Prerequisites for using Apache Hadoop as a source Permissions for Hive as a source Permissions for HDFS as a source Permissions for HDFS as a target Connecting to Apache Hadoop as a source Connecting to Hive and HDFS Connecting to Amazon EMR as a target

Connecting to Apache Hadoop databases with the AWS Schema Conversion Tool

You can use the AWS SCT command line interface (CLI) to migrate from Apache Hadoop to Amazon EMR. AWS SCT uses your Amazon S3 bucket as a temporary storage for your data during migration.

AWS SCT supports as a source Apache Hadoop version 2.2.0 and higher. Also, AWS SCT supports Apache Hive version 0.13.0 and higher.

AWS SCT supports as a target Amazon EMR version 6.3.0 and higher. Also, AWS SCT supports as a target Apache Hadoop version 2.6.0 and higher, and Apache Hive version 0.13.0 and higher.

Topics

Prerequisites for using Apache Hadoop as a source
Permissions for using Hive as a source
Permissions for using HDFS as a source
Permissions for using HDFS as a target
Connecting to Apache Hadoop as a source
Connecting to your source Hive and HDFS services
Connecting to Amazon EMR as a target

Prerequisites for using Apache Hadoop as a source

The following prerequisites are required to connect to Apache Hadoop with the AWS SCT CLI.

Create an Amazon S3 bucket to store data during the migration. You can then copy data to Amazon EMR HDFS or use Amazon S3 as a data repository for your Hadoop workloads. For more information, see Creating a bucket in the Amazon S3 User Guide.
Create an AWS Identity and Access Management (IAM) role with the AmazonS3FullAccess policy. AWS SCT uses this IAM role to access your Amazon S3 bucket.
Take a note of your AWS secret key and AWS secret access key. For more information about AWS access keys, see Managing access keys in the IAM User Guide.
Create and configure a target Amazon EMR cluster. For more information, see Getting started with Amazon EMR in the Amazon EMR Management Guide.
Install the distcp utility on your source Apache Hadoop cluster. Also, install the s3-dist-cp utility on your target Amazon EMR cluster. Make sure that your database users have permissions to run these utilities.
Configure the core-site.xml file in your source Hadoop cluster to use the s3a protocol. To do so, set the fs.s3a.aws.credentials.provider parameter to one of the following values.
- org.apache.hadoop.fs.s3a.TemporaryAWSCredentialsProvider
- org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider
- org.apache.hadoop.fs.s3a.AnonymousAWSCredentialsProvider
- org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider
You can add the following code example into the core-site.xml file.
```
<property>
  <name>fs.s3a.aws.credentials.provider</name>
  <value>org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider</value>
</property>
```
The preceding example shows one of the four options from the preceding list of options. If you don't set the fs.s3a.aws.credentials.provider parameter in the core-site.xml file, AWS SCT chooses the provider automatically.

Permissions for using Hive as a source

The permissions required for a Hive source user are as follows:

READ access to the source data folders and to the source Amazon S3 bucket
READ+WRITE access to the intermediate and target Amazon S3 buckets

To increase the migration speed, we recommend that you run compaction for ACID-transactional source tabes.

The permissions required for an Amazon EMR Hive target user are as follows:

READ access to the target Amazon S3 bucket
READ+WRITE access to the intermediate Amazon S3 bucket
READ+WRITE access to the target HDFS folders

Permissions for using HDFS as a source

The permissions required for HDFS as a source are as follows:

EXECUTE for the NameNode
EXECUTE+READ for all source folders and files that you include in the migration project
READ+WRITE for the tmp directory in the NameNode to run Spark jobs and store files before the migration to Amazon S3

In HDFS, all operations require traversal access. Traversal access demands the EXECUTE permission on all existing components of the path, except for the final path component. For example, for any operation accessing /foo/bar/baz, your user must have EXECUTE permission on /, /foo, and /foo/bar.

