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From bridg...@apache.org
Subject [25/51] [partial] drill-site git commit: Cleaning up deleted content.
Date Sat, 25 Feb 2017 00:15:34 GMT
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-    <li><a href="/docs/learn-drill-with-the-mapr-sandbox/">Learn Drill with the MapR Sandbox</a></li>
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-  <li>Lession 2: Run Queries with ANSI SQL</li>
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-    <div class="main-content">
-
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-        <a class="edit-link" href="https://github.com/apache/drill/blob/gh-pages/_docs/tutorials/learn-drill-with-the-mapr-sandbox/040-lesson-2-run-queries-with-ansi-sql.md" target="_blank"><i class="fa fa-pencil-square-o"></i></a>
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-        <h1>Lession 2: Run Queries with ANSI SQL</h1>
-
-      </div>
-
-      <link href="/css/docpage.css" rel="stylesheet" type="text/css">
-
-      <div class="int_text" align="left">
-        
-          <h2 id="goal">Goal</h2>
-
-<p>This lesson shows how to do some standard SQL analysis in Apache Drill: for
-example, summarizing data by using simple aggregate functions and connecting
-data sources by using joins. Note that Apache Drill provides ANSI SQL support,
-not a “SQL-like” interface.</p>
-
-<h2 id="queries-in-this-lesson">Queries in This Lesson</h2>
-
-<p>Now that you know what the data sources look like in their raw form, using
-select * queries, try running some simple but more useful queries on each data
-source. These queries demonstrate how Drill supports ANSI SQL constructs and
-also how you can combine data from different data sources in a single SELECT
-statement.</p>
-
-<ul>
-<li>Show an aggregate query on a single file or table. Use GROUP BY, WHERE, HAVING, and ORDER BY clauses.</li>
-<li>Perform joins between Hive, MapR-DB, and file system data sources.</li>
-<li>Use table and column aliases.</li>
-<li>Create a Drill view.</li>
-</ul>
-
-<h2 id="aggregation">Aggregation</h2>
-
-<h3 id="set-the-schema-to-hive:">Set the schema to hive:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; use hive;
-+------------+------------+
-|     ok     |  summary   |
-+------------+------------+
-| true       | Default schema changed to &#39;hive&#39; |
-+------------+------------+
-1 row selected
-</code></pre></div>
-<h3 id="return-sales-totals-by-month:">Return sales totals by month:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select `month`, sum(order_total)
-from orders group by `month` order by 2 desc;
-+------------+------------+
-| month | EXPR$1 |
-+------------+------------+
-| June | 950481 |
-| May | 947796 |
-| March | 836809 |
-| April | 807291 |
-| July | 757395 |
-| October | 676236 |
-| August | 572269 |
-| February | 532901 |
-| September | 373100 |
-| January | 346536 |
-+------------+------------+
-</code></pre></div>
-<p>Drill supports SQL aggregate functions such as SUM, MAX, AVG, and MIN.
-Standard SQL clauses work in the same way in Drill queries as in relational
-database queries.</p>
-
-<p>Note that back ticks are required for the “month” column only because “month”
-is a reserved word in SQL.</p>
-
-<h3 id="return-the-top-20-sales-totals-by-month-and-state:">Return the top 20 sales totals by month and state:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select `month`, state, sum(order_total) as sales from orders group by `month`, state
-order by 3 desc limit 20;
-+------------+------------+------------+
-|   month    |   state    |   sales    |
-+------------+------------+------------+
-| May        | ca         | 119586     |
-| June       | ca         | 116322     |
-| April      | ca         | 101363     |
-| March      | ca         | 99540      |
-| July       | ca         | 90285      |
-| October    | ca         | 80090      |
-| June       | tx         | 78363      |
-| May        | tx         | 77247      |
-| March      | tx         | 73815      |
-| August     | ca         | 71255      |
-| April      | tx         | 68385      |
-| July       | tx         | 63858      |
-| February   | ca         | 63527      |
-| June       | fl         | 62199      |
-| June       | ny         | 62052      |
-| May        | fl         | 61651      |
-| May        | ny         | 59369      |
-| October    | tx         | 55076      |
-| March      | fl         | 54867      |
-| March      | ny         | 52101      |
-+------------+------------+------------+
-20 rows selected
-</code></pre></div>
-<p>Note the alias for the result of the SUM function. Drill supports column
-aliases and table aliases.</p>
-
-<h2 id="having-clause">HAVING Clause</h2>
-
-<p>This query uses the HAVING clause to constrain an aggregate result.</p>
-
