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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Tutorial: Load streaming data from Apache Kafka · Apache Druid</title><meta name="viewport" content="width=device-width"/><link rel="canonical" href="https://druid.apache.org/docs/0.23.0/tutorials/tutorial-kafka.html"/><meta name="generator" content="Docusaurus"/><meta name="description" content="&lt;!--"/><meta name="docsearch:language" content="en"/><meta name="docsearch:version" content="0.23.0" /><meta property="og:title" content="Tutorial: Load streaming data from Apache Kafka · Apache Druid"/><meta property="og:type" content="website"/><meta property="og:url" content="https://druid.apache.org/index.html"/><meta property="og:description" content="&lt;!--"/><meta property="og:image" content="https://druid.apache.org/img/druid_nav.png"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://druid.apache.org/img/druid_nav.png"/><link rel="shortcut icon" href="/img/favicon.png"/><link rel="stylesheet" href="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.css"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script async="" src="https://www.googletagmanager.com/gtag/js?id=UA-131010415-1"></script><script>
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class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle collapsible">Getting started<span class="arrow"><svg width="24" height="24" viewBox="0 0 24 24"><path fill="#565656" d="M7.41 15.41L12 10.83l4.59 4.58L18 14l-6-6-6 6z"></path><path d="M0 0h24v24H0z" fill="none"></path></svg></span></h3><ul class="hide"><li class="navListItem"><a class="navItem" href="/docs/0.23.0/design/index.html">Introduction to Apache Druid</a></li><li class="navListItem"><a class="navItem" href="/docs/0.23.0/tutorials/index.html">Quickstart</a></li><li class="navListItem"><a class="navItem" href="/docs/0.23.0/tutorials/docker.html">Docker</a></li><li class="navListItem"><a class="navItem" href="/docs/0.23.0/operations/single-server.html">Single server deployment</a></li><li class="navListItem"><a class="navItem" href="/docs/0.23.0/tutorials/cluster.html">Clustered deployment</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle collapsible">Tutorials<span class="arrow"><svg 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<h2><a class="anchor" aria-hidden="true" id="getting-started"></a><a href="#getting-started" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Getting started</h2>
<p>This tutorial demonstrates how to load data into Apache Druid from a Kafka stream, using Druid's Kafka indexing service.</p>
<p>For this tutorial, we'll assume you've already downloaded Druid as described in
the <a href="/docs/0.23.0/tutorials/index.html">quickstart</a> using the <code>micro-quickstart</code> single-machine configuration and have it
running on your local machine. You don't need to have loaded any data yet.</p>
<h2><a class="anchor" aria-hidden="true" id="download-and-start-kafka"></a><a href="#download-and-start-kafka" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Download and start Kafka</h2>
<p><a href="http://kafka.apache.org/">Apache Kafka</a> is a high throughput message bus that works well with
Druid. For this tutorial, we will use Kafka 2.7.0. To download Kafka, issue the following
commands in your terminal:</p>
<pre><code class="hljs css language-bash">curl -O https://archive.apache.org/dist/kafka/2.7.0/kafka_2.13-2.7.0.tgz
tar -xzf kafka_2.13-2.7.0.tgz
<span class="hljs-built_in">cd</span> kafka_2.13-2.7.0
</code></pre>
<p>Start a Kafka broker by running the following command in a new terminal:</p>
<pre><code class="hljs css language-bash">./bin/kafka-server-start.sh config/server.properties
</code></pre>
<p>Run this command to create a Kafka topic called <em>wikipedia</em>, to which we'll send data:</p>
<pre><code class="hljs css language-bash">./bin/kafka-topics.sh --create --topic wikipedia --bootstrap-server localhost:9092
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="load-data-into-kafka"></a><a href="#load-data-into-kafka" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Load data into Kafka</h2>
<p>Let's launch a producer for our topic and send some data!</p>
<p>In your Druid directory, run the following command:</p>
<pre><code class="hljs css language-bash"><span class="hljs-built_in">cd</span> quickstart/tutorial
gunzip -c wikiticker-2015-09-12-sampled.json.gz &gt; wikiticker-2015-09-12-sampled.json
</code></pre>
<p>In your Kafka directory, run the following command, where {PATH_TO_DRUID} is replaced by the path to the Druid directory:</p>
<pre><code class="hljs css language-bash"><span class="hljs-built_in">export</span> KAFKA_OPTS=<span class="hljs-string">"-Dfile.encoding=UTF-8"</span>
./bin/kafka-console-producer.sh --broker-list localhost:9092 --topic wikipedia &lt; {PATH_TO_DRUID}/quickstart/tutorial/wikiticker-2015-09-12-sampled.json
</code></pre>
<p>The previous command posted sample events to the <em>wikipedia</em> Kafka topic.
