =&gt; "/etc/logstash/compute_string_size.rb" script_params =&gt; { "source_field" =&gt; "description" } } ruby { path =&gt; "/etc/logstash/compute_string_size.rb" script_params =&gt; { "source_field" =&gt; "extra" } } } output { # ... }</pre><h4>Testing</h4>We now have&nbsp;tests that check if the calculation of the string size, the tagging when field is missing and usage of the size classes works correctly. We can confirm the&nbsp;tests pass by using Logstash's "test and exit" flag:<pre>% bin/logstash -e "filter { ruby { path =&gt; 'compute_string_size.rb' } }" -t [2018-01-23T23:56:01,805][INFO ][logstash.filters.ruby.script] Test run complete {:script_path=&gt;"compute_string_size.rb", :results=&gt;{:passed=&gt;4, :failed=&gt;0, :errored=&gt;0}} Configuration OK [2018-01-23T23:56:01,819][INFO ][logstash.runner ] Using config.test_and_exit mode. Config Validation Result: OK. Exiting Logstash</pre><h4>Sharing</h4>That's right! Now that we've encapsulated the sizing of strings in a neat little ruby file, we can share it! I opened a repository&nbsp;to collect scripts I've created so far:&nbsp;<a href="https://github.com/jsvd/logstash-filter-ruby-scripts" target="_self">https://github.com/jsvd/logstash-filter-ruby-scripts</a>, and I welcome scripts created by others!<h4>Limitations</h4>Even though file-based scripting in the ruby filter provides code reuse and testability, there are still&nbsp;limitations&nbsp;compared to full blown Logstash plugins. The main one is lack of support for third-party libraries: if you need the custom ruby code to use a library that doesn't ship with Logstash, it's not possible to load it through the ruby filter.If you're planning to use or are already using the <a href="https://www.elastic.co/guide/en/logstash/master/logstash-centralized-pipeline-management.html" target="_self">centralized pipeline management</a>&nbsp;there's no current way of pushing through the management UI other artifacts like these ruby scripts. This is <a href="https://github.com/elastic/x-pack-logstash/issues/303" target="_self">being tracked and&nbsp;addressed</a>, but it is&nbsp;a limitation at the time of writing of this blog post.<h3>Conclusion</h3>The <a href="https://rubygems.org/gems/logstash-filter-ruby/versions/3.1.3" target="_self">new Logstash ruby filter 3.1.x</a>&nbsp;provides users with a way to remove all the inline ruby code in their pipeline configs. This code is typically hard to maintain and to validate its&nbsp;correctness. The file-based scripting feature solves&nbsp;both of these problems by allowing scripts to be parametrized and tests to be written in the file containing the script itself.If you want to use this feature it is available by default on L<a href="https://www.elastic.co/downloads/logstash" target="_self">ogstash since version 6.1</a>. Otherwise you can update the ruby plugin&nbsp;by doing `bin/logstash-plugin update logstash-filter-ruby`.

