Apache Flume: Distributed Log Collection for Hadoop by Steve Hoffman

By Steve Hoffman

movement information to Hadoop utilizing Apache Flume

Overview

  • Integrate Flume together with your info sources
  • Transcode your info en-route in Flume
  • Route and separate your information utilizing general expression matching
  • Configure failover paths and load-balancing to take away unmarried issues of failure
  • Utilize Gzip Compression for documents written to HDFS

In Detail

Apache Flume is a disbursed, trustworthy, and on hand carrier for successfully amassing, aggregating, and relocating quite a lot of log info. Its major target is to convey information from purposes to Apache Hadoop's HDFS. It has an easy and versatile structure in line with streaming info flows. it really is powerful and fault tolerant with many failover and restoration mechanisms.

Apache Flume: dispensed Log assortment for Hadoop covers issues of HDFS and streaming data/logs, and the way Flume can get to the bottom of those difficulties. This e-book explains the generalized structure of Flume, including relocating facts to/from databases, NO-SQL-ish information shops, in addition to optimizing functionality. This e-book comprises real-world eventualities on Flume implementation.

Apache Flume: allotted Log assortment for Hadoop begins with an architectural evaluate of Flume after which discusses each one part intimately. It courses you thru the entire set up strategy and compilation of Flume.

It offers you a heads-up on how you can use channels and channel selectors. for every architectural part (Sources, Channels, Sinks, Channel Processors, Sink teams, etc) a few of the implementations may be lined intimately besides configuration techniques. you should use it to customise Flume on your particular wishes. There are guidelines given on writing customized implementations besides that will assist you research and enforce them.

  • By the tip, you need to be in a position to build a chain of Flume brokers to move your streaming info and logs out of your structures into Hadoop in close to actual time.
  • What you are going to examine from this book

    • Understand the Flume architecture
    • Download and set up open resource Flume from Apache
    • Discover while to exploit a reminiscence or file-backed channel
    • Understand and configure the Hadoop dossier method (HDFS) sink
    • Learn find out how to use sink teams to create redundant info flows
    • Configure and use numerous assets for eating data
    • Inspect info documents and path to assorted or a number of locations according to payload content
    • Transform information en-route to Hadoop
    • Monitor your information flows

    Approach

    A starter advisor that covers Apache Flume in detail.

    Who this publication is written for

    Apache Flume: allotted Log assortment for Hadoop is meant for those that are chargeable for relocating datasets into Hadoop in a well timed and trustworthy demeanour like software program engineers, database directors, and knowledge warehouse administrators.

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    Additional resources for Apache Flume: Distributed Log Collection for Hadoop

    Example text

    CallTimeout is the amount of time the HDFS sink will wait for HDFS operations to return a success (or failure) before giving up. If your Hadoop cluster is particularly slow (for instance a development or virtual cluster) you may need to set this value higher to avoid errors. Keep in mind that your channel will overflow if you cannot sustain higher write throughput than input rate to your channel. idleTimeout property if set to a non-zero value, is the time Flume will wait to automatically close an idle file.

    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. Possible (or intentional) duplicate events are a fact of ingesting streaming data. Some people will run periodic MapReduce jobs to clean the data (and removing duplicates while they are at it). Others will just account for duplicates when they run their MapReduce jobs, which saves additional post processing. In practice you will probably do both.

    IdleTimeout property if set to a non-zero value, is the time Flume will wait to automatically close an idle file. I have never used this since hdfs. fileRollInterval handles closing of files each roll period and if the channel is idle it will not open a new file. This setting seems to have been created as an alternative roll mechanism to the size, time, and event count mechanisms already discussed. You may want as much data written to a file as possible and only close it when there is really no more data.

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