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From bridg...@apache.org
Subject [26/51] [partial] drill-site git commit: site update
Date Fri, 24 Feb 2017 21:44:44 GMT
http://git-wip-us.apache.org/repos/asf/drill-site/blob/d9845dc4/docs/aggregate-and-aggregate-statistical/index.html
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-                <li class="toctree-l3 current"><a class="reference internal" href="/docs/aggregate-and-aggregate-statistical/">Aggregate and Aggregate Statistical</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/set/">SET</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/drop-view/">DROP VIEW</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/from-clause/">FROM Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/group-by-clause/">GROUP BY Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/having-clause/">HAVING Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/limit-clause/">LIMIT Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/offset-clause/">OFFSET Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/order-by-clause/">ORDER BY Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/union-set-operator/">UNION Set Operator</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/where-clause/">WHERE Clause</a></li>
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-                <li class="toctree-l3"><a class="reference internal" href="/docs/with-clause/">WITH Clause</a></li>
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-        <h2 id="aggregate-functions">Aggregate Functions</h2>
-
-<p>The following tables list the aggregate and aggregate statistical functions that you can use in 
-Drill queries:</p>
-
-<table><thead>
-<tr>
-<th><strong>Function</strong></th>
-<th><strong>Argument Type</strong></th>
-<th><strong>Return Type</strong></th>
-</tr>
-</thead><tbody>
-<tr>
-<td>AVG(expression)</td>
-<td>SMALLINT, INTEGER, BIGINT, FLOAT, DOUBLE, DECIMAL, INTERVAL</td>
-<td>DECIMAL for any integer-type argument, DOUBLE for a floating-point argument, otherwise the same as the argument data type</td>
-</tr>
-<tr>
-<td>COUNT(*)</td>
-<td><em>-</em></td>
-<td>BIGINT</td>
-</tr>
-<tr>
-<td>COUNT([DISTINCT] expression)</td>
-<td>any</td>
-<td>BIGINT</td>
-</tr>
-<tr>
-<td>MAX(expression)</td>
-<td>BINARY, DECIMAL, VARCHAR, DATE, TIME, or TIMESTAMP</td>
-<td>same as argument type</td>
-</tr>
-<tr>
-<td>MIN(expression)</td>
-<td>BINARY, DECIMAL, VARCHAR, DATE, TIME, or TIMESTAMP</td>
-<td>same as argument type</td>
-</tr>
-<tr>
-<td>SUM(expression)</td>
-<td>SMALLINT, INTEGER, BIGINT, FLOAT, DOUBLE, DECIMAL, INTERVAL</td>
-<td>BIGINT for SMALLINT or INTEGER arguments, DECIMAL for BIGINT arguments, DOUBLE for floating-point arguments, otherwise the same as the argument data type</td>
-</tr>
-</tbody></table>
-
-<p>* In this release, Drill disables the DECIMAL data type, including casting to DECIMAL and reading DECIMAL types from Parquet and Hive. You can <a href="docs/supported-data-types/#enabling-the-decimal-type">enable the DECIMAL type</a>, but this is not recommended.</p>
-
-<p>AVG, COUNT, MIN, MAX, and SUM accept ALL and DISTINCT keywords. The default is ALL.</p>
-
-<p>These examples of aggregate functions use the <code>cp</code> storage plugin to access a the <a href="/docs/querying-json-files/"><code>employee.json</code></a> file installed with Drill. By default, JSON reads numbers as double-precision floating point numbers. These examples assume that you are using the default option <a href="/docs/json-data-model/#handling-type-differences">all_text_mode</a> set to false.</p>
-
-<h2 id="avg">AVG</h2>
-
-<p>Averages a column of all records in a data source. Averages a column of one or more groups of records. Which records to include in the calculation can be based on a condition.</p>
-
-<h3 id="avg-syntax">AVG Syntax</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT AVG([ALL | DISTINCT] aggregate_expression)
-FROM tables
-WHERE conditions;
-
-SELECT expression1, expression2, ... expression_n,
-       AVG([ALL | DISTINCT] aggregate_expression)
-FROM tables
-WHERE conditions
-GROUP BY expression1, expression2, ... expression_n;
-</code></pre></div>
-<p>Expressions listed within the AVG function and must be included in the GROUP BY clause. </p>
