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From a..@apache.org
Subject [07/13] drill git commit: DRILL-2315: Confluence conversion plus fixes
Date Thu, 26 Feb 2015 01:01:16 GMT
http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/query/query-fs/001-query-json.md
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diff --git a/_docs/drill-docs/query/query-fs/001-query-json.md b/_docs/drill-docs/query/query-fs/001-query-json.md
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index 048903b..0000000
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+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: "Querying JSON Files"
-parent: "Querying a File System"
----
-Your Drill installation includes a sample JSON file located in Drill's
-classpath. The sample JSON file, `employee.json`, contains fictitious employee
-data. Use SQL syntax to query the sample `JSON` file.
-
-To view the data in the `employee.json` file, submit the following SQL query
-to Drill:
-
-``0: jdbc:drill:zk=local> SELECT * FROM cp.`employee.json`;``
-
-The query returns the following results:
-
-**Example of partial output**
-
-    +-------------+------------+------------+------------+-------------+-----------+
-    | employee_id | full_name  | first_name | last_name  | position_id | position_ |
-    +-------------+------------+------------+------------+-------------+-----------+
-    | 1101        | Steve Eurich | Steve      | Eurich     | 16          | Store T |
-    | 1102        | Mary Pierson | Mary       | Pierson    | 16          | Store T |
-    | 1103        | Leo Jones  | Leo        | Jones      | 16          | Store Tem |
-    | 1104        | Nancy Beatty | Nancy      | Beatty     | 16          | Store T |
-    | 1105        | Clara McNight | Clara      | McNight    | 16          | Store  |
-    | 1106        | Marcella Isaacs | Marcella   | Isaacs     | 17          | Stor |
-    | 1107        | Charlotte Yonce | Charlotte  | Yonce      | 17          | Stor |
-    | 1108        | Benjamin Foster | Benjamin   | Foster     | 17          | Stor |
-    | 1109        | John Reed  | John       | Reed       | 17          | Store Per |
-    | 1110        | Lynn Kwiatkowski | Lynn       | Kwiatkowski | 17          | St |
-    | 1111        | Donald Vann | Donald     | Vann       | 17          | Store Pe |
-    | 1112        | William Smith | William    | Smith      | 17          | Store  |
-    | 1113        | Amy Hensley | Amy        | Hensley    | 17          | Store Pe |
-    | 1114        | Judy Owens | Judy       | Owens      | 17          | Store Per |
-    | 1115        | Frederick Castillo | Frederick  | Castillo   | 17          | S |
-    | 1116        | Phil Munoz | Phil       | Munoz      | 17          | Store Per |
-    | 1117        | Lori Lightfoot | Lori       | Lightfoot  | 17          | Store |
-    ...
-    +-------------+------------+------------+------------+-------------+-----------+
-    1,155 rows selected (0.762 seconds)
-    0: jdbc:drill:zk=local>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/query/query-fs/002-query-parquet.md
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diff --git a/_docs/drill-docs/query/query-fs/002-query-parquet.md b/_docs/drill-docs/query/query-fs/002-query-parquet.md
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----
-title: "Querying Parquet Files"
-parent: "Querying a File System"
----
-Your Drill installation includes a `sample-date` directory with Parquet files
-that you can query. Use SQL syntax to query the `region.parquet` and
-`nation.parquet` files in the `sample-data` directory.
-
-**Note:** Your Drill installation location may differ from the examples used here. The examples assume that Drill was installed in embedded mode on your machine following the [Apache Drill in 10 Minutes ](https://cwiki.apache.org/confluence/display/DRILL/Apache+Drill+in+10+Minutes)tutorial. If you installed Drill in distributed mode, or your `sample-data` directory differs from the location used in the examples, make sure to change the `sample-data` directory to the correct location before you run the queries.
-
-#### Region File
-
-If you followed the Apache Drill in 10 Minutes instructions to install Drill
-in embedded mode, the path to the parquet file varies between operating
-systems.
-
-To view the data in the `region.parquet` file, issue the query appropriate for
-your operating system:
-
-  * Linux  
-``SELECT * FROM dfs.`/opt/drill/apache-drill-0.4.0-incubating/sample-
-data/region.parquet`; ``
-
-   * Mac OS X  
-``SELECT * FROM dfs.`/Users/max/drill/apache-drill-0.4.0-incubating/sample-
-data/region.parquet`;``
-
-   * Windows  
-``SELECT * FROM dfs.`C:\drill\apache-drill-0.4.0-incubating\sample-
-data\region.parquet`;``
-
-The query returns the following results:
-
-    +------------+------------+
-    |   EXPR$0   |   EXPR$1   |
-    +------------+------------+
-    | AFRICA     | lar deposits. blithely final packages cajole. regular waters ar |
-    | AMERICA    | hs use ironic, even requests. s |
-    | ASIA       | ges. thinly even pinto beans ca |
-    | EUROPE     | ly final courts cajole furiously final excuse |
-    | MIDDLE EAST | uickly special accounts cajole carefully blithely close reques |
-    +------------+------------+
-    5 rows selected (0.165 seconds)
-    0: jdbc:drill:zk=local>
-
-#### Nation File
-
-If you followed the Apache Drill in 10 Minutes instructions to install Drill
-in embedded mode, the path to the parquet file varies between operating
-systems.
-
-To view the data in the `nation.parquet` file, issue the query appropriate for
-your operating system:
-
-  * Linux  
-``SELECT * FROM dfs.`/opt/drill/apache-drill-0.4.0-incubating/sample-
-data/nation.parquet`; ``
-
-  * Mac OS X  
-``SELECT * FROM dfs.`/Users/max/drill/apache-drill-0.4.0-incubating/sample-
-data/nation.parquet`;``
-
-  * Windows  
-``SELECT * FROM dfs.`C:\drill\apache-drill-0.4.0-incubating\sample-
-data\nation.parquet`;``
-
-The query returns the following results:
-
-    +------------+------------+------------+------------+
-    |   EXPR$0   |   EXPR$1   |   EXPR$2   |   EXPR$3   |
-    +------------+------------+------------+------------+
-    | 0          | 0          | ALGERIA    |  haggle. carefully final deposits det |
-    | 1          | 1          | ARGENTINA  | al foxes promise slyly according to t |
-    | 2          | 1          | BRAZIL     | y alongside of the pending deposits.  |
-    | 3          | 1          | CANADA     | eas hang ironic, silent packages. sly |
-    | 4          | 4          | EGYPT      | y above the carefully unusual theodol |
-    | 5          | 0          | ETHIOPIA   | ven packages wake quickly. regu |
-    | 6          | 3          | FRANCE     | refully final requests. regular, iron |
-    | 7          | 3          | GERMANY    | l platelets. regular accounts x-ray:  |
-    | 8          | 2          | INDIA      | ss excuses cajole slyly across the pa |
-    | 9          | 2          | INDONESIA  |  slyly express asymptotes. regular de |
-    | 10         | 4          | IRAN       | efully alongside of the slyly final d |
-    | 11         | 4          | IRAQ       | nic deposits boost atop the quickly f |
-    | 12         | 2          | JAPAN      | ously. final, express gifts cajole a |
-    | 13         | 4          | JORDAN     | ic deposits are blithely about the ca |
-    | 14         | 0          | KENYA      |  pending excuses haggle furiously dep |
-    | 15         | 0          | MOROCCO    | rns. blithely bold courts among the c |
-    | 16         | 0          | MOZAMBIQUE | s. ironic, unusual asymptotes wake bl |
-    | 17         | 1          | PERU       | platelets. blithely pending dependenc |
-    | 18         | 2          | CHINA      | c dependencies. furiously express not |
-    | 19         | 3          | ROMANIA    | ular asymptotes are about the furious |
-    | 20         | 4          | SAUDI ARABIA | ts. silent requests haggle. closely |
-    | 21         | 2          | VIETNAM    | hely enticingly express accounts. eve |
-    | 22         | 3          | RUSSIA     |  requests against the platelets use n |
-    | 23         | 3          | UNITED KINGDOM | eans boost carefully special requ |
-    | 24         | 1          | UNITED STATES | y final packages. slow foxes cajol |
-    +------------+------------+------------+------------+
-    25 rows selected (2.401 seconds)
-    0: jdbc:drill:zk=local>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/query/query-fs/003-query-text.md
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diff --git a/_docs/drill-docs/query/query-fs/003-query-text.md b/_docs/drill-docs/query/query-fs/003-query-text.md
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----
-title: "Querying Plain Text Files"
-parent: "Querying a File System"
----
-You can use Drill to access both structured file types and plain text files
-(flat files). This section shows a few simple examples that work on flat
-files:
-
-  * CSV files (comma-separated values)
-  * TSV files (tab-separated values)
-  * PSV files (pipe-separated values)
-
-The examples here show CSV files, but queries against TSV and PSV files return
-equivalent results. However, make sure that your registered storage plugins
-recognize the appropriate file types and extensions. For example, the
-following configuration expects PSV files (files with a pipe delimiter) to
-have a `tbl` extension, not a `psv` extension. Drill returns a "file not
-found" error if references to files in queries do not match these conditions.
-
-    "formats": {
-        "psv": {
-          "type": "text",
-          "extensions": [
-            "tbl"
-          ],
-          "delimiter": "|"
-        }
-
-## SELECT * FROM a CSV File
-
-The first query selects rows from a `.csv` text file. The file contains seven
-records:
-
-    $ more plays.csv
- 
-    1599,As You Like It
-    1601,Twelfth Night
-    1594,Comedy of Errors
-    1595,Romeo and Juliet
-    1596,The Merchant of Venice
-    1610,The Tempest
-    1599,Hamlet
-
-Drill recognizes each row as an array of values and returns one column for
-each row.
-
-0: jdbc:drill:zk=local> select * from dfs.`/Users/brumsby/drill/plays.csv`;
- 
-    +------------+
-    |  columns   |
-    +------------+
-    | ["1599","As You Like It"] |
-    | ["1601","Twelfth Night"] |
-    | ["1594","Comedy of Errors"] |
-    | ["1595","Romeo and Juliet"] |
-    | ["1596","The Merchant of Venice"] |
-    | ["1610","The Tempest"] |
-    | ["1599","Hamlet"] |
-    +------------+
-    7 rows selected (0.089 seconds)
-
-## Columns[n] Syntax
-
-You can use the `COLUMNS[n]` syntax in the SELECT list to return these CSV
-rows in a more readable, column by column, format. (This syntax uses a zero-
-based index, so the first column is column `0`.)
