This article describes one of the many ways to import data into AWS DynamoDB database. The option explained here uses Amazon EMR and Hive. Using Amazon EMR and Hive you can quickly and efficiently process large amounts of data, such as importing data from Amazon S3 into a DynamoDB table. What this example accomplishes? Every day an external datasource sends a csv file with about 1000 records to S3 bucket. A lambda function that will get triggered when an csv object is placed into an S3 bucket. Lambda function will start a EMR job with steps includes: Create a Hive table that references data stored in DynamoDB. Creating a hive table that references a location in Amazon S3. Load data form S3 table to DynamoDB table. The following diagram shows the architecture of the process. Prerequisites Basic understanding of CloudFormation. Basic understanding of EMR. Setup an AWS account. Install . Serverless Framework Now, Let’s start Before getting started, Install the Serverless Framework. Open up a terminal and type npm install -g serverless. There is a yml file (serverless.yml) in the project directory. Let’s start to define a set of objects in template file as below: S3 bucket There are 2 S3 buckets, is for EMR logs, is to store csv files. LogBucket S3BucketCsvimport - ServerSideEncryptionByDefault: Resources: LogBucket: Type: AWS::S3::Bucket Properties: AccessControl: Private S3BucketCsvimport: Type: AWS::S3::Bucket Properties: AccessControl: Private BucketEncryption: ServerSideEncryptionConfiguration: SSEAlgorithm: AES256 BucketName: ${self:custom.csvImportBucketName} DynamoDB table A DynamoDB table to load csv data from S3. Resources: ContactsTable: Type: AWS::DynamoDB::Table Properties: TableName: ${self:custom.contactsTable} SSESpecification: SSEEnabled: true AttributeDefinitions: - AttributeName: id AttributeType: S KeySchema: - AttributeName: id KeyType: HASH ProvisionedThroughput: ReadCapacityUnits: ${self:custom.tableThroughputs.${self:provider.stage}} WriteCapacityUnits: ${self:custom.tableThroughputs.${self:provider.stage}} StreamSpecification: StreamViewType: NEW_AND_OLD_IMAGES Lambda function configuration Adding lambda function configuration to serverless.yml. it will be triggered by S3 new objected created event, the lambda function will then start a EMR job flow to process data importing. startEMRJob: handler: src/handler.start_emr_job environment: CONTACTS_TABLE: ${self:custom.contactsTable} SUBNET_ID: ${self:custom.vpc.subsetId} EMR_LOGS_BUCKET: Ref: LogBucket CSV_IMPORT_BUCKET: ${self:custom.csvImportBucketName} events: - s3: bucket: ${self:custom.csvImportBucketName} event: s3:ObjectCreated:* rules: - prefix: uploads/ - suffix: .csv existing: true IAM role We also need to create IAM role for the lambda function, so our lambda function has permission to start EMR job flow. iamRoleStatements: provider: - Effect: "Allow" Action : - "iam:PassRole" Resource : - arn:aws:iam::#{ AWS ::AccountId}:role/EMR_DefaultRole - arn:aws:iam::#{ AWS ::AccountId}:role/EMR_EC2_DefaultRole - Effect: "Allow" Action : - "elasticmapreduce:RunJobFlow" Resource : "*" - Effect: "Allow" Action : - "s3:PutObject" Resource : - "Fn::Join" : - "" - - "arn:aws:s3:::" - ${ self :custom.csvImportBucketName} - "/*" - Effect: "Allow" Action : - "dynamodb:*" Resource : - "Fn::GetAtt" : [ContactsTable, Arn] - "Fn::Join" : - "/" - - { "Fn::GetAtt" : [ContactsTable, Arn] } - "index/*" Adding lambda function Let’s add a lambda function to create an AWS EMR cluster and adding the step details such as the location of the hive scripts, arguments etc. We can use the boto3 lib for EMR, in order to create a cluster and submit the job dynamically from lambda function. logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) put_dump_record_to_db() put_step_scripts_to_s3() cluster_id = emr.run_job_flow( Applications=[ { }, { }, { }, { }, { }, { }, ], Instances={ { }, { } ], }, Configurations=[ { } }, ], Steps=[ { } }, { } }, { } }, { } } ], ) logger.error(e) table.put_item( root_path = Path(__file__).parent.parent import boto3 import logging import os from datetime import datetime from pathlib import Path emr = boto3.client( 'emr' ) s3 = boto3.resource( 's3' ) dynamodb = boto3.resource( 'dynamodb' ) def start_emr_job ( event, context ): try : Name= 'test_emr_job' , LogUri= "s3://{}" . format (os.environ[ 'EMR_LOGS_BUCKET' ]), ReleaseLabel= 'emr-5.18.0' , 'Name' : 'Hadoop' 'Name' : 'Livy' 'Name' : 'Pig' 'Name' : 'Hue' 'Name' : 'Hue' 'Name' : 'Hive' 'InstanceGroups' : [ 'Name' : "Master nodes" , 'Market' : 'ON_DEMAND' , 'InstanceRole' : 'MASTER' , 'InstanceType' : 'm1.medium' , 'InstanceCount' : 1 , 'Name' : "Slave nodes" , 'Market' : 'ON_DEMAND' , 'InstanceRole' : 'CORE' , 'InstanceType' : 'm1.medium' , 'InstanceCount' : 2 , 'KeepJobFlowAliveWhenNoSteps' : False , 'TerminationProtected' : False , 'Ec2SubnetId' : os.environ[ 'SUBNET_ID' ], 'Classification' : 'hive-site' , 'Properties' : { 'hive.execution.engine' : 'mr' 'Name' : 'creating dynamodb table' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step1.