Now we will use Druid's Kafka indexing service to ingest messages from our newly created topic.</p>
<h2><a class="anchor" aria-hidden="true" id="loading-data-with-the-data-loader"></a><a href="#loading-data-with-the-data-loader" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Loading data with the data loader</h2>
<p>Navigate to <a href="http://localhost:8888">localhost:8888</a> and click <code>Load data</code> in the console header.</p>
<p><img src="../assets/tutorial-kafka-data-loader-01.png" alt="Data loader init" title="Data loader init"></p>
<p>Select <code>Apache Kafka</code> and click <code>Connect data</code>.</p>
<p><img src="../assets/tutorial-kafka-data-loader-02.png" alt="Data loader sample" title="Data loader sample"></p>
<p>Enter <code>localhost:9092</code> as the bootstrap server and <code>wikipedia</code> as the topic.</p>
<p>Click <code>Apply</code> and make sure that the data you are seeing is correct.</p>
<p>Once the data is located, you can click &quot;Next: Parse data&quot; to go to the next step.</p>
<p><img src="../assets/tutorial-kafka-data-loader-03.png" alt="Data loader parse data" title="Data loader parse data"></p>
<p>The data loader will try to automatically determine the correct parser for the data.
In this case it will successfully determine <code>json</code>.
Feel free to play around with different parser options to get a preview of how Druid will parse your data.</p>
<p>With the <code>json</code> parser selected, click <code>Next: Parse time</code> to get to the step centered around determining your primary timestamp column.</p>
<p><img src="../assets/tutorial-kafka-data-loader-04.png" alt="Data loader parse time" title="Data loader parse time"></p>
<p>Druid's architecture requires a primary timestamp column (internally stored in a column called <code>__time</code>).
If you do not have a timestamp in your data, select <code>Constant value</code>.
In our example, the data loader will determine that the <code>time</code> column in our raw data is the only candidate that can be used as the primary time column.</p>
<p>Click <code>Next: ...</code> twice to go past the <code>Transform</code> and <code>Filter</code> steps.
You do not need to enter anything in these steps as applying ingestion time transforms and filters are out of scope for this tutorial.</p>
<p><img src="../assets/tutorial-kafka-data-loader-05.png" alt="Data loader schema" title="Data loader schema"></p>
<p>In the <code>Configure schema</code> step, you can configure which <a href="/docs/0.23.0/ingestion/data-model.html#dimensions">dimensions</a> and <a href="/docs/0.23.0/ingestion/data-model.html#metrics">metrics</a> will be ingested into Druid.
This is exactly what the data will appear like in Druid once it is ingested.
Since our dataset is very small, go ahead and turn off <a href="/docs/0.23.0/ingestion/rollup.html"><code>Rollup</code></a> by clicking on the switch and confirming the change.</p>
<p>Once you are satisfied with the schema, click <code>Next</code> to go to the <code>Partition</code> step where you can fine tune how the data will be partitioned into segments.</p>
<p><img src="../assets/tutorial-kafka-data-loader-06.png" alt="Data loader partition" title="Data loader partition"></p>
<p>Here, you can adjust how the data will be split up into segments in Druid.
