Tag Archives: hadoop

How to migrate a Hue database from an existing Amazon EMR cluster

Post Syndicated from Anvesh Ragi original https://aws.amazon.com/blogs/big-data/how-to-migrate-a-hue-database-from-an-existing-amazon-emr-cluster/

Hadoop User Experience (Hue) is an open-source, web-based, graphical user interface for use with Amazon EMR and Apache Hadoop. The Hue database stores things like users, groups, authorization permissions, Apache Hive queries, Apache Oozie workflows, and so on.

There might come a time when you want to migrate your Hue database to a new EMR cluster. For example, you might want to upgrade from an older version of the Amazon EMR AMI (Amazon Machine Image), but your Hue application and its database have had a lot of customization.You can avoid re-creating these user entities and retain query/workflow histories in Hue by migrating the existing Hue database, or remote database in Amazon RDS, to a new cluster.

By default, Hue user information and query histories are stored in a local MySQL database on the EMR cluster’s master node. However, you can create one or more Hue-enabled clusters using a configuration stored in Amazon S3 and a remote MySQL database in Amazon RDS. This allows you to preserve user information and query history that Hue creates without keeping your Amazon EMR cluster running.

This post describes the step-by-step process for migrating the Hue database from an existing EMR cluster.

Note: Amazon EMR supports different Hue versions across different AMI releases. Keep in mind the compatibility of Hue versions between the old and new clusters in this migration activity. Currently, Hue 3.x.x versions are not compatible with Hue 4.x.x versions, and therefore a migration between these two Hue versions might create issues. In addition, Hue 3.10.0 is not backward compatible with its previous 3.x.x versions.

Before you begin

First, let’s create a new testUser in Hue on an existing EMR cluster, as shown following:

You will use these credentials later to log in to Hue on the new EMR cluster and validate whether you have successfully migrated the Hue database.

Let’s get started!

Migration how-to

Follow these steps to migrate your database to a new EMR cluster and then validate the migration process.

1.) Make a backup of the existing Hue database.

Use SSH to connect to the master node of the old cluster, as shown following (if you are using Linux/Unix/macOS), and dump the Hue database to a JSON file.

$ ssh -i ~/key.pem [email protected]
$ /usr/lib/hue/build/env/bin/hue dumpdata > ./hue-mysql.json

Edit the hue-mysql.json output file by removing all JSON objects that have useradmin.userprofile in the model field, and save the file. For example, remove the objects as shown following:

{
  "pk": 1,
  "model": "useradmin.userprofile",
  "fields": {
    "last_activity": "2018-01-10T11:41:04",
    "creation_method": "HUE",
    "first_login": false,
    "user": 1,
    "home_directory": "/user/hue_admin"
  }
},

2.) Store the hue-mysql.json file on persistent storage like Amazon S3.

You can copy the file from the old EMR cluster to Amazon S3 using the AWS CLI or Secure Copy (SCP) client. For example, the following uses the AWS CLI:

$ aws s3 cp ./hue-mysql.json s3://YourBucketName/folder/

3.) Recover/reload the backed-up Hue database into the new EMR cluster.

a.) Use SSH to connect to the master node of the new EMR cluster, and stop the Hue service that is already running.

$ ssh -i ~/key.pem [email protected]x-xxx-xxx-229.us-west-2.compute.amazonaws.com
$ sudo stop hue
hue stop/waiting

b.) Connect to the Hue database—either the local MySQL database or the remote database in Amazon RDS for your cluster as shown following, using the mysql client.

$ mysql -h HOST –u USER –pPASSWORD

For a local MySQL database, you can find the hostname, user name, and password for connecting to the database in the /etc/hue/conf/hue.ini file on the master node.

[[database]]
    engine = mysql
    name = huedb
    case_insensitive_collation = utf8_unicode_ci
    test_charset = utf8
    test_collation = utf8_bin
    host = ip-172-31-37-133.us-west-2.compute.internal
    user = hue
    test_name = test_huedb
    password = QdWbL3Ai6GcBqk26
    port = 3306

Based on the preceding example configuration, the sample command is as follows. (Replace the host, user, and password details based on your EMR cluster settings.)

$ mysql -h ip-172-31-37-133.us-west-2.compute.internal -u hue -pQdWbL3Ai6GcBqk26

c.) Drop the existing Hue database with the name huedb from the MySQL server.

mysql> DROP DATABASE IF EXISTS huedb;

d.) Create a new empty database with the same name huedb.

mysql> CREATE DATABASE huedb DEFAULT CHARACTER SET utf8 DEFAULT COLLATE=utf8_bin;

e.) Now, synchronize Hue with its database huedb.

$ sudo /usr/lib/hue/build/env/bin/hue syncdb --noinput
$ sudo /usr/lib/hue/build/env/bin/hue migrate

(This populates the new huedb with all Hue tables that are required.)

f.) Log in to MySQL again, and drop the foreign key to clean tables.

mysql> SHOW CREATE TABLE huedb.auth_permission;

In the following example, replace <id value> with the actual value from the preceding output.

mysql> ALTER TABLE huedb.auth_permission DROP FOREIGN KEY
content_type_id_refs_id_<id value>;

g.) Delete the contents of the django_content_type

mysql> DELETE FROM huedb.django_content_type;

h.) Download the backed-up Hue database dump from Amazon S3 to the new EMR cluster, and load it into Hue.

$ aws s3 cp s3://YourBucketName/folder/hue-mysql.json ./
$ sudo /usr/lib/hue/build/env/bin/hue loaddata ./hue-mysql.json

i.) In MySQL, add the foreign key content_type_id back to the auth_permission

mysql> use huedb;
mysql> ALTER TABLE huedb.auth_permission ADD FOREIGN KEY (`content_type_id`) REFERENCES `django_content_type` (`id`);

j.) Start the Hue service again.

$ sudo start hue
hue start/running, process XXXX

That’s it! Now, verify whether you can successfully access the Hue UI, and sign in using your existing testUser credentials.

After a successful sign in to Hue on the new EMR cluster, you should see a similar Hue homepage as shown following with testUser as the user signed in:

Conclusion

You have now learned how to migrate an existing Hue database to a new Amazon EMR cluster and validate the migration process. If you have any similar Amazon EMR administration topics that you want to see covered in a future post, please let us know in the comments below.


Additional Reading

If you found this post useful, be sure to check out Anomaly Detection Using PySpark, Hive, and Hue on Amazon EMR and Dynamically Create Friendly URLs for Your Amazon EMR Web Interfaces.


About the Author


Anvesh Ragi is a Big Data Support Engineer with Amazon Web Services. He works closely with AWS customers to provide them architectural and engineering assistance for their data processing workflows. In his free time, he enjoys traveling and going for hikes.

Central Logging in Multi-Account Environments

Post Syndicated from matouk original https://aws.amazon.com/blogs/architecture/central-logging-in-multi-account-environments/

Centralized logging is often required in large enterprise environments for a number of reasons, ranging from compliance and security to analytics and application-specific needs.

I’ve seen that in a multi-account environment, whether the accounts belong to the same line of business or multiple business units, collecting logs in a central, dedicated logging account is an established best practice. It helps security teams detect malicious activities both in real-time and during incident response. It provides protection to log data in case it is accidentally or intentionally deleted. It also helps application teams correlate and analyze log data across multiple application tiers.

This blog post provides a solution and building blocks to stream Amazon CloudWatch log data across accounts. In a multi-account environment this repeatable solution could be deployed multiple times to stream all relevant Amazon CloudWatch log data from all accounts to a centralized logging account.

Solution Summary 

The solution uses Amazon Kinesis Data Streams and a log destination to set up an endpoint in the logging account to receive streamed logs and uses Amazon Kinesis Data Firehose to deliver log data to the Amazon Simple Storage Solution (S3) bucket. Application accounts will subscribe to stream all (or part) of their Amazon CloudWatch logs to a defined destination in the logging account via subscription filters.

Below is a diagram illustrating how the various services work together.


In logging an account, a Kinesis Data Stream is created to receive streamed log data and a log destination is created to facilitate remote streaming, configured to use the Kinesis Data Stream as its target.

The Amazon Kinesis Data Firehose stream is created to deliver log data from the data stream to S3. The delivery stream uses a generic AWS Lambda function for data validation and transformation.

In each application account, a subscription filter is created between each Amazon CloudWatch log group and the destination created for this log group in the logging account.

The following steps are involved in setting up the central-logging solution:

  1. Create an Amazon S3 bucket for your central logging in the logging account
  2. Create an AWS Lambda function for log data transformation and decoding in logging account
  3. Create a central logging stack as a logging-account destination ready to receive streamed logs and deliver them to S3
  4. Create a subscription in application accounts to deliver logs from a specific CloudWatch log group to the logging account destination
  5. Create Amazon Athena tables to query and analyze log data in your logging account

Creating a log destination in your logging account

In this section, we will setup the logging account side of the solution, providing detail on the list above. The example I use is for the us-east-1 region, however any region where required services are available could be used.

It’s important to note that your logging-account destination and application-account subscription must be in the same region. You can deploy the solution multiple times to create destinations in all required regions if application accounts use multiple regions.

Step 1: Create an S3 bucket

Use the CloudFormation template below to create S3 bucket in logging account. This template also configures the bucket to archive log data to Glacier after 60 days.


{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "CF Template to create S3 bucket for central logging",
  "Parameters":{

    "BucketName":{
      "Type":"String",
      "Default":"",
      "Description":"Central logging bucket name"
    }
  },
  "Resources":{
                        
   "CentralLoggingBucket" : {
      "Type" : "AWS::S3::Bucket",
      "Properties" : {
        "BucketName" : {"Ref": "BucketName"},
        "LifecycleConfiguration": {
            "Rules": [
                {
                  "Id": "ArchiveToGlacier",
                  "Prefix": "",
                  "Status": "Enabled",
                  "Transitions":[{
                      "TransitionInDays": "60",
                      "StorageClass": "GLACIER"
                  }]
                }
            ]
        }
      }
    }

  },
  "Outputs":{
    "CentralLogBucket":{
    	"Description" : "Central log bucket",
    	"Value" : {"Ref": "BucketName"} ,
    	"Export" : { "Name" : "CentralLogBucketName"}
    }
  }
} 

To create your central-logging bucket do the following:

  1. Save the template file to your local developer machine as “central-log-bucket.json”
  2. From the CloudFormation console, select “create new stack” and import the file “central-log-bucket.json”
  3. Fill in the parameters and complete stack creation steps (as indicated in the screenshot below)
  4. Verify the bucket has been created successfully and take a note of the bucket name

Step 2: Create data processing Lambda function

Use the template below to create a Lambda function in your logging account that will be used by Amazon Firehose for data transformation during the delivery process to S3. This function is based on the AWS Lambda kinesis-firehose-cloudwatch-logs-processor blueprint.

The function could be created manually from the blueprint or using the cloudformation template below. To find the blueprint navigate to Lambda -> Create -> Function -> Blueprints

This function will unzip the event message, parse it and verify that it is a valid CloudWatch log event. Additional processing can be added if needed. As this function is generic, it could be reused by all log-delivery streams.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create cloudwatch data processing lambda function",
  "Resources":{
      
    "LambdaRole": {
        "Type": "AWS::IAM::Role",
        "Properties": {
            "AssumeRolePolicyDocument": {
                "Version": "2012-10-17",
                "Statement": [
                    {
                        "Effect": "Allow",
                        "Principal": {
                            "Service": "lambda.amazonaws.com"
                        },
                        "Action": "sts:AssumeRole"
                    }
                ]
            },
            "Path": "/",
            "Policies": [
                {
                    "PolicyName": "firehoseCloudWatchDataProcessing",
                    "PolicyDocument": {
                        "Version": "2012-10-17",
                        "Statement": [
                            {
                                "Effect": "Allow",
                                "Action": [
                                    "logs:CreateLogGroup",
                                    "logs:CreateLogStream",
                                    "logs:PutLogEvents"
                                ],
                                "Resource": "arn:aws:logs:*:*:*"
                            }
                        ]
                    }
                }
            ]
        }
    },
      
    "FirehoseDataProcessingFunction": {
        "Type": "AWS::Lambda::Function",
        "Properties": {
            "Handler": "index.handler",
            "Role": {"Fn::GetAtt": ["LambdaRole","Arn"]},
            "Description": "Firehose cloudwatch data processing",
            "Code": {
                "ZipFile" : { "Fn::Join" : ["\n", [
                  "'use strict';",
                  "const zlib = require('zlib');",
                  "function transformLogEvent(logEvent) {",
                  "       return Promise.resolve(`${logEvent.message}\n`);",
                  "}",
                  "exports.handler = (event, context, callback) => {",
                  "    Promise.all(event.records.map(r => {",
                  "        const buffer = new Buffer(r.data, 'base64');",
                  "        const decompressed = zlib.gunzipSync(buffer);",
                  "        const data = JSON.parse(decompressed);",
                  "        if (data.messageType !== 'DATA_MESSAGE') {",
                  "            return Promise.resolve({",
                  "                recordId: r.recordId,",
                  "                result: 'ProcessingFailed',",
                  "            });",
                  "         } else {",
                  "            const promises = data.logEvents.map(transformLogEvent);",
                  "            return Promise.all(promises).then(transformed => {",
                  "                const payload = transformed.reduce((a, v) => a + v, '');",
                  "                const encoded = new Buffer(payload).toString('base64');",
                  "                console.log('---------------payloadv2:'+JSON.stringify(payload, null, 2));",
                  "                return {",
                  "                    recordId: r.recordId,",
                  "                    result: 'Ok',",
                  "                    data: encoded,",
                  "                };",
                  "           });",
                  "        }",
                  "    })).then(recs => callback(null, { records: recs }));",
                    "};"

                ]]}
            },
            "Runtime": "nodejs6.10",
            "Timeout": "60"
        }
    }

  },
  "Outputs":{
   "Function" : {
      "Description": "Function ARN",
      "Value": {"Fn::GetAtt": ["FirehoseDataProcessingFunction","Arn"]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Function" }}
    }
  }
}

To create the function follow the steps below:

  1. Save the template file as “central-logging-lambda.json”
  2. Login to logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-lambda.json” and click next
  4. Follow the steps to create the stack and verify successful creation
  5. Take a note of Lambda function arn from the output section

Step 3: Create log destination in logging account

Log destination is used as the target of a subscription from application accounts, log destination can be shared between multiple subscriptions however according to the architecture suggested in this solution all logs streamed to the same destination will be stored in the same S3 location, if you would like to store log data in different hierarchy or in a completely different bucket you need to create separate destinations.