-<h3 id="set-the-workspace-to-dfs.clicks">Set the workspace to dfs.clicks</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; use dfs.clicks;
-+------------+------------+
-|     ok     |  summary   |
-+------------+------------+
-| true       | Default schema changed to &#39;dfs.clicks&#39; |
-+------------+------------+
-1 row selected
-</code></pre></div>
-<h3 id="return-total-number-of-clicks-for-devices-that-indicate-high-click-throughs:">Return total number of clicks for devices that indicate high click-throughs:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select t.user_info.device, count(*) from `clicks/clicks.json` t 
-group by t.user_info.device
-having count(*) &gt; 1000;
-+------------+------------+
-|   EXPR$0   |   EXPR$1   |
-+------------+------------+
-| IOS5       | 11814      |
-| AOS4.2     | 5986       |
-| IOS6       | 4464       |
-| IOS7       | 3135       |
-| AOS4.4     | 1562       |
-| AOS4.3     | 3039       |
-+------------+------------+
-</code></pre></div>
-<p>The aggregate is a count of the records for each different mobile device in
-the clickstream data. Only the activity for the devices that registered more
-than 1000 transactions qualify for the result set.</p>
-
-<h2 id="union-operator">UNION Operator</h2>
-
-<p>Use the same workspace as before (dfs.clicks).</p>
-
-<h3 id="combine-clicks-activity-from-before-and-after-the-marketing-campaign">Combine clicks activity from before and after the marketing campaign</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select t.trans_id transaction, t.user_info.cust_id customer from `clicks/clicks.campaign.json` t 
-union all 
-select u.trans_id, u.user_info.cust_id  from `clicks/clicks.json` u limit 5;
-+-------------+------------+
-| transaction |  customer  |
-+-------------+------------+
-| 35232       | 18520      |
-| 31995       | 17182      |
-| 35760       | 18228      |
-| 37090       | 17015      |
-| 37838       | 18737      |
-+-------------+------------+
-</code></pre></div>
-<p>This UNION ALL query returns rows that exist in two files (and includes any
-duplicate rows from those files): <code>clicks.campaign.json</code> and <code>clicks.json</code>.</p>
-
-<h2 id="subqueries">Subqueries</h2>
-
-<h3 id="set-the-workspace-to-hive:">Set the workspace to hive:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; use hive;
-+------------+------------+
-|     ok     |  summary   |
-+------------+------------+
-| true       | Default schema changed to &#39;hive&#39; |
-+------------+------------+
-</code></pre></div>
-<h3 id="compare-order-totals-across-states:">Compare order totals across states:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select o1.cust_id, sum(o1.order_total) as ny_sales,
-(select sum(o2.order_total) from hive.orders o2
-where o1.cust_id=o2.cust_id and state=&#39;ca&#39;) as ca_sales
-from hive.orders o1 where o1.state=&#39;ny&#39; group by o1.cust_id
-order by cust_id limit 20;
-+------------+------------+------------+
-|  cust_id   |  ny_sales  |  ca_sales  |
-+------------+------------+------------+
-| 1001       | 72         | 47         |
-| 1002       | 108        | 198        |
-| 1003       | 83         | null       |
-| 1004       | 86         | 210        |
-| 1005       | 168        | 153        |
-| 1006       | 29         | 326        |
-| 1008       | 105        | 168        |
-| 1009       | 443        | 127        |
-| 1010       | 75         | 18         |
-| 1012       | 110        | null       |
-| 1013       | 19         | null       |
-| 1014       | 106        | 162        |
-| 1015       | 220        | 153        |
-| 1016       | 85         | 159        |
-| 1017       | 82         | 56         |
-| 1019       | 37         | 196        |
-| 1020       | 193        | 165        |
-| 1022       | 124        | null       |
-| 1023       | 166        | 149        |
-| 1024       | 233        | null       |
-+------------+------------+------------+
-</code></pre></div>
-<p>This example demonstrates Drill support for correlated subqueries. This query
-uses a subquery in the select list and correlates the result of the subquery
-with the outer query, using the cust_id column reference. The subquery returns
-the sum of order totals for California, and the outer query returns the
-equivalent sum, for the same cust_id, for New York.</p>
-
-<p>The result set is sorted by the cust_id and presents the sales totals side by
-side for easy comparison. Null values indicate customer IDs that did not
-register any sales in that state.</p>
-
-<h2 id="cast-function">CAST Function</h2>
-
-<h3 id="use-the-maprdb-workspace:">Use the maprdb workspace:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; use maprdb;
-+------------+------------+
-|     ok     |  summary   |
-+------------+------------+
-| true       | Default schema changed to &#39;maprdb&#39; |
-+------------+------------+
-1 row selected
-</code></pre></div>
