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From bridg...@apache.org
Subject [26/51] [partial] drill-site git commit: Cleaning up deleted content.
Date Sat, 25 Feb 2017 00:15:35 GMT
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-    <li><a href="/docs/learn-drill-with-the-mapr-sandbox/">Learn Drill with the MapR Sandbox</a></li>
-  
-  <li>Lession 1: Learn about the Data Set</li>
-</nav>
-
-  <div class="main-content-wrapper">
-    <div class="main-content">
-
-      
-        <a class="edit-link" href="https://github.com/apache/drill/blob/gh-pages/_docs/tutorials/learn-drill-with-the-mapr-sandbox/030-lesson-1-learn-about-the-data-set.md" target="_blank"><i class="fa fa-pencil-square-o"></i></a>
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-      <div class="int_title">
-        <h1>Lession 1: Learn about the Data Set</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 is simply about discovering what data is available, in what
-format, using simple SQL SELECT statements. Drill is capable of analyzing data
-without prior knowledge or definition of its schema. This means that you can
-start querying data immediately (and even as it changes), regardless of its
-format.</p>
-
-<p>The data set for the tutorial consists of:</p>
-
-<ul>
-<li>Transactional data: stored as a Hive table</li>
-<li>Product catalog and master customer data: stored as MapR-DB tables</li>
-<li>Clickstream and logs data: stored in the MapR file system as JSON files</li>
-</ul>
-
-<h2 id="queries-in-this-lesson">Queries in This Lesson</h2>
-
-<p>This lesson consists of select * queries on each data source.</p>
-
-<h2 id="before-you-begin">Before You Begin</h2>
-
-<h3 id="start-sqlline">Start SQLLine</h3>
-
-<p>If SQLLine is not already started, use a Terminal or Command window to log
-into the demo VM as root, then enter <code>sqlline</code>, as described in <a href="/docs/getting-to-know-the-drill-sandbox">&quot;Getting to Know the Sandbox&quot;</a>:</p>
-
-<p>You can run queries from the <code>sqlline</code> prompt to complete the tutorial. To exit from
-SQLLine, type:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; !quit
-</code></pre></div>
-<p>Examples in this tutorial use SQLLine. You can also execute queries using the Drill Web UI.</p>
-
-<h3 id="list-the-available-workspaces-and-databases:">List the available workspaces and databases:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; show databases;
-+-------------+
-| SCHEMA_NAME |
-+-------------+
-| hive.default |
-| dfs.default |
-| dfs.logs    |
-| dfs.root    |
-| dfs.views   |
-| dfs.clicks  |
-| dfs.tmp     |
-| sys         |
-| maprdb      |
-| cp.default  |
-| INFORMATION_SCHEMA |
-+-------------+
-12 rows selected
-</code></pre></div>
-<p>This command exposes all the metadata available from the storage
-plugins configured with Drill as a set of schemas. The Hive and
-MapR-DB databases, file system, and other data are configured in the file system. As
-you run queries in the tutorial, you will switch among these schemas by
-submitting the USE command. This behavior resembles the ability to use
-different database schemas (namespaces) in a relational database system.</p>
-
-<h2 id="query-hive-tables">Query Hive Tables</h2>
-
-<p>The orders table is a six-column Hive table defined in the Hive metastore.
