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You're reading from  Apache Flume: Distributed Log Collection for Hadoop

Product typeBook
Published inFeb 2015
Reading LevelIntermediate
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ISBN-139781784392178
Edition1st Edition
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Steven Hoffman
Steven Hoffman
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Steven Hoffman

Steve Hoffman has 32 years of experience in software development, ranging from embedded software development to the design and implementation of large-scale, service-oriented, object-oriented systems. For the last 5 years, he has focused on infrastructure as code, including automated Hadoop and HBase implementations and data ingestion using Apache Flume. Steve holds a BS in computer engineering from the University of Illinois at Urbana-Champaign and an MS in computer science from DePaul University. He is currently a senior principal engineer at Orbitz Worldwide (http://orbitz.com/). More information on Steve can be found at http://bit.ly/bacoboy and on Twitter at @bacoboy. This is the first update to Steve's first book, Apache Flume: Distributed Log Collection for Hadoop, Packt Publishing.
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Summary


In this chapter, we covered the two channel types you are most likely to use in your data processing pipelines.

The memory channel offers speed at the cost of data loss in the event of failure. Alternatively, the file channel provides a more reliable transport in that it can tolerate agent failures and restarts at a performance cost.

You will need to decide which channel is appropriate for your use cases. When trying to decide whether a memory channel is appropriate, ask yourself what the monetary cost is if you lose some data. Weigh that against the additional costs of more hardware to cover the difference in performance when deciding if you need a durable channel after all. Another consideration is whether or not the data can be resent. Not all data you might ingest into Hadoop will come from streaming application logs. If you receive "daily downloads" of data, you can get away with using a memory channel because if you encounter a problem, you can always rerun the import.

Finally...

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Apache Flume: Distributed Log Collection for Hadoop
Published in: Feb 2015Publisher: ISBN-13: 9781784392178

Author (1)

author image
Steven Hoffman

Steve Hoffman has 32 years of experience in software development, ranging from embedded software development to the design and implementation of large-scale, service-oriented, object-oriented systems. For the last 5 years, he has focused on infrastructure as code, including automated Hadoop and HBase implementations and data ingestion using Apache Flume. Steve holds a BS in computer engineering from the University of Illinois at Urbana-Champaign and an MS in computer science from DePaul University. He is currently a senior principal engineer at Orbitz Worldwide (http://orbitz.com/). More information on Steve can be found at http://bit.ly/bacoboy and on Twitter at @bacoboy. This is the first update to Steve's first book, Apache Flume: Distributed Log Collection for Hadoop, Packt Publishing.
Read more about Steven Hoffman