As noted previously, your destination and subscription have to be in the same region

Use the template below to create destination stack in logging account.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create log destination and required resources",
  "Parameters":{

    "LogBucketName":{
      "Type":"String",
      "Default":"central-log-do-not-delete",
      "Description":"Destination logging bucket"
    },
    "LogS3Location":{
      "Type":"String",
      "Default":"<BU>/<ENV>/<SOURCE_ACCOUNT>/<LOG_TYPE>/",
      "Description":"S3 location for the logs streamed to this destination; example marketing/prod/999999999999/flow-logs/"
    },
    "ProcessingLambdaARN":{
      "Type":"String",
      "Default":"",
      "Description":"CloudWatch logs data processing function"
    },
    "SourceAccount":{
      "Type":"String",
      "Default":"",
      "Description":"Source application account number"
    }
  },
    
  "Resources":{
    "MyStream": {
      "Type": "AWS::Kinesis::Stream",
      "Properties": {
        "Name": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Stream"] ]},
        "RetentionPeriodHours" : 48,
        "ShardCount": 1,
        "Tags": [
          {
            "Key": "Solution",
            "Value": "CentralLogging"
          }
       ]
      }
    },
    "LogRole" : {
      "Type"  : "AWS::IAM::Role",
      "Properties" : {
          "AssumeRolePolicyDocument" : {
              "Statement" : [ {
                  "Effect" : "Allow",
                  "Principal" : {
                      "Service" : [ {"Fn::Join": [ "", [ "logs.", { "Ref": "AWS::Region" }, ".amazonaws.com" ] ]} ]
                  },
                  "Action" : [ "sts:AssumeRole" ]
              } ]
          },         
          "Path" : "/service-role/"
      }
    },
      
    "LogRolePolicy" : {
        "Type" : "AWS::IAM::Policy",
        "Properties" : {
            "PolicyName" : {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-LogPolicy"] ]},
            "PolicyDocument" : {
              "Version": "2012-10-17",
              "Statement": [
                {
                  "Effect": "Allow",
                  "Action": ["kinesis:PutRecord"],
                  "Resource": [{ "Fn::GetAtt" : ["MyStream", "Arn"] }]
                },
                {
                  "Effect": "Allow",
                  "Action": ["iam:PassRole"],
                  "Resource": [{ "Fn::GetAtt" : ["LogRole", "Arn"] }]
                }
              ]
            },
            "Roles" : [ { "Ref" : "LogRole" } ]
        }
    },
      
    "LogDestination" : {
      "Type" : "AWS::Logs::Destination",
      "DependsOn" : ["MyStream","LogRole","LogRolePolicy"],
      "Properties" : {
        "DestinationName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Destination"] ]},
        "RoleArn": { "Fn::GetAtt" : ["LogRole", "Arn"] },
        "TargetArn": { "Fn::GetAtt" : ["MyStream", "Arn"] },
        "DestinationPolicy": { "Fn::Join" : ["",[
		
				"{\"Version\" : \"2012-10-17\",\"Statement\" : [{\"Effect\" : \"Allow\",",
                " \"Principal\" : {\"AWS\" : \"", {"Ref":"SourceAccount"} ,"\"},",
                "\"Action\" : \"logs:PutSubscriptionFilter\",",
                " \"Resource\" : \"", 
                {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]}  ,"\"}]}"

			]]}
          
          
      }
    },
      
    "S3deliveryStream": {
      "DependsOn": ["S3deliveryRole", "S3deliveryPolicy"],
      "Type": "AWS::KinesisFirehose::DeliveryStream",
      "Properties": {
        "DeliveryStreamName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-DeliveryStream"] ]},
        "DeliveryStreamType": "KinesisStreamAsSource",
        "KinesisStreamSourceConfiguration": {
            "KinesisStreamARN": { "Fn::GetAtt" : ["MyStream", "Arn"] },
            "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] }
        },
        "ExtendedS3DestinationConfiguration": {
          "BucketARN": {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]},
          "BufferingHints": {
            "IntervalInSeconds": "60",
            "SizeInMBs": "50"
          },
          "CompressionFormat": "UNCOMPRESSED",
          "Prefix": {"Ref": "LogS3Location"},
          "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] },
          "ProcessingConfiguration" : {
              "Enabled": "true",
              "Processors": [
              {
                "Parameters": [ 
                { 
                    "ParameterName": "LambdaArn",
                    "ParameterValue": {"Ref":"ProcessingLambdaARN"}
                }],
                "Type": "Lambda"
              }]
          }
        }

      }
    },
      
    "S3deliveryRole": {
      "Type": "AWS::IAM::Role",
      "Properties": {
        "AssumeRolePolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
            {
              "Sid": "",
              "Effect": "Allow",
              "Principal": {
                "Service": "firehose.amazonaws.com"
              },
              "Action": "sts:AssumeRole",
              "Condition": {
                "StringEquals": {
                  "sts:ExternalId": {"Ref":"AWS::AccountId"}
                }
              }
            }
          ]
        }
      }
    },
      
    "S3deliveryPolicy": {
      "Type": "AWS::IAM::Policy",
      "Properties": {
        "PolicyName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-FirehosePolicy"] ]},
        "PolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
            {
              "Effect": "Allow",
              "Action": [
                "s3:AbortMultipartUpload",
                "s3:GetBucketLocation",
                "s3:GetObject",
                "s3:ListBucket",
                "s3:ListBucketMultipartUploads",
                "s3:PutObject"
              ],
              "Resource": [
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}]]},
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}, "*"]]}
              ]
            },
            {
              "Effect": "Allow",
              "Action": [
                "lambda:InvokeFunction",
                "lambda:GetFunctionConfiguration",
                "logs:PutLogEvents",
                "kinesis:DescribeStream",
                "kinesis:GetShardIterator",
                "kinesis:GetRecords",
                "kms:Decrypt"
              ],
              "Resource": "*"
            }
          ]
        },
        "Roles": [{"Ref": "S3deliveryRole"}]
      }
    }

  },
  "Outputs":{
      
   "Destination" : {
      "Description": "Destination",
      "Value": {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Destination" }}
    }

  }
} 

To create log your destination and all required resources, follow these steps:

  1. Save your template as “central-logging-destination.json”
  2. Login to your logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-destination.json” and click next
  4. Fill in the parameters to configure the log destination and click Next
  5. Follow the default steps to create the stack and verify successful creation
    1. Bucket name is the same as in the “create central logging bucket” step
    2. LogS3Location is the directory hierarchy for saving log data that will be delivered to this destination
    3. ProcessingLambdaARN is as created in “create data processing Lambda function” step
    4. SourceAccount is the application account number where the subscription will be created
  6. Take a note of destination ARN as it appears in outputs section as you did above.

Step 4: Create the log subscription in your application account

In this section, we will create the subscription filter in one of the application accounts to stream logs from the CloudWatch log group to the log destination that was created in your logging account.

Create log subscription filter

The subscription filter is created between the CloudWatch log group and a destination endpoint. Asubscription could be filtered to send part (or all) of the logs in the log group. For example,you can create a subscription filter to stream only flow logs with status REJECT.

Use the CloudFormation template below to create subscription filter. Subscription filter and log destination must be in the same region.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create log subscription filter for a specific Log Group",
  "Parameters":{

    "DestinationARN":{
      "Type":"String",
      "Default":"",
      "Description":"ARN of logs destination"
    },
    "LogGroupName":{
      "Type":"String",
      "Default":"",
      "Description":"Name of LogGroup to forward logs from"
    },
    "FilterPattern":{
      "Type":"String",
      "Default":"",
      "Description":"Filter pattern to filter events to be sent to log destination; Leave empty to send all logs"
    }
  },
    
  "Resources":{
    "SubscriptionFilter" : {
      "Type" : "AWS::Logs::SubscriptionFilter",
      "Properties" : {
        "LogGroupName" : { "Ref" : "LogGroupName" },
        "FilterPattern" : { "Ref" : "FilterPattern" },
        "DestinationArn" : { "Ref" : "DestinationARN" }
      }
    }
  }
}

To create a subscription filter for one of CloudWatch log groups in your application account, follow the steps below:

  1. Save the template as “central-logging-subscription.json”
  2. Login to your application account and, from the CloudFormation console, select “create new stack”
  3. Select the file “central-logging-subscription.json” and click next
  4. Fill in the parameters as appropriate to your environment as you did above
    a.  DestinationARN is the value of obtained in “create log destination in logging account” step
    b.  FilterPatterns is the filter value for log data to be streamed to your logging account (leave empty to stream all logs in the selected log group)
    c.  LogGroupName is the log group as it appears under CloudWatch Logs
  5. Verify successful creation of the subscription

This completes the deployment process in both the logging- and application-account side. After a few minutes, log data will be streamed to the central-logging destination defined in your logging account.

Step 5: Analyzing log data

Once log data is centralized, it opens the door to run analytics on the consolidated data for business or security reasons. One of the powerful services that AWS offers is Amazon Athena.

Amazon Athena allows you to query data in S3 using standard SQL.

Follow the steps below to create a simple table and run queries on the flow logs data that has been collected from your application accounts

  1. Login to your logging account and from the Amazon Athena console, use the DDL below in your query  editor to create a new table

CREATE EXTERNAL TABLE IF NOT EXISTS prod_vpc_flow_logs (

Version INT,

Account STRING,

InterfaceId STRING,

SourceAddress STRING,

DestinationAddress STRING,

SourcePort INT,

DestinationPort INT,

Protocol INT,

Packets INT,

Bytes INT,

StartTime INT,

EndTime INT,

Action STRING,

LogStatus STRING

)

ROW FORMAT SERDE ‘org.apache.hadoop.hive.serde2.RegexSerDe’

WITH SERDEPROPERTIES (

“input.regex” = “^([^ ]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([0-9]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)$”)

LOCATION ‘s3://central-logging-company-do-not-delete/’;

2. Click ”run query” and verify a successful run/ This creates the table “prod_vpc_flow_logs”

3. You can then run queries against the table data as below:

Conclusion

By following the steps I’ve outlined, you will build a central logging solution to stream CloudWatch logs from one application account to a central logging account. This solution is repeatable and could be deployed multiple times for multiple accounts and logging requirements.

 

About the Author

Mahmoud Matouk is a Senior Cloud Infrastructure Architect. He works with our customers to help accelerate migration and cloud adoption at the enterprise level.

 

How I built a data warehouse using Amazon Redshift and AWS services in record time

Post Syndicated from Stephen Borg original https://aws.amazon.com/blogs/big-data/how-i-built-a-data-warehouse-using-amazon-redshift-and-aws-services-in-record-time/

This is a customer post by Stephen Borg, the Head of Big Data and BI at Cerberus Technologies.

Cerberus Technologies, in their own words: Cerberus is a company founded in 2017 by a team of visionary iGaming veterans. Our mission is simple – to offer the best tech solutions through a data-driven and a customer-first approach, delivering innovative solutions that go against traditional forms of working and process. This mission is based on the solid foundations of reliability, flexibility and security, and we intend to fundamentally change the way iGaming and other industries interact with technology.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

In two of my recent projects, I ran into challenges when scaling our data warehouse using on-premises infrastructure. Data was growing at many tens of gigabytes per day, and query performance was suffering. Scaling required major capital investment for hardware and software licenses, and also significant operational costs for maintenance and technical staff to keep it running and performing well. Unfortunately, I couldn’t get the resources needed to scale the infrastructure with data growth, and these projects were abandoned. Thanks to cloud data warehousing, the bottleneck of infrastructure resources, capital expense, and operational costs have been significantly reduced or have totally gone away. There is no more excuse for allowing obstacles of the past to delay delivering timely insights to decision makers, no matter how much data you have.

With Amazon Redshift and AWS, I delivered a cloud data warehouse to the business very quickly, and with a small team: me. I didn’t have to order hardware or software, and I no longer needed to install, configure, tune, or keep up with patches and version updates. Instead, I easily set up a robust data processing pipeline and we were quickly ingesting and analyzing data. Now, my data warehouse team can be extremely lean, and focus more time on bringing in new data and delivering insights. In this post, I show you the AWS services and the architecture that I used.

Handling data feeds

I have several different data sources that provide everything needed to run the business. The data includes activity from our iGaming platform, social media posts, clickstream data, marketing and campaign performance, and customer support engagements.

To handle the diversity of data feeds, I developed abstract integration applications using Docker that run on Amazon EC2 Container Service (Amazon ECS) and feed data to Amazon Kinesis Data Streams. These data streams can be used for real time analytics. In my system, each record in Kinesis is preprocessed by an AWS Lambda function to cleanse and aggregate information. My system then routes it to be stored where I need on Amazon S3 by Amazon Kinesis Data Firehose. Suppose that you used an on-premises architecture to accomplish the same task. A team of data engineers would be required to maintain and monitor a Kafka cluster, develop applications to stream data, and maintain a Hadoop cluster and the infrastructure underneath it for data storage. With my stream processing architecture, there are no servers to manage, no disk drives to replace, and no service monitoring to write.

Setting up a Kinesis stream can be done with a few clicks, and the same for Kinesis Firehose. Firehose can be configured to automatically consume data from a Kinesis Data Stream, and then write compressed data every N minutes to Amazon S3. When I want to process a Kinesis data stream, it’s very easy to set up a Lambda function to be executed on each message received. I can just set a trigger from the AWS Lambda Management Console, as shown following.

I also monitor the duration of function execution using Amazon CloudWatch and AWS X-Ray.

Regardless of the format I receive the data from our partners, I can send it to Kinesis as JSON data using my own formatters. After Firehose writes this to Amazon S3, I have everything in nearly the same structure I received but compressed, encrypted, and optimized for reading.

This data is automatically crawled by AWS Glue and placed into the AWS Glue Data Catalog. This means that I can immediately query the data directly on S3 using Amazon Athena or through Amazon Redshift Spectrum. Previously, I used Amazon EMR and an Amazon RDS–based metastore in Apache Hive for catalog management. Now I can avoid the complexity of maintaining Hive Metastore catalogs. Glue takes care of high availability and the operations side so that I know that end users can always be productive.

Working with Amazon Athena and Amazon Redshift for analysis

I found Amazon Athena extremely useful out of the box for ad hoc analysis. Our engineers (me) use Athena to understand new datasets that we receive and to understand what transformations will be needed for long-term query efficiency.

For our data analysts and data scientists, we’ve selected Amazon Redshift. Amazon Redshift has proven to be the right tool for us over and over again. It easily processes 20+ million transactions per day, regardless of the footprint of the tables and the type of analytics required by the business. Latency is low and query performance expectations have been more than met. We use Redshift Spectrum for long-term data retention, which enables me to extend the analytic power of Amazon Redshift beyond local data to anything stored in S3, and without requiring me to load any data. Redshift Spectrum gives me the freedom to store data where I want, in the format I want, and have it available for processing when I need it.

To load data directly into Amazon Redshift, I use AWS Data Pipeline to orchestrate data workflows. I create Amazon EMR clusters on an intra-day basis, which I can easily adjust to run more or less frequently as needed throughout the day. EMR clusters are used together with Amazon RDS, Apache Spark 2.0, and S3 storage. The data pipeline application loads ETL configurations from Spring RESTful services hosted on AWS Elastic Beanstalk. The application then loads data from S3 into memory, aggregates and cleans the data, and then writes the final version of the data to Amazon Redshift. This data is then ready to use for analysis. Spark on EMR also helps with recommendations and personalization use cases for various business users, and I find this easy to set up and deliver what users want. Finally, business users use Amazon QuickSight for self-service BI to slice, dice, and visualize the data depending on their requirements.