-This is a Hive external table pointing to the data stored in flat files on the
-MapR file system. The orders table contains 122,000 rows.</p>
-
-<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; |
-+------------+------------+
-</code></pre></div>
-<p>You will run the USE command throughout this tutorial. The USE command sets
-the schema for the current session.</p>
-
-<h3 id="describe-the-table:">Describe the table:</h3>
-
-<p>You can use the DESCRIBE command to show the columns and data types for a Hive
-table:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; describe orders;
-+-------------+------------+-------------+
-| COLUMN_NAME | DATA_TYPE  | IS_NULLABLE |
-+-------------+------------+-------------+
-| order_id    | BIGINT     | YES         |
-| month       | VARCHAR    | YES         |
-| cust_id     | BIGINT     | YES         |
-| state       | VARCHAR    | YES         |
-| prod_id     | BIGINT     | YES         |
-| order_total | INTEGER    | YES         |
-+-------------+------------+-------------+
-</code></pre></div>
-<p>The DESCRIBE command returns complete schema information for Hive tables based
-on the metadata available in the Hive metastore.</p>
-
-<h3 id="select-5-rows-from-the-orders-table:">Select 5 rows from the orders table:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from orders limit 5;
-+------------+------------+------------+------------+------------+-------------+
-|  order_id  |   month    |  cust_id   |   state    |  prod_id   | order_total |
-+------------+------------+------------+------------+------------+-------------+
-| 67212      | June       | 10001      | ca         | 909        | 13          |
-| 70302      | June       | 10004      | ga         | 420        | 11          |
-| 69090      | June       | 10011      | fl         | 44         | 76          |
-| 68834      | June       | 10012      | ar         | 0          | 81          |
-| 71220      | June       | 10018      | az         | 411        | 24          |
-+------------+------------+------------+------------+------------+-------------+
-</code></pre></div>
-<p>Because orders is a Hive table, you can query the data in the same way that
-you would query the columns in a relational database table. Note the use of
-the standard LIMIT clause, which limits the result set to the specified number
-of rows. You can use LIMIT with or without an ORDER BY clause.</p>
-
-<p>Drill provides seamless integration with Hive by allowing queries on Hive
-tables defined in the metastore with no extra configuration. Note that Hive is
-not a prerequisite for Drill, but simply serves as a storage plugin or data
-source for Drill. Drill also lets users query all Hive file formats (including
-custom serdes). Additionally, any UDFs defined in Hive can be leveraged as
-part of Drill queries.</p>
-
-<p>Because Drill has its own low-latency SQL query execution engine, you can
-query Hive tables with high performance and support for interactive and ad-hoc
-data exploration.</p>
-
-<h2 id="query-mapr-db-and-hbase-tables">Query MapR-DB and HBase Tables</h2>
-
-<p>The customers and products tables are MapR-DB tables. MapR-DB is an enterprise
-in-Hadoop NoSQL database. It exposes the HBase API to support application
-development. Every MapR-DB table has a row_key, in addition to one or more
-column families. Each column family contains one or more specific columns. The
-row_key value is a primary key that uniquely identifies each row.</p>
-
-<p>Drill allows direct queries on MapR-DB and HBase tables. Unlike other SQL on
-Hadoop options, Drill requires no overlay schema definitions in Hive to work
-with this data. Think about a MapR-DB or HBase table with thousands of
-columns, such as a time-series database, and the pain of having to manage
-duplicate schemas for it in Hive!</p>
-
-<h3 id="products-table">Products Table</h3>
-
-<p>The products table has two column families.</p>
-
