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<article data-swiftype-index='true'>
<a class='edit-link' href="https://github.com/apache/ignite-3/tree/main/docs/_docs/sql-reference/explain-statement.adoc" target="_blank">Edit</a>
<h1>EXPLAIN Command</h1>
<div id="preamble">
<div class="sectionbody">
<div class="paragraph">
<p>The <code>EXPLAIN</code> command is used to display the execution plan of an SQL query, showing how the query will be processed by the SQL engine.
It provides insights into the relational operators used, their configuration, and the estimated number of rows processed at each step.
This information is essential for diagnosing performance bottlenecks and understanding query optimization decisions.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="syntax">Syntax</h2>
<div class="sectionbody">
<div class="paragraph diagram-container">
<p>Diagram(
Terminal('EXPLAIN'),
Optional(
Sequence(
Choice(
0,
Terminal('PLAN'),
Terminal('MAPPING')
),
Terminal('FOR')
)
),
NonTerminal('query_or_dml')
)</p>
</div>
<div class="paragraph">
<p>If neither <code>PLAN</code> nor <code>MAPPING</code> is specified, then <code>PLAN</code> is implicit.</p>
</div>
<div class="paragraph">
<p>Parameters:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><code>PLAN</code> - explains query in terms of relational operators tree.
This representation is suitable for investigation of performance issues related to the optimizer.</p>
</li>
<li>
<p><code>MAPPING</code> - explains query in terms of mapping of query fragment to a particular node of the cluster.
This representation is suitable for investigation of performance issues related to the data colocation.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>Examples:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="sql"><span class="k">EXPLAIN</span> <span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">lineitem</span><span class="p">;</span>
<span class="k">EXPLAIN</span> <span class="n">PLAN</span> <span class="k">FOR</span> <span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">lineitem</span><span class="p">;</span>
<span class="k">EXPLAIN</span> <span class="n">MAPPING</span> <span class="k">FOR</span> <span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">lineitem</span><span class="p">;</span></code></pre>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="understanding-the-output">Understanding The Output</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Each query plan is represented as a tree-like structure composed of <a href="/docs/ignite3/latest/sql-reference/explain-operators-list"><strong>relational operators</strong></a>.</p>
</div>
<div class="paragraph">
<p>A node in the plan includes:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>A <strong>name</strong>, indicating the relational operator (e.g., <code>TableScan</code>, <code>IndexScan</code>, <code>Sort</code>, <code>Join</code> types)</p>
</li>
<li>
<p>A set of <strong>attributes</strong>, relevant to that specific operator</p>
</li>
</ul>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="text">OperatorName
attribute1: value1
attribute2: value2</code></pre>
</div>
</div>
<div class="paragraph">
<p>Examples:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="text">TableScan // Full table access
table: PUBLIC.EMP
fieldNames: [NAME, SALARY]
est: (rows=1)
IndexScan // Index-based access
table: PUBLIC.EMP
index: EMP_NAME_DESC_IDX
type: SORTED
fields: [NAME]
collation: [NAME DESC]
est: (rows=1)
Sort // Sort rows
collation: [NAME DESC NULLS LAST]
est: (rows=1)</code></pre>
</div>
</div>
<div class="sect2">
<h3 id="operator-naming">Operator Naming</h3>
<div class="paragraph">
<p>The operator name reflects the specific algorithm or strategy used.