-
-0: jdbc:drill:zk=local> select columns[0], columns[1] 
-from dfs.`/Users/brumsby/drill/plays.csv`;
- 
-    +------------+------------+
-    |   EXPR$0   |   EXPR$1   |
-    +------------+------------+
-    | 1599       | As You Like It |
-    | 1601       | Twelfth Night |
-    | 1594       | Comedy of Errors |
-    | 1595       | Romeo and Juliet |
-    | 1596       | The Merchant of Venice |
-    | 1610       | The Tempest |
-    | 1599       | Hamlet     |
-    +------------+------------+
-    7 rows selected (0.137 seconds)
-
-You can use aliases to return meaningful column names. Note that `YEAR` is a
-reserved word, so the `Year` alias must be enclosed by back ticks.
-
-    0: jdbc:drill:zk=local> select columns[0] as `Year`, columns[1] as Play 
-    from dfs.`/Users/brumsby/drill/plays.csv`;
- 
-    +------------+------------+
-    |    Year    |    Play    |
-    +------------+------------+
-    | 1599       | As You Like It |
-    | 1601       | Twelfth Night |
-    | 1594       | Comedy of Errors |
-    | 1595       | Romeo and Juliet |
-    | 1596       | The Merchant of Venice |
-    | 1610       | The Tempest |
-    | 1599       | Hamlet     |
-    +------------+------------+
-    7 rows selected (0.113 seconds)
-
-You cannot refer to the aliases in subsequent clauses of the query. Use the
-original `columns[n]` syntax, as shown in the WHERE clause for the following
-example:
-
-0: jdbc:drill:zk=local> select columns[0] as `Year`, columns[1] as Play 
-from dfs.`/Users/brumsby/drill/plays.csv` where columns[0]>1599;
- 
-    +------------+------------+
-    |    Year    |    Play    |
-    +------------+------------+
-    | 1601       | Twelfth Night |
-    | 1610       | The Tempest |
-    +------------+------------+
-    2 rows selected (0.201 seconds)
-
-Note that the restriction with the use of aliases applies to queries against
-all data sources.
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/query/query-fs/004-query-dir.md
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diff --git a/_docs/drill-docs/query/query-fs/004-query-dir.md b/_docs/drill-docs/query/query-fs/004-query-dir.md
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----
-title: "Querying Directories"
-parent: "Querying a File System"
----
-You can store multiple files in a directory and query them as if they were a
-single entity. You do not have to explicitly join the files. The files must be
-compatible, in the sense that they must have comparable data types and columns
-in the same order. This type of query is not limited to text files; you can
-also query directories of JSON files, for example.
-
-For example, assume that a `testdata` directory contains two files with the
-same structure: `plays.csv` and `moreplays.csv`. The first file contains 7
-records and the second file contains 3 records. The following query returns
-the "union" of the two files, ordered by the first column:
-
-    0: jdbc:drill:zk=local> select columns[0] as `Year`, columns[1] as Play 
-    from dfs.`/Users/brumsby/drill/testdata` order by 1;
- 
-    +------------+------------+
-    |    Year    |    Play    |
-    +------------+------------+
-    | 1594       | Comedy of Errors |
-    | 1595       | Romeo and Juliet |
-    | 1596       | The Merchant of Venice |
-    | 1599       | As You Like It |
-    | 1599       | Hamlet     |
-    | 1601       | Twelfth Night |
-    | 1606       | Macbeth    |
-    | 1606       | King Lear  |
-    | 1609       | The Winter's Tale |
-    | 1610       | The Tempest |
-    +------------+------------+
-    10 rows selected (0.296 seconds)
-
-You can drill down further and automatically query subdirectories as well. For
-example, assume that you have a logs directory that contains a subdirectory
-for each year and subdirectories for each month (1 through 12). The month
-directories contain JSON files.
-
-    [root@ip-172-16-1-200 logs]# pwd
-    /mapr/drilldemo/labs/clicks/logs
-    [root@ip-172-16-1-200 logs]# ls
-    2012  2013  2014
-    [root@ip-172-16-1-200 logs]# cd 2013
-    [root@ip-172-16-1-200 2013]# ls
-    1  10  11  12  2  3  4  5  6  7  8  9
-
-You can query all of these files, or a subset, by referencing the file system
-once in a Drill query. For example, the following query counts the number of
-records in all of the files inside the `2013` directory:
-
-    0: jdbc:drill:> select count(*) from MFS.`/mapr/drilldemo/labs/clicks/logs/2013` ;
-    +------------+
-    |   EXPR$0   |
-    +------------+
-    | 24000      |
-    +------------+
-    1 row selected (2.607 seconds)
-
-You can also use variables `dir0`, `dir1`, and so on, to refer to
-subdirectories in your workspace path. For example, assume that `bob.logdata`
-is a workspace that points to the `logs` directory, which contains multiple
-subdirectories: `2012`, `2013`, and `2014`. The following query constrains
-files inside the subdirectory named `2013`. The variable `dir0` refers to the
-first level down from logs, `dir1` to the next level, and so on.
-
-    0: jdbc:drill:> use bob.logdata;
-    +------------+------------+
-    |     ok     |  summary   |
-    +------------+------------+
-    | true       | Default schema changed to 'bob.logdata' |
-    +------------+------------+
-    1 row selected (0.305 seconds)
- 
-    0: jdbc:drill:> select * from logs where dir0='2013' limit 10;
-    +------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+
-    |    dir0    |    dir1    |  trans_id  |    date    |    time    |  cust_id   |   device   |   state    |  camp_id   |  keywords   |
-    +------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+
-    | 2013       | 2          | 12115      | 02/23/2013 | 19:48:24   | 3          | IOS5       | az         | 5          | who's       |
-    | 2013       | 2          | 12127      | 02/26/2013 | 19:42:03   | 11459      | IOS5       | wa         | 10         | for         |
-    | 2013       | 2          | 12138      | 02/09/2013 | 05:49:01   | 1          | IOS6       | ca         | 7          | minutes     |
-    | 2013       | 2          | 12139      | 02/23/2013 | 06:58:20   | 1          | AOS4.4     | ms         | 7          | i           |
-    | 2013       | 2          | 12145      | 02/10/2013 | 10:14:56   | 10         | IOS5       | mi         | 6          | wrong       |
-    | 2013       | 2          | 12157      | 02/15/2013 | 02:49:22   | 102        | IOS5       | ny         | 5          | want        |
-    | 2013       | 2          | 12176      | 02/19/2013 | 08:39:02   | 28         | IOS5       | or         | 0          | and         |
-    | 2013       | 2          | 12194      | 02/24/2013 | 08:26:17   | 125445     | IOS5       | ar         | 0          | say         |
-    | 2013       | 2          | 12236      | 02/05/2013 | 01:40:05   | 10         | IOS5       | nj         | 2          | sir         |
-    | 2013       | 2          | 12249      | 02/03/2013 | 04:45:47   | 21725      | IOS5       | nj         | 5          | no          |
-    +------------+------------+------------+------------+------------+------------+------------+------------+------------+-------------+
-    10 rows selected (0.583 seconds)
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/rn/001-0.5.0rn.md
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diff --git a/_docs/drill-docs/rn/001-0.5.0rn.md b/_docs/drill-docs/rn/001-0.5.0rn.md
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+++ /dev/null
@@ -1,29 +0,0 @@
----
-title: "Apache Drill 0.5.0 Release Notes"
-parent: "Release Notes"
----
-
-Apache Drill 0.5.0, the first beta release for Drill, is designed to help
-enthusiasts start working and experimenting with Drill. It also continues the
-Drill monthly release cycle as we drive towards general availability.
-
-The 0.5.0 release is primarily a bug fix release, with [more than 100 JIRAs](h
-ttps://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12313820&versi
-on=12324880) closed, but there are some notable features. For information
-about the features, see the [Apache Drill Blog for the 0.5.0
-release](https://blogs.apache.org/drill/entry/apache_drill_beta_release_see).
-
-This release is available as [binary](http://www.apache.org/dyn/closer.cgi/inc
-ubator/drill/drill-0.5.0-incubating/apache-drill-0.5.0-incubating.tar.gz) and 
-[source](http://www.apache.org/dyn/closer.cgi/incubator/drill/drill-0.5.0-incu
-bating/apache-drill-0.5.0-incubating-src.tar.gz) tarballs that are compiled
-against Apache Hadoop. Drill has been tested against MapR, Cloudera, and
-Hortonworks Hadoop distributions. There are associated build profiles and
-JIRAs that can help you run Drill against your preferred distribution.
-
-Apache Drill 0.5.0 Key Notes and Limitations
-
-  * The current release supports in memory and beyond memory execution. However, you must disable memory-intensive hash aggregate and hash join operations to leverage this functionality.
-  * While the Drill execution engine supports dynamic schema changes during the course of a query, some operators have yet to implement support for this behavior, such as Sort. Others operations, such as streaming aggregate, may have partial support that leads to unexpected results.
-  * There are known issues with joining text files without using an intervening view. See [DRILL-1401](https://issues.apache.org/jira/browse/DRILL-1401) for more information.
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/rn/002-0.4.0rn.md
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diff --git a/_docs/drill-docs/rn/002-0.4.0rn.md b/_docs/drill-docs/rn/002-0.4.0rn.md
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@@ -1,42 +0,0 @@
----
-title: "Apache Drill 0.4.0 Release Notes"
-parent: "Release Notes"
----
-The 0.4.0 release is a developer preview release, designed to help enthusiasts
-start to work with and experiment with Drill. It is the first Drill release
-that provides distributed query execution.
-
-This release is built upon [more than 800
-JIRAs](https://issues.apache.org/jira/browse/DRILL/fixforversion/12324963/).
-It is a pre-beta release on the way towards Drill. As a developer snapshot,
-the release contains a large number of outstanding bugs that will make some
-use cases challenging. Feel free to consult outstanding issues [targeted for
-the 0.5.0
-release](https://issues.apache.org/jira/browse/DRILL/fixforversion/12324880/)
-to see whether your use case is affected.
-
-To read more about this release and new features introduced, please view the
-[0.4.0 announcement blog
-entry](https://blogs.apache.org/drill/entry/announcing_apache_drill_0_4).
-
-The release is available as both [binary](http://www.apache.org/dyn/closer.cgi
-/incubator/drill/drill-0.4.0-incubating/apache-drill-0.4.0-incubating.tar.gz)
-and [source](http://www.apache.org/dyn/closer.cgi/incubator/drill/drill-0.4.0-
-incubating/apache-drill-0.4.0-incubating-src.tar.gz) tarballs. In both cases,
-these are compiled against Apache Hadoop. Drill has also been tested against
-MapR, Cloudera and Hortonworks Hadoop distributions and there are associated
-build profiles or JIRAs that can help you run against your preferred
-distribution.
-
-Some Key Notes & Limitations
-
-  * The current release supports in memory and beyond memory execution. However, users must disable memory-intensive hash aggregate and hash join operations to leverage this functionality.
-  * In many cases,merge join operations return incorrect results.
-  * Use of a local filter in a join “on” clause when using left, right or full outer joins may result in incorrect results.