The following code example demonstrates how to grant EXECUTE+READ permissions for your source folders and files, and READ+WRITE permissions for the tmp directory.


hadoop fs –chmod –R 744 /user/hdfs-data
hadoop fs –chmod –R 766 /tmp

Permissions for using HDFS as a target

The permissions required for Amazon EMR HDFS as a target are as follows:

EXECUTE for the NameNode of the target Amazon EMR cluster
READ+WRITE for the target HDFS folders where you will store data after migration

Connecting to Apache Hadoop as a source

You can use Apache Hadoop as a source in AWS SCT version 1.0.670 or higher. You can migrate Hadoop clusters to Amazon EMR only in the AWS SCT command line interface (CLI). Before you start, familiarize yourself with the command line interface of AWS SCT. For more information, see CLI Reference for AWS Schema Conversion Tool.

To connect to Apache Hadoop in the AWS SCT CLI

Create a new AWS SCT CLI script or edit an existing scenario template. For example, you can download and edit the HadoopMigrationTemplate.scts template. For more information, see Getting CLI scenarios.
Configure the AWS SCT application settings such as the driver location and log folder.

Download the required JDBC driver and specify the location where you store the file. For more information, see Installing JDBC drivers for AWS Schema Conversion Tool.

The following code example shows you how to add the path to the Apache Hive driver. After you run this code example, AWS SCT stores log files in the c:\sct folder.
```
SetGlobalSettings
    -save: 'true'
    -settings: '{
        "hive_driver_file": "c:\\sct\\HiveJDBC42.jar",
        "log_folder": "c:\\sct",
        "console_log_folder": "c:\\sct"
    }'
/
```
You can use this example and the following examples in Windows.
Create a new AWS SCT project.

The following code example creates the hadoop_emr project in the c:\sct folder.
```
CreateProject
    -name: 'hadoop_emr'
    -directory: 'c:\sct'
/
```
Add your source Hadoop cluster to the project.

Use the AddSourceCluster command to connect to the source Hadoop cluster. Make sure that you provide values for the following mandatory parameters: name, host, port, and user. Other parameters are optional.

The following code example adds the source Hadoop cluster. This example sets HADOOP_SOURCE as a name of the source cluster. Use this object name to add Hive and HDFS services to the project and create mapping rules.
```
AddSourceCluster
    -name: 'HADOOP_SOURCE'
    -vendor: 'HADOOP'
    -host: 'hadoop_address'
    -port: '22'
    -user: 'hadoop_user'
    -password: 'hadoop_password'
    -useSSL: 'true'
    -privateKeyPath: 'c:\path\name.pem'
    -passPhrase: 'hadoop_passphrase'
/
```
In the preceding example, replace hadoop_address with the IP address of your Hadoop cluster. If needed, configure the value of the port option. Next, replace hadoop_user and hadoop_password with the name of your Hadoop user and the password for this user. For path\name, enter the name and path to the PEM file for your source Hadoop cluster.
Save your CLI script. Next, add the connection information for your Hive and HDFS services.

Connecting to your source Hive and HDFS services

You can connect to your source Hive and HDFS services with the AWS SCT CLI. To connect to Apache Hive, use the Hive JDBC driver version 2.3.4 or higher. For more information, see Installing JDBC drivers for AWS Schema Conversion Tool.

AWS SCT connects to Apache Hive with the hadoop cluster user. To do so, use the AddSourceClusterHive and AddSourceClusterHDFS commands. You can use one of the following approaches.

Create a new SSH tunnel.

For createTunnel, enter true. For host, enter the internal IP address of your source Hive or HDFS service. For port, enter the service port of your Hive or HDFS service.

Next, enter your Hive or HDFS credentials for user and password. For more information about SSH tunnels, see Set up an SSH tunnel to the primary node using local port forwarding in the Amazon EMR Management Guide.
Use an existing SSH tunnel.

For host, enter localhost. For port, enter the local port from the SSH tunnel parameters.
Connect to your Hive and HDFS services directly.

