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+ <li class="toctree-l3"><a class="reference internal" href="/docs/querying-complex-data-introduction/">Querying Complex Data Introduction</a></li>
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+ <li class="toctree-l3"><a class="reference internal" href="/docs/date-time-and-timestamp/">Date, Time, and Timestamp</a></li>
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+ <li class="toctree-l3"><a class="reference internal" href="/docs/handling-different-data-types/">Handling Different Data Types</a></li>
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+ <li class="toctree-l3"><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/kvgen/">KVGEN</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/repeated-count/">REPEATED_COUNT</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/repeated-contains/">REPEATED_CONTAINS</a></li>
+
+ </ul>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/query-directory-functions/">Query Directory Functions</a></li>
+
+
+
+ <li class="toctree-l2"><a href="javascript: void(0);">SQL Commands</a></li>
+ <ul style="display: none">
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/supported-sql-commands/">Supported SQL Commands</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/alter-session-command/">ALTER SESSION Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/alter-system-command/">ALTER SYSTEM Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/create-table-as-ctas-command/">CREATE TABLE AS (CTAS) Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/create-view-command/">CREATE VIEW Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/describe-command/">DESCRIBE Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/explain-commands/">EXPLAIN Commands</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/select-statements/">SELECT Statements</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/show-databases-and-show-schemas-command/">SHOW DATABASES and SHOW SCHEMAS Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/show-files-command/">SHOW FILES Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/show-tables-command/">SHOW TABLES Command</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/use-command/">USE Command</a></li>
+
+ </ul>
+
+
+
+ <li class="toctree-l2"><a href="javascript: void(0);">SQL Conditional Expressions</a></li>
+ <ul style="display: none">
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/case/">CASE</a></li>
+
+ </ul>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/reserved-keywords/">Reserved Keywords</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/sql-extensions/">SQL Extensions</a></li>
+
+
+ </ul>
+
+
+
+ <li class="toctree-l1"><a href="javascript: void(0);">Data Sources and File Formats</a></li>
+ <ul style="display: none">
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/data-sources-and-file-formats-introduction/">Data Sources and File Formats Introduction</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/hive-to-drill-data-type-mapping/">Hive-to-Drill Data Type Mapping</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/deploying-and-using-a-hive-udf/">Deploying and Using a Hive UDF</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/parquet-format/">Parquet Format</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/json-data-model/">JSON Data Model</a></li>
+
+
+ </ul>
+
+
+
+ <li class="toctree-l1"><a href="javascript: void(0);">Develop Custom Functions</a></li>
+ <ul style="display: none">
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/develop-custom-functions-introduction/">Develop Custom Functions Introduction</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/develop-a-simple-function/">Develop a Simple Function</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/developing-an-aggregate-function/">Developing an Aggregate Function</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/adding-custom-functions-to-drill/">Adding Custom Functions to Drill</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/using-custom-functions-in-queries/">Using Custom Functions in Queries</a></li>
+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/custom-function-interfaces/">Custom Function Interfaces</a></li>
+
+
+ </ul>
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+ <ul style="display: none">
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/compiling-drill-from-source/">Compiling Drill from Source</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/drill-patch-review-tool/">Drill Patch Review Tool</a></li>
+
+ </ul>
+
+
+
+ <li class="toctree-l2"><a href="javascript: void(0);">Contribute to Drill</a></li>
+ <ul style="display: none">
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/apache-drill-contribution-guidelines/">Apache Drill Contribution Guidelines</a></li>
+
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+
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+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/rpc-overview/">RPC Overview</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/query-stages/">Query Stages</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/useful-research/">Useful Research</a></li>
+
+ <li class="toctree-l3"><a class="reference internal" href="/docs/value-vectors/">Value Vectors</a></li>
+
+ </ul>
+
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+
+
+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/apache-drill-m1-release-notes-apache-drill-alpha/">Apache Drill M1 Release Notes (Apache Drill Alpha)</a></li>
+
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+
+ <li class="toctree-l2"><a class="reference internal" href="/docs/apache-drill-0-6-0-release-notes/">Apache Drill 0.6.0 Release Notes</a></li>