-  * Because of known memory leaks and memory overrun issues you may need more memory and you may need to restart the system in some cases.
-  * Some types of complex expressions, especially those involving empty arrays may fail or return incorrect results.
-  * While the Drill execution engine supports dynamic schema changes during the course of a query, some operators have yet to implement support for this behavior (such as Sort). Others operations (such as streaming aggregate) may have partial support that leads to unexpected results.
-  * Protobuf, UDF, query plan interfaces and all interfaces are subject to change in incompatible ways.
-  * Multiplication of some types of DECIMAL(28+,*) will return incorrect result.
-
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/rn/003-alpha-rn.md
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diff --git a/_docs/drill-docs/rn/003-alpha-rn.md b/_docs/drill-docs/rn/003-alpha-rn.md
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----
-title: "Apache Drill M1 Release Notes (Apache Drill Alpha)"
-parent: "Release Notes"
----
-### Milestone 1 Goals
-
-The first release of Apache Drill is designed as a technology preview for
-people to better understand the architecture and vision. It is a functional
-release tying to piece together the key components of a next generation MPP
-query engine. It is designed to allow milestone 2 (M2) to focus on
-architectural analysis and performance optimization.
-
-  * Provide a new optimistic DAG execution engine for data analysis
-  * Build a new columnar shredded in-memory format and execution model that minimizes data serialization/deserialization costs and operator complexity
-  * Provide a model for runtime generated functions and relational operators that minimizes complexity and maximizes performance
-  * Support queries against columnar on disk format (Parquet) and JSON
-  * Support the most common set of standard SQL read-only phrases using ANSI standards. Includes: SELECT, FROM, WHERE, HAVING, ORDER, GROUP BY, IN, DISTINCT, LEFT JOIN, RIGHT JOIN, INNER JOIN
-  * Support schema-on-read querying and execution
-  * Build a set of columnar operation primitives including Merge Join, Sort, Streaming Aggregate, Filter, Selection Vector removal.
-  * Support unlimited level of subqueries and correlated subqueries
-  * Provided an extensible query-language agnostic JSON-base logical data flow syntax.
-  * Support complex data type manipulation via logical plan operations
-
-### Known Issues
-
-SQL Parsing  
-Because Apache Drill is built to support late-bound changing schemas while SQL
-is statically typed, there are couple of special requirements that are
-required writing SQL queries. These are limited to the current release and
-will be correct in a future milestone release.
-
-  * All tables are exposed as a single map field that contains
-  * Drill Alpha doesn't support implicit or explicit casts outside those required above.
-  * Drill Alpha does not include, there are currently a couple of differences for how to write a query in In order to query against
-
-UDFs
-
-  * Drill currently supports simple and aggregate functions using scalar, repeated and
-  * Nested data support incomplete. Drill Alpha supports nested data structures as well repeated fields. However,
-  * asd
-
-
-
-

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+++ /dev/null
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----
-title: "Apache Drill 0.6.0 Release Notes (Apache Drill Alpha)"
-parent: "Release Notes"
----
-Apache Drill 0.6.0, the second beta release for Drill, is designed to help
-enthusiasts start working and experimenting with Drill. It also continues the
-Drill monthly release cycle as we drive towards general availability.
-
-This release is available as [binary](http://www.apache.org/dyn/closer.cgi/inc
-ubator/drill/drill-0.5.0-incubating/apache-drill-0.5.0-incubating.tar.gz) and 
-[source](http://www.apache.org/dyn/closer.cgi/incubator/drill/drill-0.5.0-incu
-bating/apache-drill-0.5.0-incubating-src.tar.gz) tarballs that are compiled
-against Apache Hadoop. Drill has been tested against MapR, Cloudera, and
-Hortonworks Hadoop distributions. There are associated build profiles and
-JIRAs that can help you run Drill against your preferred distribution.
-
-Apache Drill 0.6.0 Key Features
-
-This release is primarily a bug fix release, with [more than 30 JIRAs closed](
-https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12313820&vers
-ion=12327472), but there are some notable features:
-
-  * Direct ANSI SQL access to MongoDB, using the latest [MongoDB Plugin for Apache Drill](/confluence/display/DRILL/MongoDB+Plugin+for+Apache+Drill)
-  * Filesystem query performance improvements with partition pruning
-  * Ability to use the file system as a persistent store for query profiles and diagnostic information
-  * Window function support (alpha)
-
-Apache Drill 0.6.0 Key Notes and Limitations
-
-  * The current release supports in-memory and beyond-memory execution. However, you must disable memory-intensive hash aggregate and hash join operations to leverage this functionality.
-  * While the Drill execution engine supports dynamic schema changes during the course of a query, some operators have yet to implement support for this behavior, such as Sort. Other operations, such as streaming aggregate, may have partial support that leads to unexpected results.
-

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----
-title: "Apache Drill 0.7.0 Release Notes (Apache Drill Alpha)"
-parent: "Release Notes"
----
-Apache Drill 0.7.0, the third beta release for Drill, is designed to help
-enthusiasts start working and experimenting with Drill. It also continues the
-Drill monthly release cycle as we drive towards general availability.
-
-This release is available as
-[binary](http://www.apache.org/dyn/closer.cgi/drill/drill-0.7.0/apache-
-drill-0.7.0.tar.gz) and
-[source](http://www.apache.org/dyn/closer.cgi/drill/drill-0.7.0/apache-
-drill-0.7.0-src.tar.gz) tarballs that are compiled against Apache Hadoop.
-Drill has been tested against MapR, Cloudera, and Hortonworks Hadoop
-distributions. There are associated build profiles and JIRAs that can help you
-run Drill against your preferred distribution
-
-Apache Drill 0.7.0 Key Features
-
-  * No more dependency on UDP/Multicast - Making it possible for Drill to work well in the following scenarios:
-
-    * UDP multicast not enabled (as in EC2)
-
-    * Cluster spans multiple subnets
-
-    * Cluster has multihome configuration
-
-  * New functions to natively work with nested data - KVGen and Flatten 
-
-  * Support for Hive 0.13 (Hive 0.12 with Drill is not supported any more) 
-
-  * Improved performance when querying Hive tables and File system through partition pruning
-
-  * Improved performance for HBase with LIKE operator pushdown
-
-  * Improved memory management
-
-  * Drill web UI monitoring and query profile improvements
-
-  * Ability to parse files without explicit extensions using default storage format specification
-
-  * Fixes for dealing with complex/nested data objects in Parquet/JSON
-
-  * Fast schema return - Improved experience working with BI/query tools by returning metadata quickly
-
-  * Several hang related fixes
-
-  * Parquet writer fixes for handling large datasets
-
-  * Stability improvements in ODBC and JDBC drivers
-
-Apache Drill 0.7.0 Key Notes and Limitations
-
-  * The current release supports in-memory and beyond-memory execution. However, you must disable memory-intensive hash aggregate and hash join operations to leverage this functionality.
-  * While the Drill execution engine supports dynamic schema changes during the course of a query, some operators have yet to implement support for this behavior, such as Sort. Other operations, such as streaming aggregate, may have partial support that leads to unexpected results.
-

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----
-title: "Data Types"
-parent: "SQL Reference"
----
-You can use the following SQL data types in your Drill queries:
-
-#### Binary Types
-
-  * BYTEA
-  * VARBINARY
-
-#### Character Types
-
-  * CHAR
-  * VARCHAR 
-  * TEXT
-
-#### Date/Time Data Types
-
-  * DATE
-  * INTERVAL
-    * Interval Year (stores year and month)
-
-    * Interval Day (stores day, hour, minute, seconds, and milliseconds)
-  * TIME
-  * TIMESTAMP
-
-Refer to [Supported Date/Time Data Type formats](https://cwiki.apache.org/conf
-luence/pages/viewpage.action?pageId=44304039).
-
-#### Integer
-
-  * BIGINT
-
-  * INT
-  * SMALLINT
-
-  * TINYINT
-
-#### Numeric Types
-
-  * DECIMAL
-  * FLOAT 
-  * DOUBLE PRECISION (FLOAT 8)
-  * REAL (FLOAT 4) 
-
-#### Boolean Types
-
-  * FALSE
-  * NULL
-  * TRUE
-
-# Complex Data Types
-
-Drill provides map and array data types to work with complex and nested data
-structures. For analysis of complex data, a more modern JSON-style approach to
-writing queries is more effective than using standard SQL functions.
-
-The following table provides descriptions and examples of the complex data
-types:
-
-<table><tbody>
-  <tr><th>Data Type</th>
-  <th>Description</th>
-  <th>Example</th></tr>
-    <tr>
-      <td valign="top">Map</td>
-      <td valign="top">A map is a set of name/value pairs. </br>
-      A value in an map can be a scalar type, </br>
-      such as string or int, or a map can be a </br>
-      complex type, such as an array or another map.</td>
-      <td valign="top">Map with scalar type values:</br><code>&nbsp;&nbsp;&quot;phoneNumber&quot;: { &quot;areaCode&quot;: &quot;622&quot;, &quot;number&quot;: &quot;1567845&quot;}</code></br>Map with complex type value:<code></br>&nbsp;&nbsp;{ &quot;citiesLived&quot; : [ { &quot;place&quot; : &quot;Los Angeles&quot;,</br>        
-      &nbsp;&nbsp;&nbsp;&nbsp;&quot;yearsLived&quot; : [ &quot;1989&quot;,</br>
-      &nbsp;&nbsp;&nbsp;&nbsp;            &quot;1993&quot;,</br>            
-      &nbsp;&nbsp;&nbsp;&nbsp;&quot;1998&quot;,</br>            
-      &nbsp;&nbsp;&nbsp;&nbsp;&quot;2002&quot;</br>
-      &nbsp;&nbsp;&nbsp;&nbsp;          ]</br>      
-      &nbsp;&nbsp;
-      &nbsp;} ] }</code></td>
-    </tr>
-    <tr>
-      <td valign="top">Array</td>
-      <td valign="top">An array is a repeated list of values. </br>
-      A value in an array can be a scalar type, </br>
-      such as string or int, or an array can be a</br> 
-      complex type, such as a map or another array.</td>
-      <td valign="top">Array with scalar values:</br><code>&nbsp;&nbsp;&quot;yearsLived&quot;: [&quot;1990&quot;, &quot;1993&quot;, &quot;1998&quot;, &quot;2008&quot;]</code></br>Array with complex type values:</br><code>&nbsp;&nbsp;&quot;children&quot;:</br>&nbsp;&nbsp;[ { &quot;age&quot; : &quot;10&quot;, </br>   &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&quot;gender&quot; : &quot;Male&quot;,</br>    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&quot;name&quot; : &quot;Earl&quot;</br> &nbsp;&nbsp;&nbsp;&nbsp; }, </br> &nbsp;&nbsp;&nbsp;&nbsp;{ &quot;age&quot; : &quot;6&quot;,</br>    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&quot;gender&quot; : &quot;Male&quot;,</br>    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&quot;name&quot; : &quot;Sam&quot;</br>  &nbsp;&nbsp;&nbsp;&nbsp;},</br>  &nbsp;&nbsp;&nbsp;&nbsp;{ &quot;age&quot; : &quot;8&quot;,</br>    &nbsp;&nbsp;&nbsp;&nbsp;&quot;gender&quot; : &quot;Male&quot;,  </br>  &nbsp;&nbsp;&nbsp;&nbsp;&quot;name&quot; : &quot;Kit&quot; </br> &nbsp;&nbsp;&nbsp;&nbsp
 ;}</br>&nbsp;&nbsp;]</code></td>
-    </tr>
-  </tbody></table>
-
-
-<!--| Data Type | Description | Example |  
-| --------- | ----------- | --------| 
-| Map       | A map is a set of name/value pairs. A value in an map can be a scalar type, such as string or int, or a map can be a complex type, such as an array or another map.| Map with scalar type values: "phoneNumber": { "areaCode": "622", "number": "1567845"} Map with complex type value :{ "citiesLived" : [ { "place" : "Los Angeles", "yearsLived" : [ "1989", "1993", "1998", "2002"] } ] } |
-|Array| An array is a repeated list of values. A value in an array can be a scalar type, such as string or int, or an array can be a complex type, such as a map or another array.| Array with scalar type values: "yearsLived": ["1990"", "1993", "1998", "2008"] Array with complex type value :"children" : [ { "age" : "10", "gender" : "Male", "name": "Earl" }, {"age" : "6", "gender": "Male", "name": "Sam"}, { "age" : "8", "gender" : "Male", "name" : "Kit" }] |
--->
\ No newline at end of file

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----
-title: "Operators"
-parent: "SQL Reference"
----
-You can use various types of operators in your Drill queries to perform
-operations on your data.
