Examples: Setting connection types and options - Amazon Glue
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Examples: Setting connection types and options

The sample code in this section demonstrates how to set connection types and connection options when connecting to extract, transform, and load (ETL) sources and sinks. The code shows how to specify connection types and connection options in both Python and Scala for connections to MongoDB and Amazon DocumentDB (with MongoDB compatibility). The code is similar for connecting to other data stores that Amazon Glue supports.


When you create an ETL job that connects to Amazon DocumentDB, for the Connections job property, you must designate a connection object that specifies the virtual private cloud (VPC) in which Amazon DocumentDB is running. For the connection object, the connection type must be JDBC, and the JDBC URL must be mongo://<DocumentDB_host>:27017.

Connecting with Python

The following Python script demonstrates using connection types and connection options for reading and writing to MongoDB and Amazon DocumentDB.

import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext, SparkConf from awsglue.context import GlueContext from awsglue.job import Job import time ## @params: [JOB_NAME] args = getResolvedOptions(sys.argv, ['JOB_NAME']) sc = SparkContext() glueContext = GlueContext(sc) spark = glueContext.spark_session job = Job(glueContext) job.init(args['JOB_NAME'], args) output_path = "s3://some_bucket/output/" + str(time.time()) + "/" mongo_uri = "mongodb://<mongo-instanced-ip-address>:27017" mongo_ssl_uri = "mongodb://<mongo-instanced-ip-address>:27017" documentdb_uri = "mongodb://<mongo-instanced-ip-address>:27017" write_uri = "mongodb://<mongo-instanced-ip-address>:27017" documentdb_write_uri = "mongodb://<mongo-instanced-ip-address>:27017" read_docdb_options = { "uri": documentdb_uri, "database": "test", "collection": "coll", "username": "username", "password": "1234567890", "ssl": "true", "ssl.domain_match": "false", "partitioner": "MongoSamplePartitioner", "partitionerOptions.partitionSizeMB": "10", "partitionerOptions.partitionKey": "_id" } read_mongo_options = { "uri": mongo_uri, "database": "test", "collection": "coll", "username": "username", "password": "pwd", "partitioner": "MongoSamplePartitioner", "partitionerOptions.partitionSizeMB": "10", "partitionerOptions.partitionKey": "_id"} ssl_mongo_options = { "uri": mongo_ssl_uri, "database": "test", "collection": "coll", "ssl": "true", "ssl.domain_match": "false" } write_mongo_options = { "uri": write_uri, "database": "test", "collection": "coll", "username": "username", "password": "pwd" } write_documentdb_options = { "uri": documentdb_write_uri, "database": "test", "collection": "coll", "username": "username", "password": "pwd" } # Get DynamicFrame from MongoDB and DocumentDB dynamic_frame = glueContext.create_dynamic_frame.from_options(connection_type="mongodb", connection_options=read_mongo_options) dynamic_frame2 = glueContext.create_dynamic_frame.from_options(connection_type="documentdb", connection_options=read_docdb_options) # Write DynamicFrame to MongoDB and DocumentDB glueContext.write_dynamic_frame.from_options(dynamic_frame, connection_type="mongodb", connection_options=write_mongo_options) glueContext.write_dynamic_frame.from_options(dynamic_frame2, connection_type="documentdb", connection_options=write_documentdb_options) job.commit()

Connecting with Scala

The following Scala script demonstrates using connection types and connection options for reading and writing to MongoDB and Amazon DocumentDB.

import com.amazonaws.services.glue.GlueContext import com.amazonaws.services.glue.MappingSpec import com.amazonaws.services.glue.errors.CallSite import com.amazonaws.services.glue.util.GlueArgParser import com.amazonaws.services.glue.util.Job import com.amazonaws.services.glue.util.JsonOptions import com.amazonaws.services.glue.DynamicFrame import org.apache.spark.SparkContext import scala.collection.JavaConverters._ object GlueApp { val DEFAULT_URI: String = "mongodb://<mongo-instanced-ip-address>:27017" val WRITE_URI: String = "mongodb://<mongo-instanced-ip-address>:27017" val DOC_URI: String = "mongodb://<mongo-instanced-ip-address>:27017" val DOC_WRITE_URI: String = "mongodb://<mongo-instanced-ip-address>:27017" lazy val defaultJsonOption = jsonOptions(DEFAULT_URI) lazy val documentDBJsonOption = jsonOptions(DOC_URI) lazy val writeJsonOption = jsonOptions(WRITE_URI) lazy val writeDocumentDBJsonOption = jsonOptions(DOC_WRITE_URI) def main(sysArgs: Array[String]): Unit = { val spark: SparkContext = new SparkContext() val glueContext: GlueContext = new GlueContext(spark) val args = GlueArgParser.getResolvedOptions(sysArgs, Seq("JOB_NAME").toArray) Job.init(args("JOB_NAME"), glueContext, args.asJava) // Get DynamicFrame from MongoDB and DocumentDB val resultFrame: DynamicFrame = glueContext.getSource("mongodb", defaultJsonOption).getDynamicFrame() val resultFrame2: DynamicFrame = glueContext.getSource("documentdb", documentDBJsonOption).getDynamicFrame() // Write DynamicFrame to MongoDB and DocumentDB glueContext.getSink("mongodb", writeJsonOption).writeDynamicFrame(resultFrame) glueContext.getSink("documentdb", writeJsonOption).writeDynamicFrame(resultFrame2) Job.commit() } private def jsonOptions(uri: String): JsonOptions = { new JsonOptions( s"""{"uri": "${uri}", |"database":"test", |"collection":"coll", |"username": "username", |"password": "pwd", |"ssl":"true", |"ssl.domain_match":"false", |"partitioner": "MongoSamplePartitioner", |"partitionerOptions.partitionSizeMB": "10", |"partitionerOptions.partitionKey": "_id"}""".stripMargin) } }

For more information, see the following: