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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.
--->
-
-# Configuring CarbonData
- This tutorial guides you through the advanced configurations of CarbonData :
- 
- * [System Configuration](#system-configuration)
- * [Performance Configuration](#performance-configuration)
- * [Miscellaneous Configuration](#miscellaneous-configuration)
- * [Spark Configuration](#spark-configuration)
- 
- 
-##  System Configuration
-This section provides the details of all the configurations required for the 
CarbonData System.
-
-<b><p align="center">System Configuration in carbon.properties</p></b>
-
-| Property | Default Value | Description |
-|----------------------------|-------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| carbon.storelocation | /user/hive/warehouse/carbon.store | Location where 
CarbonData will create the store, and write the data in its own format. NOTE: 
Store location should be in HDFS. |
-| carbon.ddl.base.hdfs.url | hdfs://hacluster/opt/data | 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. |
-| carbon.badRecords.location | /opt/Carbon/Spark/badrecords | Path where the 
bad records are stored. |
-| carbon.kettle.home | $SPARK_HOME/carbonlib/carbonplugins | Configuration for 
loading the data with kettle. |
-| carbon.data.file.version | 2 | 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.|                    
-
-##  Performance Configuration
-This section provides the details of all the configurations required for 
CarbonData Performance Optimization.
-
-<b><p align="center">Performance Configuration in carbon.properties</p></b>
-
-* **Data Loading Configuration**
-
-| Parameter | Default Value | Description | Range |
-|--------------------------------------|---------------|----------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| carbon.sort.file.buffer.size | 20 | File read buffer size used during 
sorting. This value is expressed in MB. | Min=1 and Max=100 |
-| carbon.graph.rowset.size | 100000 | Rowset size exchanged between data load 
graph steps. | Min=500 and Max=1000000 |
-| carbon.number.of.cores.while.loading | 6 | Number of cores to be used while 
loading data. |  |
-| carbon.sort.size | 500000 | Record count to sort and write intermediate 
files to temp. |  |
-| carbon.enableXXHash | true | Algorithm for hashmap for hashkey calculation. 
|  |
-| carbon.number.of.cores.block.sort | 7 | Number of cores to use for block 
sort while loading data. |  |
-| carbon.max.driver.lru.cache.size | -1 | 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. |  |
-| carbon.max.executor.lru.cache.size | -1 | 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. |  |
-| carbon.merge.sort.prefetch | true | Enable prefetch of data during merge 
sort while reading data from sort temp files in data loading. |  |
-| carbon.update.persist.enable | true | Enabling this parameter considers 
persistent data. Enabling this will reduce the execution time of UPDATE 
operation. |  |
-
-
-
-* **Compaction Configuration**
-  
-| Parameter | Default Value | Description | Range |
-|-----------------------------------------------|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
-| carbon.number.of.cores.while.compacting | 2 | Number of cores which are used 
to write data during compaction. |  |
-| carbon.compaction.level.threshold | 4, 3 | 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. 
| Valid values are from 0-100. |
-| carbon.major.compaction.size | 1024 | 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. |  |
-| carbon.horizontal.compaction.enable | true | 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. |  |
-| carbon.horizontal.UPDATE.compaction.threshold | 1 | 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. | Values between 1 to 10000. |
-| carbon.horizontal.DELETE.compaction.threshold | 1 | 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. | Values 
between 1 to 10000. |
-
-  
-
-* **Query Configuration**
-  
-| Parameter | Default Value | Description | Range |
-|--------------------------------------|---------------|---------------------------------------------------|---------------------------|
-| carbon.number.of.cores | 4 | Number of cores to be used while querying. |  |
-| carbon.inmemory.record.size | 120000 | Number of records to be in memory 
while querying. | Min=100000 and Max=240000 |
-| carbon.enable.quick.filter | false | Improves the performance of filter 
query. |  |
-| no.of.cores.to.load.blocks.in.driver | 10 | Number of core to load the 
blocks in driver. |  |
-
-
-##   Miscellaneous Configuration
-
-<b><p align="center">Extra Configuration in carbon.properties</p></b>
-
-* **Time format for CarbonData** 
-
-| Parameter | Default Format | Description |
-|-------------------------|---------------------|--------------------------------------------------------------|
-| carbon.timestamp.format | yyyy-MM-dd HH:mm:ss | Timestamp format of input 
data used for timestamp data type. |
-
-* **Dataload Configuration**
-  
-| Parameter | Default Value | Description |
-|---------------------------------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| carbon.sort.file.write.buffer.size | 10485760 | File write buffer size used 
during sorting. |
-| carbon.lock.type | LOCALLOCK | 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. |
-| carbon.sort.intermediate.files.limit | 20 | Minimum number of intermediate 
files after which merged sort can be started. |
-| carbon.block.meta.size.reserved.percentage | 10 | Space reserved in 
percentage for writing block meta data in CarbonData file. |
-| carbon.csv.read.buffersize.byte | 1048576 | csv reading buffer size. |
-| high.cardinality.value | 100000 | To identify and apply compression for 
non-high cardinality columns. |
-| carbon.merge.sort.reader.thread | 3 | Maximum no of threads used for reading 
intermediate files for final merging. |
-| carbon.load.metadata.lock.retries | 3 | Maximum number of retries to get the 
metadata lock for loading data to table. |
-| carbon.load.metadata.lock.retry.timeout.sec | 5 | Interval between the 
retries to get the lock. |
-| carbon.tempstore.location | /opt/Carbon/TempStoreLoc | Temporary store 
location. By default it takes System.getProperty("java.io.tmpdir"). |
-| carbon.load.log.counter | 500000 | Data loading records count logger. |   
-
-
-* **Compaction Configuration**
-
-| Parameter | Default Value | Description |
-|-----------------------------------|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| carbon.numberof.preserve.segments | 0 | 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. |
-| carbon.allowed.compaction.days | 0 | 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. |
-| carbon.enable.auto.load.merge | false | To enable compaction while data 
loading. |
-
- 
-* **Query Configuration**
-
-| Parameter | Default Value | Description |
-|--------------------------|---------------|-----------------------------------------------------------------------------------------------|
-| max.query.execution.time | 60 | Maximum time allowed for one query to be 
executed. The value is in minutes. |
-| carbon.enableMinMax | true | Min max is feature added to enhance query 
performance. To disable this feature, set it false. | 
-  
-* **Global Dictionary Configurations**
-  
-| Parameter | Default Value | Description |
-|---------------------------------------|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| high.cardinality.identify.enable | true | 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 > configured value of high.cardinalityEqually, the value of 
cardinality is higher than the threshold.value of cardinality/ row number x 100 
> configured value of high.cardinality.row.count.percentageEqually, the ratio 
of the cardinality value to data row number is higher than the configured 
percentage. |
-| high.cardinality.threshold | 1000000 | high.cardinality.threshold | 1000000 
| It is a threshold to identify high cardinality of the columns.If the value of 
columns' cardinality > the configured value, then the columns are excluded from 
dictionary encoding. |
-| high.cardinality.row.count.percentage | 80 | 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 > 
configured value of high.cardinality.row.count.percentageThe value of the 
parameter must be larger than 0. |
-| carbon.cutOffTimestamp | 1970-01-01 05:30:00 | 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. |
-| carbon.timegranularity | SECOND | The property used to set the data 
granularity level DAY, HOUR, MINUTE, or SECOND. |
-  
-##  Spark Configuration
- <b><p align="center">Spark Configuration Reference in 
spark-defaults.conf</p></b>
- 
-| Parameter | Default Value | Description |
-|----------------------------------------|--------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| spark.driver.memory | 1g | Amount of memory to be used by the driver 
process. |
-| spark.executor.memory | 1g | Amount of memory to be used per executor 
process. |
-| spark.sql.bigdata.register.analyseRule | 
org.apache.spark.sql.hive.acl.CarbonAccessControlRules | 
CarbonAccessControlRules need to be set for enabling Access Control. |
-   
- 
\ No newline at end of file

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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.