-
-<h3 id="avg-examples">AVG Examples</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">ALTER SESSION SET `store.json.all_text_mode` = false;
-+-------+------------------------------------+
-|  ok   |              summary               |
-+-------+------------------------------------+
-| true  | store.json.all_text_mode updated.  |
-+-------+------------------------------------+
-1 row selected (0.073 seconds)
-</code></pre></div>
-<p>Take a look at the salaries of employees having IDs 1139, 1140, and 1141. These are the salaries that subsequent examples will average and sum.</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT * FROM cp.`employee.json` WHERE employee_id IN (1139, 1140, 1141);
-+--------------+------------------+-------------+------------+--------------+--------------------------+-----------+----------------+-------------+------------------------+-------------+----------------+------------------+-----------------+---------+-----------------------+
-| employee_id  |    full_name     | first_name  | last_name  | position_id  |      position_title      | store_id  | department_id  | birth_date  |       hire_date        |   salary    | supervisor_id  | education_level  | marital_status  | gender  |    management_role    |
-+--------------+------------------+-------------+------------+--------------+--------------------------+-----------+----------------+-------------+------------------------+-------------+----------------+------------------+-----------------+---------+-----------------------+
-| 1139         | Jeanette Belsey  | Jeanette    | Belsey     | 12           | Store Assistant Manager  | 18        | 11             | 1972-05-12  | 1998-01-01 00:00:00.0  | 10000.0000  | 17             | Graduate Degree  | S               | M       | Store Management      |
-| 1140         | Mona Jaramillo   | Mona        | Jaramillo  | 13           | Store Shift Supervisor   | 18        | 11             | 1961-09-24  | 1998-01-01 00:00:00.0  | 8900.0000   | 1139           | Partial College  | S               | M       | Store Management      |
-| 1141         | James Compagno   | James       | Compagno   | 15           | Store Permanent Checker  | 18        | 15             | 1914-02-02  | 1998-01-01 00:00:00.0  | 6400.0000   | 1139           | Graduate Degree  | S               | M       | Store Full Time Staf  |
-+--------------+------------------+-------------+------------+--------------+--------------------------+-----------+----------------+-------------+------------------------+-------------+----------------+------------------+-----------------+---------+-----------------------+
-3 rows selected (0.284 seconds)
-</code></pre></div><div class="highlight"><pre><code class="language-text" data-lang="text">SELECT AVG(salary) FROM cp.`employee.json` WHERE employee_id IN (1139, 1140, 1141);
-+--------------------+
-|       EXPR$0       |
-+--------------------+
-| 8433.333333333334  |
-+--------------------+
-1 row selected (0.208 seconds)
-
-SELECT AVG(ALL salary) FROM cp.`employee.json` WHERE employee_id IN (1139, 1140, 1141);
-+--------------------+
-|       EXPR$0       |
-+--------------------+
-| 8433.333333333334  |
-+--------------------+
-1 row selected (0.17 seconds)
-
-SELECT AVG(DISTINCT salary) FROM cp.`employee.json`;
-+---------------------+
-|       EXPR$0        |
-+---------------------+
-| 12773.333333333334  |
-+---------------------+
-1 row selected (0.384 seconds)
-</code></pre></div><div class="highlight"><pre><code class="language-text" data-lang="text">SELECT education_level, AVG(salary) FROM cp.`employee.json` GROUP BY education_level;
-+----------------------+---------------------+
-|   education_level    |       EXPR$1        |
-+----------------------+---------------------+
-| Graduate Degree      | 4392.823529411765   |
-| Bachelors Degree     | 4492.404181184669   |
-| Partial College      | 4047.1180555555557  |
-| High School Degree   | 3516.1565836298932  |
-| Partial High School  | 3511.0852713178297  |
-+----------------------+---------------------+
-5 rows selected (0.495 seconds)
-</code></pre></div>
-<h2 id="count">COUNT</h2>
-
-<p>Returns the number of rows that match the given criteria.</p>
