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From chenliang...@apache.org
Subject [2/3] incubator-carbondata-site git commit: update pmc and committer link
Date Mon, 20 Feb 2017 23:51:57 GMT
http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/9ebca155/content/docs/latest_htmls/overview-of-carbondata.html
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Overview</h1><p>This tutorial provides a detailed overview about :</p>
+<ul>
+  <li><a href="#introduction">Introduction</a></li>
+  <li><a href="#features">Features</a></li>
+</ul><h2>Introduction</h2><p>CarbonData is a fully indexed columnar and Hadoop native data-store for processing heavy analytical workloads and detailed queries on big data. CarbonData allows faster interactive query using advanced columnar storage, index, compression and encoding techniques to improve computing efficiency, which helps in speeding up queries by an order of magnitude faster over PetaBytes of data.</p><p>In customer benchmarks, CarbonData has proven to manage Petabyte of data running on extraordinarily low-cost hardware and answers queries around 10 times faster than the current open source solutions (column-oriented SQL on Hadoop data-stores).</p><p>Some of the salient features of CarbonData are :</p>
+<ul>
+  <li>Low-Latency for various types of data access patterns like Sequential, Random and OLAP.</li>
+  <li>Fast query on fast data.</li>
+  <li>Space efficiency.</li>
+  <li>General format available on Hadoop-ecosystem.</li>
+</ul><h2>Features</h2><p>CarbonData file format is a columnar store in HDFS. It has many features that a modern columnar format has, such as splittable, compression schema, complex data type etc and CarbonData has following unique features:</p>
+<ul>
+  <li><p>Unique Data Organization: Though CarbonData stores data in Columnar format, it differs from traditional Columnar formats as the columns in each row-group(Data Block) is sorted independent of the other columns. Though this arrangement requires CarbonData to store the row-number mapping against each column value, it makes it possible to use binary search for faster filtering and since the values are sorted, same/similar values come together which yields better compression and offsets the storage overhead required by the row number mapping.</p></li>
+  <li><p>Advanced Push Down Optimizations: CarbonData pushes as much of query processing as possible close to the data to minimize the amount of data being read, processed, converted and transmitted/shuffled. Using projections and filters it reads only the required columns form the store and also reads only the rows that match the filter conditions provided in the query.</p></li>
+  <li><p>Multi Level Indexing: CarbonData uses multiple indices at various levels to enable faster search and speed up query processing.</p></li>
+  <li><p>Dictionary Encoding: Most databases and big data SQL data stores employ columnar encoding to achieve data compression by storing small integers numbers (surrogate value) instead of full string values. However, almost all existing databases and data stores divide the data into row groups containing anywhere from few thousand to a million rows and employ dictionary encoding only within each row group. Hence, the same column value can have different surrogate values in different row groups. So, while reading the data, conversion from surrogate value to actual value needs to be done immediately after the data is read from the disk. But CarbonData employs global surrogate key which means that a common dictionary is maintained for the full store on one machine/node. So CarbonData can perform all the query processing work such as grouping/aggregation, sorting etc on light weight surrogate values. The conversion from surrogate to actual values needs to be done only on the final res
 ult. This procedure improves performance on two aspects. Conversion from surrogate values to actual values is done only for the final result rows which are much less than the actual rows read from the store. All query processing and computation such as grouping/aggregation, sorting, and so on is done on lightweight surrogate values which requires less memory and CPU time compared to actual values.</p></li>
+  <li><p>Deep Spark Integration: It has built-in spark integration for Spark 1.6.2, 2.1 and interfaces for Spark SQL, DataFrame API and query optimization. It supports bulk data ingestion and allows saving of spark dataframes as CarbonData files.</p></li>
+  <li><p>Update Delete Support: It supports batch updates like daily update scenarios for OLAP and Base+Delta file based design.</p></li>
+  <li><p>Bucketing : It is a technique that is used for uniform distribution of data across files in CarbonData. It enhances the performance of join queries. While loading the data, records are placed into buckets based on hashing algorithm. During the execution of join queries the records can be fetched from buckets with out need of shuffling.This feature is used to distribute/organize the table/partition data into multiple files placing similar records in same file.</p></li>
+  <li><p>Global Multi Dimensional Keys(MDK) based B+Tree Index for all non- measure columns: Aids in quickly locating the row groups(Data Blocks) that contain the data matching search/filter criteria.</p></li>
+  <li><p>Min-Max Index for all columns: Aids in quickly locating the row groups(Data Blocks) that contain the data matching search/filter criteria.</p></li>
+  <li><p>Data Block level Inverted Index for all columns: Aids in quickly locating the rows that contain the data matching search/filter criteria within a row group(Data Blocks).</p></li>
+  <li><p>Store data along with index: Significantly accelerates query performance and reduces the I/O scans and CPU resources, when there are filters in the query. CarbonData index consists of multiple levels of indices. A processing framework can leverage this index to reduce the task it needs to schedule and process. It can also do skip scan in more finer grain units (called blocklet) in task side scanning instead of scanning the whole file.</p></li>
+  <li><p>Operable encoded data: It supports efficient compression and global encoding schemes and can query on compressed/encoded data. The data can be converted just before returning the results to the users, which is "late materialized".</p></li>
+  <li><p>Column group: Allows multiple columns to form a column group that would be stored as row format. This reduces the row reconstruction cost at query time.</p></li>
+  <li><p>Support for various use cases with one single Data format: Examples are interactive OLAP-style query, Sequential Access (big scan) and Random Access (narrow scan).</p></li>
+</ul>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Quick Start</h1><p>This tutorial provides a quick introduction to using CarbonData.</p><h2>Prerequisites</h2>
+<ul>
+  <li><a href="https://github.com/apache/incubator-carbondata/blob/master/build">Installation and building CarbonData</a>.</li>
+  <li>Create a sample.csv file using the following commands. The CSV file is required for loading data into CarbonData.</li>
+</ul><p><code>
+cd carbondata
+cat &gt; sample.csv &lt;&lt; EOF
+id,name,city,age
+1,david,shenzhen,31
+2,eason,shenzhen,27
+3,jarry,wuhan,35
+EOF
+</code></p><h2>Interactive Analysis with Spark Shell Version 2.1</h2><p>Apache Spark Shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. Please visit <a href="http://spark.apache.org/docs/latest/">Apache Spark Documentation</a> for more details on Spark shell.</p><h4>Basics</h4><p>Start Spark shell by running the following command in the Spark directory:</p><p><code>
+./bin/spark-shell --jars &lt;carbondata assembly jar path&gt;
+</code></p><p>In this shell, SparkSession is readily available as 'spark' and Spark context is readily available as 'sc'.</p><p>In order to create a CarbonSession we will have to configure it explicitly in the following manner :</p>
+<ul>
+  <li>Import the following :</li>
+</ul><p><code>
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.CarbonSession._
+</code></p>
+<ul>
+  <li>Create a CarbonSession :</li>
+</ul><p><code>
+val carbon = SparkSession
+            .builder()
+            .config(sc.getConf)
+            .getOrCreateCarbonSession()
+</code></p><h4>Executing Queries</h4><h5>Creating a Table</h5><p><code>
+scala&gt;carbon.sql(&quot;CREATE TABLE IF NOT EXISTS test_table
+     (id string, name string, city string, age Int)
+     STORED BY &#39;carbondata&#39;&quot;)
+</code></p><h5>Loading Data to a Table</h5><p><code>
+scala&gt;carbon.sql(&quot;LOAD DATA INPATH &#39;sample.csv file path&#39; INTO TABLE test_table&quot;)
+</code> NOTE:Please provide the real file path of sample.csv for the above script.</p><h6>Query Data from a Table</h6><p>``` scala&gt;carbon.sql("SELECT * FROM test_table").show()</p><p>scala&gt;carbon.sql("SELECT city, avg(age), sum(age)  FROM test_table GROUP BY city").show() ```</p><h2>Interactive Analysis with Spark Shell Version 1.6</h2><h4>Basics</h4><p>Start Spark shell by running the following command in the Spark directory:</p><p><code>
