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<h1>Introduction to pydruid</h1>
<p class="text-muted">by <span class="author text-uppercase">Igal Levy</span> · April 15, 2014</p>
<p>We&#39;ve already written about pairing <a href="/blog/2014/02/03/rdruid-and-twitterstream.html">R with RDruid</a>, but Python has powerful and free open-source analysis tools too. Collectively, these are often referred to as the <a href="http://www.scipy.org/stackspec.html">SciPy Stack</a>. To pair SciPy&#39;s analytic power with the advantages of querying time-series data in Druid, we created the pydruid connector. This allows Python users to query Druid&mdash;and export the results to useful formats&mdash;in a way that makes sense to them.</p>
<h2 id="getting-started">Getting Started</h2>
<p>pydruid should run with Python 2.x, and is known to run with Python 2.7.5.</p>
<p>Install pydruid in the same way as you&#39;d install any other Python module on your system. The simplest way is:</p>
<div class="highlight"><pre><code class="language-bash" data-lang="bash"><span></span>pip install pydruid
</code></pre></div>
<p>You should also install Pandas to execute the simple examples below:</p>
<div class="highlight"><pre><code class="language-bash" data-lang="bash"><span></span>pip install pandas
</code></pre></div>
<p>When you import pydruid into your example, it will try to load Pandas as well.</p>
<h2 id="run-the-druid-wikipedia-example">Run the Druid Wikipedia Example</h2>
<p><a href="/downloads.html">Download Druid</a> and unpack Druid. If you are not familiar with Druid, see this <a href="/docs/latest/Tutorial:-A-First-Look-at-Druid.html">introductory tutorial</a>.</p>
<p>From the Druid home directory, start the Druid Realtime node:</p>
<div class="highlight"><pre><code class="language-bash" data-lang="bash"><span></span><span class="nv">$DRUID_HOME</span>/run_example_server.sh
</code></pre></div>
<p>When prompted, choose the &quot;wikipedia&quot; example. After the Druid realtime node is done starting up, messages should appear that start with the following:</p>
<div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>2014-04-03 18:01:32,852 INFO [wikipedia-incremental-persist] ...
</code></pre></div>
<p>These messages confirm that the realtime node is ingesting data from the Wikipedia edit stream, and that data can be queried.</p>
<h2 id="write-execute-and-submit-a-pydruid-query">Write, Execute, and Submit a pydruid Query</h2>
<p>Let&#39;s say we want to see the top few languages for Wikipedia articles, in terms of number of edits. This is the query we could post directly to Druid:</p>
<div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span>
<span class="nt">&quot;queryType&quot;</span><span class="p">:</span> <span class="s2">&quot;topN&quot;</span><span class="p">,</span>
<span class="nt">&quot;dataSource&quot;</span><span class="p">:</span> <span class="s2">&quot;wikipedia&quot;</span><span class="p">,</span>
<span class="nt">&quot;dimension&quot;</span><span class="p">:</span> <span class="s2">&quot;language&quot;</span><span class="p">,</span>
<span class="nt">&quot;threshold&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
<span class="nt">&quot;metric&quot;</span><span class="p">:</span> <span class="s2">&quot;edit_count&quot;</span><span class="p">,</span>
<span class="nt">&quot;granularity&quot;</span><span class="p">:</span> <span class="s2">&quot;all&quot;</span><span class="p">,</span>
<span class="nt">&quot;filter&quot;</span><span class="p">:</span> <span class="p">{</span>
<span class="nt">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;selector&quot;</span><span class="p">,</span>
<span class="nt">&quot;dimension&quot;</span><span class="p">:</span> <span class="s2">&quot;namespace&quot;</span><span class="p">,</span>
<span class="nt">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;article&quot;</span>
<span class="p">},</span>
<span class="nt">&quot;aggregations&quot;</span><span class="p">:</span> <span class="p">[</span>
<span class="p">{</span>
<span class="nt">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;longSum&quot;</span><span class="p">,</span>
<span class="nt">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;edit_count&quot;</span><span class="p">,</span>
<span class="nt">&quot;fieldName&quot;</span><span class="p">:</span> <span class="s2">&quot;count&quot;</span>
<span class="p">}</span>
<span class="p">],</span>
<span class="nt">&quot;intervals&quot;</span><span class="p">:[</span><span class="s2">&quot;2013-06-01T00:00/2020-01-01T00&quot;</span><span class="p">]</span>
<span class="p">}</span>