-<h3 id="return-customer-data-with-appropriate-data-types">Return customer data with appropriate data types</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select cast(row_key as int) as cust_id, cast(t.personal.name as varchar(20)) as name, 
-cast(t.personal.gender as varchar(10)) as gender, cast(t.personal.age as varchar(10)) as age,
-cast(t.address.state as varchar(4)) as state, cast(t.loyalty.agg_rev as dec(7,2)) as agg_rev, 
-cast(t.loyalty.membership as varchar(20)) as membership
-from customers t limit 5;
-+------------+------------+------------+------------+------------    +------------+------------+
-|  cust_id   |    name    |   gender   |    age     |   state    |  agg_rev   | membership |
-+------------+------------+------------+------------+------------+------------+------------+
-| 10001      | &quot;Corrine Mecham&quot; | &quot;FEMALE&quot;   | &quot;15-20&quot;    | &quot;va&quot;       | 197.00     | &quot;silver&quot;   |
-| 10005      | &quot;Brittany Park&quot; | &quot;MALE&quot;     | &quot;26-35&quot;    | &quot;in&quot;       | 230.00     | &quot;silver&quot;   |
-| 10006      | &quot;Rose Lokey&quot; | &quot;MALE&quot;     | &quot;26-35&quot;    | &quot;ca&quot;       | 250.00     | &quot;silver&quot;   |
-| 10007      | &quot;James Fowler&quot; | &quot;FEMALE&quot;   | &quot;51-100&quot;   | &quot;me&quot;       | 263.00     | &quot;silver&quot;   |
-| 10010      | &quot;Guillermo Koehler&quot; | &quot;OTHER&quot;    | &quot;51-100&quot;   | &quot;mn&quot;       | 202.00     | &quot;silver&quot;   |
-+------------+------------+------------+------------+------------+------------+------------+
-5 rows selected
-</code></pre></div>
-<p>Note the following features of this query:</p>
-
-<ul>
-<li>The CAST function is required for every column in the table. This function returns the MapR-DB/HBase binary data as readable integers and strings. Alternatively, you can use CONVERT_TO/CONVERT_FROM functions to decode the string columns. CONVERT_TO/CONVERT_FROM are more efficient than CAST in most cases. Use only CONVERT_TO to convert binary types to any type other than VARCHAR.</li>
-<li>The row_key column functions as the primary key of the table (a customer ID in this case).</li>
-<li>The table alias t is required; otherwise the column family names would be parsed as table names and the query would return an error.</li>
-</ul>
-
-<h3 id="remove-the-quotes-from-the-strings:">Remove the quotes from the strings:</h3>
-
-<p>You can use the regexp_replace function to remove the quotes around the
-strings in the query results. For example, to return a state name va instead
-of “va”:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select cast(row_key as int), regexp_replace(cast(t.address.state as varchar(10)),&#39;&quot;&#39;,&#39;&#39;)
-from customers t limit 1;
-+------------+------------+
-|   EXPR$0   |   EXPR$1   |
-+------------+------------+
-| 10001      | va         |
-+------------+------------+
-1 row selected
-</code></pre></div>
-<h2 id="create-view-command">CREATE VIEW Command</h2>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; use dfs.views;
-+------------+------------+
-| ok | summary |
-+------------+------------+
-| true | Default schema changed to &#39;dfs.views&#39; |
-+------------+------------+
-</code></pre></div>
-<h3 id="use-a-mutable-workspace:">Use a mutable workspace:</h3>
-
-<p>A mutable (or writable) workspace is a workspace that is enabled for “write”
-operations. This attribute is part of the storage plugin configuration. You
-can create Drill views and tables in mutable workspaces.</p>
-
-<h3 id="create-a-view-on-a-mapr-db-table">Create a view on a MapR-DB table</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; create or replace view custview as select cast(row_key as int) as cust_id,
-cast(t.personal.name as varchar(20)) as name, 
-cast(t.personal.gender as varchar(10)) as gender, 
-cast(t.personal.age as varchar(10)) as age, 
-cast(t.address.state as varchar(4)) as state,
-cast(t.loyalty.agg_rev as dec(7,2)) as agg_rev,
-cast(t.loyalty.membership as varchar(20)) as membership
-from maprdb.customers t;
-+------------+------------+
-|     ok     |  summary   |
-+------------+------------+
-| true       | View &#39;custview&#39; replaced successfully in &#39;dfs.views&#39; schema |
-+------------+------------+
-1 row selected
-</code></pre></div>
-<p>Drill provides CREATE OR REPLACE VIEW syntax similar to relational databases
-to create views. Use the OR REPLACE option to make it easier to update the
-view later without having to remove it first. Note that the FROM clause in
-this example must refer to maprdb.customers. The MapR-DB tables are not
-directly visible to the dfs.views workspace.</p>
-
-<p>Unlike a traditional database where views typically are DBA/developer-driven
-operations, file system-based views in Drill are very lightweight. A view is
-simply a special file with a specific extension (.drill). You can store views
-even in your local file system or point to a specific workspace. You can
-specify any query against any Drill data source in the body of the CREATE VIEW
-statement.</p>
-
-<p>Drill provides a decentralized metadata model. Drill is able to query metadata
-defined in data sources such as Hive, HBase, and the file system. Drill also
-supports the creation of metadata in the file system.</p>
-
-<h3 id="query-data-from-the-view:">Query data from the view:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from custview limit 1;
-+------------+------------+------------+------------+------------+------------+------------+
-|  cust_id   |    name    |   gender   |    age     |   state    |  agg_rev   | membership |
-+------------+------------+------------+------------+------------+------------+------------+
-| 10001      | &quot;Corrine Mecham&quot; | &quot;FEMALE&quot;   | &quot;15-20&quot;    | &quot;va&quot;       | 197.00     | &quot;silver&quot;   |
-+------------+------------+------------+------------+------------+------------+------------+
-</code></pre></div>
-<p>Once the users get an idea on what data is available by exploring it directly
-from file system , views can be used as a way to take the data in downstream
-tools like Tableau, Microstrategy etc for downstream analysis and
-visualization. For these tools, a view appears simply as a “table” with
-selectable “columns” in it.</p>
-
-<h2 id="query-across-data-sources">Query Across Data Sources</h2>
-
-<p>Continue using <code>dfs.views</code> for this query.</p>
-
-<h3 id="join-the-customers-view-and-the-orders-table:">Join the customers view and the orders table:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select membership, sum(order_total) as sales from hive.orders, custview
-where orders.cust_id=custview.cust_id
-group by membership order by 2;
-+------------+------------+
-| membership |   sales    |
-+------------+------------+
-| &quot;basic&quot;    | 380665     |
-| &quot;silver&quot;   | 708438     |
-| &quot;gold&quot;     | 2787682    |
-+------------+------------+
-3 rows selected
-</code></pre></div>
-<p>In this query, we are reading data from a MapR-DB table (represented by
-custview) and combining it with the order information in Hive. When doing
-cross data source queries such as this, you need to use fully qualified
-table/view names. For example, the orders table is prefixed by “hive,” which
-is the storage plugin name registered with Drill. We are not using any prefix
-for “custview” because we explicitly switched the dfs.views workspace where
-custview is stored.</p>
-
-<p>Note: If the results of any of your queries appear to be truncated because the
-rows are wide, set the maximum width of the display to 10000:</p>
-
-<p>Do not use a semicolon for this SET command.</p>
-
-<h3 id="join-the-customers,-orders,-and-clickstream-data:">Join the customers, orders, and clickstream data:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select custview.membership, sum(orders.order_total) as sales from hive.orders, custview,
-dfs.`/mapr/demo.mapr.com/data/nested/clicks/clicks.json` c 
-where orders.cust_id=custview.cust_id and orders.cust_id=c.user_info.cust_id 
-group by custview.membership order by 2;
-+------------+------------+
-| membership |   sales    |
-+------------+------------+
-| &quot;basic&quot;    | 372866     |
-| &quot;silver&quot;   | 728424     |
-| &quot;gold&quot;     | 7050198    |
-+------------+------------+
-3 rows selected
-</code></pre></div>
-<p>This three-way join selects from three different data sources in one query:</p>
-
-<ul>
-<li>hive.orders table</li>
-<li>custview (a view of the HBase customers table)</li>
-<li>clicks.json file</li>
-</ul>
-
-<p>The join column for both sets of join conditions is the cust_id column. The
-views workspace is used for this query so that custview can be accessed. The
-hive.orders table is also visible to the query.</p>
-
-<p>However, note that the JSON file is not directly visible from the views
-workspace, so the query specifies the full path to the file:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">dfs.`/mapr/demo.mapr.com/data/nested/clicks/clicks.json`
-</code></pre></div>
-<h2 id="what&#39;s-next">What&#39;s Next</h2>
-
-<p>Go to <a href="/docs/lession-3-run-queries-on-complex-data-types">Lesson 3: Run Queries on Complex Data Types</a>. </p>
-
-      
-        
-          <div class="doc-nav">
-  
-  <span class="previous-toc"><a href="/docs/lession-1-learn-about-the-data-set/">← Lession 1: Learn about the Data Set</a></span><span class="next-toc"><a href="/docs/lession-3-run-queries-on-complex-data-types/">Lession 3: Run Queries on Complex Data Types →</a></span>
-</div>
-
-      
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