For host, enter the IP address or hostname of your source Hive or HDFS service. For port, enter the service port of your Hive or HDFS service. Next, enter your Hive or HDFS credentials for user and password.

To connect to Hive and HDFS in the AWS SCT CLI

Open your CLI script which includes the connection information for your source Hadoop cluster. Make sure that you use the name of the Hadoop cluster that you defined in the previous step.
Add your source Hive service to the project.

Use the AddSourceClusterHive command to connect the source Hive service. Make sure that you provide values for the following mandatory parameters: user, password, cluster, name, and port. Other parameters are optional.

The following code example creates a tunnel for AWS SCT to work with your Hive service. This source Hive service runs on the same PC as AWS SCT. This example uses the HADOOP_SOURCE source cluster from the previous example.
```
AddSourceClusterHive
    -cluster: 'HADOOP_SOURCE'
    -name: 'HIVE_SOURCE'
    -host: 'localhost'
    -port: '10005'
    -user: 'hive_user'
    -password: 'hive_password'
    -createTunnel: 'true'
    -localPort: '10005'
    -remoteHost: 'hive_remote_address'
    -remotePort: 'hive_port'
/
```
The following code example connects to your Hive service without a tunnel.
```
AddSourceClusterHive
    -cluster: 'HADOOP_SOURCE'
    -name: 'HIVE_SOURCE'
    -host: 'hive_address'
    -port: 'hive_port'
    -user: 'hive_user'
    -password: 'hive_password'
/
```
In the preceding examples, replace hive_user and hive_password with the name of your Hive user and the password for this user.

Next, replace hive_address and hive_port with the NameNode IP address and port of your source Hadoop cluster.

For hive_remote_address, you might use the default value 127.0.0.1 or the NameNode IP address of your source Hive service.
Add your source HDFS service to the project.

Use the AddSourceClusterHDFS command to connect the source HDFS service. Make sure that you provide values for the following mandatory parameters: user, password, cluster, name, and port. Other parameters are optional.

Make sure that your user has the required permissions to migrate data from your source HDFS service. For more information, see Permissions for using Hive as a source.

The following code example creates a tunnel for AWS SCT to work with your Apache HDFS service. This example uses the HADOOP_SOURCE source cluster that you created before.
```
AddSourceClusterHDFS
    -cluster: 'HADOOP_SOURCE'
    -name: 'HDFS_SOURCE'
    -host: 'localhost'
    -port: '9005'
    -user: 'hdfs_user'
    -password: 'hdfs_password'
    -createTunnel: 'true'
    -localPort: '9005'
    -remoteHost: 'hdfs_remote_address'
    -remotePort: 'hdfs_port'
/
```
The following code connects to your Apache HDFS service without a tunnel.
```
AddSourceClusterHDFS
    -cluster: 'HADOOP_SOURCE'
    -name: 'HDFS_SOURCE'
    -host: 'hdfs_address'
    -port: 'hdfs_port'
    -user: 'hdfs_user'
    -password: 'hdfs_password'
/
```
In the preceding examples, replace hdfs_user and hdfs_password with the name of your HDFS user and the password for this user.

Next, replace hdfs_address and hdfs_port with the NameNode IP address and port of your source Hadoop cluster.

For hdfs_remote_address, you might use the default value 127.0.0.1 or the NameNode IP address of your source Hive service.
Save your CLI script. Next, add the connection information for your target Amazon EMR cluster, and the migration commands.

Connecting to Amazon EMR as a target

You can connect to your target Amazon EMR cluster with the AWS SCT CLI. To do so, you authorize inbound traffic and use SSH. In this case, AWS SCT has all required permissions to work with your Amazon EMR cluster. For more information, see Before you connect and Connect to the primary node using SSH in the Amazon EMR Management Guide.

AWS SCT connects to Amazon EMR Hive with the hadoop cluster user. To connect to Amazon EMR Hive, use the Hive JDBC driver version 2.6.2.1002 or higher. For more information, see Installing JDBC drivers for AWS Schema Conversion Tool.

