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<h2 class="title"><a id="_query_phase"></a>Query Phase<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/060_Distributed_Search/05_Query_phase.asciidoc">edit</a></h2>
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<p>During the initial <em>query phase</em>, the query is broadcast to a shard copy (a
primary or replica shard) of every shard in the index. Each shard executes
the search locally and builds a <em>priority queue</em> of matching documents.</p>
<div class="sidebar">
<div class="titlepage"><div><div>
<p class="title"><strong>Priority Queue</strong></p>
</div></div></div>
<p>A <em>priority queue</em> is just a sorted list that holds the <em>top-n</em> matching
documents. The size of the priority queue depends on the pagination
parameters <code class="literal">from</code> and <code class="literal">size</code>. For example, the following search request
would require a priority queue big enough to hold 100 documents:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">GET /_search
{
"from": 90,
"size": 10
}</pre>
</div>
</div>
<p>The query phase process is depicted in <a class="xref" href="_query_phase.html#img-distrib-search" title="Query phase of distributed search">Figure 14, “Query phase of distributed search”</a>.</p>
<div id="img-distrib-search" class="imageblock">
<div class="content">
<img src="images/elas_0901.png" alt="Query phase of distributed search">
</div>
<div class="title">Figure 14. Query phase of distributed search</div>
</div>
<p>The query phase consists of the following three steps:</p>
<div class="olist orderedlist">
<ol class="orderedlist">
<li class="listitem">
The client sends a <code class="literal">search</code> request to <code class="literal">Node 3</code>, which creates an empty
priority queue of size <code class="literal">from + size</code>.
</li>
<li class="listitem">
<code class="literal">Node 3</code> forwards the search request to a primary or replica copy of every
shard in the index. Each shard executes the query locally and adds the
results into a local sorted priority queue of size <code class="literal">from + size</code>.
</li>
<li class="listitem">
Each shard returns the doc IDs and sort values of all the docs in its
priority queue to the coordinating node, <code class="literal">Node 3</code>, which merges these
values into its own priority queue to produce a globally sorted list of
results.
</li>
</ol>
</div>
<p>When a search request is sent to a node, that node becomes the coordinating
node. It is the job of this node to broadcast the search request to all
involved shards, and to gather their responses into a globally sorted result
set that it can return to the client.</p>
<p>The first step is to broadcast the request to a shard copy of every node in
the index. Just like <a class="xref" href="distrib-read.html" title="Retrieving a Document">document <code class="literal">GET</code> requests</a>, search requests
can be handled by a primary shard or by any of its replicas. This is how more
replicas (when combined with more hardware) can increase search throughput.
A coordinating node will round-robin through all shard copies on subsequent
requests in order to spread the load.</p>
<p>Each shard executes the query locally and builds a sorted priority queue of
length <code class="literal">from + size</code>—in other words, enough results to satisfy the global
search request all by itself. It returns a lightweight list of results to the
coordinating node, which contains just the doc IDs and any values required for
sorting, such as the <code class="literal">_score</code>.</p>
<p>The coordinating node merges these shard-level results into its own sorted
priority queue, which represents the globally sorted result set. Here the query
phase ends.</p>
<div class="note admon">
<div class="icon"></div>
<div class="admon_content">
<p>An index can consist of one or more primary shards, so a search request
against a single index needs to be able to combine the results from multiple
shards. A search against <em>multiple</em> or <em>all</em> indices works in exactly the same
way—​there are just more shards involved.</p>
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