+./bin/spark-shell --jars &lt;carbondata assembly jar path&gt;
+</code></p><p>NOTE: In this shell, SparkContext is readily available as sc.</p>
+<ul>
+  <li>In order to execute the Queries we need to import CarbonContext:</li>
+</ul><p><code>
+import org.apache.spark.sql.CarbonContext
+</code></p>
+<ul>
+  <li>Create an instance of CarbonContext in the following manner :</li>
+</ul><p><code>
+val cc = new CarbonContext(sc)
+</code></p><p>NOTE: By default store location is pointed to "../carbon.store", user can provide own store location to CarbonContext like new CarbonContext(sc, storeLocation).</p><h4>Executing Queries</h4><h5>Creating a Table</h5><p><code>
+scala&gt;cc.sql(&quot;CREATE TABLE IF NOT EXISTS test_table
+     (id string, name string, city string, age Int)
+     STORED BY &#39;carbondata&#39;&quot;)
+</code> To see the table created :</p><p><code>
+scala&gt;cc.sql(&quot;SHOW TABLES&quot;).show()
+</code></p><h5>Loading Data to a Table</h5><p><code>
+scala&gt;cc.sql(&quot;LOAD DATA INPATH &#39;sample.csv file path&#39;
+      INTO TABLE test_table&quot;)
+</code> NOTE:Please provide the real file path of sample.csv for the above script.</p><h5>Query Data from a Table</h5><p><code>
+scala&gt;cc.sql(&quot;SELECT * FROM test_table&quot;).show()
+scala&gt;cc.sql(&quot;SELECT city, avg(age), sum(age)
+      FROM test_table GROUP BY city&quot;).show()
+</code></p>
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+<h1>Data Types</h1><h4>CarbonData supports the following data types:</h4>
+<ul>
+  <li>Numeric Types</li>
+  <li>SMALLINT</li>
+  <li>INT/INTEGER</li>
+  <li>BIGINT</li>
+  <li>DOUBLE</li>
+  <li>DECIMAL</li>
+  <li><p>Date/Time Types</p></li>
+  <li>TIMESTAMP</li>
+  <li><p>String Types</p></li>
+  <li>STRING</li>
+  <li><p>Complex Types</p>
+  <ul>
+    <li>arrays: ARRAY<code>&lt;data_type&gt;</code></li>
+    <li>structs: STRUCT<code>&lt;col_name : data_type COMMENT col_comment, ...&gt;</code></li>
+  </ul></li>
+</ul>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Troubleshooting</h1><p>This tutorial is designed to provide troubleshooting for end users and developers who are building, deploying, and using CarbonData.</p>
+<ul>
+  <li><a href="#failed-to-load-thrift-libraries">Failed to load thrift libraries</a></li>
+  <li><a href="#failed-to-launch-the-spark-shell">Failed to launch the Spark Shell</a></li>
+  <li><a href="#query-failure-with-generic-error-on-the-beeline">Query Failure with Generic Error on the Beeline</a></li>
+  <li><a href="#failed-to-execute-load-query-on-cluster">Failed to execute load query on cluster</a></li>
+  <li><a href="#failed-to-execute-insert-query-on-cluster">Failed to execute insert query on cluster</a></li>
+  <li><a href="#failed-to-connect-to-hiveuser-with-thrift">Failed to connect to hiveuser with thrift</a></li>
+  <li><a href="#failure-to-read-the-metastore-db-during-table-creation">Failure to read the metastore db during table creation</a></li>
+  <li><a href="#failed-to-load-data-on-the-cluster">Failed to load data on the cluster</a></li>
+  <li><a href="#failed-to-insert-data-on-the-cluster">Failed to insert data on the cluster</a></li>
+  <li><a href="#failed-to-execute-concurrent-operations">Failed to execute Concurrent Operations</a></li>
+  <li><a href="#failed-to-create-a-table-with-a-single-numeric-column">Failed to create a table with a single numeric column</a></li>
+  <li><a href="#data-failure-because-of-bad-records">Data Failure because of Bad Records</a></li>
+</ul><h2>Failed to load thrift libraries</h2><p><strong>Symptom</strong></p><p>Thrift throws following exception :</p><p><code>
+  thrift: error while loading shared libraries:
+  libthriftc.so.0: cannot open shared object file: No such file or directory
+</code></p><p><strong>Possible Cause</strong></p><p>The complete path to the directory containing the libraries is not configured correctly.</p><p><strong>Procedure</strong></p><p>Follow the steps below to ensure loading of libraries appropriately :</p>
+<ol>
+  <li><p>For ubuntu you have to add a custom.conf file to /etc/ld.so.conf.d  For example,</p><p><code>
+ sudo gedit /etc/ld.so.conf.d/randomLibs.conf
+</code></p><p>Inside this file you are supposed to configure the complete path to the directory that contains all the libraries that you wish to add to the system, let us say /home/ubuntu/localLibs</p></li>
+  <li><p>To ensure your library location ,check for existence of libthrift.so</p></li>
+  <li><p>Save and run the following command to update the system with this libs.</p><p><code>
+  sudo ldconfig
+</code></p><p>Note : Remember to add only the path to the directory, not the full path for that file, all the libraries inside that path will be automatically indexed.</p></li>
+</ol><h2>Failed to launch the Spark Shell</h2><p><strong>Symptom</strong></p><p>The shell prompts the following error :</p><p><code>
+  org.apache.spark.sql.CarbonContext$$anon$$apache$spark$sql$catalyst$analysis
+  $OverrideCatalog$_setter_$org$apache$spark$sql$catalyst$analysis
+  $OverrideCatalog$$overrides_$e
+</code></p><p><strong>Possible Cause</strong></p><p>The Spark Version and the selected Spark Profile do not match.</p><p><strong>Procedure</strong></p>
+<ol>
+  <li><p>Ensure your spark version and selected profile for spark are correct.</p></li>
+  <li><p>Use the following command :</p><p><code>
+ &quot;mvn -Pspark-2.1 -Dspark.version {yourSparkVersion} clean package&quot;
+</code></p><p>Note : Refrain from using "mvn clean package" without specifying the profile.</p></li>
+</ol><h2>Query Failure with Generic Error on the Beeline</h2><p><strong>Symptom</strong></p><p>Query fails on the executor side and generic error message is printed on the beeline console</p><p><img src="../../../src/site/markdown/images/query_failure_beeline.png?raw=true" alt="Query Failure Beeline" /></p><p><strong>Possible Causes</strong></p>
+<ul>
+  <li>In Query flow, Table B-Tree will be loaded into memory on the driver side and filter condition is validated against the min-max of each block to identify false positive,  Once the blocks are selected, based on number of available executors, blocks will be distributed to each executor node as shown in below driver logs snapshot</li>
+</ul><p><img src="../../../src/site/markdown/images/query_failure_logs.png?raw=true" alt="Query Failure Logs" /></p>
+<ul>
+  <li><p>When the error occurs in driver side while b-tree loading or block distribution, detail error message will be printed on the beeline console and error trace will be printed on the driver logs.</p></li>
+  <li><p>When the error occurs in the executor side, generic error message will be printed as shown in issue description.</p></li>
+</ul><p><img src="../../../src/site/markdown/images/query_failure_job_details.png?raw=true" alt="Query Failure Job Details" /></p>
+<ul>
+  <li>Details of the failed stages can be seen in the Spark Application UI by clicking on the failed stages on the failed job as shown in previous snapshot</li>
+</ul><p><img src="../../../src/site/markdown/images/query_failure_spark_ui.png?raw=true" alt="Query Failure Spark UI" /></p><p><strong>Procedure</strong></p><p>Details of the error can be analyzed in details using executor logs available in stdout</p><p><img src="../../../src/site/markdown/images/query_failure_procedure.png?raw=true" alt="Query Failure Spark UI" /></p><p>Below snapshot shows executor logs with error message for query failure which can be helpful to locate the error</p><p><img src="../../../src/site/markdown/images/query_failure_issue.png?raw=true" alt="Query Failure Spark UI" /> </p><h2>Failed to execute load query on cluster.</h2><p><strong>Symptom</strong></p><p>Load query failed with the following exception:</p><p><code>
+  Dictionary file is locked for updation.
+</code></p><p><strong>Possible Cause</strong></p><p>The carbon.properties file is not identical in all the nodes of the cluster.</p><p><strong>Procedure</strong></p><p>Follow the steps to ensure the carbon.properties file is consistent across all the nodes:</p>
+<ol>
+  <li><p>Copy the carbon.properties file from the master node to all the other nodes in the cluster.  For example, you can use ssh to copy this file to all the nodes.</p></li>
+  <li><p>For the changes to take effect, restart the Spark cluster.</p></li>
+</ol><h2>Failed to execute insert query on cluster.</h2><p><strong>Symptom</strong></p><p>Load query failed with the following exception:</p><p><code>
+  Dictionary file is locked for updation.
+</code></p><p><strong>Possible Cause</strong></p><p>The carbon.properties file is not identical in all the nodes of the cluster.</p><p><strong>Procedure</strong></p><p>Follow the steps to ensure the carbon.properties file is consistent across all the nodes:</p>
+<ol>
+  <li><p>Copy the carbon.properties file from the master node to all the other nodes in the cluster.  For example, you can use scp to copy this file to all the nodes.</p></li>
+  <li><p>For the changes to take effect, restart the Spark cluster.</p></li>
+</ol><h2>Failed to connect to hiveuser with thrift</h2><p><strong>Symptom</strong></p><p>We get the following exception :</p><p><code>
+  Cannot connect to hiveuser.