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'DYNAMODBTABLE={}' . format ( os.environ[ "CONTACTS_TABLE" ])] 'Name' : 'creating csv table' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step2.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'INPUT=s3://{}' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] 'Name' : 'adding partition' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step3.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'INPUT=s3://{}' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] 'Name' : 'import date to dynamodb' , 'ActionOnFailure' : 'TERMINATE_CLUSTER' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step4.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] VisibleToAllUsers= True , JobFlowRole= 'EMR_EC2_DefaultRole' , ServiceRole= 'EMR_DefaultRole' , logger.info( 'cluster {} created with the step...' . format ( cluster_id[ 'JobFlowId' ])) except Exception as e: raise def put_dump_record_to_db (): table = dynamodb.Table(os.environ[ "CONTACTS_TABLE" ]) if table.item_count == 0 : Item={ 'id' : 'NA' , 'full_name' : 'demo user' , 'gender' : 'M' , 'address' : 'NA' , 'language' : [ "English" ]}) def put_step_scripts_to_s3 (): scripts = [ "scripts/step1.q" , "scripts/step2.q" , "scripts/step3.q" , "scripts/step4.q" ] for script in scripts: s3.Bucket(os.environ[ 'CSV_IMPORT_BUCKET' ]).upload_file( '{}/{}' . format (root_path, script), script) Hive scripts And finally…let’s add Hive script. Step 1. Creating mapping between Hive and S3 We will create an external Hive table that maps to the csv data file. dynamodb_contacts ( TBLPROPERTIES -- Create external table for dynamodb table. CREATE EXTERNAL TABLE IF NOT EXISTS id string ,full_name string , email string , gender string ,address string , language array < string >) STORED BY 'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHandler' ( "dynamodb.table.name" = "${DYNAMODBTABLE}" , "dynamodb.column.mapping" = "id:id,full_name:full_name,email:email,gender:gender,address:address,language:language" ); Step 2. Creating mapping between Hive and DynamoDB Establish a mapping between Hive and the Features table in DynamoDB. TBLPROPERTIES ( -- Create table using csv in s3 CREATE EXTERNAL TABLE IF NOT EXISTS csv_contacts ( id string ,first_name string , last_name string , email string , gender string , address string , language array < string > ) PARTITIONED BY (created_date string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY ',' LOCATION '${INPUT}/uploads/' 'serialization.null.format' = '' , 'skip.header.line.count' = '1' ); Step3. Load the table with data in S3 Add current date as partition and load table with csv data in S3. -- add current date as partition ALTER TABLE csv_contacts add IF NOT EXISTS PARTITION (created_date= '${TODAY}' ) LOCATION '${INPUT}/uploads/created_date=${TODAY}/' ; Step4. Import a table from Amazon S3 to DynamoDB Using following Hive script to write data from Amazon S3 to DynamoDB. -- Import csv data to DynamoDB table INSERT OVERWRITE TABLE dynamodb_contacts SELECT DISTINCT id , CONCAT_WS ( ' ' ,first_name,last_name),email, gender, address, language FROM csv_contacts WHERE created_date >= '${TODAY}' that Amazon EMR operations on a DynamoDB table count as read/write operations, and are subject to the table’s provisioned throughput settings. for more details please visit . Note EMR document Now let’s deploy the service and test it out! $sls deploy --stage dev After script deployed, copy a csv file to S3 bucket with prefix, eg. created_date={CURRENT_DATE} $aws s3 cp csv /contacts.csv s3:/ /myemr.csv.import.dev/u ploads /created_date=2020-02-03/ contacts.csv Then we can go to AWS EMR console and check the progress of the EMR steps. It will take several minutes to complete all the steps and cluster will terminate automatically after running above steps. Next We can go to AWS DynamoDB console to verify that the data has been loaded into DynamoDB: Eventually Importing process took about 6 mins to load 1000 records total 76kb into DynamoDB table with write capacity units 10 and no auto scaling enabled. That’s about it, Thanks for reading! I hope you have found this article useful, You can find the complete project in my . GitHub repo