--->
-
-# Data Management
-This tutorial is going to introduce you to the conceptual details of data 
management like:
-
-* [Loading Data](#loading-data)
-* [Deleting Data](#deleting-data)
-* [Compacting Data](#compacting-data)
-* [Updating Data](#updating-data)
-
-## Loading Data
-
-* **Scenario**
-
-   After creating a table, you can load data to the table using the [LOAD 
DATA](dml-operation-on-carbondata.md) 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.
-
-* **Procedure**
-  
-   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.
-   
-   
-| Status | Description |
-|-----------------|------------------------------------------------------------------------------------------------------------|
-| Success | All the data is loaded into table and no bad records found. |
-| Partial Success | Data is loaded into table and bad records are found. Bad 
records are stored at carbon.badrecords.location. |
-   
-   In case of failure, the error will be logged in error log. Details of loads 
can be seen with [SHOW SEGMENTS](dml-operation-on-carbondata.md) command. The 
show segment command output consists of :
-   
-   - SegmentSequenceID
-   - START_TIME OF LOAD
-   - END_TIME OF LOAD 
-   - LOAD STATUS
- 
-   The latest load will be displayed first in the output.
-   
-   Refer to [DML operations on CarbonData](dml-operation-on-carbondata.md) for 
load commands.
-   
-   
-## Deleting Data  
-
-* **Scenario**
-   
-   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.
-   
-* **Procedure** 
-
-   The loaded data can be deleted in the following ways:
-   
-   * Delete by Segment ID
-      
-      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.
-      
-| SegmentSequenceId | Status            | Load Start Time      | Load End Time 
       |
-|-------------------|-------------------|----------------------|----------------------|
-| 0                 | Success           | 2015-11-19 19:14:... | 2015-11-19 
19:14:... |
-| 1                 | Marked for Update | 2015-11-19 19:54:... | 2015-11-19 
20:08:... |
-| 2                 | Marked for Delete | 2015-11-19 20:25:... | 2015-11-19 
20:49:... |
-
-   * Delete by Date Field
-   
-      If the table contains date field, you can delete the data based on a 
specific date.
-
-   * Delete by Record
-
-      To delete records from CarbonData table based on some filter 
Condition(s).
-      
-      For delete commands refer to [DML operations on 
CarbonData](dml-operation-on-carbondata.md).
-      
-   * **NOTE**:
-    
-     - 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.
-     
-     - 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.
-     
-     - If the user wants to force delete the segment physically then he can 
use CLEAN FILES Command.
-        
-Example :
-          
-```
-CLEAN FILES FOR TABLE table1
-```
-
- This DML will physically delete the segment which are "Marked for delete" 
immediately.
-
-## Compacting Data
-      
-* **Scenario**
-  
-  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.  
-      
-* **Procedure**
-
-  There are two types of compaction Minor and Major compaction.
-  
-  - **Minor Compaction**
-    
-     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.
-     
-     - Level 1: Merging of the segments which are not yet compacted.
-     - Level 2: Merging of the compacted segments again to form a bigger 
segment. 
-  - **Major Compaction**
-     
-     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. 
-      
-   There are number of parameters related to Compaction that can be set in 
carbon.properties file 
-   
-| Parameter | Default | Application | Description | Valid Values |
-|-----------------------------------------|---------|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
-| carbon.compaction.level.threshold | 4, 3 | Minor | 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. | NA |
-| carbon.major.compaction.size | 1024 MB | Major | Major compaction size can 
be configured using this parameter. Sum of the segments which is below this 
threshold will be merged. | NA |
-| carbon.numberof.preserve.segments | 0 | Minor/Major | 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. | 0-100 |
-| carbon.allowed.compaction.days | 0 | Minor/Major | 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. | 0-100 |
-| carbon.number.of.cores.while.compacting | 2 | Minor/Major | Number of cores 
which is used to write data during compaction. | 0-100 |   
-  
-   For compaction commands refer to [DDL operations on 
CarbonData](ddl-operation-on-carbondata.md)
-
-## Updating Data
-
-* **Scenario**
-
-    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.
-
-* **Procedure**
-
-    To update we need to specify the column expression with an optional filter 
condition(s).
-
-    For update commands refer to [DML operations on 
CarbonData](dml-operation-on-carbondata.md).
-
-
-    
-
-
-
-
- 
- 

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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.
--->
-
-# DDL Operations on CarbonData
-This tutorial guides you through the data definition language support provided 
by CarbonData.
-
-## Overview 
-The following DDL operations are supported in CarbonData :
-
-* [CREATE TABLE](#create-table)
-* [SHOW TABLE](#show-table)
-* [DROP TABLE](#drop-table)
-* [COMPACTION](#compaction)
-* [BUCKETING](#bucketing)
-
-## CREATE TABLE
-  This command can be used to create a CarbonData table by specifying the list 
of fields along with the table properties.
-```
-   CREATE TABLE [IF NOT EXISTS] [db_name.]table_name
-                    [(col_name data_type , ...)]
-   STORED BY 'carbondata'
-   [TBLPROPERTIES (property_name=property_value, ...)]
-   // All Carbon's additional table options will go into properties
-```
-
-### Parameter Description
-
-| Parameter | Description | Optional |
-|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------|----------|
-| db_name | Name of the database. Database name should consist of alphanumeric 
characters and underscore(_) special character. | Yes |
-| field_list | Comma separated List of fields with data type. The field names 
should consist of alphanumeric characters and underscore(_) special character. 
| No |
-| table_name | The name of the table in Database. Table Name should consist of 
alphanumeric characters and underscore(_) special character. | No |
-| STORED BY | "org.apache.carbondata.format", identifies and creates a 
CarbonData table. | No |
-| TBLPROPERTIES | List of CarbonData table properties. |  |
-
-### Usage Guidelines
-
-   Following are the guidelines for using table properties.
-
-   - **Dictionary Encoding Configuration**
-
-       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.
