Hive application specifics for earlier AMI versions of Amazon EMR - Amazon EMR
Log filesSplit input functionalityThrift service portsUse Hive to recover partitionsPass a Hive variable to a scriptSpecify an external metastore locationConnect to Hive using JDBC
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Hive application specifics for earlier AMI versions of Amazon EMR

Log files

Using Amazon EMR AMI versions 2.x and 3.x, Hive logs are saved to /mnt/var/log/apps/. In order to support concurrent versions of Hive, the version of Hive that you run determines the log file name, as shown in the following table.

Hive version Log file name
0.13.1 hive.log
Note

Beginning with this version, Amazon EMR uses an unversioned file name, hive.log. Minor versions share the same log location as the major version.

0.11.0 hive_0110.log
Note

Minor versions of Hive 0.11.0, such as 0.11.0.1, share the same log file location as Hive 0.11.0.

0.8.1 hive_081.log
Note

Minor versions of Hive 0.8.1, such as Hive 0.8.1.1, share the same log file location as Hive 0.8.1.

0.7.1 hive_07_1.log
Note

Minor versions of Hive 0.7.1, such as Hive 0.7.1.3 and Hive 0.7.1.4, share the same log file location as Hive 0.7.1.

0.7 hive_07.log
0.5 hive_05.log
0.4 hive.log

Split input functionality

To implement split input functionality using Hive versions earlier than 0.13.1 (Amazon EMR AMI versions earlier 3.11.0), use the following:


hive> set hive.input.format=org.apache.hadoop.hive.ql.io.HiveCombineSplitsInputFormat;
hive> set mapred.min.split.size=100000000;

This functionality was deprecated with Hive 0.13.1. To get the same split input format functionality in Amazon EMR AMI Version 3.11.0, use the following:

set hive.hadoop.supports.splittable.combineinputformat=true;

Thrift service ports

Thrift is an RPC framework that defines a compact binary serialization format used to persist data structures for later analysis. Normally, Hive configures the server to operate on the following ports.

Hive version Port number
Hive 0.13.1 10000
Hive 0.11.0 10004
Hive 0.8.1 10003
Hive 0.7.1 10002
Hive 0.7 10001
Hive 0.5 10000

For more information about thrift services, see http://wiki.apache.org/thrift/.

Use Hive to recover partitions

Amazon EMR includes a statement in the Hive query language that recovers the partitions of a table from table data located in Amazon S3. The following example shows this. 


CREATE EXTERNAL TABLE (json string) raw_impression 
PARTITIONED BY (dt string) 
LOCATION 's3://elastic-mapreduce/samples/hive-ads/tables/impressions';
ALTER TABLE logs RECOVER PARTITIONS;

The partition directories and data must be at the location specified in the table definition and must be named according to the Hive convention: for example, dt=2009-01-01.

Note

After Hive 0.13.1 this capability is supported natively using msck repair table and therefore recover partitions is not supported. For more information, see https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL.

Pass a Hive variable to a script

To pass a variable into a Hive step using the Amazon CLI, type the following command, replace myKey with the name of your EC2 key pair, and replace amzn-s3-demo-bucket with your bucket name. In this example, SAMPLE is a variable value preceded by the -d switch. This variable is defined in the Hive script as: ${SAMPLE}.

Note

Linux line continuation characters (\) are included for readability. They can be removed or used in Linux commands. For Windows, remove them or replace with a caret (^).

aws emr create-cluster --name "Test cluster" --ami-version 3.9 \
--applications Name=Hue Name=Hive Name=Pig \
--use-default-roles --ec2-attributes KeyName=myKey \
--instance-type m3.xlarge --instance-count 3 \
--steps Type=Hive,Name="Hive Program",ActionOnFailure=CONTINUE,\
Args=[-f,s3://elasticmapreduce/samples/hive-ads/libs/response-time-stats.q,-d,\
INPUT=s3://elasticmapreduce/samples/hive-ads/tables,-d,OUTPUT=s3://amzn-s3-demo-bucket/hive-ads/output/,\
-d,SAMPLE=s3://elasticmapreduce/samples/hive-ads/]

Specify an external metastore location

The following procedure shows you how to override the default configuration values for the Hive metastore location and start a cluster using the reconfigured metastore location.