-
-<h3 id="count-syntax">COUNT Syntax</h3>
-
-<p><code>SELECT COUNT([DISTINCT | ALL] column) FROM . . .</code><br>
-<code>SELECT COUNT(*) FROM . . .</code>  </p>
-
-<ul>
-<li>column<br>
-Returns the number of values of the specified column.<br></li>
-<li>DISTINCT column<br>
-Returns the number of distinct values in the column.<br></li>
-<li>ALL column<br>
-Returns the number of values of the specified column.<br></li>
-<li>* (asterisk)
-Returns the number of records in the table.</li>
-</ul>
-
-<h3 id="count-examples">COUNT Examples</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT COUNT(DISTINCT salary) FROM cp.`employee.json`;
-+---------+
-| EXPR$0  |
-+---------+
-| 48      |
-+---------+
-1 row selected (0.159 seconds)
-
-SELECT COUNT(ALL salary) FROM cp.`employee.json`;
-+---------+
-| EXPR$0  |
-+---------+
-| 1155    |
-+---------+
-1 row selected (0.106 seconds)
-
-SELECT COUNT(salary) FROM cp.`employee.json`;
-+---------+
-| EXPR$0  |
-+---------+
-| 1155    |
-+---------+
-1 row selected (0.102 seconds)
-
-SELECT COUNT(*) FROM cp.`employee.json`;
-+---------+
-| EXPR$0  |
-+---------+
-| 1155    |
-+---------+
-1 row selected (0.174 seconds)
-</code></pre></div>
-<h2 id="min-and-max-functions">MIN and MAX Functions</h2>
-
-<p>These functions return the smallest and largest values of the selected columns, respectively.</p>
-
-<h3 id="min-and-max-syntax">MIN and MAX Syntax</h3>
-
-<p>MIN(column)<br>
-MAX(column)</p>
-
-<h3 id="min-and-max-examples">MIN and MAX Examples</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT MIN(salary) FROM cp.`employee.json`;
-+---------+
-| EXPR$0  |
-+---------+
-| 20.0    |
-+---------+
-1 row selected (0.138 seconds)
-
-SELECT MAX(salary) FROM cp.`employee.json`;
-+----------+
-|  EXPR$0  |
-+----------+
-| 80000.0  |
-+----------+
-1 row selected (0.139 seconds)
-</code></pre></div>
-<p>Use a correlated subquery to find the names and salaries of the lowest paid employees:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT full_name, SALARY FROM cp.`employee.json` WHERE salary = (SELECT MIN(salary) FROM cp.`employee.json`);
-+------------------------+---------+
-|       full_name        | SALARY  |
-+------------------------+---------+
-| Leopoldo Renfro        | 20.0    |
-| Donna Brockett         | 20.0    |
-| Laurie Anderson        | 20.0    |
-. . .
-</code></pre></div>
-<h2 id="sum-function">SUM Function</h2>
-
-<p>Returns the total of a numeric column.</p>
-
-<h3 id="sum-syntax">SUM syntax</h3>
-
-<p><code>SUM(column)</code></p>
-
-<h3 id="examples">Examples</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">SELECT SUM(ALL salary) FROM cp.`employee.json`;
-+------------+
-|   EXPR$0   |
-+------------+
-| 4642640.0  |
-+------------+
-1 row selected (0.123 seconds)
-
-SELECT SUM(DISTINCT salary) FROM cp.`employee.json`;
-+-----------+
-|  EXPR$0   |
-+-----------+
-| 613120.0  |
-+-----------+
-1 row selected (0.309 seconds)
-
-SELECT SUM(salary) FROM cp.`employee.json` WHERE employee_id IN (1139, 1140, 1141);
-+----------+
-|  EXPR$0  |
-+----------+
-| 25300.0  |
-+----------+
-1 row selected (1.995 seconds)
-</code></pre></div>
-<h2 id="aggregate-statistical-functions">Aggregate Statistical Functions</h2>
-
-<p>Drill provides following aggregate statistics functions:</p>
-
-<ul>
-<li>stddev(expression)<br>
-An alias for stddev_samp</li>
-<li>stddev_pop(expression)
-Population standard deviate of input values</li>
-<li>stddev_samp(expression)
-Sample standard deviate of input values</li>
-<li>variance(expression)
-An alias for var_samp</li>
-<li>var_pop(expression)
-Population variance of input values (the population standard deviated squared)</li>
-<li>var_samp(expression)
-Sample variance of input values (sample standard deviation squared)</li>
-</ul>
-
-<p>These functions take a SMALLINT, INTEGER, BIGINT, FLOAT, DOUBLE, or DECIMAL expression as the argument. If the expression is FLOAT, the function returns  DOUBLE; otherwise, the function returns DECIMAL. As previously mentioned, DECIMAL is disabled. You can <a href="docs/supported-data-types/#enabling-the-decimal-type">enable the DECIMAL type</a>, but this is not recommended.</p>
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