-```
-       TBLPROPERTIES ("DICTIONARY_EXCLUDE"="column1, column2")
-       TBLPROPERTIES ("DICTIONARY_INCLUDE"="column1, column2")
-```
-
-   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.
-
-   - **Row/Column Format Configuration**
-
-       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.
-```
-TBLPROPERTIES ("COLUMN_GROUPS"="(column1, column3),
-(Column4,Column5,Column6)")
-```
-
-   - **Table Block Size Configuration**
-
-     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.
-```
-       TBLPROPERTIES ("TABLE_BLOCKSIZE"="512 MB")
-```
-
-  Here 512 MB means the block size of this table is 512 MB, you can also set 
it as 512M or 512.
-
-   - **Inverted Index Configuration**
-
-      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.
-```
-       TBLPROPERTIES ("NO_INVERTED_INDEX"="column1, column3")
-```
-
-  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.
-
-   NOTE:
-
-   - By default all columns other than numeric datatype are treated as 
dimensions and all columns of numeric datatype are treated as measures.
-
-   - 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.
-
-### Example:
-```
-    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'='(productNumber,productName)',
-                     'DICTIONARY_EXCLUDE'='storeCity',
-                     'DICTIONARY_INCLUDE'='productNumber',
-                     'NO_INVERTED_INDEX'='productBatch')
-```
-
-## SHOW TABLE
-
-  This command can be used to list all the tables in current database or all 
the tables of a specific database.
-```
-  SHOW TABLES [IN db_Name];
-```
-
-### Parameter Description
-| Parameter  | Description                                                     
                          | Optional |
-|------------|-------------------------------------------------------------------------------------------|----------|
-| IN db_Name | Name of the database. Required only if tables of this specific 
database are to be listed. | Yes      |
-
-### Example:
-```
-  SHOW TABLES IN ProductSchema;
-```
-
-## DROP TABLE
-
- This command is used to delete an existing table.
-```
-  DROP TABLE [IF EXISTS] [db_name.]table_name;
-```
-
-### Parameter Description
-| Parameter | Description | Optional |
-|-----------|-------------| -------- |
-| db_Name | Name of the database. If not specified, current database will be 
selected. | YES |
-| table_name | Name of the table to be deleted. | NO |
-
-### Example:
-```
-  DROP TABLE IF EXISTS productSchema.productSalesTable;
-```
-
-## COMPACTION
-
-This command merges the specified number of segments into one segment. This 
enhances the query performance of the table.
-```
-  ALTER TABLE [db_name.]table_name COMPACT 'MINOR/MAJOR';
-```
-
-  To get details about Compaction refer to [Data 
Management](data-management.md)
-
-### Parameter Description
-
-| Parameter | Description | Optional |
-| ------------- | -----| ----------- |
-| db_name | Database name, if it is not specified then it uses current 
database. | YES |
-| table_name | The name of the table in provided database.| NO |
-
-### Syntax
-
-- **Minor Compaction**
-```
-ALTER TABLE table_name COMPACT 'MINOR';
-```
-- **Major Compaction**
-```
-ALTER TABLE table_name COMPACT 'MAJOR';
-```
-
-## BUCKETING
-
-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.
-
-```
-   CREATE TABLE [IF NOT EXISTS] [db_name.]table_name
-                    [(col_name data_type, ...)]
-   STORED BY 'carbondata'
-   TBLPROPERTIES(“BUCKETNUMBER”=”noOfBuckets”,
-   “BUCKETCOLUMNS”=’’columnname”)
-```
-  
-## Parameter Description
-
-| Parameter    | Description   | Optional      |
-|---------------       
|------------------------------------------------------------------------------------------------------------------------------
 |----------     |
-| BUCKETNUMBER         | Specifies the number of Buckets to be created.        
| No    |
-| BUCKETCOLUMNS        | Specify the columns to be considered for Bucketing    
| No    |
-
-## Usage Guidelines
-
-- The feature is supported for Spark 1.6.2 onwards, but the performance 
optimization is evident from Spark 2.1 onwards.
-
-- Bucketing can not be performed for columns of Complex Data Types.
-
-- Columns in the BUCKETCOLUMN parameter must be only dimension. The 
BUCKETCOLUMN parameter can not be a measure or a combination of measures and 
dimensions.
-
-
-## Example :
-
-```
- 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,productNumber)',
-                  'DICTIONARY_EXCLUDE'='productName',
-                  'DICTIONARY_INCLUDE'='productNumber',
-                  'NO_INVERTED_INDEX'='productBatch',
-                  'BUCKETNUMBER'='4',
-                  'BUCKETCOLUMNS'='productName')
- ```
-

http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/4f8753c1/src/site/markdown/dml-operation-on-carbondata.md
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b/src/site/markdown/dml-operation-on-carbondata.md
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+++ /dev/null
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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.
--->
-
-# DML Operations on CarbonData
-This tutorial guides you through the data manipulation language support 
provided by CarbonData.
-
-## Overview 
-The following DML operations are supported in CarbonData :
-
-* [LOAD DATA](#load-data)
-* [INSERT DATA INTO A CARBONDATA TABLE](#insert-data-into-a-carbondata-table)
-* [SHOW SEGMENTS](#show-segments)
-* [DELETE SEGMENT BY ID](#delete-segment-by-id)
-* [DELETE SEGMENT BY DATE](#delete-segment-by-date)
-* [UPDATE CARBONDATA TABLE](#update-carbondata-table)
-* [DELETE RECORDS FROM CARBONDATA TABLE](#delete-records-from-carbondata-table)
-
-## LOAD DATA
-
-This command loads the user data in raw format to the CarbonData specific data 
format store, this allows CarbonData to provide good performance while querying 
the data.
-Please visit [Data Management](data-management.md) for more details on LOAD.
-
-### Syntax
-
-```
-LOAD DATA [LOCAL] INPATH 'folder_path' 
-INTO TABLE [db_name.]table_name 
-OPTIONS(property_name=property_value, ...)
-```
-
-OPTIONS are not mandatory for data loading process. Inside OPTIONS user can 
provide either of any options like DELIMITER, QUOTECHAR, ESCAPECHAR, MULTILINE 
as per requirement.
-
-NOTE: The path shall be canonical path.
-
-### Parameter Description
-
-| Parameter     | Description                                                  
        | Optional |
-| ------------- | 
---------------------------------------------------------------------| -------- 
|
-| folder_path   | Path of raw csv data folder or file.                         
        | NO       |
-| db_name       | Database name, if it is not specified then it uses the 
current database. | YES      |
-| table_name    | The name of the table in provided database.                  
        | NO       |
-| OPTIONS       | Extra options provided to Load                               
        | YES      |
- 
-
-### Usage Guidelines
-
-You can use the following options to load data:
-
-- **DELIMITER:** Delimiters can be provided in the load command.
-    
-    ``` 
-    OPTIONS('DELIMITER'=',')
-    ```
-
-- **QUOTECHAR:** Quote Characters can be provided in the load command.