+</code></p><p><strong>Possible Cause</strong></p><p>The external process does not have permission to access.</p><p><strong>Procedure</strong></p><p>Ensure that the Hiveuser in mysql must allow its access to the external processes.</p><h2>Failure to read the metastore db during table creation.</h2><p><strong>Symptom</strong></p><p>We get the following exception on trying to connect :</p><p><code>
+  Cannot read the metastore db
+</code></p><p><strong>Possible Cause</strong></p><p>The metastore db is dysfunctional.</p><p><strong>Procedure</strong></p><p>Remove the metastore db from the carbon.metastore in the Spark Directory.</p><h2>Failed to load data on the cluster</h2><p><strong>Symptom</strong></p><p>Data loading fails with the following exception :</p><p><code>
+   Data Load failure exeception
+</code></p><p><strong>Possible Cause</strong></p><p>The following issue can cause the failure :</p>
+<ol>
+  <li><p>The core-site.xml, hive-site.xml, yarn-site and carbon.properties are not consistent across all nodes of the cluster.</p></li>
+  <li><p>Path to hdfs ddl is not configured correctly in the carbon.properties.</p></li>
+</ol><p><strong>Procedure</strong></p><p>Follow the steps to ensure the following configuration files are consistent across all the nodes:</p>
+<ol>
+  <li><p>Copy the core-site.xml, hive-site.xml, yarn-site,carbon.properties files from the master node to all the other nodes in the cluster.  For example, you can use scp to copy this file to all the nodes.</p><p>Note : Set the path to hdfs ddl in carbon.properties in the master node.</p></li>
+  <li><p>For the changes to take effect, restart the Spark cluster.</p></li>
+</ol><h2>Failed to insert data on the cluster</h2><p><strong>Symptom</strong></p><p>Insertion fails with the following exception :</p><p><code>
+   Data Load failure exeception
+</code></p><p><strong>Possible Cause</strong></p><p>The following issue can cause the failure :</p>
+<ol>
+  <li><p>The core-site.xml, hive-site.xml, yarn-site and carbon.properties are not consistent across all nodes of the cluster.</p></li>
+  <li><p>Path to hdfs ddl is not configured correctly in the carbon.properties.</p></li>
+</ol><p><strong>Procedure</strong></p><p>Follow the steps to ensure the following configuration files are consistent across all the nodes:</p>
+<ol>
+  <li><p>Copy the core-site.xml, hive-site.xml, yarn-site,carbon.properties files from the master node to all the other nodes in the cluster.  For example, you can use scp to copy this file to all the nodes.</p><p>Note : Set the path to hdfs ddl in carbon.properties in the master node.</p></li>
+  <li><p>For the changes to take effect, restart the Spark cluster.</p></li>
+</ol><h2>Failed to execute Concurrent Operations.</h2><p><strong>Symptom</strong></p><p>Execution of Concurrent Operations (Load,Insert,Update) on table by multiple workers fails with the following exception :</p><p><code>
+   Table is locked for updation.
+</code></p><p><strong>Possible Cause</strong></p><p>Concurrency not supported.</p><p><strong>Procedure</strong></p><p>Worker must wait for the query execution to complete and the table to release the lock for another query execution to succeed..</p><h2>Failed to create a table with a single numeric column.</h2><p><strong>Symptom</strong></p><p>Execution fails with the following exception :</p><p><code>
+   Table creation fails.
+</code></p><p><strong>Possible Cause</strong></p><p>Behavior not supported.</p><p><strong>Procedure</strong></p><p>A single column that can be considered as dimension is mandatory for table creation.</p><h2>Data Failure because of Bad Records</h2><p><strong>Symptom</strong></p><p>Data Loading fails with the following exception</p><p><code>
+   Error: java.lang.Exception: Data load failed due to Bad record
+</code></p><p><strong>Possible Causes</strong></p><p>The parameter BAD_RECORDS_ACTION has not been specified in the Query.</p><p><strong>Procedure</strong></p><p>Set the following parameter in the load command OPTIONS as shown below :</p><p>'BAD_RECORDS_ACTION'='FORCE?</p><p><em>Example :</em></p><p><code>
+   LOAD DATA INPATH &#39;hdfs://hacluster/user/loader/moredata01.csv&#39; INTO TABLE flow_carbon_256b OPTIONS(&#39;DELIMITER&#39;=&#39;,&#39;, &#39;BAD_RECORDS_ACTION&#39;=&#39;FORCE&#39;);
+</code></p>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Useful Tips</h1><p>This tutorial guides you to create CarbonData Tables and optimize performance. The following sections will elaborate on the above topics :</p>
+<ul>
+  <li><a href="#suggestions-to-create-carbondata-table">Suggestions to create CarbonData Table</a></li>
+  <li><a href="#configurations-for-optimizing-carbondata-performance">Configurations For Optimizing CarbonData Performance</a></li>
+</ul><h2>Suggestions to Create CarbonData Table</h2><p>Recently CarbonData was used to analyze performance of Telecommunication field. The results of the analysis for table creation with dimensions ranging from 10 thousand to 10 billion rows and 100 to 300 columns have been summarized below. </p><p>The following table describes some of the columns from the table used.</p><p><strong>Table Column Description</strong></p>
+<table>
+  <thead>
+    <tr>
+      <th>Column Name </th>
+      <th>Data Type </th>
+      <th>Cardinality </th>
+      <th>Attribution </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>msisdn </td>
+      <td>String </td>
+      <td>30 million </td>
+      <td>Dimension </td>
+    </tr>
+    <tr>
+      <td>BEGIN_TIME </td>
+      <td>BigInt </td>
+      <td>10 Thousand </td>
+      <td>Dimension </td>
+    </tr>
+    <tr>
+      <td>HOST </td>
+      <td>String </td>
+      <td>1 million </td>
+      <td>Dimension </td>
+    </tr>
+    <tr>
+      <td>Dime_1 </td>
+      <td>String </td>
+      <td>1 Thousand </td>
+      <td>Dimension </td>
+    </tr>
+    <tr>
+      <td>counter_1 </td>
+      <td>Numeric(20,0) </td>
+      <td>NA </td>
+      <td>Measure </td>
+    </tr>
+    <tr>
+      <td>... </td>
+      <td>... </td>
+      <td>NA </td>
+      <td>Measure </td>
+    </tr>
+    <tr>
+      <td>counter_100 </td>
+      <td>Numeric(20,0) </td>
+      <td>NA </td>
+      <td>Measure </td>
+    </tr>
+  </tbody>
+</table><p>CarbonData has more than 50 test cases, on the basis of these we have following suggestions to enhance the query performance :</p>
+<ul>
+  <li><strong>Put the frequently-used column filter in the beginning</strong></li>
+</ul><p>For example, MSISDN filter is used in most of the query then we must put the MSISDN in the first column. The create table command can be modified as suggested below :</p><p><code>
+  create table carbondata_table(
+  msisdn String,
+  ...
+  )STORED BY &#39;org.apache.carbondata.format&#39; 
+  TBLPROPERTIES ( &#39;DICTIONARY_EXCLUDE&#39;=&#39;MSISDN,..&#39;,
+  &#39;DICTIONARY_INCLUDE&#39;=&#39;...&#39;);
+</code></p><p>Now the query with MSISDN in the filter will be more efficient.</p>
+<ul>
+  <li><strong>Put the frequently-used columns in the order of low to high cardinality</strong></li>
+</ul><p>If the table in the specified query has multiple columns which are frequently used to filter the results, it is suggested to put  the columns in the order of cardinality low to high. This ordering of frequently used columns improves the compression ratio and  enhances the performance of queries with filter on these columns.</p><p>For example if MSISDN, HOST and Dime_1 are frequently-used columns, then the column order of table is suggested as  Dime_1&gt;HOST&gt;MSISDN as Dime_1 has the lowest cardinality.  The create table command can be modified as suggested below :</p><p><code>
+  create table carbondata_table(
+  Dime_1 String,
+  HOST String,
+  MSISDN String,
+  ...
+  )STORED BY &#39;org.apache.carbondata.format&#39; 
+  TBLPROPERTIES ( &#39;DICTIONARY_EXCLUDE&#39;=&#39;MSISDN,HOST..&#39;,
+  &#39;DICTIONARY_INCLUDE&#39;=&#39;Dime_1..&#39;);
+</code></p>
+<ul>
+  <li><strong>Put the Dimension type columns in order of low to high cardinality</strong></li>
+</ul><p>If the columns used to filter are not frequently used, then it is suggested to order all the columns of dimension type in order of low to high cardinality. The create table command can be modified as below :</p><p><code>
+  create table carbondata_table(
+  Dime_1 String,
+  BEGIN_TIME bigint
+  HOST String,
+  MSISDN String,
+  ...