Each AWS service in this architecture plays its part in saving precious time that’s crucial for delivery and getting different departments in the business on board. I found the services easy to set up and use, and all have proven to be highly reliable for our use as our production environments. When the architecture was in place, scaling out was either completely handled by the service, or a matter of a simple API call, and crucially doesn’t require me to change one line of code. Increasing shards for Kinesis can be done in a minute by editing a stream. Increasing capacity for Lambda functions can be accomplished by editing the megabytes allocated for processing, and concurrency is handled automatically. EMR cluster capacity can easily be increased by changing the master and slave node types in Data Pipeline, or by using Auto Scaling. Lastly, RDS and Amazon Redshift can be easily upgraded without any major tasks to be performed by our team (again, me).

In the end, using AWS services including Kinesis, Lambda, Data Pipeline, and Amazon Redshift allows me to keep my team lean and highly productive. I eliminated the cost and delays of capital infrastructure, as well as the late night and weekend calls for support. I can now give maximum value to the business while keeping operational costs down. My team pushed out an agile and highly responsive data warehouse solution in record time and we can handle changing business requirements rapidly, and quickly adapt to new data and new user requests.


Additional Reading

If you found this post useful, be sure to check out Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server and Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift.


About the Author

Stephen Borg is the Head of Big Data and BI at Cerberus Technologies. He has a background in platform software engineering, and first became involved in data warehousing using the typical RDBMS, SQL, ETL, and BI tools. He quickly became passionate about providing insight to help others optimize the business and add personalization to products. He is now the Head of Big Data and BI at Cerberus Technologies.

 

 

 

Build a Multi-Tenant Amazon EMR Cluster with Kerberos, Microsoft Active Directory Integration and EMRFS Authorization

Post Syndicated from Songzhi Liu original https://aws.amazon.com/blogs/big-data/build-a-multi-tenant-amazon-emr-cluster-with-kerberos-microsoft-active-directory-integration-and-emrfs-authorization/

One of the challenges faced by our customers—especially those in highly regulated industries—is balancing the need for security with flexibility. In this post, we cover how to enable multi-tenancy and increase security by using EMRFS (EMR File System) authorization, the Amazon S3 storage-level authorization on Amazon EMR.

Amazon EMR is an easy, fast, and scalable analytics platform enabling large-scale data processing. EMRFS authorization provides Amazon S3 storage-level authorization by configuring EMRFS with multiple IAM roles. With this functionality enabled, different users and groups can share the same cluster and assume their own IAM roles respectively.

Simply put, on Amazon EMR, we can now have an Amazon EC2 role per user assumed at run time instead of one general EC2 role at the cluster level. When the user is trying to access Amazon S3 resources, Amazon EMR evaluates against a predefined mappings list in EMRFS configurations and picks up the right role for the user.

In this post, we will discuss what EMRFS authorization is (Amazon S3 storage-level access control) and show how to configure the role mappings with detailed examples. You will then have the desired permissions in a multi-tenant environment. We also demo Amazon S3 access from HDFS command line, Apache Hive on Hue, and Apache Spark.

EMRFS authorization for Amazon S3

There are two prerequisites for using this feature:

  1. Users must be authenticated, because EMRFS needs to map the current user/group/prefix to a predefined user/group/prefix. There are several authentication options. In this post, we launch a Kerberos-enabled cluster that manages the Key Distribution Center (KDC) on the master node, and enable a one-way trust from the KDC to a Microsoft Active Directory domain.
  2. The application must support accessing Amazon S3 via Applications that have their own S3FileSystem APIs (for example, Presto) are not supported at this time.

EMRFS supports three types of mapping entries: user, group, and Amazon S3 prefix. Let’s use an example to show how this works.

Assume that you have the following three identities in your organization, and they are defined in the Active Directory:

To enable all these groups and users to share the EMR cluster, you need to define the following IAM roles:

In this case, you create a separate Amazon EC2 role that doesn’t give any permission to Amazon S3. Let’s call the role the base role (the EC2 role attached to the EMR cluster), which in this example is named EMR_EC2_RestrictedRole. Then, you define all the Amazon S3 permissions for each specific user or group in their own roles. The restricted role serves as the fallback role when the user doesn’t belong to any user/group, nor does the user try to access any listed Amazon S3 prefixes defined on the list.

Important: For all other roles, like emrfs_auth_group_role_data_eng, you need to add the base role (EMR_EC2_RestrictedRole) as the trusted entity so that it can assume other roles. See the following example:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "ec2.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    },
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::511586466501:role/EMR_EC2_RestrictedRole"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

The following is an example policy for the admin user role (emrfs_auth_user_role_admin_user):

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "s3:*",
            "Resource": "*"
        }
    ]
}

We are assuming the admin user has access to all buckets in this example.

The following is an example policy for the data science group role (emrfs_auth_group_role_data_sci):

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::emrfs-auth-data-science-bucket-demo/*",
                "arn:aws:s3:::emrfs-auth-data-science-bucket-demo"
            ],
            "Action": [
                "s3:*"
            ]
        }
    ]
}

This role grants all Amazon S3 permissions to the emrfs-auth-data-science-bucket-demo bucket and all the objects in it. Similarly, the policy for the role emrfs_auth_group_role_data_eng is shown below:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::emrfs-auth-data-engineering-bucket-demo/*",
                "arn:aws:s3:::emrfs-auth-data-engineering-bucket-demo"
            ],
            "Action": [
                "s3:*"
            ]
        }
    ]
}

Example role mappings configuration

To configure EMRFS authorization, you use EMR security configuration. Here is the configuration we use in this post

Consider the following scenario.

First, the admin user admin1 tries to log in and run a command to access Amazon S3 data through EMRFS. The first role emrfs_auth_user_role_admin_user on the mapping list, which is a user role, is mapped and picked up. Then admin1 has access to the Amazon S3 locations that are defined in this role.

Then a user from the data engineer group (grp_data_engineering) tries to access a data bucket to run some jobs. When EMRFS sees that the user is a member of the grp_data_engineering group, the group role emrfs_auth_group_role_data_eng is assumed, and the user has proper access to Amazon S3 that is defined in the emrfs_auth_group_role_data_eng role.

Next, the third user comes, who is not an admin and doesn’t belong to any of the groups. After failing evaluation of the top three entries, EMRFS evaluates whether the user is trying to access a certain Amazon S3 prefix defined in the last mapping entry. This type of mapping entry is called the prefix type. If the user is trying to access s3://emrfs-auth-default-bucket-demo/, then the prefix mapping is in effect, and the prefix role emrfs_auth_prefix_role_default_s3_prefix is assumed.

If the user is not trying to access any of the Amazon S3 paths that are defined on the list—which means it failed the evaluation of all the entries—it only has the permissions defined in the EMR_EC2RestrictedRole. This role is assumed by the EC2 instances in the cluster.

In this process, all the mappings defined are evaluated in the defined order, and the first role that is mapped is assumed, and the rest of the list is skipped.

Setting up an EMR cluster and mapping Active Directory users and groups

Now that we know how EMRFS authorization role mapping works, the next thing we need to think about is how we can use this feature in an easy and manageable way.

Active Directory setup

Many customers manage their users and groups using Microsoft Active Directory or other tools like OpenLDAP. In this post, we create the Active Directory on an Amazon EC2 instance running Windows Server and create the users and groups we will be using in the example below. After setting up Active Directory, we use the Amazon EMR Kerberos auto-join capability to establish a one-way trust from the KDC running on the EMR master node to the Active Directory domain on the EC2 instance. You can use your own directory services as long as it talks to the LDAP (Lightweight Directory Access Protocol).

To create and join Active Directory to Amazon EMR, follow the steps in the blog post Use Kerberos Authentication to Integrate Amazon EMR with Microsoft Active Directory.

After configuring Active Directory, you can create all the users and groups using the Active Directory tools and add users to appropriate groups. In this example, we created users like admin1, dataeng1, datascientist1, grp_data_engineering, and grp_data_science, and then add the users to the right groups.

Join the EMR cluster to an Active Directory domain

For clusters with Kerberos, Amazon EMR now supports automated Active Directory domain joins. You can use the security configuration to configure the one-way trust from the KDC to the Active Directory domain. You also configure the EMRFS role mappings in the same security configuration.

The following is an example of the EMR security configuration with a trusted Active Directory domain EMRKRB.TEST.COM and the EMRFS role mappings as we discussed earlier:

The EMRFS role mapping configuration is shown in this example:

We will also provide an example AWS CLI command that you can run.

Launching the EMR cluster and running the tests

Now you have configured Kerberos and EMRFS authorization for Amazon S3.

Additionally, you need to configure Hue with Active Directory using the Amazon EMR configuration API in order to log in using the AD users created before. The following is an example of Hue AD configuration.

[
  {
    "Classification":"hue-ini",
    "Properties":{

    },
    "Configurations":[
      {
        "Classification":"desktop",
        "Properties":{

        },
        "Configurations":[
          {
            "Classification":"ldap",
            "Properties":{

            },
            "Configurations":[
              {
                "Classification":"ldap_servers",
                "Properties":{

                },
                "Configurations":[
                  {
                    "Classification":"AWS",
                    "Properties":{
                      "base_dn":"DC=emrkrb,DC=test,DC=com",
                      "ldap_url":"ldap://emrkrb.test.com",
                      "search_bind_authentication":"false",
                      "bind_dn":"CN=adjoiner,CN=users,DC=emrkrb,DC=test,DC=com",
                      "bind_password":"Abc123456",
                      "create_users_on_login":"true",
                      "nt_domain":"emrkrb.test.com"
                    },
                    "Configurations":[

                    ]
                  }
                ]
              }
            ]
          },
          {
            "Classification":"auth",
            "Properties":{
              "backend":"desktop.auth.backend.LdapBackend"
            },
            "Configurations":[

            ]
          }
        ]
      }
    ]
  }

Note: In the preceding configuration JSON file, change the values as required before pasting it into the software setting section in the Amazon EMR console.

Now let’s use this configuration and the security configuration you created before to launch the cluster.

In the Amazon EMR console, choose Create cluster. Then choose Go to advanced options. On the Step1: Software and Steps page, under Edit software settings (optional), paste the configuration in the box.

The rest of the setup is the same as an ordinary cluster setup, except in the Security Options section. In Step 4: Security, under Permissions, choose Custom, and then choose the RestrictedRole that you created before.

Choose the appropriate subnets (these should meet the base requirement in order for a successful Active Directory join—see the Amazon EMR Management Guide for more details), and choose the appropriate security groups to make sure it talks to the Active Directory. Choose a key so that you can log in and configure the cluster.

Most importantly, choose the security configuration that you created earlier to enable Kerberos and EMRFS authorization for Amazon S3.

You can use the following AWS CLI command to create a cluster.

aws emr create-cluster --name "TestEMRFSAuthorization" \ 
--release-label emr-5.10.0 \ --instance-type m3.xlarge \ 
--instance-count 3 \ 
--ec2-attributes InstanceProfile=EMR_EC2_DefaultRole,KeyName=MyEC2KeyPair \ --service-role EMR_DefaultRole \ 
--security-configuration MyKerberosConfig \ 
--configurations file://hue-config.json \
--applications Name=Hadoop Name=Hive Name=Hue Name=Spark \ 
--kerberos-attributes Realm=EC2.INTERNAL, \ KdcAdminPassword=<YourClusterKDCAdminPassword>, \ ADDomainJoinUser=<YourADUserLogonName>,ADDomainJoinPassword=<YourADUserPassword>, \ 
CrossRealmTrustPrincipalPassword=<MatchADTrustPwd>

Note: If you create the cluster using CLI, you need to save the JSON configuration for Hue into a file named hue-config.json and place it on the server where you run the CLI command.

After the cluster gets into the Waiting state, try to connect by using SSH into the cluster using the Active Directory user name and password.

ssh -l [email protected] <EMR IP or DNS name>

Quickly run two commands to show that the Active Directory join is successful:

  1. id [user name] shows the mapped AD users and groups in Linux.
  2. hdfs groups [user name] shows the mapped group in Hadoop.

Both should return the current Active Directory user and group information if the setup is correct.

Now, you can test the user mapping first. Log in with the admin1 user, and run a Hadoop list directory command:

hadoop fs -ls s3://emrfs-auth-data-science-bucket-demo/

Now switch to a user from the data engineer group.

Retry the previous command to access the admin’s bucket. It should throw an Amazon S3 Access Denied exception.

When you try listing the Amazon S3 bucket that a data engineer group member has accessed, it triggers the group mapping.

hadoop fs -ls s3://emrfs-auth-data-engineering-bucket-demo/

It successfully returns the listing results. Next we will test Apache Hive and then Apache Spark.

 

To run jobs successfully, you need to create a home directory for every user in HDFS for staging data under /user/<username>. Users can configure a step to create a home directory at cluster launch time for every user who has access to the cluster. In this example, you use Hue since Hue will create the home directory in HDFS for the user at the first login. Here Hue also needs to be integrated with the same Active Directory as explained in the example configuration described earlier.

First, log in to Hue as a data engineer user, and open a Hive Notebook in Hue. Then run a query to create a new table pointing to the data engineer bucket, s3://emrfs-auth-data-engineering-bucket-demo/table1_data_eng/.

You can see that the table was created successfully. Now try to create another table pointing to the data science group’s bucket, where the data engineer group doesn’t have access.

It failed and threw an Amazon S3 Access Denied error.

Now insert one line of data into the successfully create table.

Next, log out, switch to a data science group user, and create another table, test2_datasci_tb.

The creation is successful.

The last task is to test Spark (it requires the user directory, but Hue created one in the previous step).

Now let’s come back to the command line and run some Spark commands.

Login to the master node using the datascientist1 user:

Start the SparkSQL interactive shell by typing spark-sql, and run the show tables command. It should list the tables that you created using Hive.

As a data science group user, try select on both tables. You will find that you can only select the table defined in the location that your group has access to.

Conclusion

EMRFS authorization for Amazon S3 enables you to have multiple roles on the same cluster, providing flexibility to configure a shared cluster for different teams to achieve better efficiency. The Active Directory integration and group mapping make it much easier for you to manage your users and groups, and provides better auditability in a multi-tenant environment.


Additional Reading

If you found this post useful, be sure to check out Use Kerberos Authentication to Integrate Amazon EMR with Microsoft Active Directory and Launching and Running an Amazon EMR Cluster inside a VPC.


About the Authors

Songzhi Liu is a Big Data Consultant with AWS Professional Services. He works closely with AWS customers to provide them Big Data & Machine Learning solutions and best practices on the Amazon cloud.

 

 

 

 

Reactive Microservices Architecture on AWS

Post Syndicated from Sascha Moellering original https://aws.amazon.com/blogs/architecture/reactive-microservices-architecture-on-aws/

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements.

Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven.

Being message driven is perhaps the most important characteristic of reactive systems. Asynchronous messaging helps in the design of loosely coupled systems, which is a key factor for scalability. In order to build a highly decoupled system, it is important to isolate services from each other. As already described, isolation is an important aspect of the microservices pattern. Indeed, reactive systems and microservices are a natural fit.

Implemented Use Case
This reference architecture illustrates a typical ad-tracking implementation.

Many ad-tracking companies collect massive amounts of data in near-real-time. In many cases, these workloads are very spiky and heavily depend on the success of the ad-tech companies’ customers. Typically, an ad-tracking-data use case can be separated into a real-time part and a non-real-time part. In the real-time part, it is important to collect data as fast as possible and ask several questions including:,  “Is this a valid combination of parameters?,””Does this program exist?,” “Is this program still valid?”