-<table ><colgroup><col /><col /></colgroup><tbody><tr><td ><span style="color: rgb(0,0,0);">Column Family</span></td><td ><span style="color: rgb(0,0,0);">Columns</span></td></tr><tr><td ><span style="color: rgb(0,0,0);">details</span></td><td ><span style="color: rgb(0,0,0);">name</br></span><span style="color: rgb(0,0,0);">category</span></td></tr><tr><td ><span style="color: rgb(0,0,0);">pricing</span></td><td ><span style="color: rgb(0,0,0);">price</span></td></tr></tbody></table>  
-
-<p>The products table contains 965 rows.</p>
-
-<h3 id="customers-table">Customers Table</h3>
-
-<p>The Customers table has three column families.</p>
-
-<table ><colgroup><col /><col /></colgroup><tbody><tr><td ><span style="color: rgb(0,0,0);">Column Family</span></td><td ><span style="color: rgb(0,0,0);">Columns</span></td></tr><tr><td ><span style="color: rgb(0,0,0);">address</span></td><td ><span style="color: rgb(0,0,0);">state</span></td></tr><tr><td ><span style="color: rgb(0,0,0);">loyalty</span></td><td ><span style="color: rgb(0,0,0);">agg_rev</br></span><span style="color: rgb(0,0,0);">membership</span></td></tr><tr><td ><span style="color: rgb(0,0,0);">personal</span></td><td ><span style="color: rgb(0,0,0);">age</br></span><span style="color: rgb(0,0,0);">gender</span></td></tr></tbody></table>  
-  
-
-<p>The customers table contains 993 rows.</p>
-
-<h3 id="set-the-workspace-to-maprdb:">Set the workspace to maprdb:</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; |
-+------------+------------+
-</code></pre></div>
-<h3 id="describe-the-tables:">Describe the tables:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; describe customers;
-+-------------+------------+-------------+
-| COLUMN_NAME | DATA_TYPE  | IS_NULLABLE |
-+-------------+------------+-------------+
-| row_key     | ANY        | NO          |
-| address     | (VARCHAR(1), ANY) MAP | NO          |
-| loyalty     | (VARCHAR(1), ANY) MAP | NO          |
-| personal    | (VARCHAR(1), ANY) MAP | NO          |
-+-------------+------------+-------------+
-
-0: jdbc:drill:&gt; describe products;
-+-------------+------------+-------------+
-| COLUMN_NAME | DATA_TYPE  | IS_NULLABLE |
-+-------------+------------+-------------+
-| row_key     | ANY        | NO          |
-| details     | (VARCHAR(1), ANY) MAP | NO          |
-| pricing     | (VARCHAR(1), ANY) MAP | NO          |
-+-------------+------------+-------------+
-</code></pre></div>
-<p>Unlike the Hive example, the DESCRIBE command does not return the full schema
-up to the column level. Wide-column NoSQL databases such as MapR-DB and HBase
-can be schema-less by design; every row has its own set of column name-value
-pairs in a given column family, and the column value can be of any data type,
-as determined by the application inserting the data.</p>
-
-<p>A “MAP” complex type in Drill represents this variable column name-value
-structure, and “ANY” represents the fact that the column value can be of any
-data type. Observe the row_key, which is also simply bytes and has the type
-ANY.</p>
-
-<h3 id="select-5-rows-from-the-products-table:">Select 5 rows from the products table:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from products limit 5;
-+------------+------------+------------+
-| row_key | details | pricing |
-+------------+------------+------------+
-| [B@a1a3e25 | {&quot;category&quot;:&quot;bGFwdG9w&quot;,&quot;name&quot;:&quot;IlNvbnkgbm90ZWJvb2si&quot;} | {&quot;price&quot;:&quot;OTU5&quot;} |
-| [B@103a43af | {&quot;category&quot;:&quot;RW52ZWxvcGVz&quot;,&quot;name&quot;:&quot;IzEwLTQgMS84IHggOSAxLzIgUHJlbWl1bSBEaWFnb25hbCBTZWFtIEVudmVsb3Blcw==&quot;} | {&quot;price&quot;:&quot;MT |
-| [B@61319e7b | {&quot;category&quot;:&quot;U3RvcmFnZSAmIE9yZ2FuaXphdGlvbg==&quot;,&quot;name&quot;:&quot;MjQgQ2FwYWNpdHkgTWF4aSBEYXRhIEJpbmRlciBSYWNrc1BlYXJs&quot;} | {&quot;price&quot; |
-| [B@9bcf17 | {&quot;category&quot;:&quot;TGFiZWxz&quot;,&quot;name&quot;:&quot;QXZlcnkgNDk4&quot;} | {&quot;price&quot;:&quot;Mw==&quot;} |
-| [B@7538ef50 | {&quot;category&quot;:&quot;TGFiZWxz&quot;,&quot;name&quot;:&quot;QXZlcnkgNDk=&quot;} | {&quot;price&quot;:&quot;Mw==&quot;} |
-</code></pre></div>
-<p>Given that Drill requires no up front schema definitions indicating data