-
-    ```
-    OPTIONS('QUOTECHAR'='"')
-    ```
-
-- **COMMENTCHAR:** Comment Characters can be provided in the load command if 
user want to comment lines.
-
-    ```
-    OPTIONS('COMMENTCHAR'='#')
-    ```
-
-- **FILEHEADER:** Headers can be provided in the LOAD DATA command if headers 
are missing in the source files.
-
-    ```
-    OPTIONS('FILEHEADER'='column1,column2') 
-    ```
-
-- **MULTILINE:** CSV with new line character in quotes.
-
-    ```
-    OPTIONS('MULTILINE'='true') 
-    ```
-
-- **ESCAPECHAR:** Escape char can be provided if user want strict validation 
of escape character on CSV.
-
-    ```
-    OPTIONS('ESCAPECHAR'='\') 
-    ```
-
-- **COMPLEX_DELIMITER_LEVEL_1:** Split the complex type data column in a row 
(eg., a$b$c --> Array = {a,b,c}).
-
-    ```
-    OPTIONS('COMPLEX_DELIMITER_LEVEL_1'='$') 
-    ```
-
-- **COMPLEX_DELIMITER_LEVEL_2:** Split the complex type nested data column in 
a row. Applies level_1 delimiter & applies level_2 based on complex data type 
(eg., a:b$c:d --> Array> = {{a,b},{c,d}}).
-
-    ```
-    OPTIONS('COMPLEX_DELIMITER_LEVEL_2'=':') 
-    ```
-
-- **ALL_DICTIONARY_PATH:** All dictionary files path.
-
-    ```
-    OPTIONS('ALL_DICTIONARY_PATH'='/opt/alldictionary/data.dictionary')
-    ```
-
-- **COLUMNDICT:** Dictionary file path for specified column.
-
-    ```
-    OPTIONS('COLUMNDICT'='column1:dictionaryFilePath1,
-    column2:dictionaryFilePath2')
-    ```
-
-    NOTE: ALL_DICTIONARY_PATH and COLUMNDICT can't be used together.
-    
-- **DATEFORMAT:** Date format for specified column.
-
-    ```
-    OPTIONS('DATEFORMAT'='column1:dateFormat1, column2:dateFormat2')
-    ```
-
-    NOTE: Date formats are specified by date pattern strings. The date pattern 
letters in CarbonData are same as in JAVA. Refer to 
[SimpleDateFormat](http://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html).
-
-- **USE_KETTLE:** This option is used to specify whether to use kettle for 
loading data or not. By default kettle is not used for data loading.
-
-    ```
-    OPTIONS('USE_KETTLE'='FALSE')
-    ```
-
-   Note :  It is recommended to set the value for this option as false.
-
-- **SINGLE_PASS:** Single Pass Loading enables single job to finish data 
loading with dictionary generation on the fly. It enhances performance in the 
scenarios where the subsequent data loading after initial load involves fewer 
incremental updates on the dictionary.
-
-   This option specifies whether to use single pass for loading data or not. 
By default this option is set to FALSE.
-
-    ```
-    OPTIONS('SINGLE_PASS'='TRUE')
-    ```
-
-   Note :
-
-   * If this option is set to TRUE then data loading will take less time.
-
-   * If this option is set to some invalid value other than TRUE or FALSE then 
it uses the default value.
-### Example:
-
-```
-LOAD DATA local inpath '/opt/rawdata/data.csv' INTO table carbontable
-options('DELIMITER'=',', 'QUOTECHAR'='"','COMMENTCHAR'='#',
-'FILEHEADER'='empno,empname,designation,doj,workgroupcategory,
- workgroupcategoryname,deptno,deptname,projectcode,
- projectjoindate,projectenddate,attendance,utilization,salary',
-'MULTILINE'='true','ESCAPECHAR'='\','COMPLEX_DELIMITER_LEVEL_1'='$',
-'COMPLEX_DELIMITER_LEVEL_2'=':',
-'ALL_DICTIONARY_PATH'='/opt/alldictionary/data.dictionary',
-'USE_KETTLE'='FALSE',
-'SINGLE_PASS'='TRUE'
-)
-```
-
-
-## INSERT DATA INTO A CARBONDATA TABLE
-
-This command inserts data into a CarbonData table. It is defined as a 
combination of two queries Insert and Select query respectively. It inserts 
records from a source table into a target CarbonData table. The source table 
can be a Hive table, Parquet table or a CarbonData table itself. It comes with 
the functionality to aggregate the records of a table by performing Select 
query on source table and load its corresponding resultant records into a 
CarbonData table.
-
-**NOTE** :  The client node where the INSERT command is executing, must be 
part of the cluster.
-
-### Syntax
-
-```
-INSERT INTO TABLE <CARBONDATA TABLE> SELECT * FROM sourceTableName 
-[ WHERE { <filter_condition> } ];
-```
-
-You can also omit the `table` keyword and write your query as:
- 
-```
-INSERT INTO <CARBONDATA TABLE> SELECT * FROM sourceTableName 
-[ WHERE { <filter_condition> } ];
-```
-
-### Parameter Description
-
-| Parameter | Description |
-|--------------|---------------------------------------------------------------------------------|
-| CARBON TABLE | The name of the Carbon table in which you want to perform the 
insert operation. |
-| sourceTableName | The table from which the records are read and inserted 
into destination CarbonData table. |
-
-### Usage Guidelines
-The following condition must be met for successful insert operation :
-
-- The source table and the CarbonData table must have the same table schema.
-- The table must be created.
-- Overwrite is not supported for CarbonData table.
-- The data type of source and destination table columns should be same, else 
the data from source table will be treated as bad records and the INSERT 
command fails.
-- INSERT INTO command does not support partial success if bad records are 
found, it will fail.
-- Data cannot be loaded or updated in source table while insert from source 
table to target table is in progress.
-
-To enable data load or update during insert operation, configure the following 
property to true.
-
-```
-carbon.insert.persist.enable=true
-```
-
-By default the above configuration will be false.
-
-**NOTE**: Enabling this property will reduce the performance.
-
-### Examples
-```
-INSERT INTO table1 SELECT item1 ,sum(item2 + 1000) as result FROM 
-table2 group by item1;
-```
-
-```
-INSERT INTO table1 SELECT item1, item2, item3 FROM table2 
-where item2='xyz';
-```
-
-```
-INSERT INTO table1 SELECT * FROM table2 
-where exists (select * from table3 
-where table2.item1 = table3.item1);
-```
-
-**The Status Success/Failure shall be captured in the driver log.**
-
-## SHOW SEGMENTS
-
-This command is used to get the segments of CarbonData table.
-
-```
-SHOW SEGMENTS FOR TABLE [db_name.]table_name 
-LIMIT number_of_segments;
-```
-
-### Parameter Description
-
-| Parameter          | Description                                             
             | Optional |
-| ------------------ | 
---------------------------------------------------------------------| 
---------|
-| db_name            | Database name, if it is not specified then it uses the 
current database. | YES      |
-| table_name         | The name of the table in provided database.             
             | NO       |
-| number_of_segments | Limit the output to this number.                        
             | YES      |
-
-### Example:
-
-```
-SHOW SEGMENTS FOR TABLE CarbonDatabase.CarbonTable LIMIT 4;
-```
-
-## DELETE SEGMENT BY ID
-
-This command is used to delete segment by using the segment ID. Each segment 
has a unique segment ID associated with it. 
