Incremental Backup of Data from HDP to Azure using Falcon for Disaster Recovery and Burst capacity

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Incremental Backup of Data from HDP to Azure using Falcon for Disaster Recovery and Burst capacity

Introduction

Apache Falcon simplifies the configuration of data motion with: replication; lifecycle management; lineage and traceability. This provides data governance consistency across Hadoop components.

Scenario

In this tutorial we will walk through a scenario where email data gets processed on multiple HDP 2.2 clusters around the country then gets backed up hourly on a cloud hosted cluster . In our example:

• This cluster is hosted on Windows Azure.
• Data arrives from all the West Coast production servers. The input data feeds are often late for up to 4 hrs.

The goal is to clean the raw data to remove sensitive information like credit card numbers and make it available to our marketing data science team for customer churn analysis.

To simulate this scenario, we have a pig script grabbing the freely available Enron emails from the internet and feeding it into the pipeline.

Prerequisite

• A cluster with Apache Hadoop 2.2 configured
• A cluster with Apache Falcon configured

The easiest way to meet the above prerequisites is to download the HDP Sandbox

After downloading the environment, confirm that Apache Falcon is running. Below are the steps to validate that:

1. if Ambari is not configured on your Sandbox, go http://127.0.0.1:8000/about/ and enable Ambari.

1. Once Ambari is enabled, navigate to Ambari at http://127.0.0.1:8080, login with username and password ofadmin and admin respectively. Then check if Falcon is running.

1. If Falcon is not running, start Falcon:

Steps for the Scenario

1. Create cluster specification XML file
2. Create feed (aka dataset) specification XML file * Reference cluster specification
3. Create the process specification XML file * Reference cluster specification – defines where the process runs * Reference feed specification – defines the datasets that the process manipulates

We have already created the necessary xml files. In this step we are going to download the specifications and use them to define the topology and submit the storm job.

Staging the component of the App on HDFS

In this step we will stage the pig script and the necessary folder structure for inbound and outbound feeds on the HDFS:

First download this zip file called falcon.zip to your local host machine.

Navigate using your browser to the Hue – File Browser interface at http://127.0.0.1:8000/filebrowser/ to explore the HDFS.

Navigate to /user/ambari-qa folder like below:

Now we will upload the zip file we just downloaded:

This should also unzip the zip file and create a folder structure with a folder called falcon.

Setting up the destination storage on Microsoft Azure

Login to the Windows Azure portal at http://manage.windowsazure.com

Create a storage account

Wait for the storage account to be provisioned

Copy the access key and the account name in a text document. We will use the access key and the account name in later steps

The other information you will want to note down is the blob endpoint of the storage account we just created

Click on the Containers tab and create a new container called myfirstcontainer.

Configuring access to Azure Blob store from Hadoop

Login to Ambari – http://127.0.0.1:8080 with the credentials admin and admin.

Then click on HDFS from the bar on the left and then select the Configs tab.

Scroll down to the bottom of the page to the Custom hdfs-site section and click on Add property...

In the Add Property dialog, the key name will start with fs.azure.account.key. followed by your blob endpoint that you noted down in a previous step. The value will be the Azure storage key that you noted down in a previous step. Once you have filled in the values click the Add button:

Once you are back out of the new key dialog you will have to Save it by clicking on the green Save button:

Then restart all the service by clicking on the orange Restart button:

Wait for all the restart to complete

Now let’s test if we can access our container on the Azure Blob Store.

SSH in to the VM:

ssh root@127.0.0.1 -p 2222;

The password is hadoop

hdfs dfs -ls -R wasb://myfirstcontainer@saptak.blob.core.windows.net/

Issue the command from our cluster on the SSH’d terminal

Staging the specifications

From the SSH session, first we will change our user to ambari-qa. Type:

su ambari-qa

Go to the users home directory:

cd ~

Download the topology, feed and process definitions:

wget http://hortonassets.s3.amazonaws.com/tutorial/falcon/falconDemo.zip

Unzip the file:

unzip ./falconDemo.zip

Change Directory to the folder created:

cd falconChurnDemo/

Now let’s modify the cleansedEmailFeed.xml to point the backup cluster to our Azure Blob Store container.

Use vi to edit the file:

Modify the value of location element of the backupCluster

to look like this:

Then save it and quit vi.

Submit the entities to the cluster:

Cluster Specification

Cluster specification is one per cluster.

See below for a sample cluster specification file.

Back to our scenario, lets submit the ‘oregon cluster’ entity to Falcon. This signifies the primary Hadoop cluster located in the Oregon data center.

falcon entity -type cluster -submit -file oregonCluster.xml

Then lets submit the ‘virginia cluster’ entity to Falcon. This signifies the backup Hadoop cluster located in the Virginia data center

falcon entity -type cluster -submit -file virginiaCluster.xml

If you view the XML file you will see how the cluster location and purpose has been captured in the XML file.

Feed Specification

A feed (a.k.a dataset) signifies a location of data and its associated replication policy and late arrival cut-off time.

See below for a sample feed (a.k.a dataset) specification file.

Back to our scenario, let’s submit the source of the raw email feed. This feed signifies the raw emails that are being downloaded into the Hadoop cluster. These emails will be used by the email cleansing process.

falcon entity -type feed -submit -file rawEmailFeed.xml

Now let’s define the feed entity which will handle the end of the pipeline to store the cleansed email. This feed signifies the emails produced by the cleanse email process. It also takes care of replicating the cleansed email dataset to the backup cluster (virginia cluster)

falcon entity -type feed -submit -file cleansedEmailFeed.xml

Process

A process defines configuration for a workflow. A workflow is a directed acyclic graph(DAG) which defines the job for the workflow engine. A process definition defines the configurations required to run the workflow job. For example, process defines the frequency at which the workflow should run, the clusters on which the workflow should run, the inputs and outputs for the workflow, how the workflow failures should be handled, how the late inputs should be handled and so on.

Here is an example of what a process specification looks like:

Back to our scenario, let’s submit the ingest and the cleanse process respectively:

The ingest process is responsible for calling the Oozie workflow that downloads the raw emails from the web into the primary Hadoop cluster under the location specified in the rawEmailFeed.xml It also takes care of handling late data arrivals

falcon entity -type process -submit -file emailIngestProcess.xml

The cleanse process is responsible for calling the pig script that cleans the raw emails and produces the clean emails that are then replicated to the backup Hadoop cluster

falcon entity -type process -submit -file cleanseEmailProcess.xml

Schedule the Falcon entities

So, all that is left now is to schedule the feeds and processes to get it going.

Ingest the feed

falcon entity -type feed -schedule -name rawEmailFeed

falcon entity -type process -schedule -name rawEmailIngestProcess

Cleanse the emails

falcon entity -type feed -schedule -name cleansedEmailFeed

falcon entity -type process -schedule -name cleanseEmailProcess

Processing

In a few seconds you should notice that that Falcon has started ingesting files from the internet and dumping them to new folders like below on HDFS:

In a couple of minutes you should notice a new folder called processed under which the files processed through the data pipeline are being emitted:

We just created an end-to-end data pipeline to process data. The power of the Apache Falcon framework is its flexibility to work with pretty much any open source or proprietary data processing products out there.