Because response time has a huge impact on conversion rate in advertising, it is important for advertisers to respond as fast as possible. This information should be kept in memory to reduce communication overhead with the caching infrastructure. The tracking application itself should be as lightweight and scalable as possible. For example, the application shouldn’t have any shared mutable state and it should use reactive paradigms. In our implementation, one main application is responsible for this real-time part. It collects and validates data, responds to the client as fast as possible, and asynchronously sends events to backend systems.

The non-real-time part of the application consumes the generated events and persists them in a NoSQL database. In a typical tracking implementation, clicks, cookie information, and transactions are matched asynchronously and persisted in a data store. The matching part is not implemented in this reference architecture. Many ad-tech architectures use frameworks like Hadoop for the matching implementation.

The system can be logically divided into the data collection partand the core data updatepart. The data collection part is responsible for collecting, validating, and persisting the data. In the core data update part, the data that is used for validation gets updated and all subscribers are notified of new data.

Components and Services

Main Application
The main application is implemented using Java 8 and uses Vert.x as the main framework. Vert.x is an event-driven, reactive, non-blocking, polyglot framework to implement microservices. It runs on the Java virtual machine (JVM) by using the low-level IO library Netty. You can write applications in Java, JavaScript, Groovy, Ruby, Kotlin, Scala, and Ceylon. The framework offers a simple and scalable actor-like concurrency model. Vert.x calls handlers by using a thread known as an event loop. To use this model, you have to write code known as “verticles.” Verticles share certain similarities with actors in the actor model. To use them, you have to implement the verticle interface. Verticles communicate with each other by generating messages in  a single event bus. Those messages are sent on the event bus to a specific address, and verticles can register to this address by using handlers.

With only a few exceptions, none of the APIs in Vert.x block the calling thread. Similar to Node.js, Vert.x uses the reactor pattern. However, in contrast to Node.js, Vert.x uses several event loops. Unfortunately, not all APIs in the Java ecosystem are written asynchronously, for example, the JDBC API. Vert.x offers a possibility to run this, blocking APIs without blocking the event loop. These special verticles are called worker verticles. You don’t execute worker verticles by using the standard Vert.x event loops, but by using a dedicated thread from a worker pool. This way, the worker verticles don’t block the event loop.

Our application consists of five different verticles covering different aspects of the business logic. The main entry point for our application is the HttpVerticle, which exposes an HTTP-endpoint to consume HTTP-requests and for proper health checking. Data from HTTP requests such as parameters and user-agent information are collected and transformed into a JSON message. In order to validate the input data (to ensure that the program exists and is still valid), the message is sent to the CacheVerticle.

This verticle implements an LRU-cache with a TTL of 10 minutes and a capacity of 100,000 entries. Instead of adding additional functionality to a standard JDK map implementation, we use Google Guava, which has all the features we need. If the data is not in the L1 cache, the message is sent to the RedisVerticle. This verticle is responsible for data residing in Amazon ElastiCache and uses the Vert.x-redis-client to read data from Redis. In our example, Redis is the central data store. However, in a typical production implementation, Redis would just be the L2 cache with a central data store like Amazon DynamoDB. One of the most important paradigms of a reactive system is to switch from a pull- to a push-based model. To achieve this and reduce network overhead, we’ll use Redis pub/sub to push core data changes to our main application.

Vert.x also supports direct Redis pub/sub-integration, the following code shows our subscriber-implementation:

vertx.eventBus().<JsonObject>consumer(REDIS_PUBSUB_CHANNEL_VERTX, received -> {

JsonObject value = received.body().getJsonObject("value");

String message = value.getString("message");

JsonObject jsonObject = new JsonObject(message);

eb.send(CACHE_REDIS_EVENTBUS_ADDRESS, jsonObject);

});

redis.subscribe(Constants.REDIS_PUBSUB_CHANNEL, res -> {

if (res.succeeded()) {

LOGGER.info("Subscribed to " + Constants.REDIS_PUBSUB_CHANNEL);

} else {

LOGGER.info(res.cause());

}

});

The verticle subscribes to the appropriate Redis pub/sub-channel. If a message is sent over this channel, the payload is extracted and forwarded to the cache-verticle that stores the data in the L1-cache. After storing and enriching data, a response is sent back to the HttpVerticle, which responds to the HTTP request that initially hit this verticle. In addition, the message is converted to ByteBuffer, wrapped in protocol buffers, and send to an Amazon Kinesis Data Stream.

The following example shows a stripped-down version of the KinesisVerticle:

public class KinesisVerticle extends AbstractVerticle {

private static final Logger LOGGER = LoggerFactory.getLogger(KinesisVerticle.class);

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";

@Override

public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

eventStream = System.getenv(STREAM_NAME) == null ? "EventStream" : System.getenv(STREAM_NAME);

eb.consumer(Constants.KINESIS_EVENTBUS_ADDRESS, message -> {

try {

TrackingMessage trackingMessage = Json.decodeValue((String)message.body(), TrackingMessage.class);

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);

message.reply("OK");

}

catch (KinesisException exc) {

LOGGER.error(exc);

}

});

}

Kinesis Consumer
This AWS Lambda function consumes data from an Amazon Kinesis Data Stream and persists the data in an Amazon DynamoDB table. In order to improve testability, the invocation code is separated from the business logic. The invocation code is implemented in the class KinesisConsumerHandler and iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to protocol buffers and converted into a Java object. Those Java objects are passed to the business logic, which persists the data in a DynamoDB table. In order to improve duration of successive Lambda calls, the DynamoDB-client is instantiated lazily and reused if possible.

Redis Updater
From time to time, it is necessary to update core data in Redis. A very efficient implementation for this requirement is using AWS Lambda and Amazon Kinesis. New core data is sent over the AWS Kinesis stream using JSON as data format and consumed by a Lambda function. This function iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to String and converted into a Java object. The Java object is passed to the business logic and stored in Redis. In addition, the new core data is also sent to the main application using Redis pub/sub in order to reduce network overhead and converting from a pull- to a push-based model.

The following example shows the source code to store data in Redis and notify all subscribers:

public void updateRedisData(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

Map<String, String> map = marshal(jsonString);

String statusCode = jedis.hmset(trackingMessage.getProgramId(), map);

}

catch (Exception exc) {

if (null == logger)

exc.printStackTrace();

else

logger.log(exc.getMessage());

}

}

public void notifySubscribers(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);

}

catch (final IOException e) {

log(e.getMessage(), logger);

}

}

Similarly to our Kinesis Consumer, the Redis-client is instantiated somewhat lazily.

Infrastructure as Code
As already outlined, latency and response time are a very critical part of any ad-tracking solution because response time has a huge impact on conversion rate. In order to reduce latency for customers world-wide, it is common practice to roll out the infrastructure in different AWS Regions in the world to be as close to the end customer as possible. AWS CloudFormation can help you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources that you want (for example, Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation takes care of provisioning and configuring those resources for you. Our reference architecture can be rolled out in different Regions using an AWS CloudFormation template, which sets up the complete infrastructure (for example, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS) cluster, Lambda functions, DynamoDB table, Amazon ElastiCache cluster, etc.).

Conclusion
In this blog post we described reactive principles and an example architecture with a common use case. We leveraged the capabilities of different frameworks in combination with several AWS services in order to implement reactive principles—not only at the application-level but also at the system-level. I hope I’ve given you ideas for creating your own reactive applications and systems on AWS.

About the Author

Sascha Moellering is a Senior Solution Architect. Sascha is primarily interested in automation, infrastructure as code, distributed computing, containers and JVM. He can be reached at [email protected]

 

 

Success at Apache: A Newbie’s Narrative

Post Syndicated from mikesefanov original https://yahooeng.tumblr.com/post/170536010891

yahoodevelopers:

Kuhu Shukla (bottom center) and team at the 2017 DataWorks Summit


By Kuhu Shukla

This post first appeared here on the Apache Software Foundation blog as part of ASF’s “Success at Apache” monthly blog series.

As I sit at my desk on a rather frosty morning with my coffee, looking up new JIRAs from the previous day in the Apache Tez project, I feel rather pleased. The latest community release vote is complete, the bug fixes that we so badly needed are in and the new release that we tested out internally on our many thousand strong cluster is looking good. Today I am looking at a new stack trace from a different Apache project process and it is hard to miss how much of the exceptional code I get to look at every day comes from people all around the globe. A contributor leaves a JIRA comment before he goes on to pick up his kid from soccer practice while someone else wakes up to find that her effort on a bug fix for the past two months has finally come to fruition through a binding +1.

Yahoo – which joined AOL, HuffPost, Tumblr, Engadget, and many more brands to form the Verizon subsidiary Oath last year – has been at the frontier of open source adoption and contribution since before I was in high school. So while I have no historical trajectories to share, I do have a story on how I found myself in an epic journey of migrating all of Yahoo jobs from Apache MapReduce to Apache Tez, a then-new DAG based execution engine.

Oath grid infrastructure is through and through driven by Apache technologies be it storage through HDFS, resource management through YARN, job execution frameworks with Tez and user interface engines such as Hive, Hue, Pig, Sqoop, Spark, Storm. Our grid solution is specifically tailored to Oath’s business-critical data pipeline needs using the polymorphic technologies hosted, developed and maintained by the Apache community.

On the third day of my job at Yahoo in 2015, I received a YouTube link on An Introduction to Apache Tez. I watched it carefully trying to keep up with all the questions I had and recognized a few names from my academic readings of Yarn ACM papers. I continued to ramp up on YARN and HDFS, the foundational Apache technologies Oath heavily contributes to even today. For the first few weeks I spent time picking out my favorite (necessary) mailing lists to subscribe to and getting started on setting up on a pseudo-distributed Hadoop cluster. I continued to find my footing with newbie contributions and being ever more careful with whitespaces in my patches. One thing was clear – Tez was the next big thing for us. By the time I could truly call myself a contributor in the Hadoop community nearly 80-90% of the Yahoo jobs were now running with Tez. But just like hiking up the Grand Canyon, the last 20% is where all the pain was. Being a part of the solution to this challenge was a happy prospect and thankfully contributing to Tez became a goal in my next quarter.

The next sprint planning meeting ended with me getting my first major Tez assignment – progress reporting. The progress reporting in Tez was non-existent – “Just needs an API fix,”  I thought. Like almost all bugs in this ecosystem, it was not easy. How do you define progress? How is it different for different kinds of outputs in a graph? The questions were many.

I, however, did not have to go far to get answers. The Tez community actively came to a newbie’s rescue, finding answers and posing important questions. I started attending the bi-weekly Tez community sync up calls and asking existing contributors and committers for course correction. Suddenly the team was much bigger, the goals much more chiseled. This was new to anyone like me who came from the networking industry, where the most open part of the code are the RFCs and the implementation details are often hidden. These meetings served as a clean room for our coding ideas and experiments. Ideas were shared, to the extent of which data structure we should pick and what a future user of Tez would take from it. In between the usual status updates and extensive knowledge transfers were made.

Oath uses Apache Pig and Apache Hive extensively and most of the urgent requirements and requests came from Pig and Hive developers and users. Each issue led to a community JIRA and as we started running Tez at Oath scale, new feature ideas and bugs around performance and resource utilization materialized. Every year most of the Hadoop team at Oath travels to the Hadoop Summit where we meet our cohorts from the Apache community and we stand for hours discussing the state of the art and what is next for the project. One such discussion set the course for the next year and a half for me.

We needed an innovative way to shuffle data. Frameworks like MapReduce and Tez have a shuffle phase in their processing lifecycle wherein the data from upstream producers is made available to downstream consumers. Even though Apache Tez was designed with a feature set corresponding to optimization requirements in Pig and Hive, the Shuffle Handler Service was retrofitted from MapReduce at the time of the project’s inception. With several thousands of jobs on our clusters leveraging these features in Tez, the Shuffle Handler Service became a clear performance bottleneck. So as we stood talking about our experience with Tez with our friends from the community, we decided to implement a new Shuffle Handler for Tez. All the conversation points were tracked now through an umbrella JIRA TEZ-3334 and the to-do list was long. I picked a few JIRAs and as I started reading through I realized, this is all new code I get to contribute to and review. There might be a better way to put this, but to be honest it was just a lot of fun! All the whiteboards were full, the team took walks post lunch and discussed how to go about defining the API. Countless hours were spent debugging hangs while fetching data and looking at stack traces and Wireshark captures from our test runs. Six months in and we had the feature on our sandbox clusters. There were moments ranging from sheer frustration to absolute exhilaration with high fives as we continued to address review comments and fixing big and small issues with this evolving feature.

As much as owning your code is valued everywhere in the software community, I would never go on to say “I did this!” In fact, “we did!” It is this strong sense of shared ownership and fluid team structure that makes the open source experience at Apache truly rewarding. This is just one example. A lot of the work that was done in Tez was leveraged by the Hive and Pig community and cross Apache product community interaction made the work ever more interesting and challenging. Triaging and fixing issues with the Tez rollout led us to hit a 100% migration score last year and we also rolled the Tez Shuffle Handler Service out to our research clusters. As of last year we have run around 100 million Tez DAGs with a total of 50 billion tasks over almost 38,000 nodes.

In 2018 as I move on to explore Hadoop 3.0 as our future release, I hope that if someone outside the Apache community is reading this, it will inspire and intrigue them to contribute to a project of their choice. As an astronomy aficionado, going from a newbie Apache contributor to a newbie Apache committer was very much like looking through my telescope - it has endless possibilities and challenges you to be your best.

About the Author:

Kuhu Shukla is a software engineer at Oath and did her Masters in Computer Science at North Carolina State University. She works on the Big Data Platforms team on Apache Tez, YARN and HDFS with a lot of talented Apache PMCs and Committers in Champaign, Illinois. A recent Apache Tez Committer herself she continues to contribute to YARN and HDFS and spoke at the 2017 Dataworks Hadoop Summit on “Tez Shuffle Handler: Shuffling At Scale With Apache Hadoop”. Prior to that she worked on Juniper Networks’ router and switch configuration APIs. She likes to participate in open source conferences and women in tech events. In her spare time she loves singing Indian classical and jazz, laughing, whale watching, hiking and peering through her Dobsonian telescope.

Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

Post Syndicated from Trevor Roberts original https://aws.amazon.com/blogs/big-data/simplify-querying-nested-json-with-the-aws-glue-relationalize-transform/

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods.

Let’s look at how Relationalize can help you with a sample use case.

An example of Relationalize in action

Suppose that the developers of a video game want to use a data warehouse like Amazon Redshift to run reports on player behavior based on data that is stored in JSON. Sample 1 shows example user data from the game. The player named “user1” has characteristics such as race, class, and location in nested JSON data. Further down, the player’s arsenal information includes additional nested JSON data. If the developers want to ETL this data into their data warehouse, they might have to resort to nested loops or recursive functions in their code.