To create a metastore located outside of the EMR cluster

  1. Create a MySQL or Aurora database using Amazon RDS.

    For information about how to create an Amazon RDS database, see Getting started with Amazon RDS.

  2. Modify your security groups to allow JDBC connections between your database and the ElasticMapReduce-Master security group.

    For information about how to modify your security groups for access, see Amazon RDS security groups in the Amazon RDS User Guide.

  3. Set the JDBC configuration values in hive-site.xml:

    1. Create a hive-site.xml configuration file containing the following:

      <configuration>
  <property>
    <name>javax.jdo.option.ConnectionURL</name>
    <value>jdbc:mariadb://hostname:3306/hive?createDatabaseIfNotExist=true</value>
    <description>JDBC connect string for a JDBC metastore</description>
  </property>
  <property>
    <name>javax.jdo.option.ConnectionUserName</name>
    <value>hive</value>
    <description>Username to use against metastore database</description>
  </property>
  <property>
    <name>javax.jdo.option.ConnectionPassword</name>
    <value>password</value>
    <description>Password to use against metastore database</description>
  </property>
</configuration>

      hostname is the DNS address of the Amazon RDS instance running the database. username and password are the credentials for your database. For more information about connecting to MySQL and Aurora database instances, see Connecting to a DB instance running the MySQL database engine and Connecting to an Aurora DB cluster in the Amazon RDS User Guide.

      The JDBC drivers are installed by Amazon EMR.

      Note

      The value property should not contain any spaces or carriage returns. It should appear all on one line.

    2. Save your hive-site.xml file to a location on Amazon S3, such as s3://amzn-s3-demo-bucket/hive-site.xml.

  4. Create a cluster, specifying the Amazon S3 location of the customized hive-site.xml file.

    The following example command demonstrates an Amazon CLI command that does this.

    Note

    Linux line continuation characters (\) are included for readability. They can be removed or used in Linux commands. For Windows, remove them or replace with a caret (^).

    aws emr create-cluster --name "Test cluster" --ami-version 3.10 \
--applications Name=Hue Name=Hive Name=Pig \
--use-default-roles --ec2-attributes KeyName=myKey \
--instance-type m3.xlarge --instance-count 3 \
--bootstrap-actions Name="Install Hive Site Configuration",\
Path="s3://region.elasticmapreduce/libs/hive/hive-script",\
Args=["--base-path","s3://elasticmapreduce/libs/hive","--install-hive-site",\
"--hive-site=s3://amzn-s3-demo-bucket/hive-site.xml","--hive-versions","latest"]

Connect to Hive using JDBC

To connect to Hive via JDBC requires you to download the JDBC driver and install a SQL client. The following example demonstrates using SQL Workbench/J to connect to Hive using JDBC.

To download JDBC drivers

  1. Download and extract the drivers appropriate to the versions of Hive that you want to access. The Hive version differs depending on the AMI that you choose when you create an Amazon EMR cluster.

  2. Install SQL Workbench/J. For more information, see Installing and starting SQL Workbench/J in the SQL Workbench/J Manual User's Manual.

  3. Create an SSH tunnel to the cluster master node. The port for connection is different depending on the version of Hive. Example commands are provided in the tables below for Linux ssh users and PuTTY commands for Windows users

    Linux SSH commands
    Hive version Command
    0.13.1 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10000:localhost:10000 hadoop@master-public-dns-name
    0.11.0 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10004:localhost:10004 hadoop@master-public-dns-name
    0.8.1 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10003:localhost:10003 hadoop@master-public-dns-name
    0.7.1 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10002:localhost:10002 hadoop@master-public-dns-name
    0.7 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10001:localhost:10001 hadoop@master-public-dns-name
    0.5 ssh -o ServerAliveInterval=10 -i path-to-key-file -N -L 10000:localhost:10000 hadoop@master-public-dns-name
    Windows PuTTY tunnel settings
    Hive version Tunnel settings
    0.13.1 Source port: 10000 Destination: master-public-dns-name:10000
    0.11.0 Source port: 10004 Destination: master-public-dns-name:10004
    0.8.1 Source port: 10003 Destination: master-public-dns-name:10003