Since this is a small dataset, there are no adjustments that need to be made in this step.</p>
<p>Click <code>Next: Tune</code> to go to the tuning step.</p>
<p><img src="../assets/tutorial-kafka-data-loader-07.png" alt="Data loader tune" title="Data loader tune"></p>
<p>In the <code>Tune</code> step is it <em>very important</em> to set <code>Use earliest offset</code> to <code>True</code> since we want to consume the data from the start of the stream.
There are no other changes that need to be made here, so click <code>Next: Publish</code> to go to the <code>Publish</code> step.</p>
<p><img src="../assets/tutorial-kafka-data-loader-08.png" alt="Data loader publish" title="Data loader publish"></p>
<p>Let's name this datasource <code>wikipedia-kafka</code>.</p>
<p>Finally, click <code>Next</code> to review your spec.</p>
<p><img src="../assets/tutorial-kafka-data-loader-09.png" alt="Data loader spec" title="Data loader spec"></p>
<p>This is the spec you have constructed.
Feel free to go back and make changes in previous steps to see how changes will update the spec.
Similarly, you can also edit the spec directly and see it reflected in the previous steps.</p>
<p>Once you are satisfied with the spec, click <code>Submit</code> and an ingestion task will be created.</p>
<p><img src="../assets/tutorial-kafka-data-loader-10.png" alt="Tasks view" title="Tasks view"></p>
<p>You will be taken to the task view with the focus on the newly created supervisor.</p>
<p>The task view is set to auto refresh, wait until your supervisor launches a task.</p>
<p>When a tasks starts running, it will also start serving the data that it is ingesting.</p>
<p>Navigate to the <code>Datasources</code> view from the header.</p>
<p><img src="../assets/tutorial-kafka-data-loader-11.png" alt="Datasource view" title="Datasource view"></p>
<p>When the <code>wikipedia-kafka</code> datasource appears here it can be queried.</p>
<p><em>Note:</em> if the datasource does not appear after a minute you might have not set the supervisor to read from the start of the stream (in the <code>Tune</code> step).</p>
<p>At this point, you can go to the <code>Query</code> view to run SQL queries against the datasource.</p>
<p>Since this is a small dataset, you can simply run a <code>SELECT * FROM &quot;wikipedia-kafka&quot;</code> query to see your results.</p>
<p><img src="../assets/tutorial-kafka-data-loader-12.png" alt="Query view" title="Query view"></p>
<p>Check out the <a href="/docs/0.23.0/tutorials/tutorial-query.html">query tutorial</a> to run some example queries on the newly loaded data.</p>
<h3><a class="anchor" aria-hidden="true" id="submit-a-supervisor-via-the-console"></a><a href="#submit-a-supervisor-via-the-console" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Submit a supervisor via the console</h3>
<p>In the console, click <code>Submit supervisor</code> to open the submit supervisor dialog.</p>
<p><img src="../assets/tutorial-kafka-submit-supervisor-01.png" alt="Submit supervisor" title="Submit supervisor"></p>
<p>Paste in this spec and click <code>Submit</code>.</p>
<pre><code class="hljs css language-json">{
<span class="hljs-attr">"type"</span>: <span class="hljs-string">"kafka"</span>,
<span class="hljs-attr">"spec"</span> : {
<span class="hljs-attr">"dataSchema"</span>: {
<span class="hljs-attr">"dataSource"</span>: <span class="hljs-string">"wikipedia"</span>,
<span class="hljs-attr">"timestampSpec"</span>: {
<span class="hljs-attr">"column"</span>: <span class="hljs-string">"time"</span>,
<span class="hljs-attr">"format"</span>: <span class="hljs-string">"auto"</span>
},
<span class="hljs-attr">"dimensionsSpec"</span>: {
<span class="hljs-attr">"dimensions"</span>: [
<span class="hljs-string">"channel"</span>,
<span class="hljs-string">"cityName"</span>,
<span class="hljs-string">"comment"</span>,
<span class="hljs-string">"countryIsoCode"</span>,
