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Quick Start

This tutorial provides a quick introduction to use CarbonData. To follow along with this guide, download a packaged release of CarbonData from the CarbonData website. Alternatively, it can be created following Building CarbonData steps.

Prerequisites

  • CarbonData supports Spark versions up to 2.4. Please download Spark package from Spark website

  • 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

Integration

Integration with Execution Engines

CarbonData can be integrated with Spark, Presto, Flink and Hive execution engines. The below documentation guides on Installing and Configuring with these execution engines.

Spark

Installing and Configuring CarbonData to run locally with Spark SQL CLI

Installing and Configuring CarbonData to run locally with Spark Shell

Installing and Configuring CarbonData on Standalone Spark Cluster

Installing and Configuring CarbonData on Spark on YARN Cluster

Installing and Configuring CarbonData Thrift Server for Query Execution

Notebook

Using CarbonData in notebook
Using CarbonData to visualization in notebook

Presto

Installing and Configuring CarbonData on Presto

Hive

Installing and Configuring CarbonData on Hive

Integration with Storage Engines

HDFS

CarbonData supports read and write with HDFS

S3

CarbonData supports read and write with S3

Alluxio

CarbonData supports read and write with Alluxio

Installing and Configuring CarbonData to run locally with Spark SQL CLI

This will work with spark 2.3+ versions. In Spark SQL CLI, it uses CarbonExtensions to customize the SparkSession with CarbonData's parser, analyzer, optimizer and physical planning strategy rules in Spark. To enable CarbonExtensions, we need to add the following configuration.

Key Value
spark.sql.extensions org.apache.spark.sql.CarbonExtensions

Start Spark SQL CLI by running the following command in the Spark directory:

./bin/spark-sql --conf spark.sql.extensions=org.apache.spark.sql.CarbonExtensions --jars <carbondata assembly jar path>
Creating a Table
CREATE TABLE IF NOT EXISTS test_table (
  id string,
  name string,
  city string,
  age Int)
STORED AS carbondata;

NOTE: CarbonExtensions only support "STORED AS carbondata" and "USING carbondata"

Loading Data to a Table
LOAD DATA INPATH '/local-path/sample.csv' INTO TABLE test_table;

LOAD DATA INPATH 'hdfs://hdfs-path/sample.csv' INTO TABLE test_table;

insert into table test_table select '1', 'name1', 'city1', 1;

NOTE: Please provide the real file path of sample.csv for the above script. If you get "tablestatus.lock" issue, please refer to FAQ

Query Data from a Table
SELECT * FROM test_table;

SELECT city, avg(age), sum(age)
FROM test_table
GROUP BY city;

Installing and Configuring CarbonData to run locally with Spark Shell

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 for more details on the Spark shell.

Basics

Option 1: Using CarbonSession (deprecated since 2.0)

Start Spark shell by running the following command in the Spark directory:

./bin/spark-shell --jars <carbondata assembly jar path>

NOTE: Path where packaged release of CarbonData was downloaded or assembly jar will be available after building CarbonData and can be copied from ./assembly/target/scala-2.1x/apache-carbondata_xxx.jar

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("<carbon_store_path>")

NOTE

  • By default metastore location points to ../carbon.metastore, user can provide own metastore location to CarbonSession like SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("<carbon_store_path>", "<local metastore path>").
  • Data storage location can be specified by <carbon_store_path>, like /carbon/data/store, hdfs://localhost:9000/carbon/data/store or s3a://carbon/data/store.
Option 2: Using SparkSession with CarbonExtensions(since 2.0)

Start Spark shell by running the following command in the Spark directory:

./bin/spark-shell --conf spark.sql.extensions=org.apache.spark.sql.CarbonExtensions --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 SparkSession we will have to configure it explicitly in the following manner :

  • Import the following :
import org.apache.spark.sql.SparkSession

NOTE

  • In this flow, we can use the built-in SparkSession spark instead of carbon. We also can create a new SparkSession instead of the built-in SparkSession spark if need. It need to add "org.apache.spark.sql.CarbonExtensions" into spark configuration "spark.sql.extensions". 
    val spark = SparkSession
  .builder()
  .config(sc.getConf)
  .enableHiveSupport
  .config("spark.sql.extensions","org.apache.spark.sql.CarbonExtensions")
  .getOrCreate()
  • Data storage location can be specified by "spark.sql.warehouse.dir".

Executing Queries

Creating a Table

NOTE : We use the built-in SparkSession spark in the following

carbon.sql(
           s"""
              | CREATE TABLE IF NOT EXISTS test_table(
              |   id string,
              |   name string,
              |   city string,
              |   age Int)
              | STORED AS carbondata
           """.stripMargin)

NOTE: The following table list all supported syntax:

create table SparkSession with CarbonExtensions CarbonSession
STORED AS carbondata yes yes
USING carbondata yes yes
STORED BY 'carbondata' no yes
STORED BY 'org.apache.carbondata.format' no yes

We suggest to use CarbonExtensions instead of CarbonSession.

Loading Data to a Table
carbon.sql("LOAD DATA INPATH '/local-path/sample.csv' INTO TABLE test_table")

carbon.sql("LOAD DATA INPATH 'hdfs://hdfs-path/sample.csv' INTO TABLE test_table")

NOTE: Please provide the real file path of sample.csv for the above script. If you get "tablestatus.lock" issue, please refer to FAQ

Query Data from a Table
carbon.sql("SELECT * FROM test_table").show()

carbon.sql(
           s"""
              | SELECT city, avg(age), sum(age)
              | FROM test_table
              | GROUP BY city
           """.stripMargin).show()

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 project and get the assembly jar from ./assembly/target/scala-2.1x/apache-carbondata_xxx.jar.