  4. Add the JDBC driver to SQL Workbench.

    1. In the Select Connection Profile dialog box, choose Manage Drivers.

    2. Choose the Create a new entry (blank page) icon.

    3. In the Name field, type Hive JDBC.

    4. For Library, click the Select the JAR file(s) icon.

    5. Select JAR files as shown in the following table.

      Hive driver version JAR files to add
      0.13.1 
      hive_metastore.jar
hive_service.jar
HiveJDBC3.jar
libfb303-0.9.0.jar
libthrift-0.9.0.jar
log4j-1.2.14.jar
ql.jar
slf4j-api-1.5.8.jar
slf4j-log4j12-1.5.8.jar
TCLIServiceClient.jar
      0.11.0 
      hadoop-core-1.0.3.jar
hive-exec-0.11.0.jar
hive-jdbc-0.11.0.jar
hive-metastore-0.11.0.jar
hive-service-0.11.0.jar
libfb303-0.9.0.jar
commons-logging-1.0.4.jar
slf4j-api-1.6.1.jar
      0.8.1 
      hadoop-core-0.20.205.jar
hive-exec-0.8.1.jar
hive-jdbc-0.8.1.jar
hive-metastore-0.8.1.jar
hive-service-0.8.1.jar
libfb303-0.7.0.jar
libthrift-0.7.0.jar
log4j-1.2.15.jar    
slf4j-api-1.6.1.jar
slf4j-log4j12-1.6.1.jar
      0.7.1 
      hadoop-0.20-core.jar
hive-exec-0.7.1.jar
hive-jdbc-0.7.1.jar
hive-metastore-0.7.1.jar
hive-service-0.7.1.jar
libfb303.jar
commons-logging-1.0.4.jar    
slf4j-api-1.6.1.jar
slf4j-log4j12-1.6.1.jar
      0.7 
      hadoop-0.20-core.jar
hive-exec-0.7.0.jar
hive-jdbc-0.7.0.jar
hive-metastore-0.7.0.jar
hive-service-0.7.0.jar
libfb303.jar
commons-logging-1.0.4.jar    
slf4j-api-1.5.6.jar
slf4j-log4j12-1.5.6.jar
      0.5 
      hadoop-0.20-core.jar
hive-exec-0.5.0.jar
hive-jdbc-0.5.0.jar
hive-metastore-0.5.0.jar
hive-service-0.5.0.jar
libfb303.jar
log4j-1.2.15.jar
commons-logging-1.0.4.jar

    6. In the Please select one driver dialog box, select a driver according to the following table and click OK.

      Hive version Driver classname
      0.13.1 
      com.amazon.hive.jdbc3.HS2Driver
      0.11.0 
      org.apache.hadoop.hive.jdbc.HiveDriver.jar
      0.8.1 
      org.apache.hadoop.hive.jdbc.HiveDriver.jar
      0.7.1 
      org.apache.hadoop.hive.jdbc.HiveDriver.jar
      0.7 
      org.apache.hadoop.hive.jdbc.HiveDriver.jar
      0.5 
      org.apache.hadoop.hive.jdbc.HiveDriver.jar

  5. When you return to the Select Connection Profile dialog box, verify that the Driver field is set to Hive JDBC and provide the JDBC connection string in the URL field according to the following table.

    Hive version JDBC connection string
    0.13.1 jdbc:hive2://localhost:10000/default
    0.11.0 jdbc:hive://localhost:10004/default
    0.8.1 jdbc:hive://localhost:10003/default

    If your cluster uses AMI version 3.3.1 or later, in the Select Connection Profile dialog box, type hadoop in the Username field.