+  )STORED BY &#39;org.apache.carbondata.format&#39; 
+  TBLPROPERTIES ( &#39;DICTIONARY_EXCLUDE&#39;=&#39;MSISDN,HOST,IMSI..&#39;,
+  &#39;DICTIONARY_INCLUDE&#39;=&#39;Dime_1,END_TIME,BEGIN_TIME..&#39;);
+</code></p>
+<ul>
+  <li><strong>For measure type columns with non high accuracy, replace Numeric(20,0) data type with Double data type</strong></li>
+</ul><p>For columns of measure type, not requiring high accuracy, it is suggested to replace Numeric data type with Double to enhance query performance. The create table command can be modified as below :</p><p><code>
+  create table carbondata_table(
+  Dime_1 String,
+  BEGIN_TIME bigint
+  HOST String,
+  MSISDN String,
+  counter_1 double,
+  counter_2 double,
+  ...
+  counter_100 double
+  )STORED BY &#39;org.apache.carbondata.format&#39; 
+  TBLPROPERTIES ( &#39;DICTIONARY_EXCLUDE&#39;=&#39;MSISDN,HOST,IMSI&#39;,
+  &#39;DICTIONARY_INCLUDE&#39;=&#39;Dime_1,END_TIME,BEGIN_TIME&#39;);
+</code>  The result of performance analysis of test-case shows reduction in query execution time from 15 to 3 seconds, thereby improving performance by nearly 5 times.</p>
+<ul>
+  <li><strong>Columns of incremental character should be re-arranged at the end of dimensions</strong></li>
+</ul><p>Consider the following scenario where data is loaded each day and the start_time is incremental for each load, it is suggested to put start_time at the end of dimensions. </p><p>Incremental values are efficient in using min/max index. The create table command can be modified as below :</p><p><code>
+  create table carbondata_table(
+  Dime_1 String,
+  HOST String,
+  MSISDN String,
+  counter_1 double,
+  counter_2 double,
+  BEGIN_TIME bigint,
+  ...
+  counter_100 double
+  )STORED BY &#39;org.apache.carbondata.format&#39; 
+  TBLPROPERTIES ( &#39;DICTIONARY_EXCLUDE&#39;=&#39;MSISDN,HOST,IMSI&#39;,
+  &#39;DICTIONARY_INCLUDE&#39;=&#39;Dime_1,END_TIME,BEGIN_TIME&#39;); 
+</code></p>
+<ul>
+  <li><strong>Avoid adding high cardinality columns to dictionary</strong></li>
+</ul><p>If the system has low memory configuration, then it is suggested to exclude high cardinality columns from the dictionary to enhance load performance. Creation of dictionary for high cardinality columns at time of load will degrade load performance due to excessive memory usage. </p><p>By default CarbonData determines the cardinality at the first data load and allows for dictionary creation only if the cardinality is less than 1 million.</p><h2>Configurations for Optimizing CarbonData Performance</h2><p>Recently we did some performance POC on CarbonData for Finance and telecommunication Field. It involved detailed queries and aggregation scenarios. After the completion of POC, some of the configurations impacting the performance have been identified and tabulated below :</p>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Location </th>
+      <th>Used For </th>
+      <th>Description </th>
+      <th>Tuning </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.sort.intermediate.files.limit </td>
+      <td>spark/carbonlib/carbon.properties </td>
+      <td>Data loading </td>
+      <td>During the loading of data, local temp is used to sort the data. This number specifies the minimum number of intermediate files after which the merge sort has to be initiated. </td>
+      <td>Increasing the parameter to a higher value will improve the load performance. For example, when we increase the value from 20 to 100, it increases the data load performance from 35MB/S to more than 50MB/S. Higher values of this parameter consumes more memory during the load. </td>
+    </tr>
+    <tr>
+      <td>carbon.number.of.cores.while.loading </td>
+      <td>spark/carbonlib/carbon.properties </td>
+      <td>Data loading </td>
+      <td>Specifies the number of cores used for data processing during data loading in CarbonData. </td>
+      <td>If you have more number of CPUs, then you can increase the number of CPUs, which will increase the performance. For example if we increase the value from 2 to 4 then the CSV reading performance can increase about 1 times </td>
+    </tr>
+    <tr>
+      <td>carbon.compaction.level.threshold </td>
+      <td>spark/carbonlib/carbon.properties </td>
+      <td>Data loading and Querying </td>
+      <td>For minor compaction, specifies the number of segments to be merged in stage 1 and number of compacted segments to be merged in stage 2. </td>
+      <td>Each CarbonData load will create one segment, if every load is small in size it will generate many small file over a period of time impacting the query performance. Configuring this parameter will merge the small segment to one big segment which will sort the data and improve the performance. For Example in one telecommunication scenario, the performance improves about 2 times after minor compaction. </td>
+    </tr>
+    <tr>
+      <td>spark.sql.shuffle.partitions </td>
+      <td>spark/con/spark-defaults.conf </td>
+      <td>Querying </td>
+      <td>The number of task started when spark shuffle. </td>
+      <td>The value can be 1 to 2 times as much as the executor cores. In an aggregation scenario, reducing the number from 200 to 32 reduced the query time from 17 to 9 seconds. </td>
+    </tr>
+    <tr>
+      <td>num-executors/executor-cores/executor-memory </td>
+      <td>spark/con/spark-defaults.conf </td>
+      <td>Querying </td>
+      <td>The number of executors, CPU cores, and memory used for CarbonData query. </td>
+      <td>In the bank scenario, we provide the 4 CPUs cores and 15 GB for each executor which can get good performance. This 2 value does not mean more the better. It needs to be configured properly in case of limited resources. For example, In the bank scenario, it has enough CPU 32 cores each node but less memory 64 GB each node. So we cannot give more CPU but less memory. For example, when 4 cores and 12GB for each executor. It sometimes happens GC during the query which impact the query performance very much from the 3 second to more than 15 seconds. In this scenario need to increase the memory or decrease the CPU cores. </td>
+    </tr>
+    <tr>
+      <td>carbon.detail.batch.size </td>
+      <td>spark/carbonlib/carbon.properties </td>
+      <td>Data loading </td>
+      <td>The buffer size to store records, returned from the block scan. </td>
+      <td>In limit scenario this parameter is very important. For example your query limit is 1000. But if we set this value to 3000 that means we get 3000 records from scan but spark will only take 1000 rows. So the 2000 remaining are useless. In one Finance test case after we set it to 100, in the limit 1000 scenario the performance increase about 2 times in comparison to if we set this value to 12000. </td>
+    </tr>
+    <tr>
+      <td>carbon.use.local.dir </td>
+      <td>spark/carbonlib/carbon.properties </td>
+      <td>Data loading </td>
+      <td>Whether use YARN local directories for multi-table load disk load balance </td>
+      <td>If this is set it to true CarbonData will use YARN local directories for multi-table load disk load balance, that will improve the data load performance. </td>
+    </tr>
+  </tbody>
+</table>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>User Guide</h1><p>Welcome to Apache CarbonData. Apache CarbonData(incubating) is a new big data file format for faster interactive query using advanced columnar storage, index, compression and encoding techniques to improve computing efficiency, which helps in speeding up queries by an order of magnitude faster over PetaBytes of data. This user guide provides a detailed description about the CarbonData and its features.</p><p>Let's get started !</p>
+<ul>
+  <li><a href="overview-of-carbondata.md">Overview</a>
+  <ul>
+    <li>Introduction</li>
+    <li>Features</li>
+    <li><a href="supported-data-types-in-carbondata.md">Data Types</a></li>
+    <li><a href="file-structure-of-carbondata.md">CarbonData File Structure</a></li>
+  </ul></li>
+  <li><a href="installation-guide.md">Installation Guide</a>
+  <ul>
+    <li>Installing and Configuring CarbonData on Standalone Spark Cluster</li>
+    <li>Installing and Configuring CarbonData on "Spark on YARN Cluster</li>
+  </ul></li>
+  <li><a href="configuration-parameters.md">Configuring CarbonData</a>
+  <ul>
+    <li>System Configuration</li>
+    <li>Performance Configuration</li>
+    <li>Miscellaneous Configuration</li>
+    <li>Spark Configuration</li>
+  </ul></li>
+  <li><a href="using-carbondata.md">Using CarbonData</a>
+  <ul>
+    <li><a href="data-management.md">Data Management</a></li>
+    <li><a href="ddl-operation-on-carbondata.md">DDL Operations on CarbonData</a></li>
+    <li><a href="dml-operation-on-carbondata.md">DML Operations on CarbonData</a></li>
+  </ul></li>
+</ul>
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+<h1>Using CarbonData</h1><p>This tutorial discusses the disciplines related to management of data in Apache CarbonData. Following below each section is a brief introduction to respective disciplines related to data management.</p><h2>Data Management</h2><p>This section shall be dealing with the disciplines related to managing data in the application, focusing on conceptual details related to operations like load data, delete data, update data and Compacting Data.</p><p>For complete details refer to <a href="data-management.md">Data Management</a></p><h2>Data Definition Language Support</h2><p>This section deals with the aspects related to creation and modification of the structure of database. It shall discuss in detail about</p>