-Using this segment ID, you can remove the segment.
-
-The following command will get the segmentID.
-
-```
-SHOW SEGMENTS FOR Table dbname.tablename LIMIT number_of_segments
-```
-
-After you retrieve the segment ID of the segment that you want to delete, 
execute the following command to delete the selected segment.
-
-```
-DELETE SEGMENT segment_sequence_id1, segments_sequence_id2, .... 
-FROM TABLE tableName
-```
-
-### Parameter Description
-| Parameter  | Description                                                     
     | Optional |
-| -----------| 
---------------------------------------------------------------------|----------|
-| segment_id | Segment Id of the load.                                         
     | NO       |
-| db_name    | Database name, if it is not specified then it uses the current 
database. | YES      |
-| table_name | The name of the table in provided database.                     
     | NO       |
-
-### Example:
-
-```
-DELETE SEGMENT 0 FROM TABLE CarbonDatabase.CarbonTable;
-DELETE SEGMENT 0.1,5,8 FROM TABLE CarbonDatabase.CarbonTable;
-```
-  NOTE: Here 0.1 is compacted segment sequence id. 
-
-## DELETE SEGMENT BY DATE
-
-This command will allow to delete the CarbonData segment(s) from the store 
based on the date provided by the user in the DML command. 
-The segment created before the particular date will be removed from the 
specific stores.
-
-```
-DELETE FROM TABLE [schema_name.]table_name 
-WHERE[DATE_FIELD]BEFORE [DATE_VALUE]
-```
-
-### Parameter Description
-
-| Parameter  | Description                                                     
                                   | Optional |
-| ---------- | 
---------------------------------------------------------------------------------------------------|
 -------- |
-| DATE_VALUE | Valid segment load start time value. All the segments before 
this specified date will be deleted. | NO       |
-| db_name    | Database name, if it is not specified then it uses the current 
database.                               | YES      |
-| table_name | The name of the table in provided database.                     
                                   | NO       |
-
-### Example:
-
-```
- DELETE SEGMENTS FROM TABLE CarbonDatabase.CarbonTable 
- WHERE STARTTIME BEFORE '2017-06-01 12:05:06';  
-```
-
-## Update CarbonData Table
-This command will allow to update the carbon table based on the column 
expression and optional filter conditions.
-
-### Syntax
-
-```
- UPDATE <table_name>
- SET (column_name1, column_name2, ... column_name n) =
- (column1_expression , column2_expression . .. column n_expression )
- [ WHERE { <filter_condition> } ];
-```
-
-alternatively the following the command can also be used for updating the 
CarbonData Table :
-
-```
-UPDATE <table_name>
-SET (column_name1, column_name2,) =
-(select sourceColumn1, sourceColumn2 from sourceTable
-[ WHERE { <filter_condition> } ] )
-[ WHERE { <filter_condition> } ];
-```
-
-### Parameter Description
-
-| Parameter | Description |
-|--------------|---------------------------------------------------------------------------------|
-| table_name | The name of the Carbon table in which you want to perform the 
update operation. |
-| column_name | The destination columns to be updated. |
-| sourceColumn | The source table column values to be updated in destination 
table. |
-| sourceTable | The table from which the records are updated into destination 
Carbon table. |
-
-### Usage Guidelines
-The following conditions must be met for successful updation :
-
-- The update command fails if multiple input rows in source table are matched 
with single row in destination table.
-- If the source table generates empty records, the update operation will 
complete successfully without updating the table.
-- If a source table row does not correspond to any of the existing rows in a 
destination table, the update operation will complete successfully without 
updating the table.
-- In sub-query, if the source table and the target table are same, then the 
update operation fails.
-- If the sub-query used in UPDATE statement contains aggregate method or group 
by query, then the UPDATE operation fails.
-
-### Examples
-
- Update is not supported for queries that contain aggregate or group by.
-
-```
- UPDATE t_carbn01 a
- SET (a.item_type_code, a.profit) = ( SELECT b.item_type_cd,
- sum(b.profit) from t_carbn01b b
- WHERE item_type_cd =2 group by item_type_code);
-```
-
-Here the Update Operation fails as the query contains aggregate function 
sum(b.profit) and group by clause in the sub-query.
-
-
-```
-UPDATE carbonTable1 d
-SET(d.column3,d.column5 ) = (SELECT s.c33 ,s.c55
-FROM sourceTable1 s WHERE d.column1 = s.c11)
-WHERE d.column1 = 'china' EXISTS( SELECT * from table3 o where o.c2 > 1);
-```
-
-
-```
-UPDATE carbonTable1 d SET (c3) = (SELECT s.c33 from sourceTable1 s
-WHERE d.column1 = s.c11)
-WHERE exists( select * from iud.other o where o.c2 > 1);
-```
-
-
-```
-UPDATE carbonTable1 SET (c2, c5 ) = (c2 + 1, concat(c5 , "y" ));
-```
-
-
-```
-UPDATE carbonTable1 d SET (c2, c5 ) = (c2 + 1, "xyx")
-WHERE d.column1 = 'india';
-```
-
-
-```
-UPDATE carbonTable1 d SET (c2, c5 ) = (c2 + 1, "xyx")
-WHERE d.column1 = 'india'
-and EXISTS( SELECT * FROM table3 o WHERE o.column2 > 1);
-```
-
-**The Status Success/Failure shall be captured in the driver log and the 
client.**
-
-
-## Delete Records from CarbonData Table
-This command allows us to delete records from CarbonData table.
-
-### Syntax
-
-```
-DELETE FROM table_name [WHERE expression];
-```
-
-### Parameter Description
-
-| Parameter | Description |
-|--------------|-----------------------------------------------------------------------|
-| table_name | The name of the Carbon table in which you want to perform the 
delete. |
-
-
-### Examples
-
-```
-DELETE FROM columncarbonTable1 d WHERE d.column1  = 'china';
-```
-
-```
-DELETE FROM dest WHERE column1 IN ('china', 'USA');
-```
-
-```
-DELETE FROM columncarbonTable1
-WHERE column1 IN (SELECT column11 FROM sourceTable2);
-```
-
-```
-DELETE FROM columncarbonTable1
-WHERE column1 IN (SELECT column11 FROM sourceTable2 WHERE
-column1 = 'USA');
-```
-
-```
-DELETE FROM columncarbonTable1 WHERE column2 >= 4
-```
-
-**The Status Success/Failure shall be captured in the driver log and the 
client.**

http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/4f8753c1/src/site/markdown/faq.md
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diff --git a/src/site/markdown/faq.md b/src/site/markdown/faq.md
deleted file mode 100644
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+++ /dev/null
@@ -1,77 +0,0 @@
-<!--
-    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.