Sample 1: Nested JSON

{
	"player": {
		"username": "user1",
		"characteristics": {
			"race": "Human",
			"class": "Warlock",
			"subclass": "Dawnblade",
			"power": 300,
			"playercountry": "USA"
		},
		"arsenal": {
			"kinetic": {
				"name": "Sweet Business",
				"type": "Auto Rifle",
				"power": 300,
				"element": "Kinetic"
			},
			"energy": {
				"name": "MIDA Mini-Tool",
				"type": "Submachine Gun",
				"power": 300,
				"element": "Solar"
			},
			"power": {
				"name": "Play of the Game",
				"type": "Grenade Launcher",
				"power": 300,
				"element": "Arc"
			}
		},
		"armor": {
			"head": "Eye of Another World",
			"arms": "Philomath Gloves",
			"chest": "Philomath Robes",
			"leg": "Philomath Boots",
			"classitem": "Philomath Bond"
		},
		"location": {
			"map": "Titan",
			"waypoint": "The Rig"
		}
	}
}

Instead, the developers can use the Relationalize transform. Sample 2 shows what the transformed data looks like.

Sample 2: Flattened JSON

{
    "player.username": "user1",
    "player.characteristics.race": "Human",
    "player.characteristics.class": "Warlock",
    "player.characteristics.subclass": "Dawnblade",
    "player.characteristics.power": 300,
    "player.characteristics.playercountry": "USA",
    "player.arsenal.kinetic.name": "Sweet Business",
    "player.arsenal.kinetic.type": "Auto Rifle",
    "player.arsenal.kinetic.power": 300,
    "player.arsenal.kinetic.element": "Kinetic",
    "player.arsenal.energy.name": "MIDA Mini-Tool",
    "player.arsenal.energy.type": "Submachine Gun",
    "player.arsenal.energy.power": 300,
    "player.arsenal.energy.element": "Solar",
    "player.arsenal.power.name": "Play of the Game",
    "player.arsenal.power.type": "Grenade Launcher",
    "player.arsenal.power.power": 300,
    "player.arsenal.power.element": "Arc",
    "player.armor.head": "Eye of Another World",
    "player.armor.arms": "Philomath Gloves",
    "player.armor.chest": "Philomath Robes",
    "player.armor.leg": "Philomath Boots",
    "player.armor.classitem": "Philomath Bond",
    "player.location.map": "Titan",
    "player.location.waypoint": "The Rig"
}

You can then write the data to a database or to a data warehouse. You can also write it to delimited text files, such as in comma-separated value (CSV) format, or columnar file formats such as Optimized Row Columnar (ORC) format. You can use either of these format types for long-term storage in Amazon S3. Storing the transformed files in S3 provides the additional benefit of being able to query this data using Amazon Athena or Amazon Redshift Spectrum. You can further extend the usefulness of the data by performing joins between data stored in S3 and the data stored in an Amazon Redshift data warehouse.

Before we get started…

In my example, I took two preparatory steps that save some time in your ETL code development:

  1. I stored my data in an Amazon S3 bucket and used an AWS Glue crawler to make my data available in the AWS Glue data catalog. You can find instructions on how to do that in Cataloging Tables with a Crawler in the AWS Glue documentation. The AWS Glue database name I used was “blog,” and the table name was “players.” You can see these values in use in the sample code that follows.
  2. I deployed a Zeppelin notebook using the automated deployment available within AWS Glue. If you already used an AWS Glue development endpoint to deploy a Zeppelin notebook, you can skip the deployment instructions. Otherwise, let’s quickly review how to deploy Zeppelin.

Deploying a Zeppelin notebook with AWS Glue

The following steps are outlined in the AWS Glue documentation, and I include a few screenshots here for clarity.

First, create two IAM roles:

Next, in the AWS Glue Management Console, choose Dev endpoints, and then choose Add endpoint.

Specify a name for the endpoint and the AWS Glue IAM role that you created.

On the networking screen, choose Skip Networking because our code only communicates with S3.

Complete the development endpoint process by providing a Secure Shell (SSH) public key and confirming your settings.

When your new development endpoint’s Provisioning status changes from PROVISIONING to READY, choose your endpoint, and then for Actions choose Create notebook server.

Enter the notebook server details, including the role you previously created and a security group with inbound access allowed on TCP port 443.

Doing this automatically launches an AWS CloudFormation template. The output specifies the URL that you can use to access your Zeppelin notebook with the username and password you specified in the wizard.

How do we flatten nested JSON?

With my data loaded and my notebook server ready, I accessed Zeppelin, created a new note, and set my interpreter to spark. I used some Python code that AWS Glue previously generated for another job that outputs to ORC. Then I added the Relationalize transform. You can see the resulting Python code in Sample 3.­

Sample 3: Python code to transform the nested JSON and output it to ORC

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
#from awsglue.transforms import Relationalize

# Begin variables to customize with your information
glue_source_database = "blog"
glue_source_table = "players"
glue_temp_storage = "s3://blog-example-edz/temp"
glue_relationalize_output_s3_path = "s3://blog-example-edz/output-flat"
dfc_root_table_name = "root" #default value is "roottable"
# End variables to customize with your information

glueContext = GlueContext(spark.sparkContext)
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = glue_source_database, table_name = glue_source_table, transformation_ctx = "datasource0")
dfc = Relationalize.apply(frame = datasource0, staging_path = glue_temp_storage, name = dfc_root_table_name, transformation_ctx = "dfc")
blogdata = dfc.select(dfc_root_table_name)
blogdataoutput = glueContext.write_dynamic_frame.from_options(frame = blogdata, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path}, format = "orc", transformation_ctx = "blogdataoutput")

What exactly is going on in this script?

After the import statements, we instantiate a GlueContext object, which allows us to work with the data in AWS Glue. Next, we create a DynamicFrame (datasource0) from the “players” table in the AWS Glue “blog” database. We use this DynamicFrame to perform any necessary operations on the data structure before it’s written to our desired output format. The source files remain unchanged.

We then run the Relationalize transform (Relationalize.apply()) with our datasource0 as one of the parameters. Another important parameter is the name parameter, which is a key that identifies our data after the transformation completes.

The Relationalize.apply() method returns a DynamicFrameCollection, and this is stored in the dfc variable. Before we can write our data to S3, we need to select the DynamicFrame from the DynamicFrameCollection object. We do this with the dfc.select() method. The correct DynamicFrame is stored in the blogdata variable.

You might be curious why a DynamicFrameCollection was returned when we started with a single DynamicFrame. This return value comes from the way Relationalize treats arrays in the JSON document: A DynamicFrame is created for each array. Together with the root data structure, each generated DynamicFrame is added to a DynamicFrameCollection when Relationalize completes its work. Although we didn’t have any arrays in our data, it’s good to keep this in mind. Finally, we output (blogdataoutput) the root DynamicFrame to ORC files in S3.

Using the transformed data

One of the use cases we discussed earlier was using Amazon Athena or Amazon Redshift Spectrum to query the ORC files.

I used the following SQL DDL statements to create external tables in both services to enable queries of my data stored in Amazon S3.

Sample 4: Amazon Athena DDL

CREATE EXTERNAL TABLE IF NOT EXISTS blog.blog_data_athena_test (
  `characteristics_race` string,
  `characteristics_class` string,
  `characteristics_subclass` string,
  `characteristics_power` int,
  `characteristics_playercountry` string,
  `kinetic_name` string,
  `kinetic_type` string,
  `kinetic_power` int,
  `kinetic_element` string,
  `energy_name` string,
  `energy_type` string,
  `energy_power` int,
  `energy_element` string,
  `power_name` string,
  `power_type` string,
  `power_power` int,
  `power_element` string,
  `armor_head` string,
  `armor_arms` string,
  `armor_chest` string,
  `armor_leg` string,
  `armor_classitem` string,
  `map` string,
  `waypoint` string 
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
WITH SERDEPROPERTIES (
  'serialization.format' = '1'
) LOCATION 's3://blog-example-edz/output-flat/'
TBLPROPERTIES ('has_encrypted_data'='false');

 

Sample 5: Amazon Redshift Spectrum DDL

-- Create a Schema
-- A single schema can be used with multiple external tables.
-- This step is only required once for the external tables you create.
create external schema spectrum 
from data catalog 
database 'blog' 
iam_role 'arn:aws:iam::0123456789:role/redshift-role'
create external database if not exists;

-- Create an external table in the schema
create external table spectrum.blog(
  username VARCHAR,
  characteristics_race VARCHAR,
  characteristics_class VARCHAR,
  characteristics_subclass VARCHAR,
  characteristics_power INTEGER,
  characteristics_playercountry VARCHAR,
  kinetic_name VARCHAR,
  kinetic_type VARCHAR,
  kinetic_power INTEGER,
  kinetic_element VARCHAR,
  energy_name VARCHAR,
  energy_type VARCHAR,
  energy_power INTEGER,
  energy_element VARCHAR,
  power_name VARCHAR,
  power_type VARCHAR,
  power_power INTEGER,
  power_element VARCHAR,
  armor_head VARCHAR,
  armor_arms VARCHAR,
  armor_chest VARCHAR,
  armor_leg VARCHAR,
  armor_classItem VARCHAR,
  map VARCHAR,
  waypoint VARCHAR)
stored as orc
location 's3://blog-example-edz/output-flat';

I even ran a query, shown in Sample 6, that joined my Redshift Spectrum table (spectrum.playerdata) with data in an Amazon Redshift table (public.raids) to generate advanced reports. In the where clause, I join the two tables based on the username values that are common to both data sources.

Sample 6: Select statement with a join of Redshift Spectrum data with Amazon Redshift data

-- Get Total Raid Completions for the Hunter Class.
select spectrum.playerdata.characteristics_class as class, sum(public.raids."completions.val.raids.leviathan") as "Total Hunter Leviathan Raid Completions" from spectrum.playerdata, public.raids
where spectrum.playerdata.username = public.raids."completions.val.username"
and spectrum.playerdata.characteristics_class = 'Hunter'
group by spectrum.playerdata.characteristics_class;

Summary

This post demonstrated how simple it can be to flatten nested JSON data with AWS Glue, using the Relationalize transform to automate the conversion of nested JSON. AWS Glue also automates the deployment of Zeppelin notebooks that you can use to develop your Python automation script. Finally, AWS Glue can output the transformed data directly to a relational database, or to files in Amazon S3 for further analysis with tools such as Amazon Athena and Amazon Redshift Spectrum.

As great as Relationalize is, it’s not the only transform available with AWS Glue. You can see a complete list of available transforms in Built-In Transforms in the AWS Glue documentation. Try them out today!


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena and AWS Glue with Node.js in Production and Build a Data Lake Foundation with AWS Glue and Amazon S3.


About the Author

Trevor Roberts Jr is a Solutions Architect with AWS. He provides architectural guidance to help customers achieve success in the cloud. In his spare time, Trevor enjoys traveling to new places and spending time with family.

Tableau 10.4 Supports Amazon Redshift Spectrum with External Amazon S3 Tables

Post Syndicated from Robin Cottiss original https://aws.amazon.com/blogs/big-data/tableau-10-4-supports-amazon-redshift-spectrum-with-external-amazon-s3-tables/

This is a guest post by Robin Cottiss, strategic customer consultant, Russell Christopher, staff product manager, and Vaidy Krishnan, senior manager of product marketing, at Tableau. Tableau, in their own words, “helps anyone quickly analyze, visualize, and share information. More than 61,000 customer accounts get rapid results with Tableau in the office and on the go. Over 300,000 people use Tableau Public to share public data in their blogs and websites.”

We’re excited to announce today an update to our Amazon Redshift connector with support for Amazon Redshift Spectrum to analyze data in external Amazon S3 tables. This feature, the direct result of joint engineering and testing work performed by the teams at Tableau and AWS, was released as part of Tableau 10.3.3 and will be available broadly in Tableau 10.4.1. With this update, you can quickly and directly connect Tableau to data in Amazon Redshift and analyze it in conjunction with data in Amazon S3—all with drag-and-drop ease.

This connector is yet another in a series of market-leading integrations of Tableau with AWS’s analytics platform, with services such as Amazon Redshift, Amazon EMR, and Amazon Athena. These integrations have allowed Tableau to become the natural choice of tool for analyzing data stored on AWS. Beyond this, Tableau Server runs seamlessly in the AWS Cloud infrastructure. If you prefer to deploy all your applications inside AWS, you have a complete solution offering from Tableau.

How does support for Amazon Redshift Spectrum help you?

If you’re like many Tableau customers, you have large buckets of data stored in Amazon S3. You might need to access this data frequently and store it in a consistent, highly structured format. If so, you can provision it to a data warehouse like Amazon Redshift. You might also want to explore this S3 data on an ad hoc basis. For example, you might want to determine whether or not to provision the data, and where—options might be Hadoop, Impala, Amazon EMR, or Amazon Redshift. To do so, you can use Amazon Athena, a serverless interactive query service from AWS that requires no infrastructure setup and management.

But what if you want to analyze both the frequently accessed data stored locally in Amazon Redshift AND your full datasets stored cost-effectively in Amazon S3? What if you want the throughput of disk and sophisticated query optimization of Amazon Redshift AND a service that combines a serverless scale-out processing capability with the massively reliable and scalable S3 infrastructure? What if you want the super-fast performance of Amazon Redshift AND support for open storage formats (for example, Parquet or ORC) in S3?

To enable these AND and resolve the tyranny of ORs, AWS launched Amazon Redshift Spectrum earlier this year.

Amazon Redshift Spectrum gives you the freedom to store your data where you want, in the format you want, and have it available for processing when you need it. Since the Amazon Redshift Spectrum launch, Tableau has worked tirelessly to provide best-in-class support for this new service. With Tableau and Redshift Spectrum, you can extend your Amazon Redshift analyses out to the entire universe of data in your S3 data lakes.

This latest update has been tested by many customers with very positive feedback. One such customer is the world’s largest food product distributor, Sysco—you can watch their session referencing the Amazon Spectrum integration at Tableau Conference 2017. Sysco also plans to reprise its “Tableau on AWS” story again in a month’s time at AWS re:Invent.

Now, I’d like to use a concrete example to demonstrate how Tableau works with Amazon Redshift Spectrum. In this example, I also show you how and why you might want to connect to your AWS data in different ways.

The setup

I use the pipeline described following to ingest, process, and analyze data with Tableau on an AWS stack. The source data is the New York City Taxi dataset, which has 9 years’ worth of taxi rides activity (including pick-up and drop-off location, amount paid, payment type, and so on) captured in 1.2 billion records.

In this pipeline, this data lands in S3, is cleansed and partitioned by using Amazon EMR, and is then converted to a columnar Parquet format that is analytically optimized. You can point Tableau to the raw data in S3 by using Amazon Athena. You can also access the cleansed data with Tableau using Presto through your Amazon EMR cluster.

Why use Tableau this early in the pipeline? Because sometimes you want to understand what’s there and what questions are worth asking before you even start the analysis.

After you find out what those questions are and determine if this sort of analysis has long-term usefulness, you can automate and optimize that pipeline. You do this to add new data as soon as possible as it arrives, to get it to the processes and people that need it. You might also want to provision this data to a highly performant “hotter” layer (Amazon Redshift or Tableau Extract) for repeated access.

In the illustration preceding, S3 contains the raw denormalized ride data at the timestamp level of granularity. This S3 data is the fact table. Amazon Redshift has the time dimensions broken out by date, month, and year, and also has the taxi zone information.

Now imagine I want to know where and when taxi pickups happen on a certain date in a certain borough. With support for Amazon Redshift Spectrum, I can now join the S3 tables with the Amazon Redshift dimensions, as shown following.