+<ul>
+  <li>Table creation</li>
+  <li>Table deletion</li>
+  <li>Table description</li>
+  <li>Compaction</li>
+</ul><p>For complete details refer to <a href="ddl-operation-on-carbondata.md">DDL Operations on CarbonData</a></p><h2>Data Manipulation Language Support</h2><p>This section deals with the aspects related to data manipulation in database. It shall discuss in detail about selecting, loading and deleting in a database. This manipulation comprises of</p>
+<ul>
+  <li>Loading data into database tables</li>
+  <li>Retrieving existing data</li>
+  <li>Deleting data from existing tables</li>
+  <li>Deleting segments from existing tables</li>
+  <li>Updating data in existing tables</li>
+</ul><p>For complete details refer to <a href="dml-operation-on-carbondata.md">DML Operations on CarbonData</a></p>
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                                 <a href="https://github.com/apache/incubator-carbondata/blob/master/docs/How-to-contribute-to-Apache-CarbonData.md"
                                    target="_blank">Contributing to CarbonData</a></li>
                             <li>
-                                <a href="https://cwiki.apache.org/confluence/display/CARBONDATA/Committers"
-                                   target="_blank">Project Committers</a></li>
+                                <a href="https://cwiki.apache.org/confluence/display/CARBONDATA/PPMC+and+Committers+member+list"
+                                   target="_blank">Project PPMC and Committers</a></li>
                             <li><a href="meetup.html">CarbonData Meetups </a></li>
                             <li><a href="security.html">Apache CarbonData Security</a></li>
                         </ul>
@@ -383,8 +383,8 @@
                     <p class="social-icons">
                         <a href="https://www.facebook.com/carbondata/" target="_blank">
                             <i class="fa fa-facebook-square" aria-hidden="true"></i></a>
-                        <a href="https://twitter.com/search?q=%23CarbonData" target="_blank">
-                            <i class="fa fa-twitter-square" aria-hidden="true"></i></a>
+                        <a href="https://twitter.com/search?q=%23CarbonData" target="_blank"> <i
+                                class="fa fa-twitter-square" aria-hidden="true"></i></a>
                         <a> <i class="fa fa-linkedin-square" aria-hidden="true"></i></a>
                     </p>
                 </div>

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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Configuring CarbonData</h1><p>This tutorial guides you through the advanced configurations of CarbonData :</p>
+<ul>
+  <li><a href="#system-configuration">System Configuration</a></li>
+  <li><a href="#performance-configuration">Performance Configuration</a></li>
+  <li><a href="#miscellaneous-configuration">Miscellaneous Configuration</a></li>
+  <li><a href="#spark-configuration">Spark Configuration</a></li>
+</ul><h2>System Configuration</h2><p>This section provides the details of all the configurations required for the CarbonData System.</p><p><b><p align="center">System Configuration in carbon.properties</p></b></p>
+<table>
+  <thead>
+    <tr>
+      <th>Property </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.storelocation </td>
+      <td>/user/hive/warehouse/carbon.store </td>
+      <td>Location where CarbonData will create the store, and write the data in its own format. NOTE: Store location should be in HDFS. </td>
+    </tr>
+    <tr>
+      <td>carbon.ddl.base.hdfs.url </td>
+      <td>hdfs://hacluster/opt/data </td>
+      <td>This property is used to configure the HDFS relative path, the path configured in carbon.ddl.base.hdfs.url will be appended to the HDFS path configured in fs.defaultFS. If this path is configured, then user need not pass the complete path while dataload. For example: If absolute path of the csv file is hdfs://10.18.101.155:54310/data/cnbc/2016/xyz.csv, the path "hdfs://10.18.101.155:54310" will come from property fs.defaultFS and user can configure the /data/cnbc/ as carbon.ddl.base.hdfs.url. Now while dataload user can specify the csv path as /2016/xyz.csv. </td>
+    </tr>
+    <tr>
+      <td>carbon.badRecords.location </td>
+      <td>/opt/Carbon/Spark/badrecords </td>
+      <td>Path where the bad records are stored. </td>
+    </tr>
+    <tr>
+      <td>carbon.kettle.home </td>
+      <td>$SPARK_HOME/carbonlib/carbonplugins </td>
+      <td>Configuration for loading the data with kettle. </td>
+    </tr>
+    <tr>
+      <td>carbon.data.file.version </td>
+      <td>2 </td>
+      <td>If this parameter value is set to 1, then CarbonData will support the data load which is in old format(0.x version). If the value is set to 2(1.x onwards version), then CarbonData will support the data load of new format only. </td>
+    </tr>
+  </tbody>
+</table><h2>Performance Configuration</h2><p>This section provides the details of all the configurations required for CarbonData Performance Optimization.</p><p><b><p align="center">Performance Configuration in carbon.properties</p></b></p>
+<ul>
+  <li><strong>Data Loading Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+      <th>Range </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.sort.file.buffer.size </td>
+      <td>20 </td>
+      <td>File read buffer size used during sorting. This value is expressed in MB. </td>
+      <td>Min=1 and Max=100 </td>
+    </tr>
+    <tr>
+      <td>carbon.graph.rowset.size </td>
+      <td>100000 </td>
+      <td>Rowset size exchanged between data load graph steps. </td>
+      <td>Min=500 and Max=1000000 </td>
+    </tr>
+    <tr>
+      <td>carbon.number.of.cores.while.loading </td>
+      <td>6 </td>
+      <td>Number of cores to be used while loading data. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.sort.size </td>
+      <td>500000 </td>
+      <td>Record count to sort and write intermediate files to temp. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.enableXXHash </td>
+      <td>true </td>
+      <td>Algorithm for hashmap for hashkey calculation. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.number.of.cores.block.sort </td>
+      <td>7 </td>
+      <td>Number of cores to use for block sort while loading data. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.max.driver.lru.cache.size </td>
+      <td>-1 </td>
+      <td>Max LRU cache size upto which data will be loaded at the driver side. This value is expressed in MB. Default value of -1 means there is no memory limit for caching. Only integer values greater than 0 are accepted. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.max.executor.lru.cache.size </td>
+      <td>-1 </td>
+      <td>Max LRU cache size upto which data will be loaded at the executor side. This value is expressed in MB. Default value of -1 means there is no memory limit for caching. Only integer values greater than 0 are accepted. If this parameter is not configured, then the carbon.max.driver.lru.cache.size value will be considered. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.merge.sort.prefetch </td>
+      <td>true </td>
+      <td>Enable prefetch of data during merge sort while reading data from sort temp files in data loading. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.update.persist.enable </td>
+      <td>true </td>
+      <td>Enabling this parameter considers persistent data. Enabling this will reduce the execution time of UPDATE operation. </td>
+      <td> </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Compaction Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+      <th>Range </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.number.of.cores.while.compacting </td>
+      <td>2 </td>
+      <td>Number of cores which are used to write data during compaction. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.compaction.level.threshold </td>
+      <td>4, 3 </td>
+      <td>This property is for minor compaction which decides how many segments to be merged. Example: If it is set as 2, 3 then minor compaction will be triggered for every 2 segments. 3 is the number of level 1 compacted segment which is further compacted to new segment. </td>
+      <td>Valid values are from 0-100. </td>
+    </tr>
+    <tr>
+      <td>carbon.major.compaction.size </td>
+      <td>1024 </td>
+      <td>Major compaction size can be configured using this parameter. Sum of the segments which is below this threshold will be merged. This value is expressed in MB. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.horizontal.compaction.enable </td>
+      <td>true </td>
+      <td>This property is used to turn ON/OFF horizontal compaction. After every DELETE and UPDATE statement, horizontal compaction may occur in case the delta (DELETE/ UPDATE) files becomes more than specified threshold. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.horizontal.UPDATE.compaction.threshold </td>
+      <td>1 </td>
+      <td>This property specifies the threshold limit on number of UPDATE delta files within a segment. In case the number of delta files goes beyond the threshold, the UPDATE delta files within the segment becomes eligible for horizontal compaction and compacted into single UPDATE delta file. </td>
+      <td>Values between 1 to 10000. </td>
+    </tr>
+    <tr>
+      <td>carbon.horizontal.DELETE.compaction.threshold </td>
+      <td>1 </td>