--->
-
-# FAQs
-
-* [What are Bad Records?](#what-are-bad-records)
-* [Where are Bad Records Stored in 
CarbonData?](#where-are-bad-records-stored-in-carbondata)
-* [How to enable Bad Record Logging?](#how-to-enable-bad-record-logging)
-* [How to ignore the Bad Records?](#how-to-ignore-the-bad-records)
-* [How to specify store location while creating carbon 
session?](#how-to-specify-store-location-while-creating-carbon-session)
-* [What is Carbon Lock Type?](#what-is-carbon-lock-type)
-* [How to resolve Abstract Method 
Error?](#how-to-resolve-abstract-method-error)
-
-## What are Bad Records?
-Records that fail to get loaded into the CarbonData due to data type 
incompatibility or are empty or have incompatible format are classified as Bad 
Records.
-
-## Where are Bad Records Stored in CarbonData?
-The bad records are stored at the location set in carbon.badRecords.location 
in carbon.properties file.
-By default **carbon.badRecords.location** specifies the following location 
``/opt/Carbon/Spark/badrecords``.
-
-## How to enable Bad Record Logging?
-While loading data we can specify the approach to handle Bad Records. In order 
to analyse the cause of the Bad Records the parameter 
``BAD_RECORDS_LOGGER_ENABLE`` must be set to value ``TRUE``. There are multiple 
approaches to handle Bad Records which can be specified  by the parameter 
``BAD_RECORDS_ACTION``.
-
-- To pad the incorrect values of the csv rows with NULL value and load the 
data in CarbonData, set the following in the query :
-```
-'BAD_RECORDS_ACTION'='FORCE'
-```
-
-- To write the Bad Records without padding incorrect values with NULL in the 
raw csv (set in the parameter **carbon.badRecords.location**), set the 
following in the query :
-```
-'BAD_RECORDS_ACTION'='REDIRECT'
-```
-
-## How to ignore the Bad Records?
-To ignore the Bad Records from getting stored in the raw csv, we need to set 
the following in the query :
-```
-'BAD_RECORDS_ACTION'='IGNORE'
-```
-
-## How to specify store location while creating carbon session?
-The store location specified while creating carbon session is used by the 
CarbonData to store the meta data like the schema, dictionary files, dictionary 
meta data and sort indexes.
-
-Try creating ``carbonsession`` with ``storepath`` specified in the following 
manner :
-```
-val carbon = 
SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession(<store_path>)
-```
-Example:
-```
-val carbon = 
SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("hdfs://localhost:9000/carbon/store
 ")
-```
-
-## What is Carbon Lock Type?
-The Apache CarbonData acquires lock on the files to prevent concurrent 
operation from modifying the same files. The lock can be of the following types 
depending on the storage location, for HDFS we specify it to be of type 
HDFSLOCK. By default it is set to type LOCALLOCK.
-The property carbon.lock.type configuration specifies the type of lock to be 
acquired during concurrent operations on table. This property can be set with 
the following values :
-- **LOCALLOCK** : This 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** : This 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 the HDFS supports, file based locking.
-
-## How to resolve Abstract Method Error?
-In order to build CarbonData project it is necessary to specify the spark 
profile. The spark profile sets the Spark Version. You need to specify the 
``spark version`` while using Maven to build project.
-
-

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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.
--->
-
-# CarbonData File Structure
-
-CarbonData files contain groups of data called blocklets, along with all 
required information like schema, offsets and indices etc, in a file header and 
footer, co-located in HDFS.
-
-The file footer can be read once to build the indices in memory, which can be 
utilized for optimizing the scans and processing for all subsequent queries.
-
-### Understanding CarbonData File Structure
-* Block : It would be as same as HDFS block, CarbonData creates one file for 
each data block, user can specify TABLE_BLOCKSIZE during creation table. Each 
file contains File Header, Blocklets and File Footer. 
-
-![CarbonData File 
Structure](../docs/images/carbon_data_file_structure_new.png?raw=true)
-
-* File Header : It contains CarbonData file version number, list of column 
schema and schema updation timestamp.
-* File Footer : it contains Number of rows, segmentinfo ,all blocklets’ info 
and index, you can find the detail from the below diagram.
-* Blocklet : Rows are grouped to form a blocklet, the size of the blocklet is 
configurable and default size is 64MB, Blocklet contains Column Page groups for 
each column.
-* Column Page Group : Data of one column and it is further divided to pages, 
it is guaranteed to be contiguous in file.
-* Page : It has the data of one column and the number of row is fixed to 32000 
size. 
-
-![CarbonData File Format](../docs/images/carbon_data_format_new.png?raw=true)
-
-### Each page contains three types of data
-* Data Page: Contains the encoded data of a column of columns.
-* Row ID Page (optional): Contains the row ID mappings used when the data page 
is stored as an inverted index.
-* RLE Page (optional): Contains additional metadata used when the data page is 
RLE coded.
-
-
-

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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.
--->
-
-# Installation Guide
-This tutorial guides you through the installation and configuration of 
CarbonData in the following two modes :
-
-* [Installing and Configuring CarbonData on Standalone Spark 
Cluster](#installing-and-configuring-carbondata-on-standalone-spark-cluster)
-* [Installing and Configuring CarbonData on “Spark on YARN” 
Cluster](#installing-and-configuring-carbondata-on-spark-on-yarn-cluster)
-
-followed by :
-
-* [Query Execution using CarbonData Thrift 
Server](#query-execution-using-carbondata-thrift-server)
-
-## Installing and Configuring CarbonData on Standalone Spark Cluster
-
-### Prerequisites
-
-   - Hadoop HDFS and Yarn should be installed and running.
-
-   - Spark should be installed and running on all the cluster nodes.
-
-   - CarbonData user should have permission to access HDFS.
-
-
-### Procedure
-
-1. [Build the 
CarbonData](https://github.com/apache/incubator-carbondata/blob/master/build/README.md)
 project and get the assembly jar from 
`./assembly/target/scala-2.1x/carbondata_xxx.jar`. 
-
-2. Copy `./assembly/target/scala-2.1x/carbondata_xxx.jar` to 
`$SPARK_HOME/carbonlib` folder.
-
-     **NOTE**: Create the carbonlib folder if it does not exist inside 
`$SPARK_HOME` path.
-
-3. Add the carbonlib folder path in the Spark classpath. (Edit 
`$SPARK_HOME/conf/spark-env.sh` file and modify the value of `SPARK_CLASSPATH` 
by appending `$SPARK_HOME/carbonlib/*` to the existing value)
-
-4. Copy the `./conf/carbon.properties.template` file from CarbonData 
repository to `$SPARK_HOME/conf/` folder and rename the file to 
`carbon.properties`.
-
-5. Copy the `./processing/carbonplugins` folder from CarbonData repository to 
`$SPARK_HOME/carbonlib/` folder.
-
-    **NOTE**: carbonplugins will contain .kettle folder.
