The SnapLogic Hadooplex in Action

I recently wrote about how SnapLogic’s Hadooplex achieves elastic scalability, running as a native YARN application in a Hadoop cluster. As a I noted in the post:

“As the workload increases, the application master requests the YARN ResourceManager to spin up more Hadooplex nodes one at a time as shown in the diagram below. This scale out occurs dynamically until either the workload starts decreasing or a maximum number of Hadooplex nodes allowed has been met.

As the workload decreases, the nodes start spinning down. This is how SnapLogic achieves elastic scaling based on the workload volumes within a Hadoop cluster utilizing the YARN ResourceManager. This is possible only if an application is a native YARN application.”

I wanted to take this further by showing what this looks like in a SnapLogic Elastic Integration Platform demonstration.  In this demo, you can see how how the Hadooplex, which is the run-time execution engine elastically scales depending on the workload.

You can read more about SnapLogic big data processing platforms in this paper and check out more SnapLogic demonstrations here. Be sure to also check out our upcoming webinar with Mark Madsen, which will focus on the new reference architecture for the enterprise data lake.

The SnapLogic Hadooplex: Achieving Elastic Scalability Using YARN

Elastic-big-dataYARN, a major advancement in Hadoop 2.0, is a resource manager that separates out the execution and processing management from the resource management capabilities of MapReduce. Like an operating system on a server, YARN is designed to allow multiple, diverse user applications to run on a multi-tenant platform.

Developers are no longer limited to writing multi-pass MapReduce programs with disadvantages like high latency, when a better option can be modeled using a directed acyclic graphic (DAG) approach.

Any application, including the likes of Spark, can be deployed onto an existing Hadoop cluster, and take advantage of YARN for scheduling and resource allocation. This is also the basic ingredient of a Hadooplex in SnapLogic – to achieve elastic scale out and scale in for integration jobs.

The per-application ApplicationMaster is, in effect, a framework specific a library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor tasks.

SnapLogic’s application master is responsible for negotiating resources with the ResourceManager. The control plane in SnapLogic is the brain (read this post on software defined integration), which holds all critical information and helps make logical decisions for scale out and scale in. The Hadooplex is the actual application itself that runs the workload.

In the this diagram you can see that the Hadooplex reports its workload information to the control plane at regular intervals. The application master gets the load information from the control plane, also at regular intervals.

Hadooplex

As the workload increases, the application master requests the YARN ResourceManager to spin up more Hadooplex nodes one at a time as shown in the diagram below. This scale out occurs dynamically until either the workload starts decreasing or a maximum number of Hadooplex nodes allowed has been met.

Hadooplex-nodes

As the workload decreases, the nodes start spinning down. This is how SnapLogic achieves elastic scaling based on the workload volumes within a Hadoop cluster utilizing the YARN ResourceManager. This is possible only if an application is a native YARN application. (Read about the importance of YARN-native here.)

Next steps:

This post originally appeared on LinkedIn.

Various Flavors of Yarn Integrations

YARN is the prerequisite for Enterprise Hadoop. It provides resource management across Hadoop clusters and extends the power of Hadoop to new technologies so that they can take advantage of cost effective, linear-scale storage and processing. It provides ISVs and developers a consistent framework for writing data access applications that run IN Hadoop.

Customers building a data lake expect to operate on the data without moving it to other systems, leveraging the processing resources of the data lake. Applications that use YARN fulfill that promise, lowering operational costs while improving quality and time-to-insight.

Integration with YARN

To harness the power or YARN, a third party application can either use YARN natively or use a YARN framework (Apache Tez, Apache slider, etc.) and if it does not use YARN it most probably reads directly from HDFS.

There are 3 broad options for integration into YARN.

  1. Full Control or YARN native: Fine grained control of cluster resources, which allows elastic scaling.
  2. Interaction through an existing YARN framework like MapReduce: Limited to one of batch or interactive or real time. No support for elastic scaling using YARN.
  3. Interaction with applications already running on a YARN framework like Hive: Limited to very specific applications or use cases for example using Hive. No support for elastic scaling using YARN.

Obviously any application, which has full control and is yarn native, provides a significant advantage to be able to do very advanced things within Hadoop using the capabilities of YARN.

This difference is necessary as the space become more interesting and confusing at the same time. Hadoop vendors like Hortonworks offer both Yarn Native and Yarn Ready certifications. Yarn ready means that an application can work with and is limited to any of the Yarn enabled applications like Hive, whereas Yarn Native means full control and fine-grained access of cluster resources.

SnapLogic is Yarn Native. This means as data volumes or workloads increase, the SnapLogic Elastic Integration Platform can automatically, elastically scale out leveraging more nodes in the Hadoop cluster on demand, and as these workloads decrease, scale down automatically. This in SnapLogic is called the Hadooplex. This blog post reviews examples of SnapLogic big data integration pipelines.
SnapLogic Hadooplex

This post originally appeared on LinkedIn. Ravi Dharnikota is a Sr. Advisor SnapLogic, working closely with customers on their big data and cloud reference architecture.