Skip to main content

Leveraging YARN to provision java apps on Cloud


Over the last few days, we have heard quite a bit of press on Hadoop Yarn. Let us continue the beat and learn a unique PAAS architecture on Hadoop Yarn.

In an article, Jaigak Song describes his idea and prototype done as part of research at SAP Labs. The source code is available at github.

The prototype has been created to provision Java applications on a PAAS environment, and start, stop their instances as required.

The following projects are included as part of source code:
PAAS Client (PaasClient)
PaasClient is a dedicated YARN client that works like a command shell to process PAAS commands…

PAAS Application Master (PaaSAppMaster)
PaasAppMaster is a YARN application master that manages a lifecycle of PAAS application instances…

PAAS Application Container (PaasAppContainer) – Jetty Web Container
PaasAppContainer is instantiated by PaasAppMaster as a YARN container according to the requested resource limit…”

 
           this.execCommand(session, "hadoop fs -rm /PAAS/" + fileName);
this.execCommand(session, String.format("hadoop fs -put ~/i827616/%s /PAAS/%s", fileName, fileName));
 
· An Admin user issues a push command in PaasClient like “push /Users/PAAS/svcA.war”, where svcA.war file is a web application file of the service named ‘svcA’. Then the PaasClient uploads the file to the Hadoop server by using ‘scp’ and ‘ssh’. There is a dedicated directory in the Hadoop file system to save War files (e.g. /PAAS/), while non-application libraries and jar files are stored in a different directory.”

Source code snapshot (click on file names to view source) :
HadoopPaas / PaasZooClient / src / com / sap / zookeeper
HadoopPaas / PaasClient / src / com / sap / hadoop / paas
HadoopPaas / PaasAppMaster / src / com / sap / hadoop / master
HadoopPaas / PaasAppContainer / src / com / sap / hadoop / client


A interesting prototype for sure, read the article and contribute to code for further cool projects on the concept.

Comments

Popular posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…