+      <td>This property specifies the threshold limit on number of DELETE delta files within a block of a segment. In case the number of delta files goes beyond the threshold, the DELETE delta files for the particular block of the segment becomes eligible for horizontal compaction and compacted into single DELETE delta file. </td>
+      <td>Values between 1 to 10000. </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Query Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+      <th>Range </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.number.of.cores </td>
+      <td>4 </td>
+      <td>Number of cores to be used while querying. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>carbon.inmemory.record.size </td>
+      <td>120000 </td>
+      <td>Number of records to be in memory while querying. </td>
+      <td>Min=100000 and Max=240000 </td>
+    </tr>
+    <tr>
+      <td>carbon.enable.quick.filter </td>
+      <td>false </td>
+      <td>Improves the performance of filter query. </td>
+      <td> </td>
+    </tr>
+    <tr>
+      <td>no.of.cores.to.load.blocks.in.driver </td>
+      <td>10 </td>
+      <td>Number of core to load the blocks in driver. </td>
+      <td> </td>
+    </tr>
+  </tbody>
+</table><h2>Miscellaneous Configuration</h2><p><b><p align="center">Extra Configuration in carbon.properties</p></b></p>
+<ul>
+  <li><strong>Time format for CarbonData</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Format </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.timestamp.format </td>
+      <td>yyyy-MM-dd HH:mm:ss </td>
+      <td>Timestamp format of input data used for timestamp data type. </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Dataload Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.sort.file.write.buffer.size </td>
+      <td>10485760 </td>
+      <td>File write buffer size used during sorting. </td>
+    </tr>
+    <tr>
+      <td>carbon.lock.type </td>
+      <td>LOCALLOCK </td>
+      <td>This configuration specifies the type of lock to be acquired during concurrent operations on table. There are following types of lock implementation: - LOCALLOCK: Lock is created on local file system as file. This lock is useful when only one spark driver (thrift server) runs on a machine and no other CarbonData spark application is launched concurrently. - HDFSLOCK: Lock is created on HDFS file system as file. This lock is useful when multiple CarbonData spark applications are launched and no ZooKeeper is running on cluster and HDFS supports file based locking. </td>
+    </tr>
+    <tr>
+      <td>carbon.sort.intermediate.files.limit </td>
+      <td>20 </td>
+      <td>Minimum number of intermediate files after which merged sort can be started. </td>
+    </tr>
+    <tr>
+      <td>carbon.block.meta.size.reserved.percentage </td>
+      <td>10 </td>
+      <td>Space reserved in percentage for writing block meta data in CarbonData file. </td>
+    </tr>
+    <tr>
+      <td>carbon.csv.read.buffersize.byte </td>
+      <td>1048576 </td>
+      <td>csv reading buffer size. </td>
+    </tr>
+    <tr>
+      <td>high.cardinality.value </td>
+      <td>100000 </td>
+      <td>To identify and apply compression for non-high cardinality columns. </td>
+    </tr>
+    <tr>
+      <td>carbon.merge.sort.reader.thread </td>
+      <td>3 </td>
+      <td>Maximum no of threads used for reading intermediate files for final merging. </td>
+    </tr>
+    <tr>
+      <td>carbon.load.metadata.lock.retries </td>
+      <td>3 </td>
+      <td>Maximum number of retries to get the metadata lock for loading data to table. </td>
+    </tr>
+    <tr>
+      <td>carbon.load.metadata.lock.retry.timeout.sec </td>
+      <td>5 </td>
+      <td>Interval between the retries to get the lock. </td>
+    </tr>
+    <tr>
+      <td>carbon.tempstore.location </td>
+      <td>/opt/Carbon/TempStoreLoc </td>
+      <td>Temporary store location. By default it takes System.getProperty("java.io.tmpdir"). </td>
+    </tr>
+    <tr>
+      <td>carbon.load.log.counter </td>
+      <td>500000 </td>
+      <td>Data loading records count logger. </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Compaction Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.numberof.preserve.segments </td>
+      <td>0 </td>
+      <td>If the user wants to preserve some number of segments from being compacted then he can set this property. Example: carbon.numberof.preserve.segments=2 then 2 latest segments will always be excluded from the compaction. No segments will be preserved by default. </td>
+    </tr>
+    <tr>
+      <td>carbon.allowed.compaction.days </td>
+      <td>0 </td>
+      <td>Compaction will merge the segments which are loaded with in the specific number of days configured. Example: If the configuration is 2, then the segments which are loaded in the time frame of 2 days only will get merged. Segments which are loaded 2 days apart will not be merged. This is disabled by default. </td>
+    </tr>
+    <tr>
+      <td>carbon.enable.auto.load.merge </td>
+      <td>false </td>
+      <td>To enable compaction while data loading. </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Query Configuration</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>max.query.execution.time </td>
+      <td>60 </td>
+      <td>Maximum time allowed for one query to be executed. The value is in minutes. </td>
+    </tr>
+    <tr>
+      <td>carbon.enableMinMax </td>
+      <td>true </td>
+      <td>Min max is feature added to enhance query performance. To disable this feature, set it false. </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><strong>Global Dictionary Configurations</strong></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>high.cardinality.identify.enable </td>
+      <td>true </td>
+      <td>If the parameter is true, the high cardinality columns of the dictionary code are automatically recognized and these columns will not be used as global dictionary encoding. If the parameter is false, all dictionary encoding columns are used as dictionary encoding. The high cardinality column must meet the following requirements: value of cardinality &gt; configured value of high.cardinalityEqually, the value of cardinality is higher than the threshold.value of cardinality/ row number x 100 &gt; configured value of high.cardinality.row.count.percentageEqually, the ratio of the cardinality value to data row number is higher than the configured percentage. </td>
+    </tr>
+    <tr>
+      <td>high.cardinality.threshold </td>
+      <td>1000000 </td>
+      <td>It is a threshold to identify high cardinality of the columns.If the value of columns' cardinality &gt; the configured value, then the columns are excluded from dictionary encoding. </td>
+    </tr>
+    <tr>
+      <td>high.cardinality.row.count.percentage </td>
+      <td>80 </td>
+      <td>Percentage to identify whether column cardinality is more than configured percent of total row count.Configuration value formula:Value of cardinality/ row number x 100 &gt; configured value of high.cardinality.row.count.percentageThe value of the parameter must be larger than 0. </td>
+    </tr>
+    <tr>
+      <td>carbon.cutOffTimestamp </td>
+      <td>1970-01-01 05:30:00 </td>
+      <td>Sets the start date for calculating the timestamp. Java counts the number of milliseconds from start of "1970-01-01 00:00:00". This property is used to customize the start of position. For example "2000-01-01 00:00:00". The date must be in the form "carbon.timestamp.format". NOTE: The CarbonData supports data store up to 68 years from the cut-off time defined. For example, if the cut-off time is 1970-01-01 05:30:00, then the data can be stored up to 2038-01-01 05:30:00. </td>
+    </tr>
+    <tr>
+      <td>carbon.timegranularity </td>
+      <td>SECOND </td>
+      <td>The property used to set the data granularity level DAY, HOUR, MINUTE, or SECOND. </td>
+    </tr>
+  </tbody>
+</table><h2>Spark Configuration</h2><p><b><p align="center">Spark Configuration Reference in spark-defaults.conf</p></b></p>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default Value </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>spark.driver.memory </td>
+      <td>1g </td>
+      <td>Amount of memory to be used for the driver process. </td>
+    </tr>
+    <tr>
+      <td>spark.executor.memory </td>
+      <td>1g </td>
+      <td>Amount of memory to be used per executor process. </td>
+    </tr>
+    <tr>
+      <td>spark.sql.bigdata.register.analyseRule </td>
+      <td>org.apache.spark.sql.hive.acl.CarbonAccessControlRules </td>
+      <td>CarbonAccessControlRules need to be set for enabling Access Control. </td>
+    </tr>
+  </tbody>
+</table>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>Data Management</h1><p>This tutorial is going to introduce you to the conceptual details of data management like:</p>
+<ul>
+  <li><a href="#loading-data">Loading Data</a></li>
+  <li><a href="#deleting-data">Deleting Data</a></li>
+  <li><a href="#compacting-data">Compacting Data</a></li>
+  <li><a href="#updating-data">Updating Data</a></li>
+</ul><h2>Loading Data</h2>
+<ul>
+  <li><strong>Scenario</strong></li>
+</ul><p>After creating a table, you can load data to the table using the <a href="dml-operation-on-carbondata.md">LOAD DATA</a> command. The loaded data is available for querying.  When data load is triggered, the data is encoded in CarbonData format and copied into HDFS CarbonData store path (specified in carbon.properties file)  in compressed, multi dimensional columnar format for quick analysis queries. The same command can be used to load new data or to  update the existing data. Only one data load can be triggered for one table. The high cardinality columns of the dictionary encoding are  automatically recognized and these columns will not be used for dictionary encoding.</p>