-
-## Logical Operators
-
-You can use the following logical operators in your Drill queries:
-
-  * AND
-  * BETWEEN
-  * IN
-  * LIKE
-  * NOT
-  * OR 
-
-## Comparison Operators
-
-You can use the following comparison operators in your Drill queries:
-
-  * <
-  * \>
-  * <=
-  * \>=
-  * =
-  * <>
-  * IS NULL
-  * IS NOT NULL
-  * IS FALSE 
-  * IS NOT FALSE
-  * IS TRUE 
-  * IS NOT TRUE
-
-## Pattern Matching Operators
-
-You can use the following pattern matching operators in your Drill queries:
-
-  * LIKE
-  * NOT LIKE
-  * SIMILAR TO
-  * NOT SIMILAR TO
-
-## Math Operators
-
-You can use the following math operators in your Drill queries:
-
-**Operator**| **Description**  
----|---  
-+| Addition  
--| Subtraction  
-*| Multiplication  
-/| Division  
-  
-## Subquery Operators
-
-You can use the following subquery operators in your Drill queries:
-
-  * EXISTS
-  * IN
-
-See [SELECT Statements](https://cwiki.apache.org/confluence/display/DRILL/SELE
-CT+Statements).
-
-## String Operators
-
-You can use the following string operators in your Drill queries:
-
-  * string || string
-  * string || non-string or non-string || string
\ No newline at end of file

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----
-title: "SQL Functions"
-parent: "SQL Reference"
----
-You can use the following types of functions in your Drill queries:
-
-  * Scalar Functions
-  * Aggregate Functions
-  * Aggregate Statistics Functions
-  * Convert Functions
-  * Nested Data Functions
-
-## Scalar Functions
-
-### Math
-
-You can use the following scalar math functions in your Drill queries:
-
-  * ABS
-  * CEIL
-  * CEILING
-  * DIV
-  * FLOOR
-  * MOD
-  * POWER 
-  * RANDOM
-  * ROUND
-  * SIGN
-  * SQRT
-  * TRUNC
-
-### String Functions
-
-The following table provides the string functions that you can use in your
-Drill queries:
-
-Function| Return Type  
---------|---  
-char_length(string) or character_length(string)| int  
-concat(str "any" [, str "any" [, ...] ])| text
-convert_from(string bytea, src_encoding name)| text 
-convert_to(string text, dest_encoding name)| bytea
-initcap(string)| text
-left(str text, n int)| text
-length(string)| int
-length(string bytes, encoding name )| int
-lower(string)| text
-lpad(string text, length int [, fill text])| text
-ltrim(string text [, characters text])| text
-position(substring in string)| int
-regexp_replace(string text, pattern text, replacement text [, flags text])|text
-replace(string text, from text, to text)| text
-right(str text, n int)| text
-rpad(string text, length int [, fill text])| text
-rtrim(string text [, characters text])| text
-strpos(string, substring)| int
-substr(string, from [, count])| text
-substring(string [from int] [for int])| text
-trim([leading | trailing | both] [characters] from string)| text
-upper(string)| text
-  
-  
-### Date/Time Functions
-
-The following table provides the date/time functions that you can use in your
-Drill queries:
-
-**Function**| **Return Type**  
----|---  
-current_date| date  
-current_time| time with time zone  
-current_timestamp| timestamp with time zone  
-date_add(date,interval expr type)| date/datetime  
-date_part(text, timestamp)| double precision  
-date_part(text, interval)| double precision  
-date_sub(date,INTERVAL expr type)| date/datetime  
-extract(field from interval)| double precision  
-extract(field from timestamp)| double precision  
-localtime| time  
-localtimestamp| timestamp  
-now()| timestamp with time zone  
-timeofday()| text  
-  
-### Data Type Formatting Functions
-
-The following table provides the data type formatting functions that you can
-use in your Drill queries:
-
-**Function**| **Return Type**  
----|---  
-to_char(timestamp, text)| text  
-to_char(int, text)| text  
-to_char(double precision, text)| text  
-to_char(numeric, text)| text  
-to_date(text, text)| date  
-to_number(text, text)| numeric  
-to_timestamp(text, text)| timestamp with time zone  
-to_timestamp(double precision)| timestamp with time zone  
-  
-## Aggregate Functions
-
-The following table provides the aggregate functions that you can use in your
-Drill queries:
-
-**Function** | **Argument Type** | **Return Type**  
-  --------   |   -------------   |   -----------
-avg(expression)| smallint, int, bigint, real, double precision, numeric, or interval| numeric for any integer-type argument, double precision for a floating-point argument, otherwise the same as the argument data type
-count(*)| _-_| bigint
-count([DISTINCT] expression)| any| bigint
-max(expression)| any array, numeric, string, or date/time type| same as argument type
-min(expression)| any array, numeric, string, or date/time type| same as argument type
-sum(expression)| smallint, int, bigint, real, double precision, numeric, or interval| bigint for smallint or int arguments, numeric for bigint arguments, double precision for floating-point arguments, otherwise the same as the argument data type
-  
-  
-## Aggregate Statistics Functions
-
-The following table provides the aggregate statistics functions that you can use in your Drill queries:
-
-**Function**| **Argument Type**| **Return Type**
-  --------  |   -------------  |   -----------
-stddev(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-stddev_pop(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-stddev_samp(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-variance(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-var_pop(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-var_samp(expression)| smallint, int, bigint, real, double precision, or numeric| double precision for floating-point arguments, otherwise numeric
-  
-  
-## Convert Functions
-
-You can use the CONVERT_TO and CONVERT_FROM functions to encode and decode
-data when you query your data sources with Drill. For example, HBase stores
-data as encoded byte arrays (VARBINARY data). When you issue a query with the
-CONVERT_FROM function on HBase, Drill decodes the data and converts it to the
-specified data type. In instances where Drill sends data back to HBase during
-a query, you can use the CONVERT_TO function to change the data type to bytes.
-
-Although you can achieve the same results by using the CAST function for some
-data types (such as VARBINARY to VARCHAR conversions), in general it is more
-efficient to use CONVERT functions when your data sources return binary data.
-When your data sources return more conventional data types, you can use the
-CAST function.
-
-The following table provides the data types that you use with the CONVERT_TO
-and CONVERT_FROM functions:
-
-**Type**| **Input Type**| **Output Type**  
----|---|---  
-BOOLEAN_BYTE| bytes(1)| boolean  
-TINYINT_BE| bytes(1)| tinyint  
-TINYINT| bytes(1)| tinyint  
-SMALLINT_BE| bytes(2)| smallint  
-SMALLINT| bytes(2)| smallint  
-INT_BE| bytes(4)| int  
-INT| bytes(4)| int  
-BIGINT_BE| bytes(8)| bigint  
-BIGINT| bytes(8)| bigint  
-FLOAT| bytes(4)| float (float4)  
-DOUBLE| bytes(8)| double (float8)  
-INT_HADOOPV| bytes(1-9)| int  
-BIGINT_HADOOPV| bytes(1-9)| bigint  
-DATE_EPOCH_BE| bytes(8)| date  
-DATE_EPOCH| bytes(8)| date  
-TIME_EPOCH_BE| bytes(8)| time  
-TIME_EPOCH| bytes(8)| time  
-UTF8| bytes| varchar  
-UTF16| bytes| var16char  
-UINT8| bytes(8)| uint8  
-  
-A common use case for CONVERT_FROM is when a data source embeds complex data
-inside a column. For example, you may have an HBase or MapR-DB table with
-embedded JSON data:
-
-    select CONVERT_FROM(col1, 'JSON') 
-    FROM hbase.table1
-    ...
-
-## Nested Data Functions
-
-This section contains descriptions of SQL functions that you can use to
-analyze nested data:
-
-  * [FLATTEN Function](/confluence/display/DRILL/FLATTEN+Function)
-  * [KVGEN Function](/confluence/display/DRILL/KVGEN+Function)
-  * [REPEATED_COUNT Function](/confluence/display/DRILL/REPEATED_COUNT+Function)
\ No newline at end of file

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----
-title: "Nested Data Functions"
-parent: "SQL Reference"
----
-This section contains descriptions of SQL functions that you can use to
-analyze nested data:
-
-  * [FLATTEN Function](/confluence/display/DRILL/FLATTEN+Function)
-  * [KVGEN Function](/confluence/display/DRILL/KVGEN+Function)
-  * [REPEATED_COUNT Function](/confluence/display/DRILL/REPEATED_COUNT+Function)
\ No newline at end of file

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----
-title: "SQL Commands Summary"
-parent: "SQL Reference"
----
-The following table provides a list of the SQL commands that Drill supports,
-with their descriptions and example syntax:
-
-<div class="table-wrap"><table class="confluenceTable"><tbody><tr><th class="confluenceTh">Command</th><th class="confluenceTh">Description</th><th class="confluenceTh">Syntax</th></tr><tr><td valign="top" class="confluenceTd">ALTER SESSION</td><td valign="top" class="confluenceTd">Changes a system setting for the duration of a session. A session ends when you quit the Drill shell. For a list of Drill options and their descriptions, refer to <a href="https://cwiki.apache.org/confluence/display/DRILL/Planning+and+Execution+Options" rel="nofollow">Planning and Execution Options</a>.</td><td valign="top" class="confluenceTd"><code>ALTER SESSION SET `option_name`='string';<br />ALTER SESSION SET `option_name`=TRUE | FALSE;</code></td></tr><tr><td valign="top" class="confluenceTd">ALTER SYSTEM</td><td valign="top" class="confluenceTd">Permanently changes a system setting. The new settings persist across all sessions. For a list of Drill options and their descriptions, refer to <a href="h
 ttps://cwiki.apache.org/confluence/display/DRILL/Planning+and+Execution+Options" rel="nofollow">Planning and Execution Options</a>.</td><td valign="top" class="confluenceTd"><code>ALTER SYSTEM `option_name`='string'<br />ALTER SYSTEM `option_name`=TRUE | FALSE;</code></td></tr><tr><td valign="top" class="confluenceTd"><p><a href="/confluence/display/DRILL/CREATE+TABLE+AS+%28CTAS%29+Command">CREATE TABLE AS<br />(CTAS)</a></p></td><td valign="top" class="confluenceTd"><p>Creates a new table and populates the new table with rows returned from a SELECT query. Use the CREATE TABLE AS (CTAS) statement in place of INSERT INTO. When you issue the CTAS command, you create a directory that contains parquet or CSV files. Each workspace in a file system has a default file type.</p><p>You can specify which writer you want Drill to use when creating a table: parquet, CSV, or JSON (as specified with <span style="line-height: 1.4285715;">the </span><code>store.format</code><span style="line-height
 : 1.4285715;"> option<span><span>).</span></span></span></p></td><td valign="top" class="confluenceTd"><code>CREATE TABLE new_table_name AS &lt;query&gt;;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">CREATE VIEW</td><td valign="top" colspan="1" class="confluenceTd"><p>Creates a new view based on the results of a SELECT query.</p></td><td valign="top" colspan="1" class="confluenceTd"><code>CREATE VIEW view_name [(column_list)] AS &lt;query&gt;;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">DROP VIEW</td><td valign="top" colspan="1" class="confluenceTd">Removes one or more views.</td><td valign="top" colspan="1" class="confluenceTd"><code>DROP VIEW view_name [, <em class="replaceable">view_name</em>] ...;     </code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd"><a href="https://cwiki.apache.org/confluence/display/DRILL/EXPLAIN+Commands" rel="nofollow">EXPLAIN PLAN FOR</a></td><td valign="top" colspan="1" class="conflue
 nceTd">Returns the physical plan for a particular query.</td><td valign="top" colspan="1" class="confluenceTd"><code>EXPLAIN PLAN FOR &lt;query&gt;;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd"><a href="https://cwiki.apache.org/confluence/display/DRILL/EXPLAIN+Commands" rel="nofollow">EXPLAIN PLAN WITHOUT IMPLEMENTATION FOR</a></td><td valign="top" colspan="1" class="confluenceTd">Returns the logical plan for a particular query.</td><td valign="top" colspan="1" class="confluenceTd"><code>EXPLAIN PLAN WITHOUT IMPLEMENTATION FOR &lt;query&gt;;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd"><a href="https://cwiki.apache.org/confluence/display/DRILL/SELECT+Statements" rel="nofollow">SELECT</a></td><td valign="top" colspan="1" class="confluenceTd">Retrieves data from tables and files.</td><td valign="top" colspan="1" class="confluenceTd"><code>[WITH subquery]<br />SELECT column_list FROM table_name <br />[WHERE clause]<br />[GROUP BY clause
 ]<br />[HAVING clause]<br />[ORDER BY clause];</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">SHOW DATABASES</td><td valign="top" colspan="1" class="confluenceTd">Returns a list of available schemas. Equivalent to SHOW SCHEMAS.</td><td valign="top" colspan="1" class="confluenceTd"><code>SHOW DATABASES;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd"><a href="https://cwiki.apache.org/confluence/display/DRILL/SHOW+FILES+Command" rel="nofollow">SHOW FILES</a></td><td valign="top" colspan="1" class="confluenceTd">Returns a list of files in a file system schema.</td><td valign="top" colspan="1" class="confluenceTd"><code>SHOW FILES IN filesystem.`schema_name`;<br />SHOW FILES FROM filesystem.`schema_name`;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">SHOW SCHEMAS</td><td valign="top" colspan="1" class="confluenceTd">Returns a list of available schemas. Equivalent to SHOW DATABASES.</td><td valign="top" colspan="1" clas
 s="confluenceTd"><code>SHOW SCHEMAS;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">SHOW TABLES</td><td valign="top" colspan="1" class="confluenceTd">Returns a list of tables for all schemas. Optionally, you can first issue the <code>USE </code>command to identify the schema for which you want to view tables.<br />For example, the following <code>USE</code> statement tells Drill that you only want information from the <code>hive.default</code> schema:<br /><code>USE hive.`default`;</code></td><td valign="top" colspan="1" class="confluenceTd"><code>SHOW TABLES;</code></td></tr><tr><td valign="top" colspan="1" class="confluenceTd">USE</td><td valign="top" colspan="1" class="confluenceTd">Change to a particular schema. When you opt to use a particular schema, Drill issues queries on that schema only.</td><td valign="top" colspan="1" class="confluenceTd"><code>USE schema_name;</code></td></tr></tbody></table></div>  
-  
-Click on any of the following links for more information about the various
-commands:
-
-  * [CREATE TABLE AS (CTAS) Command](/confluence/display/DRILL/CREATE+TABLE+AS+%28CTAS%29+Command)
-  * [EXPLAIN Commands](/confluence/display/DRILL/EXPLAIN+Commands)
-  * [SELECT Statements](/confluence/display/DRILL/SELECT+Statements)
-  * [SHOW FILES Command](/confluence/display/DRILL/SHOW+FILES+Command)
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/006-reserved-wds.md
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----
-title: "Reserved Keyswords"
-parent: "SQL Reference"
----
-When you use a reserved keyword in a Drill query, enclose the word in
-backticks. For example, if you issue the following query to Drill,  
-you must include backticks around the word TABLES because TABLES is a reserved
-keyword:
-
-``SELECT * FROM INFORMATION_SCHEMA.`TABLES`;``
-
-The following table provides the Drill reserved keywords that require back
-ticks:
-
-<div class="table-wrap"><table class="confluenceTable"><tbody><tr><td valign="top" class="confluenceTd"><h1 id="ReservedKeywords-A">A</h1><p>ABS<br />ALL<br />ALLOCATE<br />ALLOW<br />ALTER<br />AND<br />ANY<br />ARE<br />ARRAY<br />AS<br />ASENSITIVE<br />ASYMMETRIC<br />AT<br />ATOMIC<br />AUTHORIZATION<br />AVG</p><h1 id="ReservedKeywords-B">B</h1><p>BEGIN<br />BETWEEN<br />BIGINT<br />BINARY<br />BIT<br />BLOB<br />BOOLEAN<br />BOTH<br />BY</p><h1 id="ReservedKeywords-C">C</h1><p>CALL<br />CALLED<br />CARDINALITY<br />CASCADED<br />CASE<br />CAST<br />CEIL<br />CEILING<br />CHAR<br />CHARACTER<br />CHARACTER_LENGTH<br />CHAR_LENGTH<br />CHECK<br />CLOB<br />CLOSE<br />COALESCE<br />COLLATE<br />COLLECT<br />COLUMN<br />COMMIT<br />CONDITION<br />CONNECT<br />CONSTRAINT<br />CONVERT<br />CORR<br />CORRESPONDING<br />COUNT<br />COVAR_POP<br />COVAR_SAMP<br />CREATE<br />CROSS<br />CUBE<br />CUME_DIST<br />CURRENT<br />CURRENT_CATALOG<br />CURRENT_DATE<br />CURRENT_DEFAULT_TRANSFOR
 M_GROUP<br />CURRENT_PATH<br />CURRENT_ROLE<br />CURRENT_SCHEMA<br />CURRENT_TIME<br />CURRENT_TIMESTAMP<br />CURRENT_TRANSFORM_GROUP_FOR_TYPE<br />CURRENT_USER<br />CURSOR<br />CYCLE</p></td><td valign="top" class="confluenceTd"><h1 id="ReservedKeywords-D">D</h1><p>DATABASES<br />DATE<br />DAY<br />DEALLOCATE<br />DEC<br />DECIMAL<br />DECLARE<br />DEFAULT<br />DEFAULT_KW<br />DELETE<br />DENSE_RANK<br />DEREF<br />DESCRIBE<br />DETERMINISTIC<br />DISALLOW<br />DISCONNECT<br />DISTINCT<br />DOUBLE<br />DROP<br />DYNAMIC</p><h1 id="ReservedKeywords-E">E</h1><p>EACH<br />ELEMENT<br />ELSE<br />END<br />END_EXEC<br />ESCAPE<br />EVERY<br />EXCEPT<br />EXEC<br />EXECUTE<br />EXISTS<br />EXP<br />EXPLAIN<br />EXTERNAL<br />EXTRACT</p><h1 id="ReservedKeywords-F">F</h1><p>FALSE<br />FETCH<br />FILES<br />FILTER<br />FIRST_VALUE<br />FLOAT<br />FLOOR<br />FOR<br />FOREIGN<br />FREE<br />FROM<br />FULL<br />FUNCTION<br />FUSION</p><h1 id="ReservedKeywords-G">G</h1><p>GET<br />GLOBAL<br />GR
 ANT<br />GROUP<br />GROUPING</p><h1 id="ReservedKeywords-H">H</h1><p>HAVING<br />HOLD<br />HOUR</p></td><td valign="top" class="confluenceTd"><h1 id="ReservedKeywords-I">I</h1><p>IDENTITY<br />IMPORT<br />IN<br />INDICATOR<br />INNER<br />INOUT<br />INSENSITIVE<br />INSERT<br />INT<br />INTEGER<br />INTERSECT<br />INTERSECTION<br />INTERVAL<br />INTO<br />IS</p><h1 id="ReservedKeywords-J">J</h1><p>JOIN</p><h1 id="ReservedKeywords-L">L</h1><p>LANGUAGE<br />LARGE<br />LAST_VALUE<br />LATERAL<br />LEADING<br />LEFT<br />LIKE<br />LIMIT<br />LN<br />LOCAL<br />LOCALTIME<br />LOCALTIMESTAMP<br />LOWER</p><h1 id="ReservedKeywords-M">M</h1><p>MATCH<br />MAX<br />MEMBER<br />MERGE<br />METHOD<br />MIN<br />MINUTE<br />MOD<br />MODIFIES<br />MODULE<br />MONTH<br />MULTISET</p><h1 id="ReservedKeywords-N">N</h1><p>NATIONAL<br />NATURAL<br />NCHAR<br />NCLOB<br />NEW<br />NO<br />NONE<br />NORMALIZE<br />NOT<br />NULL<br />NULLIF<br />NUMERIC</p><h1 id="ReservedKeywords-O">O</h1><p>OCTET_LENGTH
 <br />OF<br />OFFSET<br />OLD<br />ON<br />ONLY<br />OPEN<br />OR<br />ORDER<br />OUT<br />OUTER<br />OVER<br />OVERLAPS<br />OVERLAY</p></td><td valign="top" colspan="1" class="confluenceTd"><h1 id="ReservedKeywords-P">P</h1><p>PARAMETER<br />PARTITION<br />PERCENTILE_CONT<br />PERCENTILE_DISC<br />PERCENT_RANK<br />POSITION<br />POWER<br />PRECISION<br />PREPARE<br />PRIMARY<br />PROCEDURE</p><h1 id="ReservedKeywords-R">R</h1><p>RANGE<br />RANK<br />READS<br />REAL<br />RECURSIVE<br />REF<br />REFERENCES<br />REFERENCING<br />REGR_AVGX<br />REGR_AVGY<br />REGR_COUNT<br />REGR_INTERCEPT<br />REGR_R2<br />REGR_SLOPE<br />REGR_SXX<br />REGR_SXY<br />RELEASE<br />REPLACE<br />RESULT<br />RETURN<br />RETURNS<br />REVOKE<br />RIGHT<br />ROLLBACK<br />ROLLUP<br />ROW<br />ROWS<br />ROW_NUMBER</p><h1 id="ReservedKeywords-S">S</h1><p>SAVEPOINT<br />SCHEMAS<br />SCOPE<br />SCROLL<br />SEARCH<br />SECOND<br />SELECT<br />SENSITIVE<br />SESSION_USER<br />SET<br />SHOW<br />SIMILAR<br />SMALLI
 NT<br />SOME<br />SPECIFIC<br />SPECIFICTYPE<br />SQL<br />SQLEXCEPTION<br />SQLSTATE<br />SQLWARNING<br />SQRT<br />START<br />STATIC<br />STDDEV_POP<br />STDDEV_SAMP<br />SUBMULTISET<br />SUBSTRING<br />SUM<br />SYMMETRIC<br />SYSTEM<br />SYSTEM_USER</p></td><td valign="top" colspan="1" class="confluenceTd"><h1 id="ReservedKeywords-T">T</h1><p>TABLE<br />TABLES<br />TABLESAMPLE<br />THEN<br />TIME<br />TIMESTAMP<br />TIMEZONE_HOUR<br />TIMEZONE_MINUTE<br />TINYINT<br />TO<br />TRAILING<br />TRANSLATE<br />TRANSLATION<br />TREAT<br />TRIGGER<br />TRIM<br />TRUE</p><h1 id="ReservedKeywords-U">U</h1><p>UESCAPE<br />UNION<br />UNIQUE<br />UNKNOWN<br />UNNEST<br />UPDATE<br />UPPER<br />USE<br />USER<br />USING</p><h1 id="ReservedKeywords-V">V</h1><p>VALUE<br />VALUES<br />VARBINARY<br />VARCHAR<br />VARYING<br />VAR_POP<br />VAR_SAMP</p><h1 id="ReservedKeywords-W">W</h1><p>WHEN<br />WHENEVER<br />WHERE<br />WIDTH_BUCKET<br />WINDOW<br />WITH<br />WITHIN<br />WITHOUT</p><h1 id="Reserve
 dKeywords-Y">Y</h1><p>YEAR</p></td></tr></tbody></table></div>
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/cmd-summary/001-create-table-as.md
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----
-title: "CREATE TABLE AS (CTAS) command"
-parent: "SQL Commands Summary"
----
-You can create tables in Drill by using the CTAS command:
-
-    CREATE TABLE new_table_name AS <query>;
-
-where query is any valid Drill query. Each table you create must have a unique
-name. You can include an optional column list for the new table. For example:
-
-    create table logtable(transid, prodid) as select transaction_id, product_id from ...
-
-You can store table data in one of three formats:
-
-  * csv
-  * parquet
-  * json
-
-The parquet and json formats can be used to store complex data.
-
-To set the output format for a Drill table, set the `store.format` option with
-the ALTER SYSTEM or ALTER SESSION command. For example:
-
-    alter session set `store.format`='json';
-
-Table data is stored in the location specified by the workspace that is in use
-when you run the CTAS statement. By default, a directory is created, using the
-exact table name specified in the CTAS statement. A .json, .csv, or .parquet
-file inside that directory contains the data.
-
-You can only create new tables in workspaces. You cannot create tables in
-other storage plugins such as Hive and HBase.
-
-You must use a writable (mutable) workspace when creating Drill tables. For
-example:
-
-	"tmp": {
-	      "location": "/tmp",
-	      "writable": true,
-	       }
-
-## Example
-
-The following query returns one row from a JSON file:
-
-	0: jdbc:drill:zk=local> select id, type, name, ppu
-	from dfs.`/Users/brumsby/drill/donuts.json`;
-	+------------+------------+------------+------------+
-	|     id     |    type    |    name    |    ppu     |
-	+------------+------------+------------+------------+
-	| 0001       | donut      | Cake       | 0.55       |
-	+------------+------------+------------+------------+
-	1 row selected (0.248 seconds)
-
-To create and verify the contents of a table that contains this row:
-
-  1. Set the workspace to a writable workspace.
-  2. Set the `store.format` option appropriately.
-  3. Run a CTAS statement that contains the query.
-  4. Go to the directory where the table is stored and check the contents of the file.
-  5. Run a query against the new table.
-
-The following sqlline output captures this sequence of steps.
-
-### Workspace Definition
-
-	"tmp": {
-	      "location": "/tmp",
-	      "writable": true,
-	       }
-
-### ALTER SESSION Command
-
-    alter session set `store.format`='json';
-
-### USE Command
-
-	0: jdbc:drill:zk=local> use dfs.tmp;
-	+------------+------------+
-	|     ok     |  summary   |
-	+------------+------------+
-	| true       | Default schema changed to 'dfs.tmp' |
-	+------------+------------+
-	1 row selected (0.03 seconds)
-
-### CTAS Command
-
-	0: jdbc:drill:zk=local> create table donuts_json as
-	select id, type, name, ppu from dfs.`/Users/brumsby/drill/donuts.json`;
-	+------------+---------------------------+
-	|  Fragment  | Number of records written |
-	+------------+---------------------------+
-	| 0_0        | 1                         |
-	+------------+---------------------------+
-	1 row selected (0.107 seconds)
-
-### File Contents
-
-	administorsmbp7:tmp brumsby$ pwd
-	/tmp
-	administorsmbp7:tmp brumsby$ cd donuts_json
-	administorsmbp7:donuts_json brumsby$ more 0_0_0.json
-	{
-	 "id" : "0001",
-	  "type" : "donut",
-	  "name" : "Cake",
-	  "ppu" : 0.55
-	}
-
-### Query Against New Table
-
-	0: jdbc:drill:zk=local> select * from donuts_json;
-	+------------+------------+------------+------------+
-	|     id     |    type    |    name    |    ppu     |
-	+------------+------------+------------+------------+
-	| 0001       | donut      | Cake       | 0.55       |
-	+------------+------------+------------+------------+
-	1 row selected (0.053 seconds)
-
-### Use a Different Output Format
-
-You can run the same sequence again with a different storage format set for
-the system or session (csv or parquet). For example, if the format is set to
-csv, and you name the table donuts_csv, the resulting file would look like
-this:
-
-	administorsmbp7:tmp brumsby$ cd donuts_csv
-	administorsmbp7:donuts_csv brumsby$ ls
-	0_0_0.csv
-	administorsmbp7:donuts_csv brumsby$ more 0_0_0.csv
-	id,type,name,ppu
-	0001,donut,Cake,0.55
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/cmd-summary/002-explain.md
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----
-title: "EXPLAIN commands"
-parent: "SQL Commands Summary"
----
-EXPLAIN is a useful tool for examining the steps that a query goes through
-when it is executed. You can use the EXPLAIN output to gain a deeper
-understanding of the parallel processing that Drill queries exploit. You can
-also look at costing information, troubleshoot performance issues, and
-diagnose routine errors that may occur when you run queries.
-
-Drill provides two variations on the EXPLAIN command, one that returns the
-physical plan and one that returns the logical plan. A logical plan takes the
-SQL query (as written by the user and accepted by the parser) and translates
-it into a logical series of operations that correspond to SQL language
-constructs (without defining the specific algorithms that will be implemented
-to run the query). A physical plan translates the logical plan into a specific
-series of steps that will be used when the query runs. For example, a logical
-plan may indicate a join step in general and classify it as inner or outer,
-but the corresponding physical plan will indicate the specific type of join
-operator that will run, such as a merge join or a hash join. The physical plan
-is operational and reveals the specific _access methods_ that will be used for
-the query.
-
-An EXPLAIN command for a query that is run repeatedly under the exact same
-conditions against the same data will return the same plan. However, if you
-change a configuration option, for example, or update the tables or files that
-you are selecting from, you are likely to see plan changes.
-
-# EXPLAIN Syntax
-
-The EXPLAIN command supports the following syntax:
-
-    explain plan [ including all attributes ] [ with implementation | without implementation ] for <query> ;
-
-where `query` is any valid SELECT statement supported by Drill.
-
-##### INCLUDING ALL ATTRIBUTES
-
-This option returns costing information. You can use this option for both
-physical and logical plans.
-
-#### WITH IMPLEMENTATION | WITHOUT IMPLEMENTATION
-
-These options return the physical and logical plan information, respectively.
-The default is physical (WITH IMPLEMENTATION).
-
-# EXPLAIN for Physical Plans
-
-The EXPLAIN PLAN FOR <query> command returns the chosen physical execution
-plan for a query statement without running the query. You can use this command
-to see what kind of execution operators Drill implements. For example, you can
-find out what kind of join algorithm is chosen when tables or files are
-joined. You can also use this command to analyze errors and troubleshoot
-queries that do not run. For example, if you run into a casting error, the
-query plan text may help you isolate the problem.
-
-Use the following syntax:
-
-    explain plan for <query> ;
-    explain plan with implementation for <query> ;
-
-The following set command increases the default text display (number of
-characters). By default, most of the plan output is not displayed.
-
-    0: jdbc:drill:zk=local> !set maxwidth 10000
-
-Do not use a semicolon to terminate set commands.