-
-6. Repeat Step 2 to Step 5 in all the nodes of the cluster.
-    
-7. In Spark node[master], configure the properties mentioned in the following 
table in `$SPARK_HOME/conf/spark-defaults.conf` file.
-
-   | Property | Value | Description |
-   
|---------------------------------|-----------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
-   | carbon.kettle.home | `$SPARK_HOME/carbonlib/carbonplugins` | Path that 
will be used by CarbonData internally to create graph for loading the data |
-   | spark.driver.extraJavaOptions | 
`-Dcarbon.properties.filepath=$SPARK_HOME/conf/carbon.properties` | A string of 
extra JVM options to pass to the driver. For instance, GC settings or other 
logging. |
-   | spark.executor.extraJavaOptions | 
`-Dcarbon.properties.filepath=$SPARK_HOME/conf/carbon.properties` | A string of 
extra JVM options to pass to executors. For instance, GC settings or other 
logging. **NOTE**: You can enter multiple values separated by space. |
-
-8. Add the following properties in `$SPARK_HOME/conf/carbon.properties` file:
-
-   | Property             | Required | Description                             
                                               | Example                        
     | Remark  |
-   
|----------------------|----------|----------------------------------------------------------------------------------------|-------------------------------------|---------|
-   | carbon.storelocation | NO       | Location where data CarbonData will 
create the store and write the data in its own format. | 
hdfs://HOSTNAME:PORT/Opt/CarbonStore      | Propose to set HDFS directory |
-   | carbon.kettle.home   | YES      | Path that will be used by CarbonData 
internally to create graph for loading the data.         | 
`$SPARK_HOME/carbonlib/carbonplugins` |         |
-
-
-9. Verify the installation. For example:
-
-   ```
-   ./spark-shell --master spark://HOSTNAME:PORT --total-executor-cores 2
-   --executor-memory 2G
-   ```
-
-**NOTE**: Make sure you have permissions for CarbonData JARs and files through 
which driver and executor will start.
-
-To get started with CarbonData : [Quick Start](quick-start-guide.md), [DDL 
Operations on CarbonData](ddl-operation-on-carbondata.md)
-
-## Installing and Configuring CarbonData on "Spark on YARN" Cluster
-
-   This section provides the procedure to install CarbonData on "Spark on 
YARN" cluster.
-
-### Prerequisites
-   * Hadoop HDFS and Yarn should be installed and running.
-   * Spark should be installed and running in all the clients.
-   * CarbonData user should have permission to access HDFS.
-
-### Procedure
-
-   The following steps are only for Driver Nodes. (Driver nodes are the one 
which starts the spark context.)
-
-1. [Build the 
CarbonData](https://github.com/apache/incubator-carbondata/blob/master/build/README.md)
 project and get the assembly jar from 
`./assembly/target/scala-2.1x/carbondata_xxx.jar` and copy to 
`$SPARK_HOME/carbonlib` folder.
-
-    **NOTE**: Create the carbonlib folder if it does not exists inside 
`$SPARK_HOME` path.
-
-2. Copy the `./processing/carbonplugins` folder from CarbonData repository to 
`$SPARK_HOME/carbonlib/` folder.
-
-    **NOTE**: carbonplugins will contain .kettle folder.
-
-3. Copy the `./conf/carbon.properties.template` file from CarbonData 
repository to `$SPARK_HOME/conf/` folder and rename the file to 
`carbon.properties`.
-
-4. Create `tar,gz` file of carbonlib folder and move it inside the carbonlib 
folder.
-
-    ```
-       cd $SPARK_HOME
-       tar -zcvf carbondata.tar.gz carbonlib/
-       mv carbondata.tar.gz carbonlib/
-    ```
-
-5. Configure the properties mentioned in the following table in 
`$SPARK_HOME/conf/spark-defaults.conf` file.
-
-   | Property | Description | Value |
-   
|---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|
-   | spark.master | Set this value to run the Spark in yarn cluster mode. | 
Set yarn-client to run the Spark in yarn cluster mode. |
-   | spark.yarn.dist.files | Comma-separated list of files to be placed in the 
working directory of each executor. |`$SPARK_HOME/conf/carbon.properties` |
-   | spark.yarn.dist.archives | Comma-separated list of archives to be 
extracted into the working directory of each executor. 
|`$SPARK_HOME/carbonlib/carbondata.tar.gz` |
-   | spark.executor.extraJavaOptions | A string of extra JVM options to pass 
to executors. For instance  **NOTE**: You can enter multiple values separated 
by space. |`-Dcarbon.properties.filepath=carbon.properties` |
-   | spark.executor.extraClassPath | Extra classpath entries to prepend to the 
classpath of executors. **NOTE**: If SPARK_CLASSPATH is defined in 
spark-env.sh, then comment it and append the values in below parameter 
spark.driver.extraClassPath |`carbondata.tar.gz/carbonlib/*` |
-   | spark.driver.extraClassPath | Extra classpath entries to prepend to the 
classpath of the driver. **NOTE**: If SPARK_CLASSPATH is defined in 
spark-env.sh, then comment it and append the value in below parameter 
spark.driver.extraClassPath. |`$SPARK_HOME/carbonlib/carbonlib/*` |
-   | spark.driver.extraJavaOptions | A string of extra JVM options to pass to 
the driver. For instance, GC settings or other logging. 
|`-Dcarbon.properties.filepath=$SPARK_HOME/conf/carbon.properties` |
-
-
-6. Add the following properties in `$SPARK_HOME/conf/carbon.properties`:
-
-   | Property | Required | Description | Example | Default Value |
-   
|----------------------|----------|----------------------------------------------------------------------------------------|-------------------------------------|---------------|
-   | carbon.storelocation | NO | Location where CarbonData will create the 
store and write the data in its own format. | 
hdfs://HOSTNAME:PORT/Opt/CarbonStore | Propose to set HDFS directory|
-   | carbon.kettle.home | YES | Path that will be used by CarbonData 
internally to create graph for loading the data. | 
carbondata.tar.gz/carbonlib/carbonplugins |  |
-
-
-7. Verify the installation.
-
-   ```
-     ./bin/spark-shell --master yarn-client --driver-memory 1g
-     --executor-cores 2 --executor-memory 2G
-   ```
-  **NOTE**: Make sure you have permissions for CarbonData JARs and files 
through which driver and executor will start.
-
-  Getting started with CarbonData : [Quick Start](quick-start-guide.md), [DDL 
Operations on CarbonData](ddl-operation-on-carbondata.md)
-
-## Query Execution Using CarbonData Thrift Server
-
-### Starting CarbonData Thrift Server.
-
-   a. cd `$SPARK_HOME`
-
-   b. Run the following command to start the CarbonData thrift server.