  2. Copy ./assembly/target/scala-2.1x/apache-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. All the carbondata related properties are configured in this file.

  5. Repeat Step 2 to Step 5 in all the nodes of the cluster.

  6. In Spark node[master], configure the properties mentioned in the following table in $SPARK_HOME/conf/spark-defaults.conf file.

Property Value Description
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.

NOTE: Please provide the real directory file path of "SPARK_HOME" instead of the "$SPARK_HOME" for the above script and there is no space on both sides of = in the 'Value' column.

  1. Verify the installation. For example:
./bin/spark-shell \
--master spark://HOSTNAME:PORT \
--total-executor-cores 2 \
--executor-memory 2G

NOTE:

  • property "carbon.storelocation" is deprecated in carbondata 2.0 version. Only the users who used this property in previous versions can still use it in carbon 2.0 version.
  • Make sure you have permissions for CarbonData JARs and files through which driver and executor will start.

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 project and get the assembly jar from ./assembly/target/scala-2.1x/apache-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 ./conf/carbon.properties.template file from CarbonData repository to $SPARK_HOME/conf/ folder and rename the file to carbon.properties. All the carbondata related properties are configured in this file.

  3. 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/
  1. 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/*
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

NOTE: Please provide the real directory file path of "SPARK_HOME" instead of the "$SPARK_HOME" for the above script and there is no space on both sides of = in the 'Value' column.

  1. Verify the installation.
./bin/spark-shell \
--master yarn-client \
--driver-memory 1G \
--executor-memory 2G \
--executor-cores 2

NOTE:

  • property "carbon.storelocation" is deprecated in carbondata 2.0 version. Only the users who used this property in previous versions can still use it in carbon 2.0 version.
  • Make sure you have permissions for CarbonData JARs and files through which driver and executor will start.
  • If use Spark + Hive 1.1.X, it needs to add carbondata assembly jar and carbondata-hive jar into parameter 'spark.sql.hive.metastore.jars' in spark-default.conf file.

Query Execution Using the Thrift Server

Option 1: Starting Thrift Server with CarbonExtensions(since 2.0)

cd $SPARK_HOME
./sbin/start-thriftserver.sh \
--conf spark.sql.extensions=org.apache.spark.sql.CarbonExtensions \
$SPARK_HOME/carbonlib/apache-carbondata-xxx.jar

Option 2: Starting CarbonData Thrift Server

a. cd $SPARK_HOME

b. Run the following command to start the CarbonData thrift server.

./bin/spark-submit \
--class org.apache.carbondata.spark.thriftserver.CarbonThriftServer \
$SPARK_HOME/carbonlib/$CARBON_ASSEMBLY_JAR
Parameter Description Example
CARBON_ASSEMBLY_JAR CarbonData assembly jar name present in the $SPARK_HOME/carbonlib/ folder. apache-carbondata-xx.jar

c. Run the following command to work with S3 storage.

./bin/spark-submit \
--class org.apache.carbondata.spark.thriftserver.CarbonThriftServer \
$SPARK_HOME/carbonlib/$CARBON_ASSEMBLY_JAR <access_key> <secret_key> <endpoint>
Parameter Description Example
CARBON_ASSEMBLY_JAR CarbonData assembly jar name present in the $SPARK_HOME/carbonlib/ folder. apache-carbondata-xx.jar
access_key Access key for S3 storage
secret_key Secret key for S3 storage
endpoint Endpoint for connecting to S3 storage

NOTE: From Spark 1.6, by default the Thrift server runs in multi-session mode. Which means each JDBC/ODBC connection owns a copy of their own SQL configuration and temporary function registry. Cached tables are still shared though. If you prefer to run the Thrift server in single-session mode and share all SQL configuration and temporary function registry, please set option spark.sql.hive.thriftServer.singleSession to true. You may either add this option to spark-defaults.conf, or pass it to spark-submit.sh via --conf:

./bin/spark-submit \
--conf spark.sql.hive.thriftServer.singleSession=true \
--class org.apache.carbondata.spark.thriftserver.CarbonThriftServer \
$SPARK_HOME/carbonlib/$CARBON_ASSEMBLY_JAR

But in single-session mode, if one user changes the database from one connection, the database of the other connections will be changed too.

Examples

  • Start with default memory and executors.
./bin/spark-submit \
--class org.apache.carbondata.spark.thriftserver.CarbonThriftServer \
$SPARK_HOME/carbonlib/apache-carbondata-xxx.jar
  • Start with Fixed executors and resources.
./bin/spark-submit \
--class org.apache.carbondata.spark.thriftserver.CarbonThriftServer \
--num-executors 3 \
--driver-memory 20G \
--executor-memory 250G \
--executor-cores 32 \
$SPARK_HOME/carbonlib/apache-carbondata-xxx.jar

Connecting to Thrift Server Using Beeline.

cd $SPARK_HOME
./bin/beeline -u jdbc:hive2://<thriftserver_host>:port

Example
./bin/beeline -u jdbc:hive2://10.10.10.10:10000

Installing and Configuring CarbonData on Presto

NOTE: CarbonData tables cannot be created nor loaded from Presto. User needs to create CarbonData Table and load data into it either with Spark or SDK or C++ SDK. Once the table is created, it can be queried from Presto.

Please refer the presto guide linked below.

prestodb guide - prestodb

prestosql guide - prestosql

Once installed the presto with carbonData as per the above guide, you can use the Presto CLI on the coordinator to query data sources in the catalog using the Presto workers.

List the schemas(databases) available

show schemas;

Selected the schema where CarbonData table resides

use carbonschema;

List the available tables

show tables;

Query from the available tables

select * from carbon_table;

Note: Create Tables and data loads should be done before executing queries as we can not create carbon table from this interface.