Moving Custom Ruby Code out of the Logstash Pipeline

Being a central component of data flow between producers and consumers, it often happens that a single Logstash is responsible for driving multiple parallel streams of events. &nbsp;The existence of these multiple isolated flows pose a few challenges for Logstash, since historically, each individual instance has supported a single pipeline, composed of an input, a filter, and an output stage.
Users have found ways of implementing multiple isolated flows in a single pipeline, mainly through conditionals: tagging events early on in the input section and then creating conditional branches through the filters and outputs, applying different sets of plugins to different tags or event values. &nbsp;This is a very common solution we often see in the community, but there are several pains users feel when implementing it:
<h3>Conditional hell</h3>While implementing isolated multiple flows using conditionals works, it's easy to see how the existence of a single pipeline and single stages of processing makes the configuration extremely verbose and hard to manage as complexity increases.
For a simple pipeline that houses two flows:
<img src="https://api.contentstack.io/v2/uploads/5991cf438853622f0911f746/download?uid=bltaea598a83ff9d133" data-sys-asset-uid="bltb99b9658d7f7c9b2" alt="blog-pipeline-log.jpg">
The corresponding Logstash pipeline configuration is already riddled with primitives (marked in bold) whose only purpose is to keep the flows separate:
<pre class="prettyprint prettyprinted"> input {
 beats { port =&gt; 3444 tag =&gt; apache }
 tcp { port =&gt; 4222 tag =&gt; firewall }
 }
 filter {
 if "apache" in [tags] { 
 dissect { ... }
 } else if "firewall" in [tags] { 
 grok { ... }
 } 
 }
 output {
 if "apache" in [tags] { 
 elasticsearch { ... }
 } else if "firewall" in [tags] { 
 tcp { ... }
 } 
 }
</pre>Unfortunately, this is not the only issue with this solution..
<h3>Lack of congestion isolation</h3>If you're familiar with how Logstash works, you know that the output section of the pipeline receives a batch of event and will not move to the next batch until all events and gone through all of the outputs. This means that, for the pipeline above, if the TCP socket destination isn't reachable, Logstash won't process other batches of events, which in turn means that Elasticsearch won't receive events, and back pressure will be applied to both the TCP input and the Beats input.
<h3>Different flows have different needs</h3>If the TCP-&gt; Grok -&gt; TCP data flow handles a very high volume of small messages and the Beats -&gt; Dissect -&gt; ES flow has large documents with less events per second, then it would be ideal to have the former flow with many workers and bigger batches, and the latter with less workers and smaller batches.
With a single pipeline, the Filters+Output section will have a single set of parameters, and will process batches mixed with events from both flows.
Also, if you're using configuration reloading, changing a grok pattern for the first flow will stop Beats from receiving data and sending to Elasticsearch.
<h3>The current solution: Multiple Logstash Instances</h3>The problems described above can be solved by having multiple Logstash instances in the same machine, which can then be managed independently, but even this solution creates other problems:
<ul>
	<li>The RPM/DEB packages (namely the init scripts) aren't meant to handle multiple instances of Logstash</li>
	<li>Each new instance of Logstash also means a whole new JVM instance too</li>
	<li>Monitoring multiple instances of Logstash is more complex, requiring the monitoring solution to ping multiple APIs, one for each instance.</li>
</ul>We're proud to announce that the solution to all of these issues will arrive in the upcoming Logstash 6.0, with the new Multiple Pipelines feature!
<h2>Multiple Pipelines</h2>Multiple pipelines is the ability to execute, in a single instance of Logstash, one or more pipelines, by reading their definitions from a configuration file called `pipelines.yml`.
This file lives in your configuration folder and looks something like this:
<pre class="prettyprint"> - pipeline.id: apache
 pipeline.batch.size: 125
 queue.type: persisted
 path.config: "/path/to/config/apache.cfg"
 queue.page_capacity: 50mb
 - pipeline.id: test
 pipeline.batch.size: 2
 pipeline.batch.delay: 1
 queue.type: memory
 config.string: "input { tcp { port =&gt; 3333 } } output { stdout {} }"
</pre>This YAML file contains a list of hashes (or dictionaries), where each one represents a pipeline, and the keys and values are setting names for that pipeline. The values of settings which are omitted fall back to their default values (configurable in the already familiar `logstash.yml`), and only pipeline specific settings can be configured (i.e. setting `node.id` will throw an error).
<h3>Launching Logstash with Multiple Pipelines</h3>By default, if Logstash is started with neither `-e` or `-f` (or their equivalents in `logstash.yml`), it will read the `pipelines.yml` file and start those pipelines. Using either of these flags causes the `pipelines.yml` to be ignored.
Reloading is also fully supported in Multiple Pipelines. If started with `-r` or if `config.reload.automatic` is set to true, the `pipelines.yml` is periodically read and individual pipelines can be reloaded, added or removed. Changes to the `path.config` files of individual pipelines will also cause those to be reloaded.
This means that our initial example can now be implemented using multiple pipelines:
<pre class="prettyprint"> - pipeline.id: apache
 path.config: "/path/to/config/apache.cfg"
 - pipeline.id: firewall
 path.config: "/path/to/config/firewall.cfg"
</pre>And the individual configuration files without the conditional hell:
<pre class="prettyprint"> # apache.cfg
 input { beats { port =&gt; 3444 } }
 filter { dissect { ... } }
 output { elasticsearch { ... } }
 # firewall.cfg
 input { tcp { port =&gt; 4222 } }
 filter { grok { ... } }
 output { tcp { ... } }
</pre>You can find documentation on the Multiple Pipelines feature <a href="/guide/en/logstash/master/multiple-pipelines.html">here</a>.
<h3>What else?</h3>If you're using X-Pack, Monitoring will support the Multiple Pipelines out of the box:
<img src="https://api.contentstack.io/v2/uploads/598e0aa4b3ba08942fce2b20/download?uid=blt8a97cb7665e445c6" data-sys-asset-uid="blt8a97cb7665e445c6" alt="blog-pipeline2.png">
<h2>Conclusion</h2>We truly hope this feature makes the lives of our many users just a little bit easier. Multiple pipelines allows you to get more out of a single Logstash instance, giving you the flexibility to process separate event flows without having to work around the constraint of a single pipeline.
If you're interested in helping testing this feature before 6.0 is out, you can become an <a href="/blog/elastic-pioneer-program-6-0">Elastic Pioneer</a> -- all you need to do is try out the latest preview release and give us feedback for an opportunity to win some sweet Elastic swag!

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Der Inhalt dieser Seite ist in der ausgewählten Sprache nicht verfügbar. Wir bei Elastic arbeiten daran, die bereitgestellten Inhalte in verschiedenen Sprachen anzubieten. Bis dahin bitten wir Sie um etwas Geduld und hoffen auf Ihr Verständnis!

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Articles by João Duarte

Software Developer, Elastic

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