To connect to Amazon EMR in the AWS SCT CLI

Open your CLI script which includes the connection information for your source Hadoop cluster. Add the target Amazon EMR credentials into this file.
Add your target Amazon EMR cluster to the project.

The following code example adds the target Amazon EMR cluster. This example sets HADOOP_TARGET as a name of the target cluster. Use this object name to add your Hive and HDFS services and an Amazon S3, bucket folder to the project and create mapping rules.
```
AddTargetCluster
	-name: 'HADOOP_TARGET'
	-vendor: 'AMAZON_EMR'
	-host: 'ec2-44-44-55-66.eu-west-1.EXAMPLE.amazonaws.com'
	-port: '22'
	-user: 'emr_user'
	-password: 'emr_password'
	-useSSL: 'true'
	-privateKeyPath: 'c:\path\name.pem'
	-passPhrase: '1234567890abcdef0!'
	-s3Name: 'S3_TARGET'
	-accessKey: 'AKIAIOSFODNN7EXAMPLE'
	-secretKey: 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
	-region: 'eu-west-1'
	-s3Path: 'doc-example-bucket/example-folder'
/
```
In the preceding example, enter your AWS resource names and Amazon EMR connection information. This includes the IP address of your Amazon EMR cluster, AWS access key, AWS secret access key, and Amazon S3 bucket. If needed, configure the value of the port variable. Next, replace emr_user and emr_password with the name of your Amazon EMR user and the password for this user. For path\name, enter the name and path to the PEM file for your target Amazon EMR cluster. For more information, see Download PEM File for EMR Cluster Access.
Add your target Amazon S3 bucket to the project.

The following code example adds the target Amazon S3 bucket. This example uses the HADOOP_TARGET cluster that you created before.
```
AddTargetClusterS3
	-cluster: 'HADOOP_TARGET'
	-Name: 'S3_TARGET'
	-accessKey: 'AKIAIOSFODNN7EXAMPLE'
	-secretKey: 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
	-region: 'eu-west-1'
	-s3Path: 'doc-example-bucket/example-folder'
/
```
In the preceding example, enter your AWS access key, AWS secret access key, and Amazon S3 bucket.
Add your target Hive service to the project.

The following code example creates a tunnel for AWS SCT to work with your target Hive service. This example uses the HADOOP_TARGET target cluster that you created before.
```
AddTargetClusterHive
    -cluster: 'HADOOP_TARGET'
    -name: 'HIVE_TARGET'
    -host: 'localhost'
    -port: '10006'
    -user: 'hive_user'
    -password: 'hive_password'
    -createTunnel: 'true'
    -localPort: '10006'
    -remoteHost: 'hive_address'
    -remotePort: 'hive_port'
/
```
In the preceding example, replace hive_user and hive_password with the name of your Hive user and the password for this user.

Next, replace hive_address with the default value 127.0.0.1 or with the NameNode IP address of your target Hive service. Next, replace hive_port with the port of your target Hive service.
Add your target HDFS service to the project.

The following code example creates a tunnel for AWS SCT to work with your Apache HDFS service. This example uses the HADOOP_TARGET target cluster that you created before.
```
AddTargetClusterHDFS
    -cluster: 'HADOOP_TARGET'
    -name: 'HDFS_TARGET'
    -host: 'localhost'
    -port: '8025'
    -user: 'hdfs_user'
    -password: 'hdfs_password'
    -createTunnel: 'true'
    -localPort: '8025'
    -remoteHost: 'hdfs_address'
    -remotePort: 'hdfs_port'
/
```
In the preceding example, replace hdfs_user and hdfs_password with the name of your HDFS user and the password for this user.

Next, replace hdfs_address and hdfs_port with the private IP address and port of the NameNode of your target HDFS service.
Save your CLI script. Next, add mapping rules and migration commands. For more information, see Migrating Hadoop workloads.

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