I can next analyze the data in Tableau to produce a borough-by-borough view of New York City ride density on Christmas Day 2015.

Or I can hone in on just Manhattan and identify pickup hotspots, with ride charges way above the average!

With Amazon Redshift Spectrum, you now have a fast, cost-effective engine that minimizes data processed with dynamic partition pruning. You can further improve query performance by reducing the data scanned. You do this by partitioning and compressing data and by using a columnar format for storage.

At the end of the day, which engine you use behind Tableau is a function of what you want to optimize for. Some possible engines are Amazon Athena, Amazon Redshift, and Redshift Spectrum, or you can bring a subset of data into Tableau Extract. Factors in planning optimization include these:

  • Are you comfortable with the serverless cost model of Amazon Athena and potential full scans? Or do you prefer the advantages of no setup?
  • Do you want the throughput of local disk?
  • Effort and time of setup. Are you okay with the lead-time of an Amazon Redshift cluster setup, as opposed to just bringing everything into Tableau Extract?

To meet the many needs of our customers, Tableau’s approach is simple: It’s all about choice. The choice of how you want to connect to and analyze your data. Throughout the history of our product and into the future, we have and will continue to empower choice for customers.

For more on how to deal with choice, as you go about making architecture decisions for your enterprise, watch this big data strategy session my friend Robin Cottiss and I delivered at Tableau Conference 2017. This session includes several customer examples leveraging the Tableau on AWS platform, and also a run-through of the aforementioned demonstration.

If you’re curious to learn more about analyzing data with Tableau on Amazon Redshift we encourage you to check out the following resources:

Hot Startups on AWS – October 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/hot-startups-on-aws-october-2017/

In 2015, the Centers for Medicare and Medicaid Services (CMS) reported that healthcare spending made up 17.8% of the U.S. GDP – that’s almost $3.2 trillion or $9,990 per person. By 2025, the CMS estimates this number will increase to nearly 20%. As cloud technology evolves in the healthcare and life science industries, we are seeing how companies of all sizes are using AWS to provide powerful and innovative solutions to customers across the globe. This month we are excited to feature the following startups:

  • ClearCare – helping home care agencies operate efficiently and grow their business.
  • DNAnexus – providing a cloud-based global network for sharing and managing genomic data.

ClearCare (San Francisco, CA)

ClearCare envisions a future where home care is the only choice for aging in place. Home care agencies play a critical role in the economy and their communities by significantly lowering the overall cost of care, reducing the number of hospital admissions, and bending the cost curve of aging. Patients receiving home care typically have multiple chronic conditions and functional limitations, driving over $190 billion in healthcare spending in the U.S. each year. To offset these costs, health insurance payers are developing in-home care management programs for patients. ClearCare’s goal is to help home care agencies leverage technology to improve costs, outcomes, and quality of life for the aging population. The company’s powerful software platform is specifically designed for use by non-medical, in-home care agencies to manage their businesses.

Founder and CEO Geoff Nudd created ClearCare because of his own grandmother’s need for care. Keeping family members and caregivers up to date on a loved one’s well being can be difficult, so Geoff created what is now ClearCare’s Family Room, which enables caregivers and agency staff to check schedules and receive real-time updates about what’s happening in the home. Since then, agencies have provided feedback on others areas of their businesses that could be streamlined. ClearCare has now built over 20 modules to help home care agencies optimize operations with services including a telephony service, billing and payroll, and more. ClearCare now serves over 4,000 home care agencies, representing 500,000 caregivers and 400,000 seniors.

Using AWS, ClearCare is able to spin up reliable infrastructure for proofs of concept and iterate on those systems to quickly get value to market. The company runs many AWS services including Amazon Elasticsearch Service, Amazon RDS, and Amazon CloudFront. Amazon EMR and Amazon Athena have enabled ClearCare to build a Hadoop-based ETL and data warehousing system that processes terabytes of data each day. By utilizing these managed services, ClearCare has been able to go from concept to customer delivery in less than three months.

To learn more about ClearCare, check out their website.

DNAnexus (Mountain View, CA)

DNAnexus is accelerating the application of genomic data in precision medicine by providing a cloud-based platform for sharing and managing genomic and biomedical data and analysis tools. The company was founded in 2009 by Stanford graduate student Andreas Sundquist and two Stanford professors Arend Sidow and Serafim Batzoglou, to address the need for scaling secondary analysis of next-generation sequencing (NGS) data in the cloud. The founders quickly learned that users needed a flexible solution to build complex analysis workflows and tools that enable them to share and manage large volumes of data. DNAnexus is optimized to address the challenges of security, scalability, and collaboration for organizations that are pursuing genomic-based approaches to health, both in clinics and research labs. DNAnexus has a global customer base – spanning North America, Europe, Asia-Pacific, South America, and Africa – that runs a million jobs each month and is doubling their storage year-over-year. The company currently stores more than 10 petabytes of biomedical and genomic data. That is equivalent to approximately 100,000 genomes, or in simpler terms, over 50 billion Facebook photos!

DNAnexus is working with its customers to help expand their translational informatics research, which includes expanding into clinical trial genomic services. This will help companies developing different medicines to better stratify clinical trial populations and develop companion tests that enable the right patient to get the right medicine. In collaboration with Janssen Human Microbiome Institute, DNAnexus is also launching Mosaic – a community platform for microbiome research.

AWS provides DNAnexus and its customers the flexibility to grow and scale research programs. Building the technology infrastructure required to manage these projects in-house is expensive and time-consuming. DNAnexus removes that barrier for labs of any size by using AWS scalable cloud resources. The company deploys its customers’ genomic pipelines on Amazon EC2, using Amazon S3 for high-performance, high-durability storage, and Amazon Glacier for low-cost data archiving. DNAnexus is also an AWS Life Sciences Competency Partner.

Learn more about DNAnexus here.

-Tina

Predict Billboard Top 10 Hits Using RStudio, H2O and Amazon Athena

Post Syndicated from Gopal Wunnava original https://aws.amazon.com/blogs/big-data/predict-billboard-top-10-hits-using-rstudio-h2o-and-amazon-athena/

Success in the popular music industry is typically measured in terms of the number of Top 10 hits artists have to their credit. The music industry is a highly competitive multi-billion dollar business, and record labels incur various costs in exchange for a percentage of the profits from sales and concert tickets.

Predicting the success of an artist’s release in the popular music industry can be difficult. One release may be extremely popular, resulting in widespread play on TV, radio and social media, while another single may turn out quite unpopular, and therefore unprofitable. Record labels need to be selective in their decision making, and predictive analytics can help them with decision making around the type of songs and artists they need to promote.

In this walkthrough, you leverage H2O.ai, Amazon Athena, and RStudio to make predictions on whether a song might make it to the Top 10 Billboard charts. You explore the GLM, GBM, and deep learning modeling techniques using H2O’s rapid, distributed and easy-to-use open source parallel processing engine. RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. This is ideal if you don’t want to connect to a server via SSH and use code editors such as vi to do analytics. RStudio is available in a desktop version, or a server version that allows you to access R via a web browser. RStudio’s Notebooks feature is used to demonstrate the execution of code and output. In addition, this post showcases how you can leverage Athena for query and interactive analysis during the modeling phase. A working knowledge of statistics and machine learning would be helpful to interpret the analysis being performed in this post.

Walkthrough

Your goal is to predict whether a song will make it to the Top 10 Billboard charts. For this purpose, you will be using multiple modeling techniques―namely GLM, GBM and deep learning―and choose the model that is the best fit.

This solution involves the following steps:

  • Install and configure RStudio with Athena
  • Log in to RStudio
  • Install R packages
  • Connect to Athena
  • Create a dataset
  • Create models

Install and configure RStudio with Athena

Use the following AWS CloudFormation stack to install, configure, and connect RStudio on an Amazon EC2 instance with Athena.

Launching this stack creates all required resources and prerequisites:

  • Amazon EC2 instance with Amazon Linux (minimum size of t2.large is recommended)
  • Provisioning of the EC2 instance in an existing VPC and public subnet
  • Installation of Java 8
  • Assignment of an IAM role to the EC2 instance with the required permissions for accessing Athena and Amazon S3
  • Security group allowing access to the RStudio and SSH ports from the internet (I recommend restricting access to these ports)
  • S3 staging bucket required for Athena (referenced within RStudio as ATHENABUCKET)
  • RStudio username and password
  • Setup logs in Amazon CloudWatch Logs (if needed for additional troubleshooting)
  • Amazon EC2 Systems Manager agent, which makes it easy to manage and patch

All AWS resources are created in the US-East-1 Region. To avoid cross-region data transfer fees, launch the CloudFormation stack in the same region. To check the availability of Athena in other regions, see Region Table.

Log in to RStudio

The instance security group has been automatically configured to allow incoming connections on the RStudio port 8787 from any source internet address. You can edit the security group to restrict source IP access. If you have trouble connecting, ensure that port 8787 isn’t blocked by subnet network ACLS or by your outgoing proxy/firewall.

  1. In the CloudFormation stack, choose Outputs, Value, and then open the RStudio URL. You might need to wait for a few minutes until the instance has been launched.
  2. Log in to RStudio with the and password you provided during setup.

Install R packages

Next, install the required R packages from the RStudio console. You can download the R notebook file containing just the code.

#install pacman – a handy package manager for managing installs
if("pacman" %in% rownames(installed.packages()) == FALSE)
{install.packages("pacman")}  
library(pacman)
p_load(h2o,rJava,RJDBC,awsjavasdk)
h2o.init(nthreads = -1)
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         2 hours 42 minutes 
##     H2O cluster version:        3.10.4.6 
##     H2O cluster version age:    4 months and 4 days !!! 
##     H2O cluster name:           H2O_started_from_R_rstudio_hjx881 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   3.30 GB 
##     H2O cluster total cores:    4 
##     H2O cluster allowed cores:  4 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     R Version:                  R version 3.3.3 (2017-03-06)
## Warning in h2o.clusterInfo(): 
## Your H2O cluster version is too old (4 months and 4 days)!
## Please download and install the latest version from http://h2o.ai/download/
#install aws sdk if not present (pre-requisite for using Athena with an IAM role)
if (!aws_sdk_present()) {
  install_aws_sdk()
}

load_sdk()
## NULL

Connect to Athena

Next, establish a connection to Athena from RStudio, using an IAM role associated with your EC2 instance. Use ATHENABUCKET to specify the S3 staging directory.

URL <- 'https://s3.amazonaws.com/athena-downloads/drivers/AthenaJDBC41-1.0.1.jar'
fil <- basename(URL)
#download the file into current working directory
if (!file.exists(fil)) download.file(URL, fil)
#verify that the file has been downloaded successfully
list.files()
## [1] "AthenaJDBC41-1.0.1.jar"
drv <- JDBC(driverClass="com.amazonaws.athena.jdbc.AthenaDriver", fil, identifier.quote="'")

con <- jdbcConnection <- dbConnect(drv, 'jdbc:awsathena://athena.us-east-1.amazonaws.com:443/',
                                   s3_staging_dir=Sys.getenv("ATHENABUCKET"),
                                   aws_credentials_provider_class="com.amazonaws.auth.DefaultAWSCredentialsProviderChain")

Verify the connection. The results returned depend on your specific Athena setup.

con
## <JDBCConnection>
dbListTables(con)
##  [1] "gdelt"               "wikistats"           "elb_logs_raw_native"
##  [4] "twitter"             "twitter2"            "usermovieratings"   
##  [7] "eventcodes"          "events"              "billboard"          
## [10] "billboardtop10"      "elb_logs"            "gdelthist"          
## [13] "gdeltmaster"         "twitter"             "twitter3"

Create a dataset

For this analysis, you use a sample dataset combining information from Billboard and Wikipedia with Echo Nest data in the Million Songs Dataset. Upload this dataset into your own S3 bucket. The table below provides a description of the fields used in this dataset.

Field Description
yearYear that song was released
songtitleTitle of the song
artistnameName of the song artist
songidUnique identifier for the song
artistidUnique identifier for the song artist
timesignatureVariable estimating the time signature of the song
timesignature_confidenceConfidence in the estimate for the timesignature
loudnessContinuous variable indicating the average amplitude of the audio in decibels
tempoVariable indicating the estimated beats per minute of the song
tempo_confidenceConfidence in the estimate for tempo
keyVariable with twelve levels indicating the estimated key of the song (C, C#, B)
key_confidenceConfidence in the estimate for key
energyVariable that represents the overall acoustic energy of the song, using a mix of features such as loudness
pitchContinuous variable that indicates the pitch of the song
timbre_0_min thru timbre_11_minVariables that indicate the minimum values over all segments for each of the twelve values in the timbre vector
timbre_0_max thru timbre_11_maxVariables that indicate the maximum values over all segments for each of the twelve values in the timbre vector
top10Indicator for whether or not the song made it to the Top 10 of the Billboard charts (1 if it was in the top 10, and 0 if not)

Create an Athena table based on the dataset

In the Athena console, select the default database, sampled, or create a new database.