For example:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><code>TableScan</code> – Full scan of a base table.</p>
</li>
<li>
<p><code>IndexScan</code> – Access via index, possibly sorted.</p>
</li>
<li>
<p><code>Sort</code> – Explicit sorting step.</p>
</li>
<li>
<p><code>HashJoin</code>, <code>MergeJoin</code>, <code>NestedLoopJoin</code> – Types of join algorithms.</p>
</li>
<li>
<p><code>Limit</code>, <code>Project</code>, <code>Exchange</code> – Execution-related transformations and controls.</p>
</li>
</ul>
</div>
</div>
<div class="sect2">
<h3 id="hierarchical-plan-structure">Hierarchical Plan Structure</h3>
<div class="paragraph">
<p>The plan is structured as a <strong>tree</strong>, where:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><strong>Leaf nodes</strong> represent data sources (e.g., <code>TableScan</code>)</p>
</li>
<li>
<p><strong>Internal nodes</strong> represent data transformations (e.g., <code>Join</code>, <code>Sort</code>)</p>
</li>
<li>
<p><strong>The root node</strong> (topmost) is the final operator that produces the result</p>
</li>
</ul>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="examples">Examples</h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="example-complex-join">Example: Complex Join</h3>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="sql"><span class="k">EXPLAIN</span> <span class="n">PLAN</span> <span class="k">FOR</span>
<span class="k">SELECT</span>
<span class="n">U</span><span class="p">.</span><span class="n">UserName</span><span class="p">,</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductName</span><span class="p">,</span> <span class="n">R</span><span class="p">.</span><span class="n">ReviewText</span><span class="p">,</span> <span class="n">R</span><span class="p">.</span><span class="n">Rating</span>
<span class="k">FROM</span> <span class="n">Users</span> <span class="n">U</span><span class="p">,</span> <span class="n">Reviews</span> <span class="n">R</span><span class="p">,</span> <span class="n">Products</span> <span class="n">P</span>
<span class="k">WHERE</span> <span class="n">U</span><span class="p">.</span><span class="n">UserID</span> <span class="o">=</span> <span class="n">R</span><span class="p">.</span><span class="n">UserID</span>
<span class="k">AND</span> <span class="n">R</span><span class="p">.</span><span class="n">ProductID</span> <span class="o">=</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductID</span>
<span class="k">AND</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductName</span> <span class="o">=</span> <span class="s1">'Product_'</span> <span class="o">||</span> <span class="o">?</span><span class="p">::</span><span class="nb">varchar</span></code></pre>
</div>
</div>
<div class="paragraph">
<p>The resulting output is:</p>
</div>
<div class="listingblock">
<div class="content">
<pre>Project
fieldNames: [USERNAME, PRODUCTNAME, REVIEWTEXT, RATING]
projection: [USERNAME, PRODUCTNAME, REVIEWTEXT, RATING]
est: (rows=16650)
HashJoin
predicate: =(USERID$0, USERID)
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING, PRODUCTID$0, PRODUCTNAME, USERID$0, USERNAME]
type: inner
est: (rows=16650)
HashJoin
predicate: =(PRODUCTID, PRODUCTID$0)
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING, PRODUCTID$0, PRODUCTNAME]
type: inner
est: (rows=16650)
Exchange
distribution: single
est: (rows=50000)
TableScan
table: PUBLIC.REVIEWS
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING]
est: (rows=50000)
Exchange
distribution: single
est: (rows=1665)
TableScan
table: PUBLIC.PRODUCTS
predicate: =(PRODUCTNAME, ||(_UTF-8'Product_', CAST(?0):VARCHAR CHARACTER SET "UTF-8"))
fieldNames: [PRODUCTID, PRODUCTNAME]
est: (rows=1665)
Exchange
distribution: single
est: (rows=10000)
TableScan
table: PUBLIC.USERS
fieldNames: [USERID, USERNAME]
est: (rows=10000)</pre>
</div>
</div>
<div class="paragraph">
<p>This execution plan represents a query that joins three tables: <code>USERS</code>, <code>REVIEWS</code>, and <code>PRODUCTS</code>, and selects four fields after filtering by product name.</p>
</div>
<div class="ulist">
<ul>
<li>
<p><strong>Project</strong> (root node):
Outputs the final selected fields — <code>USERNAME</code>, <code>PRODUCTNAME</code>, <code>REVIEWTEXT</code>, and <code>RATING</code>.</p>
</li>
<li>
<p><strong>HashJoins</strong> (two levels):
Perform the inner joins.</p>
<div class="ulist">
<ul>
<li>
<p>The first (bottom-most) joins <code>REVIEWS</code> with <code>PRODUCTS</code> on <code>PRODUCTID</code>.</p>
</li>
<li>
<p>The second joins the result with <code>USERS</code> on <code>USERID</code>.</p>
</li>
</ul>
</div>
</li>
<li>
<p><strong>TableScans</strong>:
Each table is scanned:</p>
<div class="ulist">
<ul>
<li>
<p><code>REVIEWS</code> is fully scanned.</p>
</li>
<li>
<p><code>PRODUCTS</code> is scanned with a filter on <code>PRODUCTNAME</code>.</p>
</li>
<li>
<p><code>USERS</code> is fully scanned.</p>
</li>
</ul>
</div>
</li>
<li>
<p><strong>Exchange</strong> nodes:
Indicate data redistribution between operators.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>Each node includes:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><code>fieldNames</code>: Output columns at that stage.</p>
</li>
<li>
<p><code>predicate</code>: Join or filter condition.</p>
</li>
<li>
<p><code>est</code>: Estimated number of rows at that point in the plan.</p>
</li>
</ul>
</div>
</div>
<div class="sect2">
<h3 id="example-query-mapping">Example: Query Mapping</h3>
<div class="paragraph">
<p>A result of the EXPLAIN MAPPING command includes additional metadata providing insight into how the query is mapped on cluster topology.