-types, the query returns the raw byte arrays for column values, just as they
-are stored in MapR-DB (or HBase). Observe that the column families (details
-and pricing) have the map data type and appear as JSON strings.</p>
-
-<p>In Lesson 2, you will use CAST functions to return typed data for each column.</p>
-
-<h3 id="select-5-rows-from-the-customers-table:">Select 5 rows from the customers table:</h3>
-<div class="highlight"><pre><code class="language-text" data-lang="text">+0: jdbc:drill:&gt; select * from customers limit 5;
-+------------+------------+------------+------------+
-| row_key | address | loyalty | personal |
-+------------+------------+------------+------------+
-| [B@284bae62 | {&quot;state&quot;:&quot;Imt5Ig==&quot;} | {&quot;agg_rev&quot;:&quot;IjEwMDEtMzAwMCI=&quot;,&quot;membership&quot;:&quot;ImJhc2ljIg==&quot;} | {&quot;age&quot;:&quot;IjI2LTM1Ig==&quot;,&quot;gender&quot;:&quot;Ik1B |
-| [B@7ffa4523 | {&quot;state&quot;:&quot;ImNhIg==&quot;} | {&quot;agg_rev&quot;:&quot;IjAtMTAwIg==&quot;,&quot;membership&quot;:&quot;ImdvbGQi&quot;} | {&quot;age&quot;:&quot;IjI2LTM1Ig==&quot;,&quot;gender&quot;:&quot;IkZFTUFMRSI= |
-| [B@7d13e79 | {&quot;state&quot;:&quot;Im9rIg==&quot;} | {&quot;agg_rev&quot;:&quot;IjUwMS0xMDAwIg==&quot;,&quot;membership&quot;:&quot;InNpbHZlciI=&quot;} | {&quot;age&quot;:&quot;IjI2LTM1Ig==&quot;,&quot;gender&quot;:&quot;IkZFT |
-| [B@3a5c7df1 | {&quot;state&quot;:&quot;ImtzIg==&quot;} | {&quot;agg_rev&quot;:&quot;IjMwMDEtMTAwMDAwIg==&quot;,&quot;membership&quot;:&quot;ImdvbGQi&quot;} | {&quot;age&quot;:&quot;IjUxLTEwMCI=&quot;,&quot;gender&quot;:&quot;IkZF |
-| [B@e507726 | {&quot;state&quot;:&quot;Im5qIg==&quot;} | {&quot;agg_rev&quot;:&quot;IjAtMTAwIg==&quot;,&quot;membership&quot;:&quot;ImJhc2ljIg==&quot;} | {&quot;age&quot;:&quot;IjIxLTI1Ig==&quot;,&quot;gender&quot;:&quot;Ik1BTEUi&quot; |
-+------------+------------+------------+------------+
-</code></pre></div>
-<p>Again the table returns byte data that needs to be cast to readable data
-types.</p>
-
-<h2 id="query-the-file-system">Query the File System</h2>
-
-<p>Along with querying a data source with full schemas (such as Hive) and partial
-schemas (such as MapR-DB and HBase), Drill offers the unique capability to
-perform SQL queries directly on file system. The file system could be a local
-file system, or a distributed file system such as MapR-FS, HDFS, or S3.</p>
-
-<p>In the context of Drill, a file or a directory is considered as synonymous to
-a relational database “table.” Therefore, you can perform SQL operations
-directly on files and directories without the need for up-front schema
-definitions or schema management for any model changes. The schema is
-discovered on the fly based on the query. Drill supports queries on a variety
-of file formats including text, CSV, Parquet, and JSON.</p>
-
-<p>In this example, the clickstream data coming from the mobile/web applications
-is in JSON format. The JSON files have the following structure:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">{&quot;trans_id&quot;:31920,&quot;date&quot;:&quot;2014-04-26&quot;,&quot;time&quot;:&quot;12:17:12&quot;,&quot;user_info&quot;:{&quot;cust_id&quot;:22526,&quot;device&quot;:&quot;IOS5&quot;,&quot;state&quot;:&quot;il&quot;},&quot;trans_info&quot;:{&quot;prod_id&quot;:[174,2],&quot;purch_flag&quot;:&quot;false&quot;}}
-{&quot;trans_id&quot;:31026,&quot;date&quot;:&quot;2014-04-20&quot;,&quot;time&quot;:&quot;13:50:29&quot;,&quot;user_info&quot;:{&quot;cust_id&quot;:16368,&quot;device&quot;:&quot;AOS4.2&quot;,&quot;state&quot;:&quot;nc&quot;},&quot;trans_info&quot;:{&quot;prod_id&quot;:[],&quot;purch_flag&quot;:&quot;false&quot;}}
-{&quot;trans_id&quot;:33848,&quot;date&quot;:&quot;2014-04-10&quot;,&quot;time&quot;:&quot;04:44:42&quot;,&quot;user_info&quot;:{&quot;cust_id&quot;:21449,&quot;device&quot;:&quot;IOS6&quot;,&quot;state&quot;:&quot;oh&quot;},&quot;trans_info&quot;:{&quot;prod_id&quot;:[582],&quot;purch_flag&quot;:&quot;false&quot;}}
-</code></pre></div>
-<p>The clicks.json and clicks.campaign.json files contain metadata as part of the