-
-For example, here is the top portion of the explain output for a
-COUNT(DISTINCT) query on a JSON file:
-
-	0: jdbc:drill:zk=local> !set maxwidth 10000
-	 
-	0: jdbc:drill:zk=local> explain plan for 
-	select type t, count(distinct id) 
-	from dfs.`/Users/brumsby/drill/donuts.json` 
-	where type='donut' group by type;
-	 
-	+------------+------------+
-	|    text    |    json    |
-	+------------+------------+
-	| 00-00    Screen
-	00-01      Project(t=[$0], EXPR$1=[$1])
-	00-02        Project(t=[$0], EXPR$1=[$1])
-	00-03          HashAgg(group=[{0}], EXPR$1=[COUNT($1)])
-	00-04            HashAgg(group=[{0, 1}])
-	00-05              SelectionVectorRemover
-	00-06                Filter(condition=[=(CAST($0):CHAR(5) CHARACTER SET "ISO-8859-1" COLLATE "ISO-8859-1$en_US$primary", 'donut')])
-	00-07                  Project(type=[$1], id=[$2])
-	00-08                    ProducerConsumer
-	00-09                      Scan(groupscan=[EasyGroupScan [selectionRoot=/Users/brumsby/drill/donuts.json, columns = null]])
-	...
-
-Read the text output from bottom to top to understand the sequence of
-operators that will execute the query. Note that the physical plan starts with
-a scan of the JSON file that is being queried. The selected columns are
-projected and filtered, then the aggregate function is applied.
-
-The EXPLAIN text output is followed by detailed JSON output, which is reusable
-for submitting the query via Drill APIs.
-
-	| {
-	  "head" : {
-	    "version" : 1,
-	    "generator" : {
-	      "type" : "ExplainHandler",
-	      "info" : ""
-	    },
-	    "type" : "APACHE_DRILL_PHYSICAL",
-	    "options" : [ ],
-	    "queue" : 0,
-	    "resultMode" : "EXEC"
-	  },
-	....
-
-# Costing Information
-
-Add the INCLUDING ALL ATTRIBUTES option to the EXPLAIN command to see cost
-estimates for the query plan. For example:
-
-	0: jdbc:drill:zk=local> !set maxwidth 10000
-	0: jdbc:drill:zk=local> explain plan including all attributes for 
-	select * from dfs.`/Users/brumsby/drill/donuts.json` where type='donut';
-	 
-	+------------+------------+
-	|    text    |    json    |
-	+------------+------------+
-	 
-	| 00-00    Screen: rowcount = 1.0, cumulative cost = {4.1 rows, 14.1 cpu, 0.0 io, 0.0 network}, id = 3110
-	00-01      Project(*=[$0], type=[$1]): rowcount = 1.0, cumulative cost = {4.0 rows, 14.0 cpu, 0.0 io, 0.0 network}, id = 3109
-	00-02        SelectionVectorRemover: rowcount = 1.0, cumulative cost = {3.0 rows, 6.0 cpu, 0.0 io, 0.0 network}, id = 3108
-	00-03          Filter(condition=[=(CAST($1):CHAR(5) CHARACTER SET "ISO-8859-1" COLLATE "ISO-8859-1$en_US$primary", 'donut')]): rowcount = 1.0, cumulative cost = {2.0 rows, 5.0 cpu, 0.0 io, 0.0 network}, id = 3107
-	00-04            ProducerConsumer: rowcount = 1.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io, 0.0 network}, id = 3106
-	00-05              Scan(groupscan=[EasyGroupScan [selectionRoot=/Users/brumsby/drill/donuts.json, columns = null]]): rowcount = 1.0, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io, 0.0 network}, id = 3101
-
-# EXPLAIN for Logical Plans
-
-To return the logical plan for a query (again, without actually running the
-query), use the EXPLAIN PLAN WITHOUT IMPLEMENTATION syntax:
-
-    explain plan without implementation for <query> ;
-
-For example:
-
-	0: jdbc:drill:zk=local> explain plan without implementation for 
-	select a.id 
-	from dfs.`/Users/brumsby/drill/donuts.json` a, dfs.`/Users/brumsby/drill/moredonuts.json` b 
-	where a.id=b.id;
-	 
-	+------------+------------+
-	|    text    |    json    |
-	+------------+------------+
-	| DrillScreenRel
-	  DrillProjectRel(id=[$1])
-	    DrillJoinRel(condition=[=($1, $3)], joinType=[inner])
-	      DrillScanRel(table=[[dfs, /Users/brumsby/drill/donuts.json]], groupscan=[EasyGroupScan [selectionRoot=/Users/brumsby/drill/donuts.json, columns = null]])
-	      DrillScanRel(table=[[dfs, /Users/brumsby/drill/moredonuts.json]], groupscan=[EasyGroupScan [selectionRoot=/Users/brumsby/drill/moredonuts.json, columns = null]])
-	 | {
-	  "head" : {
-	    "version" : 1,
-	    "generator" : {
-	      "type" : "org.apache.drill.exec.planner.logical.DrillImplementor",
-	      "info" : ""
-	    },
-	...
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/cmd-summary/003-select.md
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diff --git a/_docs/drill-docs/sql-ref/cmd-summary/003-select.md b/_docs/drill-docs/sql-ref/cmd-summary/003-select.md
deleted file mode 100644
index 57fc0cf..0000000
--- a/_docs/drill-docs/sql-ref/cmd-summary/003-select.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-title: "SELECT Statements"
-parent: "SQL Commands Summary"
----
-Drill supports the following ANSI standard clauses in the SELECT statement:
-
-  * WITH clause
-  * SELECT list
-  * FROM clause
-  * WHERE clause
-  * GROUP BY clause
-  * HAVING clause
-  * ORDER BY clause (with an optional LIMIT clause)
-
-You can use the same SELECT syntax in the following commands:
-
-  * CREATE TABLE AS (CTAS)
-  * CREATE VIEW
-
-INSERT INTO SELECT is not yet supported.
-
-# Column Aliases
-
-You can use named column aliases in the SELECT list to provide meaningful
-names for regular columns and computed columns, such as the results of
-aggregate functions. See the section on running queries for examples.
-
-You cannot reference column aliases in the following clauses:
-
-  * WHERE
-  * GROUP BY
-  * HAVING
-
-Because Drill works with schema-less data sources, you cannot use positional
-aliases (1, 2, etc.) to refer to SELECT list columns, except in the ORDER BY
-clause.
-
-# UNION ALL Set Operator
-
-Drill supports the UNION ALL set operator to combine two result sets. The
-distinct UNION operator is not yet supported.
-
-The EXCEPT, EXCEPT ALL, INTERSECT, and INTERSECT ALL operators are not yet
-supported.
-
-# Joins
-
-Drill supports ANSI standard joins in the FROM and WHERE clauses:
-
-  * Inner joins
-  * Left, full, and right outer joins
-
-The following types of join syntax are supported:
-
-Join type| Syntax  
----|---  
-Join condition in WHERE clause|FROM table1, table 2 WHERE table1.col1=table2.col1  
-USING join in FROM clause|FROM table1 JOIN table2 USING(col1, ...)  
-ON join in FROM clause|FROM table1 JOIN table2 ON table1.col1=table2.col1  
-NATURAL JOIN in FROM clause|FROM table 1 NATURAL JOIN table 2  
-
-Cross-joins are not yet supported. You must specify a join condition when more
-than one table is listed in the FROM clause.
-
-Non-equijoins are supported if the join also contains an equality condition on
-the same two tables as part of a conjunction:
-
-    table1.col1 = table2.col1 AND table1.c2 < table2.c2
-
-This restriction applies to both inner and outer joins.
-
-# Subqueries
-
-You can use the following subquery operators in Drill queries. These operators
-all return Boolean results.
-
-  * ALL
-  * ANY
-  * EXISTS
-  * IN
-  * SOME
-
-In general, correlated subqueries are supported. EXISTS and NOT EXISTS
-subqueries that do not contain a correlation join are not yet supported.
-

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/cmd-summary/004-show-files.md
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diff --git a/_docs/drill-docs/sql-ref/cmd-summary/004-show-files.md b/_docs/drill-docs/sql-ref/cmd-summary/004-show-files.md
deleted file mode 100644
index d0f5a83..0000000
--- a/_docs/drill-docs/sql-ref/cmd-summary/004-show-files.md
+++ /dev/null
@@ -1,65 +0,0 @@
----
-title: "SHOW FILES Command"
-parent: "SQL Commands Summary"
----
-The SHOW FILES command provides a quick report of the file systems that are
-visible to Drill for query purposes. This command is unique to Apache Drill.
-
-# Syntax
-
-The SHOW FILES command supports the following syntax.
-
-    SHOW FILES [ FROM filesystem.directory_name | IN filesystem.directory_name ];
-
-The FROM or IN clause is required if you do not specify a default file system
-first. You can do this with the USE command. FROM and IN are synonyms.
-
-The directory name is optional. (If the directory name is a Drill reserved
-word, you must use back ticks around the name.)
-
-The command returns standard Linux `stat` information for each file or
-directory, such as permissions, owner, and group values. This information is
-not specific to Drill.
-
-# Examples
-
-The following example returns information about directories and files in the
-local (`dfs`) file system.
-
-	0: jdbc:drill:> use dfs;
-	 
-	+------------+------------+
-	|     ok     |  summary   |
-	+------------+------------+
-	| true       | Default schema changed to 'dfs' |
-	+------------+------------+
-	1 row selected (0.318 seconds)
-	 
-	0: jdbc:drill:> show files;
-	+------------+-------------+------------+------------+------------+------------+-------------+------------+------------------+
-	|    name    | isDirectory |   isFile   |   length   |   owner    |   group    | permissions | accessTime | modificationTime |
-	+------------+-------------+------------+------------+------------+------------+-------------+------------+------------------+
-	| user       | true        | false      | 1          | mapr       | mapr       | rwxr-xr-x   | 2014-07-30 21:37:06.0 | 2014-07-31 22:15:53.193 |
-	| backup.tgz | false       | true       | 36272      | root       | root       | rw-r--r--   | 2014-07-31 22:09:13.0 | 2014-07-31 22:09:13.211 |
-	| JSON       | true        | false      | 1          | root       | root       | rwxr-xr-x   | 2014-07-31 15:22:42.0 | 2014-08-04 15:43:07.083 |
-	| scripts    | true        | false      | 3          | root       | root       | rwxr-xr-x   | 2014-07-31 22:10:51.0 | 2014-08-04 18:23:09.236 |
-	| temp       | true        | false      | 2          | root       | root       | rwxr-xr-x   | 2014-08-01 20:07:37.0 | 2014-08-01 20:09:42.595 |
-	| hbase      | true        | false      | 10         | mapr       | mapr       | rwxr-xr-x   | 2014-07-30 21:36:08.0 | 2014-08-04 18:31:13.778 |
-	| tables     | true        | false      | 0          | root       | root       | rwxrwxrwx   | 2014-07-31 22:14:35.0 | 2014-08-04 15:42:43.415 |
-	| CSV        | true        | false      | 4          | root       | root       | rwxrwxrwx   | 2014-07-31 17:34:53.0 | 2014-08-04
-	...