-
-   ```
-   ./bin/spark-submit --conf spark.sql.hive.thriftServer.singleSession=true
-   --class org.apache.carbondata.spark.thriftserver.CarbonThriftServer
-   $SPARK_HOME/carbonlib/$CARBON_ASSEMBLY_JAR <carbon_store_path>
-   ```
-
-| Parameter | Description | Example |
-|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
-| CARBON_ASSEMBLY_JAR | CarbonData assembly jar name present in the 
`$SPARK_HOME/carbonlib/` folder. | 
carbondata_2.10-0.1.0-incubating-SNAPSHOT-shade-hadoop2.7.2.jar |
-| carbon_store_path | This is a parameter to the CarbonThriftServer class. 
This a HDFS path where CarbonData files will be kept. Strongly Recommended to 
put same as carbon.storelocation parameter of carbon.properties. | 
`hdfs://<host_name>:port/user/hive/warehouse/carbon.store` |
-
-**Examples**
-   
-   * Start with default memory and executors.
-
-```
-./bin/spark-submit --conf spark.sql.hive.thriftServer.singleSession=true 
---class org.apache.carbondata.spark.thriftserver.CarbonThriftServer 
-$SPARK_HOME/carbonlib
-/carbondata_2.10-0.1.0-incubating-SNAPSHOT-shade-hadoop2.7.2.jar 
-hdfs://<host_name>:port/user/hive/warehouse/carbon.store
-```
-   
-   * Start with Fixed executors and resources.
-
-```
-./bin/spark-submit --conf spark.sql.hive.thriftServer.singleSession=true 
---class org.apache.carbondata.spark.thriftserver.CarbonThriftServer 
---num-executors 3 --driver-memory 20g --executor-memory 250g 
---executor-cores 32 
-/srv/OSCON/BigData/HACluster/install/spark/sparkJdbc/lib
-/carbondata_2.10-0.1.0-incubating-SNAPSHOT-shade-hadoop2.7.2.jar 
-hdfs://<host_name>:port/user/hive/warehouse/carbon.store
-```
-  
-### Connecting to CarbonData Thrift Server Using Beeline.
-
-```
-     cd $SPARK_HOME
-     ./bin/beeline jdbc:hive2://<thrftserver_host>:port
-
-     Example
-     ./bin/beeline jdbc:hive2://10.10.10.10:10000
-```
-

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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.
--->
-# Overview
-This tutorial provides a detailed overview about :
-
-* [Introduction](#introduction)
-* [Features](#features)
-
-##  Introduction
-
-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.
-
-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).
-
-Some of the salient features of CarbonData are :
-
-* Low-Latency for various types of data access patterns like Sequential, 
Random and OLAP.
-* Fast query on fast data.
-* Space efficiency.
-* General format available on Hadoop-ecosystem.
-
-##  Features
-
-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:
-
-* 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.
-
-* 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.
-
-* Multi Level Indexing: CarbonData uses multiple indices at various levels to 
enable faster search and speed up query processing.
-
-* 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 
result. Th
 is 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.
-
-* 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.
-
-* Update Delete Support: It supports batch updates like daily update scenarios 
for OLAP and Base+Delta file based design.
-
-* 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.
-
-* 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.
-
-* Min-Max Index for all columns: Aids in quickly locating the row groups(Data 
Blocks) that contain the data matching search/filter criteria.
-
-* 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).
-
-* 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.
-
-* 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".
-
-* 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.
-
-* 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).
-

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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.
--->
-
-# Quick Start
-This tutorial provides a quick introduction to using CarbonData.
-
-##  Prerequisites
-* [Installation and building 
CarbonData](https://github.com/apache/incubator-carbondata/blob/master/build).
-* Create a sample.csv file using the following commands. The CSV file is 
required for loading data into CarbonData.
-
-  ```
-  cd carbondata
-  cat > sample.csv << EOF
-  id,name,city,age
-  1,david,shenzhen,31
-  2,eason,shenzhen,27
-  3,jarry,wuhan,35
-  EOF
-  ```
-
-## Interactive Analysis with Spark Shell
-
-## Version 2.1
-
-Apache Spark Shell provides a simple way to learn the API, as well as a 
powerful tool to analyze data interactively. Please visit [Apache Spark 
Documentation](http://spark.apache.org/docs/latest/) for more details on Spark 
shell.
-
-#### Basics
-
-Start Spark shell by running the following command in the Spark directory:
-
-```
-./bin/spark-shell --jars <carbondata assembly jar path>
-```
-
-In this shell, SparkSession is readily available as `spark` and Spark context 
is readily available as `sc`.
-
-In order to create a CarbonSession we will have to configure it explicitly in 
the following manner :
-
-* Import the following :
-
-```
-import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.CarbonSession._
-```
-
-* Create a CarbonSession :
-
-```
-val carbon = 
SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("<hdfs store 
path>")
-```
-**NOTE**: By default metastore location is pointed to `../carbon.metastore`, 
user can provide own metastore location to CarbonSession like 
`SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("<hdfs 
store path>", "<local metastore path>")`
-
-#### Executing Queries
-
-##### Creating a Table
-
-```
-scala>carbon.sql("CREATE TABLE IF NOT EXISTS test_table(id string, name 
string, city string, age Int) STORED BY 'carbondata'")
-```
-
-##### Loading Data to a Table
-
-```
-scala>carbon.sql("LOAD DATA INPATH 'sample.csv file path' INTO TABLE 
test_table")
-```
-**NOTE**: Please provide the real file path of `sample.csv` for the above 
script.
-
-###### Query Data from a Table
-
-```
-scala>carbon.sql("SELECT * FROM test_table").show()
-
-scala>carbon.sql("SELECT city, avg(age), sum(age) FROM test_table GROUP BY 
city").show()
-```
-
-## Interactive Analysis with Spark Shell
-## Version 1.6
-
-#### Basics
-
-Start Spark shell by running the following command in the Spark directory:
-
-```
-./bin/spark-shell --jars <carbondata assembly jar path>
-```
-
-**NOTE**: In this shell, SparkContext is readily available as `sc`.
-
-* In order to execute the Queries we need to import CarbonContext:
-
-```
-import org.apache.spark.sql.CarbonContext
-```
-
-* Create an instance of CarbonContext in the following manner :
-
-```
-val cc = new CarbonContext(sc, "<hdfs store path>")
-```
-**NOTE**: If running on local machine without hdfs, configure the local 
machine's store path instead of hdfs store path
-
-#### Executing Queries
-
-##### Creating a Table
-
-```
-scala>cc.sql("CREATE TABLE IF NOT EXISTS test_table (id string, name string, 
city string, age Int) STORED BY 'carbondata'")
-```
-To see the table created :
-
-```
-scala>cc.sql("SHOW TABLES").show()
-```
-
-##### Loading Data to a Table
-
-```
-scala>cc.sql("LOAD DATA INPATH 'sample.csv file path' INTO TABLE test_table")
-```
-**NOTE**: Please provide the real file path of `sample.csv` for the above 
script.
-
-##### Query Data from a Table
-
-```
-scala>cc.sql("SELECT * FROM test_table").show()
-scala>cc.sql("SELECT city, avg(age), sum(age) FROM test_table GROUP BY 
city").show()
-```

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