</code></pre></div>
<p>The results should appear similar to the following:</p>
<div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">[</span> <span class="p">{</span>
<span class="nt">&quot;timestamp&quot;</span> <span class="p">:</span> <span class="s2">&quot;2014-04-03T17:59:00.000Z&quot;</span><span class="p">,</span>
<span class="nt">&quot;result&quot;</span> <span class="p">:</span> <span class="p">[</span> <span class="p">{</span>
<span class="nt">&quot;language&quot;</span> <span class="p">:</span> <span class="s2">&quot;en&quot;</span><span class="p">,</span>
<span class="nt">&quot;edit_count&quot;</span> <span class="p">:</span> <span class="mi">4726</span>
<span class="p">},</span> <span class="p">{</span>
<span class="nt">&quot;language&quot;</span> <span class="p">:</span> <span class="s2">&quot;fr&quot;</span><span class="p">,</span>
<span class="nt">&quot;edit_count&quot;</span> <span class="p">:</span> <span class="mi">1273</span>
<span class="p">},</span> <span class="p">{</span>
<span class="nt">&quot;language&quot;</span> <span class="p">:</span> <span class="s2">&quot;de&quot;</span><span class="p">,</span>
<span class="nt">&quot;edit_count&quot;</span> <span class="p">:</span> <span class="mi">857</span>
<span class="p">},</span> <span class="p">{</span>
<span class="nt">&quot;language&quot;</span> <span class="p">:</span> <span class="s2">&quot;ja&quot;</span><span class="p">,</span>
<span class="nt">&quot;edit_count&quot;</span> <span class="p">:</span> <span class="mi">176</span>
<span class="p">}</span> <span class="p">]</span>
<span class="p">}</span> <span class="p">]</span>
</code></pre></div>
<p><strong>NOTE:</strong> Due to limitations in the way the wikipedia example is set up, you may see a limited number of results appear.</p>
<p>Here&#39;s that same query in Python:</p>
<div class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pydruid.client</span> <span class="kn">import</span> <span class="o">*</span>
<span class="n">query</span> <span class="o">=</span> <span class="n">PyDruid</span><span class="p">(</span><span class="s1">&#39;http://localhost:8083&#39;</span><span class="p">,</span> <span class="s1">&#39;druid/v2/&#39;</span><span class="p">)</span>
<span class="n">top_langs</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">topn</span><span class="p">(</span>
<span class="n">datasource</span> <span class="o">=</span> <span class="s2">&quot;wikipedia&quot;</span><span class="p">,</span>
<span class="n">granularity</span> <span class="o">=</span> <span class="s2">&quot;all&quot;</span><span class="p">,</span>
<span class="n">intervals</span> <span class="o">=</span> <span class="s2">&quot;2013-06-01T00:00/2020-01-01T00&quot;</span><span class="p">,</span>
<span class="n">dimension</span> <span class="o">=</span> <span class="s2">&quot;language&quot;</span><span class="p">,</span>
<span class="nb">filter</span> <span class="o">=</span> <span class="n">Dimension</span><span class="p">(</span><span class="s2">&quot;namespace&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;article&quot;</span><span class="p">,</span>
<span class="n">aggregations</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;edit_count&quot;</span><span class="p">:</span> <span class="n">longsum</span><span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">)},</span>
<span class="n">metric</span> <span class="o">=</span> <span class="s2">&quot;edit_count&quot;</span><span class="p">,</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="mi">4</span>
<span class="p">)</span>
<span class="k">print</span> <span class="n">top_langs</span> <span class="c1"># Do this if you want to see the raw JSON</span>
</code></pre></div>
<p>Let&#39;s break this query down:</p>
<ul>
<li>query &ndash; The <code>query</code> object is instantiated with the location of the Druid realtime node. <code>query</code> exposes various querying methods, including <code>topn</code>.</li>
<li>datasource &ndash; This identifies the datasource. If Druid were ingesting from more than one datasource, this ID would identify the one we want.</li>
<li>granularity &ndash; The rollup granularity, which could be set to a specific value such as <code>minute</code> or <code>hour</code>. We want to see the sum count across the entire interval, and so we choose <code>all</code>.</li>
<li>intervals &ndash; The interval of time we&#39;re interested in. The value given is extended beyond our actual endpoints to make sure we cover all of the data.</li>
<li>dimension &ndash; The dimension we&#39;re interested in, which happens to be language. Language is an attribute of the <a href="http://meta.wikimedia.org/wiki/IRC/Channels#Raw_feeds">Wikipedia recent-changes feed&#39;s metadata</a>.</li>