-data itself (referred to as “self-describing” data). Also note that the data
-elements are complex, or nested. The initial queries below do not show how to
-unpack the nested data, but they show that easy access to the data requires no
-setup beyond the definition of a workspace.</p>
-
-<h3 id="query-nested-clickstream-data">Query nested clickstream data</h3>
-
-<h4 id="set-the-workspace-to-dfs.clicks:">Set the workspace to dfs.clicks:</h4>
-<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; |
-+------------+------------+
-</code></pre></div>
-<p>In this case, setting the workspace is a mechanism for making queries easier
-to write. When you specify a file system workspace, you can shorten references
-to files in your queries. Instead of having to provide the
-complete path to a file, you can provide the path relative to a directory
-location specified in the workspace. For example:</p>
-<div class="highlight"><pre><code class="language-text" data-lang="text">&quot;location&quot;: &quot;/mapr/demo.mapr.com/data/nested&quot;
-</code></pre></div>
-<p>Any file or directory that you want to query in this path can be referenced
-relative to this path. The clicks directory referred to in the following query
-is directly below the nested directory.</p>
-
-<h4 id="select-2-rows-from-the-clicks.json-file:">Select 2 rows from the clicks.json file:</h4>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from `clicks/clicks.json` limit 2;
-+------------+------------+------------+------------+------------+
-|  trans_id  |    date    |    time    | user_info  | trans_info |
-+------------+------------+------------+------------+------------+
-| 31920      | 2014-04-26 | 12:17:12   | {&quot;cust_id&quot;:22526,&quot;device&quot;:&quot;IOS5&quot;,&quot;state&quot;:&quot;il&quot;} | {&quot;prod_id&quot;:[174,2],&quot;purch_flag&quot;:&quot;false&quot;} |
-| 31026      | 2014-04-20 | 13:50:29   | {&quot;cust_id&quot;:16368,&quot;device&quot;:&quot;AOS4.2&quot;,&quot;state&quot;:&quot;nc&quot;} | {&quot;prod_id&quot;:[],&quot;purch_flag&quot;:&quot;false&quot;} |
-+------------+------------+------------+------------+------------+
-2 rows selected
-</code></pre></div>
-<p>Note that the FROM clause reference points to a specific file. Drill expands
-the traditional concept of a “table reference” in a standard SQL FROM clause
-to refer to a file in a local or distributed file system.</p>
-
-<p>The only special requirement is the use of back ticks to enclose the file
-path. This is necessary whenever the file path contains Drill reserved words
-or characters.</p>
-
-<h4 id="select-2-rows-from-the-campaign.json-file:">Select 2 rows from the campaign.json file:</h4>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from `clicks/clicks.campaign.json` limit 2;
-+------------+------------+------------+------------+------------+------------+
-|  trans_id  |    date    |    time    | user_info  |  ad_info   | trans_info |
-+------------+------------+------------+------------+------------+------------+
-| 35232      | 2014-05-10 | 00:13:03   | {&quot;cust_id&quot;:18520,&quot;device&quot;:&quot;AOS4.3&quot;,&quot;state&quot;:&quot;tx&quot;} | {&quot;camp_id&quot;:&quot;null&quot;} | {&quot;prod_id&quot;:[7,7],&quot;purch_flag&quot;:&quot;true&quot;} |
-| 31995      | 2014-05-22 | 16:06:38   | {&quot;cust_id&quot;:17182,&quot;device&quot;:&quot;IOS6&quot;,&quot;state&quot;:&quot;fl&quot;} | {&quot;camp_id&quot;:&quot;null&quot;} | {&quot;prod_id&quot;:[],&quot;purch_flag&quot;:&quot;false&quot;} |
-+------------+------------+------------+------------+------------+------------+
-2 rows selected
-</code></pre></div>
-<p>Notice that with a select * query, any complex data types such as maps and
-arrays return as JSON strings. You will see how to unpack this data using
-various SQL functions and operators in the next lesson.</p>
-
-<h2 id="query-logs-data">Query Logs Data</h2>
-
-<p>Unlike the previous example where we performed queries against clicks data in
-one file, logs data is stored as partitioned directories on the file system.