+<ul>
+  <li><strong>Procedure</strong></li>
+</ul><p>Data loading is a process that involves execution of multiple steps to read, sort and encode the data in CarbonData store format.  Each step is executed on different threads. After data loading process is complete, the status (success/partial success) is updated to  CarbonData store metadata. The table below lists the possible load status.</p>
+<table>
+  <thead>
+    <tr>
+      <th>Status </th>
+      <th>Description </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Success </td>
+      <td>All the data is loaded into table and no bad records found. </td>
+    </tr>
+    <tr>
+      <td>Partial Success </td>
+      <td>Data is loaded into table and bad records are found. Bad records are stored at carbon.badrecords.location. </td>
+    </tr>
+  </tbody>
+</table><p>In case of failure, the error will be logged in error log. Details of loads can be seen with <a href="dml-operation-on-carbondata.md">SHOW SEGMENTS</a> command. The show segment command output consists of :</p>
+<ul>
+  <li>SegmentSequenceID</li>
+  <li>START_TIME OF LOAD</li>
+  <li>END_TIME OF LOAD</li>
+  <li>LOAD STATUS</li>
+</ul><p>The latest load will be displayed first in the output.</p><p>Refer to <a href="dml-operation-on-carbondata.md">DML operations on CarbonData</a> for load commands.</p><h2>Deleting Data</h2>
+<ul>
+  <li><strong>Scenario</strong></li>
+</ul><p>If you have loaded wrong data into the table, or too many bad records are present and you want to modify and reload the data, you can delete required data loads.  The load can be deleted using the Segment Sequence Id or if the table contains date field then the data can be deleted using the date field.  If there are some specific records that need to be deleted based on some filter condition(s) we can delete by records.</p>
+<ul>
+  <li><strong>Procedure</strong></li>
+</ul><p>The loaded data can be deleted in the following ways:</p>
+<ul>
+  <li><p>Delete by Segment ID</p><p>After you get the segment ID of the segment that you want to delete, execute the delete command for the selected segment.  The status of deleted segment is updated to Marked for delete / Marked for Update.</p></li>
+</ul>
+<table>
+  <thead>
+    <tr>
+      <th>SegmentSequenceId </th>
+      <th>Status </th>
+      <th>Load Start Time </th>
+      <th>Load End Time </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0 </td>
+      <td>Success </td>
+      <td>2015-11-19 19:14:... </td>
+      <td>2015-11-19 19:14:... </td>
+    </tr>
+    <tr>
+      <td>1 </td>
+      <td>Marked for Update </td>
+      <td>2015-11-19 19:54:... </td>
+      <td>2015-11-19 20:08:... </td>
+    </tr>
+    <tr>
+      <td>2 </td>
+      <td>Marked for Delete </td>
+      <td>2015-11-19 20:25:... </td>
+      <td>2015-11-19 20:49:... </td>
+    </tr>
+  </tbody>
+</table>
+<ul>
+  <li><p>Delete by Date Field</p><p>If the table contains date field, you can delete the data based on a specific date.</p></li>
+  <li><p>Delete by Record</p><p>To delete records from CarbonData table based on some filter Condition(s).</p><p>For delete commands refer to <a href="dml-operation-on-carbondata.md">DML operations on CarbonData</a>.</p></li>
+  <li><p><strong>NOTE</strong>:</p>
+  <ul>
+    <li>When the delete segment DML is called, segment will not be deleted physically from the file system. Instead the segment status will be marked as "Marked for Delete". For the query execution, this deleted segment will be excluded.</li>
+  </ul>
+  <ul>
+    <li>The deleted segment will be deleted physically during the next load operation and only after the maximum query execution time configured using "max.query.execution.time". By default it is 60 minutes.</li>
+  </ul>
+  <ul>
+    <li>If the user wants to force delete the segment physically then he can use CLEAN FILES Command.</li>
+  </ul></li>
+</ul><p>Example :</p><p><code>
+CLEAN FILES FOR TABLE table1
+</code></p><p>This DML will physically delete the segment which are "Marked for delete" immediately.</p><h2>Compacting Data</h2>
+<ul>
+  <li><strong>Scenario</strong></li>
+</ul><p>Frequent data ingestion results in several fragmented CarbonData files in the store directory. Since data is sorted only within each load, the indices perform only within each  load. This means that there will be one index for each load and as number of data load increases, the number of indices also increases. As each index works only on one load,  the performance of indices is reduced. CarbonData provides provision for compacting the loads. Compaction process combines several segments into one large segment by merge sorting the data from across the segments. </p>
+<ul>
+  <li><strong>Procedure</strong></li>
+</ul><p>There are two types of compaction Minor and Major compaction.</p>
+<ul>
+  <li><p><strong>Minor Compaction</strong></p><p>In minor compaction the user can specify how many loads to be merged. Minor compaction triggers for every data load if the parameter carbon.enable.auto.load.merge is set. If any segments are available to be merged, then compaction will  run parallel with data load. There are 2 levels in minor compaction.</p>
+  <ul>
+    <li>Level 1: Merging of the segments which are not yet compacted.</li>
+    <li>Level 2: Merging of the compacted segments again to form a bigger segment.</li>
+  </ul></li>
+  <li><p><strong>Major Compaction</strong></p><p>In Major compaction, many segments can be merged into one big segment. User will specify the compaction size until which segments can be merged. Major compaction is usually done during the off-peak time. </p></li>
+</ul><p>There are number of parameters related to Compaction that can be set in carbon.properties file </p>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Default </th>
+      <th>Application </th>
+      <th>Description </th>
+      <th>Valid Values </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>carbon.compaction.level.threshold </td>
+      <td>4, 3 </td>
+      <td>Minor </td>
+      <td>This property is for minor compaction which decides how many segments to be merged. Example: If it is set as 2, 3 then minor compaction will be triggered for every 2 segments. 3 is the number of level 1 compacted segment which is further compacted to new segment. </td>
+      <td>NA </td>
+    </tr>
+    <tr>
+      <td>carbon.major.compaction.size </td>
+      <td>1024 MB </td>
+      <td>Major </td>
+      <td>Major compaction size can be configured using this parameter. Sum of the segments which is below this threshold will be merged. </td>
+      <td>NA </td>
+    </tr>
+    <tr>
+      <td>carbon.numberof.preserve.segments </td>
+      <td>0 </td>
+      <td>Minor/Major </td>
+      <td>If the user wants to preserve some number of segments from being compacted then he can set this property. Example: carbon.numberof.preserve.segments=2 then 2 latest segments will always be excluded from the compaction. No segments will be preserved by default. </td>
+      <td>0-100 </td>
+    </tr>
+    <tr>
+      <td>carbon.allowed.compaction.days </td>
+      <td>0 </td>
+      <td>Minor/Major </td>
+      <td>Compaction will merge the segments which are loaded within the specific number of days configured. Example: If the configuration is 2, then the segments which are loaded in the time frame of 2 days only will get merged. Segments which are loaded 2 days apart will not be merged. This is disabled by default. </td>
+      <td>0-100 </td>
+    </tr>
+    <tr>
+      <td>carbon.number.of.cores.while.compacting </td>
+      <td>2 </td>
+      <td>Minor/Major </td>
+      <td>Number of cores which is used to write data during compaction. </td>
+      <td>0-100 </td>
+    </tr>
+  </tbody>
+</table><p>For compaction commands refer to <a href="ddl-operation-on-carbondata.md">DDL operations on CarbonData</a></p><h2>Updating Data</h2>
+<ul>
+  <li><p><strong>Scenario</strong></p><p>Sometimes after the data has been ingested into the System, it is required to be updated. Also there may be situations where some specific columns need to be updated on the basis of column expression and optional filter conditions.</p></li>
+  <li><p><strong>Procedure</strong></p><p>To update we need to specify the column expression with an optional filter condition(s).</p><p>For update commands refer to <a href="dml-operation-on-carbondata.md">DML operations on CarbonData</a>.</p></li>
+</ul>
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+--><h1>DDL Operations on CarbonData</h1><p>This tutorial guides you through the data definition language support provided by CarbonData.</p><h2>Overview</h2><p>The following DDL operations are supported in CarbonData :</p>
+<ul>
+  <li><a href="#create-table">CREATE TABLE</a></li>
+  <li><a href="#show-table">SHOW TABLE</a></li>
+  <li><a href="#drop-table">DROP TABLE</a></li>
+  <li><a href="#compaction">COMPACTION</a></li>
+  <li><a href="#bucketing">BUCKETING</a></li>
+</ul><h2>CREATE TABLE</h2><p>This command can be used to create a CarbonData table by specifying the list of fields along with the table properties.</p><p><code>
+   CREATE TABLE [IF NOT EXISTS] [db_name.]table_name 
+                    [(col_name data_type, ...)]
+   STORED BY &#39;carbondata&#39;
+   [TBLPROPERTIES (property_name=property_value, ...)]
