Hari Shreedharan - Using Flume: Flexible, Scalable, and Reliable Data Streaming

by Hari Shreedharan

Copyright 2015 Hari Shreedharan. All rights reserved.

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978-1-449-36830-2

[LSI]

The past few years have seen tremendous growth in the development and adoption of Big Data technologies. Hadoop and related platforms are powering the next wave of data analytics over increasingly large amounts of data. The data produced today will be dwarfed by what is expected tomorrow, growing at an ever-increasing rate as the digital revolution engulfs all aspects of our existence. The barrier to entry in this new age of massive data volumes is of course the obvious one: how do you get all this data in your cluster to begin with? Clearly, this data is produced in a wide spectrum of sources spread across the enterprise, and has an interesting mix of interaction, machine, sensor, and social data among others. Any operator who has dealt with similar challenges would no doubt agree that it is nontrivial, if not downright hard, to build a system that can route this data into your clusters in a cost-effective manner.

Apache Flume is exactly built to handle this challenge.

Back in 2011 when Flume went into Incubation at The Apache Software Foundation, it was a project built by Cloudera engineers to address large-scale log data aggregation on Hadoop. Being a popular project from the beginning, it had seen a large number of new requirements ranging from event-ordering to guaranteed-delivery semantics, that came up over its initial releases. Given its popularity and high demand for complex requirements, we decided to refactor the project entirely and make it simpler, more powerful in its applicability and manageability, and allow for easy extensions where necessary. Hari and I were in the Incubator project along with a handful of other engineers who were working around the clock with the Flume community to drive this vision and implementation forward. From that time until now, Flume has graduated into its own top-level Apache project, made several stable releases, and has grown significantly rich in functionality.

Today, Flume is actively deployed and in use across the world in large numbers of data centers, sometimes spanning continental boundaries. It continues to effectively provide a super-resilient, fault-tolerant, reliable, fast, and efficient mechanism to move massive amounts of data from a variety of sources over to destination systems such as HBase, HDFS, etc. A well-planned Flume topology operates with minimal or no intervention, practically running itself indefinitely. It provides contextual routing and is able to work through downtimes, network outages, and other unpredictable/unplanned interruptions by providing the capacity to reliably store and retransmit messages when connectivity is restored. It does all of this out of the box, and yet provides the flexibility to customize any component within its implementation using fairly stable and intuitive interfaces that are widely in use.

In Using Flume, Hari provides an overview of various components within Flume, diving into details where necessary. Operators will find this book immensely valuable for understanding how to easily set up and deploy Flume pipelines. Developers will find it a handy reference to build or customize components within Flume, and to better understand its architecture and component designs. Above all, this book will give you the necessary insights for setting up continuous ingestion for HDFS and HBase the two most popular storage systems today.

With Flume deployed, you can be sure that data no matter where its produced in your enterprise, or how large its volume is will make it safely and timely into your Big Data platforms. And you can then focus your energy on getting the right insights out of your data. Good luck!

Arvind Prabhakar, CTO, StreamSets