-The logs directory has three subdirectories:</p>
-
-<ul>
-<li>2012</li>
-<li>2013</li>
-<li>2014</li>
-</ul>
-
-<p>Each of these year directories fans out to a set of numbered month
-directories, and each month directory contains a JSON file with log records
-for that month. The total number of records in all log files is 48000.</p>
-
-<p>The files in the logs directory and its subdirectories are JSON files. There
-are many of these files, but you can use Drill to query them all as a single
-data source, or to query a subset of the files.</p>
-
-<h4 id="set-the-workspace-to-dfs.logs:">Set the workspace to dfs.logs:</h4>
-<div class="highlight"><pre><code class="language-text" data-lang="text"> 0: jdbc:drill:&gt; use dfs.logs;
-+------------+------------+
-| ok | summary |
-+------------+------------+
-| true | Default schema changed to &#39;dfs.logs&#39; |
-+------------+------------+
-</code></pre></div>
-<h4 id="select-2-rows-from-the-logs-directory:">Select 2 rows from the logs directory:</h4>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select * from logs limit 2;
-+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+----------+
-| dir0 | dir1 | trans_id | date | time | cust_id | device | state | camp_id | keywords | prod_id | purch_fl |
-+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+----------+
-| 2014 | 8 | 24181 | 08/02/2014 | 09:23:52 | 0 | IOS5 | il | 2 | wait | 128 | false |
-| 2014 | 8 | 24195 | 08/02/2014 | 07:58:19 | 243 | IOS5 | mo | 6 | hmm | 107 | false |
-+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+------------+----------+
-</code></pre></div>
-<p>Note that this is flat JSON data. The dfs.clicks workspace location property
-points to a directory that contains the logs directory, making the FROM clause
-reference for this query very simple. You do not have to refer to the complete
-directory path on the file system.</p>
-
-<p>The column names dir0 and dir1 are special Drill variables that identify
-subdirectories below the logs directory. In Lesson 3, you will do more complex
-queries that leverage these dynamic variables.</p>
-
-<h4 id="find-the-total-number-of-rows-in-the-logs-directory-(all-files):">Find the total number of rows in the logs directory (all files):</h4>
-<div class="highlight"><pre><code class="language-text" data-lang="text">0: jdbc:drill:&gt; select count(*) from logs;
-+------------+
-| EXPR$0 |
-+------------+
-| 48000 |
-+------------+
-</code></pre></div>
-<p>This query traverses all of the files in the logs directory and its
-subdirectories to return the total number of rows in those files.</p>
-
-<h1 id="what&#39;s-next">What&#39;s Next</h1>
-
-<p>Go to <a href="/docs/lession-2-run-queries-with-ansi-sql">Lesson 2: Run Queries with ANSI
-SQL</a>.</p>
-
-      
-        
-          <div class="doc-nav">
-  
-  <span class="previous-toc"><a href="/docs/getting-to-know-the-drill-sandbox/">← Getting to Know the Drill Sandbox</a></span><span class="next-toc"><a href="/docs/lession-2-run-queries-with-ansi-sql/">Lession 2: Run Queries with ANSI SQL →</a></span>
-</div>
-
-      
-      </div>
-    </div>
-  </div>
-
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