<span class="hljs-string">"countryName"</span>,
<span class="hljs-string">"isAnonymous"</span>,
<span class="hljs-string">"isMinor"</span>,
<span class="hljs-string">"isNew"</span>,
<span class="hljs-string">"isRobot"</span>,
<span class="hljs-string">"isUnpatrolled"</span>,
<span class="hljs-string">"metroCode"</span>,
<span class="hljs-string">"namespace"</span>,
<span class="hljs-string">"page"</span>,
<span class="hljs-string">"regionIsoCode"</span>,
<span class="hljs-string">"regionName"</span>,
<span class="hljs-string">"user"</span>,
{ <span class="hljs-attr">"name"</span>: <span class="hljs-string">"added"</span>, <span class="hljs-attr">"type"</span>: <span class="hljs-string">"long"</span> },
{ <span class="hljs-attr">"name"</span>: <span class="hljs-string">"deleted"</span>, <span class="hljs-attr">"type"</span>: <span class="hljs-string">"long"</span> },
{ <span class="hljs-attr">"name"</span>: <span class="hljs-string">"delta"</span>, <span class="hljs-attr">"type"</span>: <span class="hljs-string">"long"</span> }
]
},
<span class="hljs-attr">"metricsSpec"</span> : [],
<span class="hljs-attr">"granularitySpec"</span>: {
<span class="hljs-attr">"type"</span>: <span class="hljs-string">"uniform"</span>,
<span class="hljs-attr">"segmentGranularity"</span>: <span class="hljs-string">"DAY"</span>,
<span class="hljs-attr">"queryGranularity"</span>: <span class="hljs-string">"NONE"</span>,
<span class="hljs-attr">"rollup"</span>: <span class="hljs-literal">false</span>
}
},
<span class="hljs-attr">"tuningConfig"</span>: {
<span class="hljs-attr">"type"</span>: <span class="hljs-string">"kafka"</span>,
<span class="hljs-attr">"reportParseExceptions"</span>: <span class="hljs-literal">false</span>
},
<span class="hljs-attr">"ioConfig"</span>: {
<span class="hljs-attr">"topic"</span>: <span class="hljs-string">"wikipedia"</span>,
<span class="hljs-attr">"inputFormat"</span>: {
<span class="hljs-attr">"type"</span>: <span class="hljs-string">"json"</span>
},
<span class="hljs-attr">"replicas"</span>: <span class="hljs-number">2</span>,
<span class="hljs-attr">"taskDuration"</span>: <span class="hljs-string">"PT10M"</span>,
<span class="hljs-attr">"completionTimeout"</span>: <span class="hljs-string">"PT20M"</span>,
<span class="hljs-attr">"consumerProperties"</span>: {
<span class="hljs-attr">"bootstrap.servers"</span>: <span class="hljs-string">"localhost:9092"</span>
}
}
}
}
</code></pre>
<p>This will start the supervisor that will in turn spawn some tasks that will start listening for incoming data.</p>
<h3><a class="anchor" aria-hidden="true" id="submit-a-supervisor-directly"></a><a href="#submit-a-supervisor-directly" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Submit a supervisor directly</h3>
<p>To start the service directly, we will need to submit a supervisor spec to the Druid overlord by running the following from the Druid package root:</p>
<pre><code class="hljs css language-bash">curl -XPOST -H<span class="hljs-string">'Content-Type: application/json'</span> -d @quickstart/tutorial/wikipedia-kafka-supervisor.json http://localhost:8081/druid/indexer/v1/supervisor
</code></pre>
<p>If the supervisor was successfully created, you will get a response containing the ID of the supervisor; in our case we should see <code>{&quot;id&quot;:&quot;wikipedia&quot;}</code>.</p>
<p>For more details about what's going on here, check out the
<a href="/docs/0.23.0/development/extensions-core/kafka-ingestion.html">Druid Kafka indexing service documentation</a>.</p>
<p>You can view the current supervisors and tasks in the Druid Console: <a href="http://localhost:8888/unified-console.html#tasks">http://localhost:8888/unified-console.md#tasks</a>.</p>
<h2><a class="anchor" aria-hidden="true" id="querying-your-data"></a><a href="#querying-your-data" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Querying your data</h2>