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+ <li class="toctree-l2"><a class="reference internal" href="/docs/apache-drill-0-9-0-release-notes/">Apache Drill 0.9.0 Release Notes</a></li>
+
+
+ </ul>
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+ <li><a href="/docs/learn-drill-with-the-mapr-sandbox/">Learn Drill with the MapR Sandbox</a></li>
+
+ <li>Lesson 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>
+
+
+ <div class="int_title">
+ <h1>Lesson 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">"Getting to Know the Sandbox"</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:> !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:> 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:> use hive;
++------------+------------+
+| ok | summary |
++------------+------------+
+| true | Default schema changed to 'hive' |
++------------+------------+
+</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:> 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:> 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:> use maprdb;
++------------+------------+
+| ok | summary |
++------------+------------+
+| true | Default schema changed to 'maprdb' |
++------------+------------+
+</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:> 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:> 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:> select * from products limit 5;
++------------+------------+------------+
+| row_key | details | pricing |
++------------+------------+------------+
+| [B@a1a3e25 | {"category":"bGFwdG9w","name":"IlNvbnkgbm90ZWJvb2si"} | {"price":"OTU5"} |
+| [B@103a43af | {"category":"RW52ZWxvcGVz","name":"IzEwLTQgMS84IHggOSAxLzIgUHJlbWl1bSBEaWFnb25hbCBTZWFtIEVudmVsb3Blcw=="} | {"price":"MT |
+| [B@61319e7b | {"category":"U3RvcmFnZSAmIE9yZ2FuaXphdGlvbg==","name":"MjQgQ2FwYWNpdHkgTWF4aSBEYXRhIEJpbmRlciBSYWNrc1BlYXJs"} | {"price" |
+| [B@9bcf17 | {"category":"TGFiZWxz","name":"QXZlcnkgNDk4"} | {"price":"Mw=="} |
+| [B@7538ef50 | {"category":"TGFiZWxz","name":"QXZlcnkgNDk="} | {"price":"Mw=="} |
+</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:> select * from customers limit 5;
++------------+------------+------------+------------+
+| row_key | address | loyalty | personal |
++------------+------------+------------+------------+
+| [B@284bae62 | {"state":"Imt5Ig=="} | {"agg_rev":"IjEwMDEtMzAwMCI=","membership":"ImJhc2ljIg=="} | {"age":"IjI2LTM1Ig==","gender":"Ik1B |
+| [B@7ffa4523 | {"state":"ImNhIg=="} | {"agg_rev":"IjAtMTAwIg==","membership":"ImdvbGQi"} | {"age":"IjI2LTM1Ig==","gender":"IkZFTUFMRSI= |
+| [B@7d13e79 | {"state":"Im9rIg=="} | {"agg_rev":"IjUwMS0xMDAwIg==","membership":"InNpbHZlciI="} | {"age":"IjI2LTM1Ig==","gender":"IkZFT |
+| [B@3a5c7df1 | {"state":"ImtzIg=="} | {"agg_rev":"IjMwMDEtMTAwMDAwIg==","membership":"ImdvbGQi"} | {"age":"IjUxLTEwMCI=","gender":"IkZF |
+| [B@e507726 | {"state":"Im5qIg=="} | {"agg_rev":"IjAtMTAwIg==","membership":"ImJhc2ljIg=="} | {"age":"IjIxLTI1Ig==","gender":"Ik1BTEUi" |
++------------+------------+------------+------------+
+</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">{"trans_id":31920,"date":"2014-04-26","time":"12:17:12","user_info":{"cust_id":22526,"device":"IOS5","state":"il"},"trans_info":{"prod_id":[174,2],"purch_flag":"false"}}
+{"trans_id":31026,"date":"2014-04-20","time":"13:50:29","user_info":{"cust_id":16368,"device":"AOS4.2","state":"nc"},"trans_info":{"prod_id":[],"purch_flag":"false"}}
+{"trans_id":33848,"date":"2014-04-10","time":"04:44:42","user_info":{"cust_id":21449,"device":"IOS6","state":"oh"},"trans_info":{"prod_id":[582],"purch_flag":"false"}}
+</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:> use dfs.clicks;
++------------+------------+
+| ok | summary |
++------------+------------+
+| true | Default schema changed to 'dfs.clicks' |
++------------+------------+
+</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">"location": "/mapr/demo.mapr.com/data/nested"
+</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:> select * from `clicks/clicks.json` limit 2;
++------------+------------+------------+------------+------------+
+| trans_id | date | time | user_info | trans_info |
++------------+------------+------------+------------+------------+
+| 31920 | 2014-04-26 | 12:17:12 | {"cust_id":22526,"device":"IOS5","state":"il"} | {"prod_id":[174,2],"purch_flag":"false"} |
+| 31026 | 2014-04-20 | 13:50:29 | {"cust_id":16368,"device":"AOS4.2","state":"nc"} | {"prod_id":[],"purch_flag":"false"} |
++------------+------------+------------+------------+------------+
+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:> 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 | {"cust_id":18520,"device":"AOS4.3","state":"tx"} | {"camp_id":"null"} | {"prod_id":[7,7],"purch_flag":"true"} |
+| 31995 | 2014-05-22 | 16:06:38 | {"cust_id":17182,"device":"IOS6","state":"fl"} | {"camp_id":"null"} | {"prod_id":[],"purch_flag":"false"} |
++------------+------------+------------+------------+------------+------------+
+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:> use dfs.logs;
++------------+------------+
+| ok | summary |
++------------+------------+
+| true | Default schema changed to 'dfs.logs' |
++------------+------------+
+</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:> 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:> 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's-next">What's Next</h1>
+
+<p>Go to <a href="/docs/lesson-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/lesson-2-run-queries-with-ansi-sql/">Lesson 2: Run Queries with ANSI SQL →</a></span>
+</div>
+
+
+ </div>
+ </div>
+ </div>
+
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