-
-The following example shows the files in a specific directory in the `dfs`
-file system:
-
-	0: jdbc:drill:> show files in dfs.CSV;
-	 
-	+------------+-------------+------------+------------+------------+------------+-------------+------------+------------------+
-	|    name    | isDirectory |   isFile   |   length   |   owner    |   group    | permissions | accessTime | modificationTime |
-	+------------+-------------+------------+------------+------------+------------+-------------+------------+------------------+
-	| customers.csv | false       | true       | 62011      | root       | root       | rw-r--r--   | 2014-08-04 18:30:39.0 | 2014-08-04 18:30:39.314 |
-	| products.csv.small | false       | true       | 34972      | root       | root       | rw-r--r--   | 2014-07-31 23:58:42.0 | 2014-07-31 23:59:16.849 |
-	| products.csv | false       | true       | 34972      | root       | root       | rw-r--r--   | 2014-08-01 06:39:34.0 | 2014-08-04 15:58:09.325 |
-	| products.csv.bad | false       | true       | 62307      | root       | root       | rw-r--r--   | 2014-08-04 15:58:02.0 | 2014-08-04 15:58:02.612 |
-	+------------+-------------+------------+------------+------------+------------+-------------+------------+------------------+
-	4 rows selected (0.165 seconds)
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/drill/blob/d959a210/_docs/drill-docs/sql-ref/data-types/001-date.md
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diff --git a/_docs/drill-docs/sql-ref/data-types/001-date.md b/_docs/drill-docs/sql-ref/data-types/001-date.md
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index 8bf4230..0000000
--- a/_docs/drill-docs/sql-ref/data-types/001-date.md
+++ /dev/null
@@ -1,148 +0,0 @@
----
-title: "Supported Date/Time Data Type Formats"
-parent: "Data Types"
----
-You must use supported `date` and `time` formats when you `SELECT` date and
-time literals or when you `CAST()` from `VARCHAR `to `date` and `time` data
-types. Apache Drill currently supports specific formats for the following
-`date` and `time` data types:
-
-  * Date
-  * Timestamp
-  * Time
-  * Interval
-    * Interval Year
-    * Interval Day
-  * Literal
-
-The following query provides an example of how to `SELECT` a few of the
-supported date and time formats as literals:
-
-    select date '2008-2-23', timestamp '2008-1-23 14:24:23', time '10:20:30' from dfs.`/tmp/input.json`;
-
-The following query provides an example where `VARCHAR` data in a file is
-`CAST()` to supported `date `and `time` formats:
-
-    select cast(col_A as date), cast(col_B as timestamp), cast(col_C as time) from dfs.`/tmp/dates.json`;
-
-`Date`, t`imestamp`, and` time` data types store values in `UTC`. Currently,
-Apache Drill does not support `timestamp` with time zone.
-
-## Date
-
-Drill supports the `date` data type in the following format:
-
-    YYYY-MM-DD (year-month-date)
-
-The following table provides some examples for the `date` data type:
-
-  | Use | Example |
-  | --- | ------- |
-  |Literal| `select date ‘2008-2-23’ from dfs.`/tmp/input.json`;`|
-  |`JSON` input | `{"date_col" : "2008-2-23"} 
-  | `CAST` from `VARCHAR`| `` select CAST(date_col as date) as CAST_DATE from dfs.`/tmp/input.json`; ``|
- 
-## Timestamp
-
-Drill supports the `timestamp` data type in the following format:
-
-    yyyy-MM-dd HH:mm:ss.SSS (year-month-date hour:minute:sec.milliseconds)
-
-The following table provides some examples for the `timestamp` data type:
-
-<table>
- <tbody>
-  <tr>
-   <th>Use</th>
-   <th>CAST Example</th>
-  </tr>
-  <tr>
-   <td valign="top">Literal</td>
-   <td valign="top"><code><span style="color: rgb(0,0,0);">select timestamp ‘2008-2-23 10:20:30.345’, timestamp ‘2008-2-23 10:20:30’ from dfs.`/tmp/input.json`;</span></code>
-   </td></tr>
-  <tr>
-   <td colspan="1" valign="top"><code>JSON</code> Input</td>
-   <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">{“timestamp_col”: “2008-2-23 15:20:30.345”}<br /></span><span style="color: rgb(0,0,0);">{“timestamp_col”: “2008-2-23 10:20:30”}</span></code><span style="color: rgb(0,0,0);">The fractional millisecond component is optional.</span></td>
-   </tr>
-   <tr>
-    <td colspan="1" valign="top"><code>CAST</code> from <code>VARCHAR</code></td>
-    <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select cast(timestamp_col as timestamp) from dfs.`/tmp/input.json`; </span></code></td>
-   </tr>
-  </tbody>
- </table>
-  
-## Time
-
-Drill supports the `time` data type in the following format:
-
-    HH:mm:ss.SSS (hour:minute:sec.milliseconds)
-
-The following table provides some examples for the` time` data type:
-
-<table><tbody><tr>
-  <th>Use</th>
-  <th>Example</th>
-  </tr>
-  <tr>
-   <td valign="top">Literal</td>
-   <td valign="top"><code><span style="color: rgb(0,0,0);">select time ‘15:20:30’, time ‘10:20:30.123’ from dfs.`/tmp/input.json`;</span></code></td>
-  </tr>
-  <tr>
-  <td colspan="1" valign="top"><code>JSON</code> Input</td>
-  <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">{“time_col” : “10:20:30.999”}<br /></span><span style="color: rgb(0,0,0);">{“time_col”: “10:20:30”}</span></code></td>
- </tr>
- <tr>
-  <td colspan="1" valign="top"><code>CAST</code> from <code>VARCHAR</code></td>
-  <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select cast(time_col as time) from dfs.`/tmp/input.json`;</span></code></td>
-</tr></tbody>
-</table>
-  
-## Interval
-
-Drill supports the `interval year` and `interval day` data types.
-
-### Interval Year
-
-The `interval year` data type stores time duration in years and months. Drill
-supports the `interval` data type in the following format:
-
-    P [qty] Y [qty] M
-
-The following table provides examples for `interval year `data type:
-
-<table ><tbody><tr>
-<th>Use</th>
-<th>Example</th></tr>
-  <tr>
-    <td valign="top">Literals</td>
-    <td valign="top"><code><span style="color: rgb(0,0,0);">select interval ‘1-2’ year to month from dfs.`/tmp/input.json`;<br /></span><span style="color: rgb(0,0,0);">select interval ‘1’ year from dfs.`/tmp/input.json`;<br /></span><span style="color: rgb(0,0,0);">select interval '13’ month from dfs.`/tmp/input.json`;</span></code></td></tr><tr>
-    <td colspan="1" valign="top"><code>JSON</code> Input</td>
-    <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">{“col” : “P1Y2M”}<br /></span><span style="color: rgb(0,0,0);">{“col” : “P-1Y2M”}<br /></span><span style="color: rgb(0,0,0);">{“col” : “P-1Y-2M”}<br /></span><span style="color: rgb(0,0,0);">{“col”: “P10M”}<br /></span><span style="color: rgb(0,0,0);">{“col”: “P5Y”}</span></code></td>
-  </tr>
-  <tr>
-    <td colspan="1" valign="top"><code>CAST</code> from <code>VARCHAR</code></td>
-    <td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select cast(col as interval year) from dfs.`/tmp/input.json`;</span></code></td>
-  </tr>
-  </tbody></table> 
-  
-### Interval Day
-
-The `interval day` data type stores time duration in days, hours, minutes, and
-seconds. You do not need to specify all fields in a given interval. Drill
-supports the `interval day` data type in the following format:
-
-    P [qty] D T [qty] H [qty] M [qty] S
-
-The following table provides examples for `interval day` data type:
-
-<div class="table-wrap"><table class="confluenceTable"><tbody><tr><th class="confluenceTh">Use</th><th class="confluenceTh">Example</th></tr><tr><td valign="top">Literal</td><td valign="top"><code><span style="color: rgb(0,0,0);">select interval '1 10:20:30.123' day to second from dfs.`/tmp/input.json`;<br /></span><span style="color: rgb(0,0,0);">select interval '1 10' day to hour from dfs.`/tmp/input.json`;<br /></span><span style="color: rgb(0,0,0);">select interval '10' day  from dfs.`/tmp/input.json`;<br /></span><span style="color: rgb(0,0,0);">select interval '10' hour  from dfs.`/tmp/input.json`;</span></code><code><span style="color: rgb(0,0,0);">select interval '10.999' second  from dfs.`/tmp/input.json`;</span></code></td></tr><tr><td colspan="1" valign="top"><code>JSON</code> Input</td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;P1DT10H20M30S&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;P1DT
 10H20M30.123S&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;P1D&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;PT10H&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;PT10.10S&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;PT20S&quot;}<br /></span><span style="color: rgb(0,0,0);">{&quot;col&quot; : &quot;PT10H10S&quot;}</span></code></td></tr><tr><td colspan="1" valign="top"><code>CAST</code> from <code>VARCHAR</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select cast(col as interval day) from dfs.`/tmp/input.json`;</span></code></td></tr></tbody></table></div> 
-  
-## Literal
-
-The following table provides a list of` date/time` literals that Drill
-supports with examples of each:
-
-<div class="table-wrap"><table class="confluenceTable"><tbody><tr><th class="confluenceTh">Format</th><th colspan="1" class="confluenceTh">Interpretation</th><th class="confluenceTh">Example</th></tr><tr><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">interval '1 10:20:30.123' day to second</span></code></td><td colspan="1" valign="top"><code>1 day, 10 hours, 20 minutes, 30 seconds, and 123 thousandths of a second</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select interval '1 10:20:30.123' day to second from dfs.`/tmp/input.json`;</span></code></td></tr><tr><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">interval '1 10' day to hour</span></code></td><td colspan="1" valign="top"><code>1 day 10 hours</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select interval '1 10' day to hour from dfs.`/tmp/input.json`;</span></code></td></tr><tr><td colspan="1" valign="top"><code><span styl
 e="color: rgb(0,0,0);">interval '10' day</span></code></td><td colspan="1" valign="top"><code>10 days</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select interval '10' day from dfs.`/tmp/input.json`;</span></code></td></tr><tr><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">interval '10' hour</span></code></td><td colspan="1" valign="top"><code>10 hours</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select interval '10' hour from dfs.`/tmp/input.json`;</span></code></td></tr><tr><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">interval '10.999' second</span></code></td><td colspan="1" valign="top"><code>10.999 seconds</code></td><td colspan="1" valign="top"><code><span style="color: rgb(0,0,0);">select interval '10.999' second from dfs.`/tmp/input.json`; </span></code></td></tr></tbody></table></div>
-
-
-


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