Run the following create table statement.

create external table if not exists billboard
(
year int,
songtitle string,
artistname string,
songID string,
artistID string,
timesignature int,
timesignature_confidence double,
loudness double,
tempo double,
tempo_confidence double,
key int,
key_confidence double,
energy double,
pitch double,
timbre_0_min double,
timbre_0_max double,
timbre_1_min double,
timbre_1_max double,
timbre_2_min double,
timbre_2_max double,
timbre_3_min double,
timbre_3_max double,
timbre_4_min double,
timbre_4_max double,
timbre_5_min double,
timbre_5_max double,
timbre_6_min double,
timbre_6_max double,
timbre_7_min double,
timbre_7_max double,
timbre_8_min double,
timbre_8_max double,
timbre_9_min double,
timbre_9_max double,
timbre_10_min double,
timbre_10_max double,
timbre_11_min double,
timbre_11_max double,
Top10 int
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://aws-bigdata-blog/artifacts/predict-billboard/data'
;

Inspect the table definition for the ‘billboard’ table that you have created. If you chose a database other than sampledb, replace that value with your choice.

dbGetQuery(con, "show create table sampledb.billboard")
##                                      createtab_stmt
## 1       CREATE EXTERNAL TABLE `sampledb.billboard`(
## 2                                       `year` int,
## 3                               `songtitle` string,
## 4                              `artistname` string,
## 5                                  `songid` string,
## 6                                `artistid` string,
## 7                              `timesignature` int,
## 8                `timesignature_confidence` double,
## 9                                `loudness` double,
## 10                                  `tempo` double,
## 11                       `tempo_confidence` double,
## 12                                       `key` int,
## 13                         `key_confidence` double,
## 14                                 `energy` double,
## 15                                  `pitch` double,
## 16                           `timbre_0_min` double,
## 17                           `timbre_0_max` double,
## 18                           `timbre_1_min` double,
## 19                           `timbre_1_max` double,
## 20                           `timbre_2_min` double,
## 21                           `timbre_2_max` double,
## 22                           `timbre_3_min` double,
## 23                           `timbre_3_max` double,
## 24                           `timbre_4_min` double,
## 25                           `timbre_4_max` double,
## 26                           `timbre_5_min` double,
## 27                           `timbre_5_max` double,
## 28                           `timbre_6_min` double,
## 29                           `timbre_6_max` double,
## 30                           `timbre_7_min` double,
## 31                           `timbre_7_max` double,
## 32                           `timbre_8_min` double,
## 33                           `timbre_8_max` double,
## 34                           `timbre_9_min` double,
## 35                           `timbre_9_max` double,
## 36                          `timbre_10_min` double,
## 37                          `timbre_10_max` double,
## 38                          `timbre_11_min` double,
## 39                          `timbre_11_max` double,
## 40                                     `top10` int)
## 41                             ROW FORMAT DELIMITED 
## 42                         FIELDS TERMINATED BY ',' 
## 43                            STORED AS INPUTFORMAT 
## 44       'org.apache.hadoop.mapred.TextInputFormat' 
## 45                                     OUTPUTFORMAT 
## 46  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
## 47                                        LOCATION
## 48    's3://aws-bigdata-blog/artifacts/predict-billboard/data'
## 49                                  TBLPROPERTIES (
## 50            'transient_lastDdlTime'='1505484133')

Run a sample query

Next, run a sample query to obtain a list of all songs from Janet Jackson that made it to the Billboard Top 10 charts.

dbGetQuery(con, " SELECT songtitle,artistname,top10   FROM sampledb.billboard WHERE lower(artistname) =     'janet jackson' AND top10 = 1")
##                       songtitle    artistname top10
## 1                       Runaway Janet Jackson     1
## 2               Because Of Love Janet Jackson     1
## 3                         Again Janet Jackson     1
## 4                            If Janet Jackson     1
## 5  Love Will Never Do (Without You) Janet Jackson 1
## 6                     Black Cat Janet Jackson     1
## 7               Come Back To Me Janet Jackson     1
## 8                       Alright Janet Jackson     1
## 9                      Escapade Janet Jackson     1
## 10                Rhythm Nation Janet Jackson     1

Determine how many songs in this dataset are specifically from the year 2010.

dbGetQuery(con, " SELECT count(*)   FROM sampledb.billboard WHERE year = 2010")
##   _col0
## 1   373

The sample dataset provides certain song properties of interest that can be analyzed to gauge the impact to the song’s overall popularity. Look at one such property, timesignature, and determine the value that is the most frequent among songs in the database. Timesignature is a measure of the number of beats and the type of note involved.

Running the query directly may result in an error, as shown in the commented lines below. This error is a result of trying to retrieve a large result set over a JDBC connection, which can cause out-of-memory issues at the client level. To address this, reduce the fetch size and run again.

#t<-dbGetQuery(con, " SELECT timesignature FROM sampledb.billboard")
#Note:  Running the preceding query results in the following error: 
#Error in .jcall(rp, "I", "fetch", stride, block): java.sql.SQLException: The requested #fetchSize is more than the allowed value in Athena. Please reduce the fetchSize and try #again. Refer to the Athena documentation for valid fetchSize values.
# Use the dbSendQuery function, reduce the fetch size, and run again
r <- dbSendQuery(con, " SELECT timesignature     FROM sampledb.billboard")
dftimesignature<- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
table(dftimesignature)
## dftimesignature
##    0    1    3    4    5    7 
##   10  143  503 6787  112   19
nrow(dftimesignature)
## [1] 7574

From the results, observe that 6787 songs have a timesignature of 4.

Next, determine the song with the highest tempo.

dbGetQuery(con, " SELECT songtitle,artistname,tempo   FROM sampledb.billboard WHERE tempo = (SELECT max(tempo) FROM sampledb.billboard) ")
##                   songtitle      artistname   tempo
## 1 Wanna Be Startin' Somethin' Michael Jackson 244.307

Create the training dataset

Your model needs to be trained such that it can learn and make accurate predictions. Split the data into training and test datasets, and create the training dataset first.  This dataset contains all observations from the year 2009 and earlier. You may face the same JDBC connection issue pointed out earlier, so this query uses a fetch size.

#BillboardTrain <- dbGetQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
#Running the preceding query results in the following error:-
#Error in .verify.JDBC.result(r, "Unable to retrieve JDBC result set for ", : Unable to retrieve #JDBC result set for SELECT * FROM sampledb.billboard WHERE year <= 2009 (Internal error)
#Follow the same approach as before to address this issue.

r <- dbSendQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
BillboardTrain <- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
BillboardTrain[1:2,c(1:3,6:10)]
##   year           songtitle artistname timesignature
## 1 2009 The Awkward Goodbye    Athlete             3
## 2 2009        Rubik's Cube    Athlete             3
##   timesignature_confidence loudness   tempo tempo_confidence
## 1                    0.732   -6.320  89.614   0.652
## 2                    0.906   -9.541 117.742   0.542
nrow(BillboardTrain)
## [1] 7201

Create the test dataset

BillboardTest <- dbGetQuery(con, "SELECT * FROM sampledb.billboard where year = 2010")
BillboardTest[1:2,c(1:3,11:15)]
##   year              songtitle        artistname key
## 1 2010 This Is the House That Doubt Built A Day to Remember  11
## 2 2010        Sticks & Bricks A Day to Remember  10
##   key_confidence    energy pitch timbre_0_min
## 1          0.453 0.9666556 0.024        0.002
## 2          0.469 0.9847095 0.025        0.000
nrow(BillboardTest)
## [1] 373

Convert the training and test datasets into H2O dataframes

train.h2o <- as.h2o(BillboardTrain)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%
test.h2o <- as.h2o(BillboardTest)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%

Inspect the column names in your H2O dataframes.

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"

Create models

You need to designate the independent and dependent variables prior to applying your modeling algorithms. Because you’re trying to predict the ‘top10’ field, this would be your dependent variable and everything else would be independent.

Create your first model using GLM. Because GLM works best with numeric data, you create your model by dropping non-numeric variables. You only use the variables in the dataset that describe the numerical attributes of the song in the logistic regression model. You won’t use these variables:  “year”, “songtitle”, “artistname”, “songid”, or “artistid”.

y.dep <- 39
x.indep <- c(6:38)
x.indep
##  [1]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [24] 29 30 31 32 33 34 35 36 37 38

Create Model 1: All numeric variables

Create Model 1 with the training dataset, using GLM as the modeling algorithm and H2O’s built-in h2o.glm function.

modelh1 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=====                                                            |   8%
  |                                                                       
  |=================================================================| 100%

Measure the performance of Model 1, using H2O’s built-in performance function.

h2o.performance(model=modelh1,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09924684
## RMSE:  0.3150347
## LogLoss:  0.3220267
## Mean Per-Class Error:  0.2380168
## AUC:  0.8431394
## Gini:  0.6862787
## R^2:  0.254663
## Null Deviance:  326.0801
## Residual Deviance:  240.2319
## AIC:  308.2319
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0   1    Error     Rate
## 0      255  59 0.187898  =59/314
## 1       17  42 0.288136   =17/59
## Totals 272 101 0.203753  =76/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.192772 0.525000 100
## 2                       max f2  0.124912 0.650510 155
## 3                 max f0point5  0.416258 0.612903  23
## 4                 max accuracy  0.416258 0.879357  23
## 5                max precision  0.813396 1.000000   0
## 6                   max recall  0.037579 1.000000 282
## 7              max specificity  0.813396 1.000000   0
## 8             max absolute_mcc  0.416258 0.455251  23
## 9   max min_per_class_accuracy  0.161402 0.738854 125
## 10 max mean_per_class_accuracy  0.124912 0.765006 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or ` 
h2o.auc(h2o.performance(modelh1,test.h2o)) 
## [1] 0.8431394

The AUC metric provides insight into how well the classifier is able to separate the two classes. In this case, the value of 0.8431394 indicates that the classification is good. (A value of 0.5 indicates a worthless test, while a value of 1.0 indicates a perfect test.)

Next, inspect the coefficients of the variables in the dataset.

dfmodelh1 <- as.data.frame(h2o.varimp(modelh1))
dfmodelh1
##                       names coefficients sign
## 1              timbre_0_max  1.290938663  NEG
## 2                  loudness  1.262941934  POS
## 3                     pitch  0.616995941  NEG
## 4              timbre_1_min  0.422323735  POS
## 5              timbre_6_min  0.349016024  NEG
## 6                    energy  0.348092062  NEG
## 7             timbre_11_min  0.307331997  NEG
## 8              timbre_3_max  0.302225619  NEG
## 9             timbre_11_max  0.243632060  POS
## 10             timbre_4_min  0.224233951  POS
## 11             timbre_4_max  0.204134342  POS
## 12             timbre_5_min  0.199149324  NEG
## 13             timbre_0_min  0.195147119  POS
## 14 timesignature_confidence  0.179973904  POS
## 15         tempo_confidence  0.144242598  POS
## 16            timbre_10_max  0.137644568  POS
## 17             timbre_7_min  0.126995955  NEG
## 18            timbre_10_min  0.123851179  POS
## 19             timbre_7_max  0.100031481  NEG
## 20             timbre_2_min  0.096127636  NEG
## 21           key_confidence  0.083115820  POS
## 22             timbre_6_max  0.073712419  POS
## 23            timesignature  0.067241917  POS
## 24             timbre_8_min  0.061301881  POS
## 25             timbre_8_max  0.060041698  POS
## 26                      key  0.056158445  POS
## 27             timbre_3_min  0.050825116  POS
## 28             timbre_9_max  0.033733561  POS
## 29             timbre_2_max  0.030939072  POS
## 30             timbre_9_min  0.020708113  POS
## 31             timbre_1_max  0.014228818  NEG
## 32                    tempo  0.008199861  POS
## 33             timbre_5_max  0.004837870  POS
## 34                                    NA <NA>

Typically, songs with heavier instrumentation tend to be louder (have higher values in the variable “loudness”) and more energetic (have higher values in the variable “energy”). This knowledge is helpful for interpreting the modeling results.

You can make the following observations from the results:

  • The coefficient estimates for the confidence values associated with the time signature, key, and tempo variables are positive. This suggests that higher confidence leads to a higher predicted probability of a Top 10 hit.
  • The coefficient estimate for loudness is positive, meaning that mainstream listeners prefer louder songs with heavier instrumentation.
  • The coefficient estimate for energy is negative, meaning that mainstream listeners prefer songs that are less energetic, which are those songs with light instrumentation.

These coefficients lead to contradictory conclusions for Model 1. This could be due to multicollinearity issues. Inspect the correlation between the variables “loudness” and “energy” in the training set.

cor(train.h2o$loudness,train.h2o$energy)
## [1] 0.7399067

This number indicates that these two variables are highly correlated, and Model 1 does indeed suffer from multicollinearity. Typically, you associate a value of -1.0 to -0.5 or 1.0 to 0.5 to indicate strong correlation, and a value of 0.1 to 0.1 to indicate weak correlation. To avoid this correlation issue, omit one of these two variables and re-create the models.

You build two variations of the original model:

  • Model 2, in which you keep “energy” and omit “loudness”
  • Model 3, in which you keep “loudness” and omit “energy”

You compare these two models and choose the model with a better fit for this use case.

Create Model 2: Keep energy and omit loudness

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:7,9:38)
x.indep
##  [1]  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh2 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=======                                                          |  10%
  |                                                                       
  |=================================================================| 100%

Measure the performance of Model 2.

h2o.performance(model=modelh2,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09922606
## RMSE:  0.3150017
## LogLoss:  0.3228213
## Mean Per-Class Error:  0.2490554
## AUC:  0.8431933
## Gini:  0.6863867
## R^2:  0.2548191
## Null Deviance:  326.0801
## Residual Deviance:  240.8247
## AIC:  306.8247
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      280 34 0.108280  =34/314
## 1       23 36 0.389831   =23/59
## Totals 303 70 0.152815  =57/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.254391 0.558140  69
## 2                       max f2  0.113031 0.647208 157
## 3                 max f0point5  0.413999 0.596026  22
## 4                 max accuracy  0.446250 0.876676  18
## 5                max precision  0.811739 1.000000   0
## 6                   max recall  0.037682 1.000000 283
## 7              max specificity  0.811739 1.000000   0
## 8             max absolute_mcc  0.254391 0.469060  69
## 9   max min_per_class_accuracy  0.141051 0.716561 131
## 10 max mean_per_class_accuracy  0.113031 0.761821 157
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh2 <- as.data.frame(h2o.varimp(modelh2))
dfmodelh2
##                       names coefficients sign
## 1                     pitch  0.700331511  NEG
## 2              timbre_1_min  0.510270513  POS
## 3              timbre_0_max  0.402059546  NEG
## 4              timbre_6_min  0.333316236  NEG
## 5             timbre_11_min  0.331647383  NEG
## 6              timbre_3_max  0.252425901  NEG
## 7             timbre_11_max  0.227500308  POS
## 8              timbre_4_max  0.210663865  POS
## 9              timbre_0_min  0.208516163  POS
## 10             timbre_5_min  0.202748055  NEG
## 11             timbre_4_min  0.197246582  POS
## 12            timbre_10_max  0.172729619  POS
## 13         tempo_confidence  0.167523934  POS
## 14 timesignature_confidence  0.167398830  POS
## 15             timbre_7_min  0.142450727  NEG
## 16             timbre_8_max  0.093377516  POS
## 17            timbre_10_min  0.090333426  POS
## 18            timesignature  0.085851625  POS
## 19             timbre_7_max  0.083948442  NEG
## 20           key_confidence  0.079657073  POS
## 21             timbre_6_max  0.076426046  POS
## 22             timbre_2_min  0.071957831  NEG
## 23             timbre_9_max  0.071393189  POS
## 24             timbre_8_min  0.070225578  POS
## 25                      key  0.061394702  POS
## 26             timbre_3_min  0.048384697  POS
## 27             timbre_1_max  0.044721121  NEG
## 28                   energy  0.039698433  POS
## 29             timbre_5_max  0.039469064  POS
## 30             timbre_2_max  0.018461133  POS
## 31                    tempo  0.013279926  POS
## 32             timbre_9_min  0.005282143  NEG
## 33                                    NA <NA>

h2o.auc(h2o.performance(modelh2,test.h2o)) 
## [1] 0.8431933

You can make the following observations:

  • The AUC metric is 0.8431933.
  • Inspecting the coefficient of the variable energy, Model 2 suggests that songs with high energy levels tend to be more popular. This is as per expectation.
  • As H2O orders variables by significance, the variable energy is not significant in this model.

You can conclude that Model 2 is not ideal for this use , as energy is not significant.