So, for the command like below:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="sql"><span class="k">EXPLAIN</span> <span class="n">MAPPING</span> <span class="k">FOR</span>
<span class="k">SELECT</span>
<span class="n">U</span><span class="p">.</span><span class="n">UserName</span><span class="p">,</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductName</span><span class="p">,</span> <span class="n">R</span><span class="p">.</span><span class="n">ReviewText</span><span class="p">,</span> <span class="n">R</span><span class="p">.</span><span class="n">Rating</span>
<span class="k">FROM</span> <span class="n">Users</span> <span class="n">U</span><span class="p">,</span> <span class="n">Reviews</span> <span class="n">R</span><span class="p">,</span> <span class="n">Products</span> <span class="n">P</span>
<span class="k">WHERE</span> <span class="n">U</span><span class="p">.</span><span class="n">UserID</span> <span class="o">=</span> <span class="n">R</span><span class="p">.</span><span class="n">UserID</span>
<span class="k">AND</span> <span class="n">R</span><span class="p">.</span><span class="n">ProductID</span> <span class="o">=</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductID</span>
<span class="k">AND</span> <span class="n">P</span><span class="p">.</span><span class="n">ProductName</span> <span class="o">=</span> <span class="s1">'Product_'</span> <span class="o">||</span> <span class="o">?</span><span class="p">::</span><span class="nb">varchar</span></code></pre>
</div>
</div>
<div class="paragraph">
<p>The resulting output is:</p>
</div>
<div class="listingblock">
<div class="content">
<pre>Fragment#0 root
distribution: single
executionNodes: [node_1]
tree:
Project
fieldNames: [USERNAME, PRODUCTNAME, REVIEWTEXT, RATING]
projection: [USERNAME, PRODUCTNAME, REVIEWTEXT, RATING]
est: (rows=1)
HashJoin
predicate: =(USERID$0, USERID)
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING, PRODUCTID$0, PRODUCTNAME, USERID$0, USERNAME]
type: inner
est: (rows=1)
HashJoin
predicate: =(PRODUCTID, PRODUCTID$0)
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING, PRODUCTID$0, PRODUCTNAME]
type: inner
est: (rows=1)
Receiver
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING]
sourceFragmentId: 1
est: (rows=1)
Receiver
fieldNames: [PRODUCTID, PRODUCTNAME]
sourceFragmentId: 2
est: (rows=1)
Receiver
fieldNames: [USERID, USERNAME]
sourceFragmentId: 3
est: (rows=1)
Fragment#1
distribution: random
executionNodes: [node_1, node_2, node_3]
partitions: [REVIEWS=[node_1={0, 2, 5, 6, 7, 8, 9, 10, 12, 13, 20}, node_2={1, 3, 11, 19, 21, 22, 23, 24}, node_3={4, 14, 15, 16, 17, 18}]]
tree:
Sender
distribution: single
targetFragmentId: 0
est: (rows=50000)
TableScan
table: PUBLIC.REVIEWS
fieldNames: [PRODUCTID, USERID, REVIEWTEXT, RATING]
est: (rows=50000)
Fragment#2
distribution: table PUBLIC.PRODUCTS in zone "Default"
executionNodes: [node_1, node_2, node_3]
partitions: [PRODUCTS=[node_1={0, 2, 5, 6, 7, 8, 9, 10, 12, 13, 20}, node_2={1, 3, 11, 19, 21, 22, 23, 24}, node_3={4, 14, 15, 16, 17, 18}]]
tree:
Sender
distribution: single
targetFragmentId: 0
est: (rows=1665)
TableScan
table: PUBLIC.PRODUCTS
predicate: =(PRODUCTNAME, ||(_UTF-8'Product_', CAST(?0):VARCHAR CHARACTER SET "UTF-8"))
fieldNames: [PRODUCTID, PRODUCTNAME]
est: (rows=1665)
Fragment#3
distribution: table PUBLIC.USERS in zone "Default"
executionNodes: [node_1, node_2, node_3]
partitions: [USERS=[node_1={0, 2, 5, 6, 7, 8, 9, 10, 12, 13, 20}, node_2={1, 3, 11, 19, 21, 22, 23, 24}, node_3={4, 14, 15, 16, 17, 18}]]
tree:
Sender
distribution: single
targetFragmentId: 0
est: (rows=10000)
TableScan
table: PUBLIC.USERS
fieldNames: [USERID, USERNAME]
est: (rows=10000)</pre>
</div>
</div>
<div class="paragraph">
<p>where:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><strong>Fragment#0</strong> means fragment with id=0</p>
</li>
<li>
<p>A <strong>root</strong> marks a fragment which is considered as root fragment, i.e. a fragment which represents user&#8217;s cursor</p>
</li>
<li>
<p>A <strong>distribution</strong> attribute provides an insight into which mapping strategy was applied to this particular fragment</p>
</li>
<li>
<p>A <strong>executionNodes</strong> attribute provides a list of nodes this fragment will be executed on</p>
</li>
<li>
<p>A <strong>partitions</strong> attribute provides an insight into which partitions of which tables will be read from which nodes</p>
</li>
<li>
<p>A <strong>tree</strong> attribute specifies which part of the relational tree corresponds to this fragment</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>The output above shows how the query is broken into multiple execution fragments and distributed across the cluster. It gives insight into both the logical execution plan and how it maps to the physical topology.</p>
</div>
<div class="paragraph">
<p>The query starts execution in <strong>Fragment#0</strong>, which serves as the root of the plan — this is where the final result is produced. It runs on a single node (<code>node_1</code>) and contains the main logic of the query, including the projection and two nested hash joins. Instead of scanning tables directly, it receives data from other fragments through <code>Receiver</code> operators. These incoming streams correspond to the <code>REVIEWS</code>, <code>PRODUCTS</code>, and <code>USERS</code> tables.</p>
</div>
<div class="paragraph">
<p>The actual table scans happen in <strong>Fragments 1 through 3</strong>, each responsible for one of the involved tables. These fragments operate in parallel across the cluster. Each performs a scan on its respective table and then sends the results back to Fragment#0.</p>
</div>
<div class="ulist">
<ul>
<li>
<p><strong>Fragment#1</strong> handles the <code>REVIEWS</code> table. It runs on all nodes and uses a random distribution strategy. Data is partitioned across nodes, and after scanning the table, results are sent upstream.</p>
</li>
<li>
<p><strong>Fragment#2</strong> is in charge of the <code>PRODUCTS</code> table. It also spans all nodes but follows a zone-based distribution linked to the table&#8217;s partitioning. There&#8217;s a filter applied to <code>PRODUCTNAME</code>, which limits the amount of data sent to the root.</p>
</li>
<li>
<p><strong>Fragment#3</strong> covers the <code>USERS</code> table. Like the others, it’s distributed and reads from table partitions spread across the cluster.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>Each fragment includes metadata such as the nodes it&#8217;s executed on, how data is partitioned, and how results are sent between fragments. This layout provides a clear view of not only how the query is logically processed, but also how the workload is split and coordinated in a distributed environment.</p>
</div>
</div>
</div>
</div>
<div class="copyright">
© 2025 The Apache Software Foundation.<br/>
Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation.
</div>
</article>
<nav class="right-nav" data-swiftype-index='false'>
<div class="toc-wrapper">
<ul class="sectlevel1">
<li><a href="#syntax">Syntax</a></li>
<li><a href="#understanding-the-output">Understanding The Output</a>
<ul class="sectlevel2">
<li><a href="#operator-naming">Operator Naming</a></li>
<li><a href="#hierarchical-plan-structure">Hierarchical Plan Structure</a></li>
</ul>
</li>
<li><a href="#examples">Examples</a>
<ul class="sectlevel2">
<li><a href="#example-complex-join">Example: Complex Join</a></li>
<li><a href="#example-query-mapping">Example: Query Mapping</a></li>
</ul>
</li>
</ul>
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