Access data stored in Amazon DynamoDB with Apache Hadoop, Apache Hive, and Apache Spark
You can use this connector to access data in Amazon DynamoDB using Apache Hadoop, Apache Hive, and Apache Spark in Amazon EMR. You can process data directly in DynamoDB using these frameworks, or join data in DynamoDB with data in Amazon S3, Amazon RDS, or other storage layers that can be accessed by Amazon EMR.
- Using Apache Hive in Amazon EMR with Amazon DynamoDB
- Accessing data in Amazon DynamoDB with Apache Spark
For more information about supported data types in DynamoDB, see Data Types for Hive and DynamoDB in the Amazon EMR Release Guide.
For more information, see Hive Commands Examples for Exporting, Importing, and Querying Data in DynamoDB in the [Amazon DynamoDB Developer Guide] dynamodb-dev-guide.
An implementation of Apache Hadoop InputFormat interface and OutputFormat are included, which allows DynamoDB AttributeValues to be directly ingested by MapReduce jobs. For an example of how to use these classes, see Set Up a Hive Table to Run Hive Commands in the Amazon EMR Release Guide, as well as their usage in the Import/Export tool classes in [DynamoDBExport.java] export-tool-source and DynamoDBImport.java.
This simple tool that makes use of the InputFormat and OutputFormat implementations provides an easy way to import to and export data from DynamoDB.
Currently the project builds against Hive 2.3.0, 1.2.1, and 1.0.0. Set this by using the hive1.version
,
hive1.2.version and
hive2.versionproperties in the root Maven
pom.xml`, respectively.
After cloning, run mvn clean install
.
Syntax to create a table using the DynamoDBStorageHandler class:
CREATE EXTERNAL TABLE hive_tablename (
hive_column1_name column1_datatype,
hive_column2_name column2_datatype
)
STORED BY 'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHandler'
TBLPROPERTIES (
"dynamodb.table.name" = "dynamodb_tablename",
"dynamodb.column.mapping" =
"hive_column1_name:dynamodb_attribute1_name,hive_column2_name:dynamodb_attribute2_name"
);
Using the DynamoDBInputFormat and DynamoDBOutputFormat classes with spark-shell
:
$ spark-shell --jars /usr/share/aws/emr/ddb/lib/emr-ddb-hadoop.jar
...
import org.apache.hadoop.io.Text;
import org.apache.hadoop.dynamodb.DynamoDBItemWritable
import org.apache.hadoop.dynamodb.read.DynamoDBInputFormat
import org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.io.LongWritable
var jobConf = new JobConf(sc.hadoopConfiguration)
jobConf.set("dynamodb.input.tableName", "myDynamoDBTable")
jobConf.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")
jobConf.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")
var orders = sc.hadoopRDD(jobConf, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])
orders.count()
java -cp target/emr-dynamodb-tools-4.2.0-SNAPSHOT.jar org.apache.hadoop.dynamodb.tools.DynamoDBExport /where/output/should/go my-dynamo-table-name
java -cp target/emr-dynamodb-tools-4.2.0-SNAPSHOT.jar org.apache.hadoop.dynamodb.tools.DynamoDBImport /where/input/data/is my-dynamo-table-name
export <path> <table-name> [<read-ratio>] [<total-segment-count>]
read-ratio: maximum percent of the specified DynamoDB table's read capacity to use for export
total-segments: number of desired MapReduce splits to use for the export
import <path> <table-name> [<write-ratio>]
write-ratio: maximum percent of the specified DynamoDB table's write capacity to use for import
To depend on the specific components in your projects, add one (or both) of the following to your
pom.xml
.
<dependency>
<groupId>com.amazon.emr</groupId>
<artifactId>emr-dynamodb-hadoop</artifactId>
<version>4.2.0</version>
</dependency>
<dependency>
<groupId>com.amazon.emr</groupId>
<artifactId>emr-dynamodb-hive</artifactId>
<version>4.2.0</version>
</dependency>
-
If you find a bug or would like to see an improvement, open an issue.
Check first to make sure there isn't one already open. We'll do our best to respond to issues and review pull-requests
-
Want to fix it yourself? Open a pull request!
If adding new functionality, include new, passing unit tests, as well as documentation. Also include a snippet in your pull request showing that all current unit tests pass. Tests are ran by default when invoking any goal for maven that results in the
package
goal being executed (mvn clean install
will run them and produce output showing such). -
Follow the Google Java Style Guide
Style is enforced at build time using the Apache Maven Checkstyle Plugin.