CreateModel 3: Keep loudness but omit energy

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:12,14:38)
x.indep
##  [1]  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh3 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |========                                                         |  12%
  |                                                                       
  |=================================================================| 100%
perfh3<-h2o.performance(model=modelh3,newdata=test.h2o)
perfh3
## H2OBinomialMetrics: glm
## 
## MSE:  0.0978859
## RMSE:  0.3128672
## LogLoss:  0.3178367
## Mean Per-Class Error:  0.264925
## AUC:  0.8492389
## Gini:  0.6984778
## R^2:  0.2648836
## Null Deviance:  326.0801
## Residual Deviance:  237.1062
## AIC:  303.1062
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      286 28 0.089172  =28/314
## 1       26 33 0.440678   =26/59
## Totals 312 61 0.144772  =54/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.273799 0.550000  60
## 2                       max f2  0.125503 0.663265 155
## 3                 max f0point5  0.435479 0.628931  24
## 4                 max accuracy  0.435479 0.882038  24
## 5                max precision  0.821606 1.000000   0
## 6                   max recall  0.038328 1.000000 280
## 7              max specificity  0.821606 1.000000   0
## 8             max absolute_mcc  0.435479 0.471426  24
## 9   max min_per_class_accuracy  0.173693 0.745763 120
## 10 max mean_per_class_accuracy  0.125503 0.775073 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh3 <- as.data.frame(h2o.varimp(modelh3))
dfmodelh3
##                       names coefficients sign
## 1              timbre_0_max 1.216621e+00  NEG
## 2                  loudness 9.780973e-01  POS
## 3                     pitch 7.249788e-01  NEG
## 4              timbre_1_min 3.891197e-01  POS
## 5              timbre_6_min 3.689193e-01  NEG
## 6             timbre_11_min 3.086673e-01  NEG
## 7              timbre_3_max 3.025593e-01  NEG
## 8             timbre_11_max 2.459081e-01  POS
## 9              timbre_4_min 2.379749e-01  POS
## 10             timbre_4_max 2.157627e-01  POS
## 11             timbre_0_min 1.859531e-01  POS
## 12             timbre_5_min 1.846128e-01  NEG
## 13 timesignature_confidence 1.729658e-01  POS
## 14             timbre_7_min 1.431871e-01  NEG
## 15            timbre_10_max 1.366703e-01  POS
## 16            timbre_10_min 1.215954e-01  POS
## 17         tempo_confidence 1.183698e-01  POS
## 18             timbre_2_min 1.019149e-01  NEG
## 19           key_confidence 9.109701e-02  POS
## 20             timbre_7_max 8.987908e-02  NEG
## 21             timbre_6_max 6.935132e-02  POS
## 22             timbre_8_max 6.878241e-02  POS
## 23            timesignature 6.120105e-02  POS
## 24                      key 5.814805e-02  POS
## 25             timbre_8_min 5.759228e-02  POS
## 26             timbre_1_max 2.930285e-02  NEG
## 27             timbre_9_max 2.843755e-02  POS
## 28             timbre_3_min 2.380245e-02  POS
## 29             timbre_2_max 1.917035e-02  POS
## 30             timbre_5_max 1.715813e-02  POS
## 31                    tempo 1.364418e-02  NEG
## 32             timbre_9_min 8.463143e-05  NEG
## 33                                    NA <NA>
h2o.sensitivity(perfh3,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501855569251422. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.2033898
h2o.auc(perfh3)
## [1] 0.8492389

You can make the following observations:

  • The AUC metric is 0.8492389.
  • From the confusion matrix, the model correctly predicts that 33 songs will be top 10 hits (true positives). However, it has 26 false positives (songs that the model predicted would be Top 10 hits, but ended up not being Top 10 hits).
  • Loudness has a positive coefficient estimate, meaning that this model predicts that songs with heavier instrumentation tend to be more popular. This is the same conclusion from Model 2.
  • Loudness is significant in this model.

Overall, Model 3 predicts a higher number of top 10 hits with an accuracy rate that is acceptable. To choose the best fit for production runs, record labels should consider the following factors:

  • Desired model accuracy at a given threshold
  • Number of correct predictions for top10 hits
  • Tolerable number of false positives or false negatives

Next, make predictions using Model 3 on the test dataset.

predict.regh <- h2o.predict(modelh3, test.h2o)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%
print(predict.regh)
##   predict        p0          p1
## 1       0 0.9654739 0.034526052
## 2       0 0.9654748 0.034525236
## 3       0 0.9635547 0.036445318
## 4       0 0.9343579 0.065642149
## 5       0 0.9978334 0.002166601
## 6       0 0.9779949 0.022005078
## 
## [373 rows x 3 columns]
predict.regh$predict
##   predict
## 1       0
## 2       0
## 3       0
## 4       0
## 5       0
## 6       0
## 
## [373 rows x 1 column]
dpr<-as.data.frame(predict.regh)
#Rename the predicted column 
colnames(dpr)[colnames(dpr) == 'predict'] <- 'predict_top10'
table(dpr$predict_top10)
## 
##   0   1 
## 312  61

The first set of output results specifies the probabilities associated with each predicted observation.  For example, observation 1 is 96.54739% likely to not be a Top 10 hit, and 3.4526052% likely to be a Top 10 hit (predict=1 indicates Top 10 hit and predict=0 indicates not a Top 10 hit).  The second set of results list the actual predictions made.  From the third set of results, this model predicts that 61 songs will be top 10 hits.

Compute the baseline accuracy, by assuming that the baseline predicts the most frequent outcome, which is that most songs are not Top 10 hits.

table(BillboardTest$top10)
## 
##   0   1 
## 314  59

Now observe that the baseline model would get 314 observations correct, and 59 wrong, for an accuracy of 314/(314+59) = 0.8418231.

It seems that Model 3, with an accuracy of 0.8552, provides you with a small improvement over the baseline model. But is this model useful for record labels?

View the two models from an investment perspective:

  • A production company is interested in investing in songs that are more likely to make it to the Top 10. The company’s objective is to minimize the risk of financial losses attributed to investing in songs that end up unpopular.
  • How many songs does Model 3 correctly predict as a Top 10 hit in 2010? Looking at the confusion matrix, you see that it predicts 33 top 10 hits correctly at an optimal threshold, which is more than half the number
  • It will be more useful to the record label if you can provide the production company with a list of songs that are highly likely to end up in the Top 10.
  • The baseline model is not useful, as it simply does not label any song as a hit.

Considering the three models built so far, you can conclude that Model 3 proves to be the best investment choice for the record label.

GBM model

H2O provides you with the ability to explore other learning models, such as GBM and deep learning. Explore building a model using the GBM technique, using the built-in h2o.gbm function.

Before you do this, you need to convert the target variable to a factor for multinomial classification techniques.

train.h2o$top10=as.factor(train.h2o$top10)
gbm.modelh <- h2o.gbm(y=y.dep, x=x.indep, training_frame = train.h2o, ntrees = 500, max_depth = 4, learn_rate = 0.01, seed = 1122,distribution="multinomial")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |===                                                              |   5%
  |                                                                       
  |=====                                                            |   7%
  |                                                                       
  |======                                                           |   9%
  |                                                                       
  |=======                                                          |  10%
  |                                                                       
  |======================                                           |  33%
  |                                                                       
  |=====================================                            |  56%
  |                                                                       
  |====================================================             |  79%
  |                                                                       
  |================================================================ |  98%
  |                                                                       
  |=================================================================| 100%
perf.gbmh<-h2o.performance(gbm.modelh,test.h2o)
perf.gbmh
## H2OBinomialMetrics: gbm
## 
## MSE:  0.09860778
## RMSE:  0.3140188
## LogLoss:  0.3206876
## Mean Per-Class Error:  0.2120263
## AUC:  0.8630573
## Gini:  0.7261146
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      266 48 0.152866  =48/314
## 1       16 43 0.271186   =16/59
## Totals 282 91 0.171582  =64/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.189757 0.573333  90
## 2                     max f2  0.130895 0.693717 145
## 3               max f0point5  0.327346 0.598802  26
## 4               max accuracy  0.442757 0.876676  14
## 5              max precision  0.802184 1.000000   0
## 6                 max recall  0.049990 1.000000 284
## 7            max specificity  0.802184 1.000000   0
## 8           max absolute_mcc  0.169135 0.496486 104
## 9 max min_per_class_accuracy  0.169135 0.796610 104
## 10 max mean_per_class_accuracy  0.169135 0.805948 104
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `
h2o.sensitivity(perf.gbmh,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501205344484314. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.1355932
h2o.auc(perf.gbmh)
## [1] 0.8630573

This model correctly predicts 43 top 10 hits, which is 10 more than the number predicted by Model 3. Moreover, the AUC metric is higher than the one obtained from Model 3.

As seen above, H2O’s API provides the ability to obtain key statistical measures required to analyze the models easily, using several built-in functions. The record label can experiment with different parameters to arrive at the model that predicts the maximum number of Top 10 hits at the desired level of accuracy and threshold.

H2O also allows you to experiment with deep learning models. Deep learning models have the ability to learn features implicitly, but can be more expensive computationally.

Now, create a deep learning model with the h2o.deeplearning function, using the same training and test datasets created before. The time taken to run this model depends on the type of EC2 instance chosen for this purpose.  For models that require more computation, consider using accelerated computing instances such as the P2 instance type.

system.time(
  dlearning.modelh <- h2o.deeplearning(y = y.dep,
                                      x = x.indep,
                                      training_frame = train.h2o,
                                      epoch = 250,
                                      hidden = c(250,250),
                                      activation = "Rectifier",
                                      seed = 1122,
                                      distribution="multinomial"
  )
)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |===                                                              |   4%
  |                                                                       
  |=====                                                            |   8%
  |                                                                       
  |========                                                         |  12%
  |                                                                       
  |==========                                                       |  16%
  |                                                                       
  |=============                                                    |  20%
  |                                                                       
  |================                                                 |  24%
  |                                                                       
  |==================                                               |  28%
  |                                                                       
  |=====================                                            |  32%
  |                                                                       
  |=======================                                          |  36%
  |                                                                       
  |==========================                                       |  40%
  |                                                                       
  |=============================                                    |  44%
  |                                                                       
  |===============================                                  |  48%
  |                                                                       
  |==================================                               |  52%
  |                                                                       
  |====================================                             |  56%
  |                                                                       
  |=======================================                          |  60%
  |                                                                       
  |==========================================                       |  64%
  |                                                                       
  |============================================                     |  68%
  |                                                                       
  |===============================================                  |  72%
  |                                                                       
  |=================================================                |  76%
  |                                                                       
  |====================================================             |  80%
  |                                                                       
  |=======================================================          |  84%
  |                                                                       
  |=========================================================        |  88%
  |                                                                       
  |============================================================     |  92%
  |                                                                       
  |==============================================================   |  96%
  |                                                                       
  |=================================================================| 100%
##    user  system elapsed 
##   1.216   0.020 166.508
perf.dl<-h2o.performance(model=dlearning.modelh,newdata=test.h2o)
perf.dl
## H2OBinomialMetrics: deeplearning
## 
## MSE:  0.1678359
## RMSE:  0.4096778
## LogLoss:  1.86509
## Mean Per-Class Error:  0.3433013
## AUC:  0.7568822
## Gini:  0.5137644
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      290 24 0.076433  =24/314
## 1       36 23 0.610169   =36/59
## Totals 326 47 0.160858  =60/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.826267 0.433962  46
## 2                     max f2  0.000000 0.588235 239
## 3               max f0point5  0.999929 0.511811  16
## 4               max accuracy  0.999999 0.865952  10
## 5              max precision  1.000000 1.000000   0
## 6                 max recall  0.000000 1.000000 326
## 7            max specificity  1.000000 1.000000   0
## 8           max absolute_mcc  0.999929 0.363219  16
## 9 max min_per_class_accuracy  0.000004 0.662420 145
## 10 max mean_per_class_accuracy  0.000000 0.685334 224
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
h2o.sensitivity(perf.dl,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.496293348880151. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.3898305
h2o.auc(perf.dl)
## [1] 0.7568822

The AUC metric for this model is 0.7568822, which is less than what you got from the earlier models. I recommend further experimentation using different hyper parameters, such as the learning rate, epoch or the number of hidden layers.

H2O’s built-in functions provide many key statistical measures that can help measure model performance. Here are some of these key terms.

MetricDescription
SensitivityMeasures the proportion of positives that have been correctly identified. It is also called the true positive rate, or recall.
SpecificityMeasures the proportion of negatives that have been correctly identified. It is also called the true negative rate.
ThresholdCutoff point that maximizes specificity and sensitivity. While the model may not provide the highest prediction at this point, it would not be biased towards positives or negatives.
PrecisionThe fraction of the documents retrieved that are relevant to the information needed, for example, how many of the positively classified are relevant
AUC

Provides insight into how well the classifier is able to separate the two classes. The implicit goal is to deal with situations where the sample distribution is highly skewed, with a tendency to overfit to a single class.

0.90 – 1 = excellent (A)

0.8 – 0.9 = good (B)

0.7 – 0.8 = fair (C)

.6 – 0.7 = poor (D)

0.5 – 0.5 = fail (F)

Here’s a summary of the metrics generated from H2O’s built-in functions for the three models that produced useful results.

Metric Model 3GBM ModelDeep Learning Model

Accuracy

(max)

0.882038

(t=0.435479)

0.876676

(t=0.442757)

0.865952

(t=0.999999)

Precision

(max)

1.0

(t=0.821606)

1.0

(t=0802184)

1.0

(t=1.0)

Recall

(max)

1.01.0

1.0

(t=0)

Specificity

(max)

1.01.0

1.0

(t=1)

Sensitivity

 

0.20338980.1355932

0.3898305

(t=0.5)

AUC0.84923890.86305730.756882

Note: ‘t’ denotes threshold.

Your options at this point could be narrowed down to Model 3 and the GBM model, based on the AUC and accuracy metrics observed earlier.  If the slightly lower accuracy of the GBM model is deemed acceptable, the record label can choose to go to production with the GBM model, as it can predict a higher number of Top 10 hits.  The AUC metric for the GBM model is also higher than that of Model 3.

Record labels can experiment with different learning techniques and parameters before arriving at a model that proves to be the best fit for their business. Because deep learning models can be computationally expensive, record labels can choose more powerful EC2 instances on AWS to run their experiments faster.

Conclusion

In this post, I showed how the popular music industry can use analytics to predict the type of songs that make the Top 10 Billboard charts. By running H2O’s scalable machine learning platform on AWS, data scientists can easily experiment with multiple modeling techniques and interactively query the data using Amazon Athena, without having to manage the underlying infrastructure. This helps record labels make critical decisions on the type of artists and songs to promote in a timely fashion, thereby increasing sales and revenue.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to build and explore a simple geospita simple GEOINT application using SparkR.


About the Authors

gopalGopal Wunnava is a Partner Solution Architect with the AWS GSI Team. He works with partners and customers on big data engagements, and is passionate about building analytical solutions that drive business capabilities and decision making. In his spare time, he loves all things sports and movies related and is fond of old classics like Asterix, Obelix comics and Hitchcock movies.

 

 

Bob Strahan, a Senior Consultant with AWS Professional Services, contributed to this post.

 

 

Open Sourcing Vespa, Yahoo’s Big Data Processing and Serving Engine

Post Syndicated from ris original https://lwn.net/Articles/734926/rss

Oath, parent company of Yahoo, has announced
that it has released Vespa as an open source
project on GitHub.
Building applications increasingly means dealing with huge amounts of data. While developers can use the the Hadoop stack to store and batch process big data, and Storm to stream-process data, these technologies do not help with serving results to end users. Serving is challenging at large scale, especially when it is necessary to make computations quickly over data while a user is waiting, as with applications that feature search, recommendation, and personalization.

By releasing Vespa, we are making it easy for anyone to build applications
that can compute responses to user requests, over large datasets, at real
time and at internet scale – capabilities that up until now, have been
within reach of only a few large companies.” (Thanks to Paul Wise)