<li>filter &ndash; Filters are used to specify a selector. In this case, we&#39;re selecting pages that have a namespace dimension with the value <code>article</code> (therefore excluding edits to Wikipedia pages that aren&#39;t articles).</li>
<li>aggregations &ndash; We&#39;re interested in obtaining the total count of edited pages, per the language dimension, and we map it to a type of aggregation available in pydruid (longsum). We also rename this <code>count</code> metric to <code>edit_count</code>.</li>
<li>metric &ndash; Names the metric to sort on.</li>
<li>threshold &ndash; Sets the maximum number of aggregated results to return.</li>
</ul>
<p>See the <a href="https://pythonhosted.org/pydruid/">pydruid documentation</a> for more information about queries.</p>
<h2 id="bringing-the-data-into-pandas">Bringing the Data Into Pandas</h2>
<p>Now that Druid is returning data, we&#39;ll pass that data to a Pandas dataframe, which allows us to analyze and visualize it:</p>
<div class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pydruid.client</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">plt</span> <span class="c1"># Need to have matplotlib installed</span>
<span class="n">query</span> <span class="o">=</span> <span class="n">PyDruid</span><span class="p">(</span><span class="s1">&#39;http://localhost:8083&#39;</span><span class="p">,</span> <span class="s1">&#39;druid/v2/&#39;</span><span class="p">)</span>
<span class="n">top_langs</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">topn</span><span class="p">(</span>
<span class="n">datasource</span> <span class="o">=</span> <span class="s2">&quot;wikipedia&quot;</span><span class="p">,</span>
<span class="n">granularity</span> <span class="o">=</span> <span class="s2">&quot;all&quot;</span><span class="p">,</span>
<span class="n">intervals</span> <span class="o">=</span> <span class="s2">&quot;2013-06-01T00:00/2020-01-01T00&quot;</span><span class="p">,</span>
<span class="n">dimension</span> <span class="o">=</span> <span class="s2">&quot;language&quot;</span><span class="p">,</span>
<span class="nb">filter</span> <span class="o">=</span> <span class="n">Dimension</span><span class="p">(</span><span class="s2">&quot;namespace&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;article&quot;</span><span class="p">,</span>
<span class="n">aggregations</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;edit_count&quot;</span><span class="p">:</span> <span class="n">longsum</span><span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">)},</span>
<span class="n">metric</span> <span class="o">=</span> <span class="s2">&quot;edit_count&quot;</span><span class="p">,</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="mi">4</span>
<span class="p">)</span>
<span class="k">print</span> <span class="n">top_langs</span> <span class="c1"># Do this if you want to see the raw JSON</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">export_pandas</span><span class="p">()</span> <span class="c1"># Client will import Pandas, no need to do so separately.</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># Don&#39;t need the timestamp column here</span>
<span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># Get a naturally numbered index</span>
<span class="k">print</span> <span class="n">df</span>
<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;language&#39;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;bar&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
<p>Printing the results gives:</p>
<div class="highlight"><pre><code class="language-text" data-lang="text"><span></span> edit_count language
1 834 en
2 256 de
3 185 fr
4 38 ja
</code></pre></div>
<p>The bar graph will look something like this:</p>
<p><img src="/img/wiki-edit-lang-plot.png" alt="Bar graph showing Wikipedia edits by language" title="Wikipedia Edits by Language"></p>
<p>If you were to repeat the query, you should see larger numbers under edit_count, since the Druid realtime node is continuing to ingest data from Wikipedia.</p>
<h2 id="conclusions">Conclusions</h2>
<p>In this blog, we showed how you can run ad-hoc queries against a data set that is being streamed into Druid. And while this is only a small example of pydruid and the power of Python, it serves as an effective introductory demonstration of the benefits of pairing Druid&#39;s ability to make data available in real-time with SciPi&#39;s powerful analytics tools.</p>
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