<p>After data is sent to the Kafka stream, it is immediately available for querying.</p>
<p>Please follow the <a href="/docs/0.23.0/tutorials/tutorial-query.html">query tutorial</a> to run some example queries on the newly loaded data.</p>
<h2><a class="anchor" aria-hidden="true" id="cleanup"></a><a href="#cleanup" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cleanup</h2>
<p>To go through any of the other ingestion tutorials, you will need to shut down the cluster and reset the cluster state by removing the contents of the <code>var</code> directory in the Druid home, as the other tutorials will write to the same &quot;wikipedia&quot; datasource.</p>
<p>You should additionally clear out any Kafka state. Do so by shutting down the Kafka broker with CTRL-C before stopping ZooKeeper and the Druid services, and then deleting the Kafka log directory at <code>/tmp/kafka-logs</code>:</p>
<pre><code class="hljs css language-bash">rm -rf /tmp/kafka-logs
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="further-reading"></a><a href="#further-reading" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Further reading</h2>
<p>For more information on loading data from Kafka streams, please see the <a href="/docs/0.23.0/development/extensions-core/kafka-ingestion.html">Druid Kafka indexing service documentation</a>.</p>
</span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/0.23.0/tutorials/tutorial-batch.html"><span class="arrow-prev">← </span><span>Loading files natively</span></a><a class="docs-next button" href="/docs/0.23.0/tutorials/tutorial-batch-hadoop.html"><span>Load from Apache Hadoop</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#getting-started">Getting started</a></li><li><a href="#download-and-start-kafka">Download and start Kafka</a></li><li><a href="#load-data-into-kafka">Load data into Kafka</a></li><li><a href="#loading-data-with-the-data-loader">Loading data with the data loader</a><ul class="toc-headings"><li><a href="#submit-a-supervisor-via-the-console">Submit a supervisor via the console</a></li><li><a href="#submit-a-supervisor-directly">Submit a supervisor directly</a></li></ul></li><li><a href="#querying-your-data">Querying your data</a></li><li><a href="#cleanup">Cleanup</a></li><li><a href="#further-reading">Further reading</a></li></ul></nav></div><footer class="nav-footer druid-footer" id="footer"><div class="container"><div class="text-center"><p><a href="/technology">Technology</a> · <a href="/use-cases">Use Cases</a> · <a href="/druid-powered">Powered by Druid</a> · <a href="/docs/0.23.0/latest">Docs</a> · <a href="/community/">Community</a> · <a href="/downloads.html">Download</a> · <a href="/faq">FAQ</a></p></div><div class="text-center"><a title="Join the user group" href="https://groups.google.com/forum/#!forum/druid-user" target="_blank"><span class="fa fa-comments"></span></a> · <a title="Follow Druid" href="https://twitter.com/druidio" target="_blank"><span class="fab fa-twitter"></span></a> · <a title="Download via Apache" href="https://www.apache.org/dyn/closer.cgi?path=/incubator/druid/{{ site.druid_versions[0].versions[0].version }}/apache-druid-{{ site.druid_versions[0].versions[0].version }}-bin.tar.gz" target="_blank"><span class="fas fa-feather"></span></a> · <a title="GitHub" href="https://github.com/apache/druid" target="_blank"><span class="fab fa-github"></span></a></div><div class="text-center license">Copyright © 2019 <a href="https://www.apache.org/" target="_blank">Apache Software Foundation</a>.<br/>Except where otherwise noted, licensed under <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>.<br/>Apache Druid, Druid, and the Druid logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</div></div></footer></div><script type="text/javascript" src="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.js"></script><script>
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