+   // All Carbon&#39;s additional table options will go into properties
+</code></p><h3>Parameter Description</h3>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Description </th>
+      <th>Optional </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>db_name </td>
+      <td>Name of the database. Database name should consist of alphanumeric characters and underscore(_) special character. </td>
+      <td>Yes </td>
+    </tr>
+    <tr>
+      <td>field_list </td>
+      <td>Comma separated List of fields with data type. The field names should consist of alphanumeric characters and underscore(_) special character. </td>
+      <td>No </td>
+    </tr>
+    <tr>
+      <td>table_name </td>
+      <td>The name of the table in Database. Table Name should consist of alphanumeric characters and underscore(_) special character. </td>
+      <td>No </td>
+    </tr>
+    <tr>
+      <td>STORED BY </td>
+      <td>"org.apache.carbondata.format", identifies and creates a CarbonData table. </td>
+      <td>No </td>
+    </tr>
+    <tr>
+      <td>TBLPROPERTIES </td>
+      <td>List of CarbonData table properties. </td>
+      <td> </td>
+    </tr>
+  </tbody>
+</table><h3>Usage Guidelines</h3><p>Following are the guidelines for using table properties.</p>
+<ul>
+  <li><p><strong>Dictionary Encoding Configuration</strong></p><p>Dictionary encoding is enabled by default for all String columns, and disabled for non-String columns. You can include and exclude columns for dictionary encoding.</p></li>
+</ul><p><code>
+       TBLPROPERTIES (&quot;DICTIONARY_EXCLUDE&quot;=&quot;column1, column2&quot;) 
+       TBLPROPERTIES (&quot;DICTIONARY_INCLUDE&quot;=&quot;column1, column2&quot;) 
+</code></p><p>Here, DICTIONARY_EXCLUDE will exclude dictionary creation. This is applicable for high-cardinality columns and is an optional parameter. DICTIONARY_INCLUDE will generate dictionary for the columns specified in the list.</p>
+<ul>
+  <li><p><strong>Row/Column Format Configuration</strong></p><p>Column groups with more than one column are stored in row format, instead of columnar format. By default, each column is a separate column group.</p></li>
+</ul><p><code>
+TBLPROPERTIES (&quot;COLUMN_GROUPS&quot;=&quot;(column1, column3),
+(Column4,Column5,Column6)&quot;) 
+</code></p>
+<ul>
+  <li><p><strong>Table Block Size Configuration</strong></p><p>The block size of table files can be defined using the property TABLE_BLOCKSIZE. It accepts only integer values. The default value is 1024 MB and supports a range of 1 MB to 2048 MB.  If you do not specify this value in the DDL command, default value is used.</p></li>
+</ul><p><code>
+       TBLPROPERTIES (&quot;TABLE_BLOCKSIZE&quot;=&quot;512 MB&quot;)
+</code></p><p>Here 512 MB means the block size of this table is 512 MB, you can also set it as 512M or 512.</p>
+<ul>
+  <li><p><strong>Inverted Index Configuration</strong></p><p>Inverted index is very useful to improve compression ratio and query speed, especially for those low-cardinality columns who are in reward position.  By default inverted index is enabled. The user can disable the inverted index creation for some columns.</p></li>
+</ul><p><code>
+       TBLPROPERTIES (&quot;NO_INVERTED_INDEX&quot;=&quot;column1, column3&quot;)
+</code></p><p>No inverted index shall be generated for the columns specified in NO_INVERTED_INDEX. This property is applicable on columns with high-cardinality and is an optional parameter.</p><p>NOTE:</p>
+<ul>
+  <li><p>By default all columns other than numeric datatype are treated as dimensions and all columns of numeric datatype are treated as measures.</p></li>
+  <li><p>All dimensions except complex datatype columns are part of multi dimensional key(MDK). This behavior can be overridden by using TBLPROPERTIES. If the user wants to keep any column (except columns of complex datatype) in multi dimensional key then he can keep the columns either in DICTIONARY_EXCLUDE or DICTIONARY_INCLUDE.</p><h3>Example:</h3><p><code>
+   CREATE TABLE IF NOT EXISTS productSchema.productSalesTable (
+                            productNumber Int,
+                            productName String, 
+                            storeCity String, 
+                            storeProvince String, 
+                            productCategory String, 
+                            productBatch String,
+                            saleQuantity Int,
+                            revenue Int)       
+   STORED BY &#39;carbondata&#39; 
+   TBLPROPERTIES (&#39;COLUMN_GROUPS&#39;=&#39;(productName,productCategory)&#39;,
+              &#39;DICTIONARY_EXCLUDE&#39;=&#39;productName&#39;,
+              &#39;DICTIONARY_INCLUDE&#39;=&#39;productNumber&#39;,
+              &#39;NO_INVERTED_INDEX&#39;=&#39;productBatch&#39;)
+</code></p></li>
+</ul><h2>SHOW TABLE</h2><p>This command can be used to list all the tables in current database or all the tables of a specific database. <code>
+  SHOW TABLES [IN db_Name];
+</code></p><h3>Parameter Description</h3>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Description </th>
+      <th>Optional </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>IN db_Name </td>
+      <td>Name of the database. Required only if tables of this specific database are to be listed. </td>
+      <td>Yes </td>
+    </tr>
+  </tbody>
+</table><h3>Example:</h3><p><code>
+  SHOW TABLES IN ProductSchema;
+</code></p><h2>DROP TABLE</h2><p>This command is used to delete an existing table.</p><p><code>
+  DROP TABLE [IF EXISTS] [db_name.]table_name;
+</code></p><h3>Parameter Description</h3>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Description </th>
+      <th>Optional </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>db_Name </td>
+      <td>Name of the database. If not specified, current database will be selected. </td>
+      <td>YES </td>
+    </tr>
+    <tr>
+      <td>table_name </td>
+      <td>Name of the table to be deleted. </td>
+      <td>NO </td>
+    </tr>
+  </tbody>
+</table><h3>Example:</h3><p><code>
+  DROP TABLE IF EXISTS productSchema.productSalesTable;
+</code></p><h2>COMPACTION</h2><p>This command merges the specified number of segments into one segment. This enhances the query performance of the table.</p><p><code>
+  ALTER TABLE [db_name.]table_name COMPACT &#39;MINOR/MAJOR&#39;;
+</code></p><p>To get details about Compaction refer to <a href="data-management.md">Data Management</a></p><h3>Parameter Description</h3>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Description </th>
+      <th>Optional </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>db_name </td>
+      <td>Database name, if it is not specified then it uses current database. </td>
+      <td>YES </td>
+    </tr>
+    <tr>
+      <td>table_name </td>
+      <td>The name of the table in provided database.</td>
+      <td>NO </td>
+    </tr>
+  </tbody>
+</table><h3>Syntax</h3>
+<ul>
+  <li><strong>Minor Compaction</strong></li>
+</ul><p><code>
+ALTER TABLE table_name COMPACT &#39;MINOR&#39;;
+</code> - <strong>Major Compaction</strong></p><p><code>
+ALTER TABLE table_name COMPACT &#39;MAJOR&#39;;
+</code></p><h2>BUCKETING</h2><p>Bucketing feature can be used to distribute/organize the table/partition data into multiple files such that similar records are present in the same file. While creating a table, a user needs to specify the columns to be used for bucketing and the number of buckets. For the selction of bucket the Hash value of columns is used.</p><p>```  CREATE TABLE [IF NOT EXISTS] [db_name.]table_name  [(col_name data_type, ...)]  STORED BY 'carbondata'  TBLPROPERTIES(?BUCKETNUMBER?=?noOfBuckets?,  ?BUCKETCOLUMNS?=??columnname?, ?TABLENAME?=?tablename?)</p><p>```</p><h2>Parameter Description</h2>
+<table>
+  <thead>
+    <tr>
+      <th>Parameter </th>
+      <th>Description </th>
+      <th>Optional </th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>BUCKETNUMBER </td>
+      <td>Specifies the number of Buckets to be created. </td>
+      <td>No </td>
+    </tr>
+    <tr>
+      <td>BUCKETCOLUMNS </td>
+      <td>Specify the columns to be considered for Bucketing </td>
+      <td>No </td>
+    </tr>
+    <tr>
+      <td>TABLENAME </td>
+      <td>The name of the table in Database. Table Name should consist of alphanumeric characters and underscore(_) special character. </td>
+      <td>Yes </td>
+    </tr>
+  </tbody>
+</table><h2>Usage Guidelines</h2>
+<ul>
+  <li><p>The feature is supported for Spark 1.6.2 onwards, but the performance optimization is evident from Spark 2.1 onwards.</p></li>
+  <li><p>Bucketing can not be performed for columns of Complex Data Types.</p></li>
+  <li><p>Columns in the BUCKETCOLUMN parameter must be either a dimension or a measure but combination of both is not supported.</p></li>
+</ul><h2>Example :</h2><p>```  CREATE TABLE IF NOT EXISTS productSchema.productSalesTable (  productNumber Int,  productName String,  storeCity String,  storeProvince String,  productCategory String,  productBatch String,  saleQuantity Int,  revenue Int)  STORED BY 'carbondata'  TBLPROPERTIES ('COLUMN_GROUPS'='(productName,productCategory)',  'DICTIONARY_EXCLUDE'='productName',  'DICTIONARY_INCLUDE'='productNumber',  'NO_INVERTED_INDEX'='productBatch',  'BUCKETNUMBER'='4',  'BUCKETCOLUMNS'='productNumber,saleQuantity',  'TABLENAME'='productSalesTable')</p><p>```</p>
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