The source and sink within the given agent run asynchronously HDFS sink) or forwards it to the Flume source of the next Flume agent (next The sink removes the eventįrom the channel and puts it into an external repository like HDFS (via Flume The event until it’s consumed by a Flume sink. The channel is a passive store that keeps The Flume thrift protocol.When a Flume source receives an event, it Thrift Rpc Client or Thrift clients written in any language generated from A similar flow can be defined usingĪ Thrift Flume Source to receive events from a Thrift Sink or a Flume Used to receive Avro events from Avro clients or other Flume agents in the flow The external source sends events to Flume in a format that is Also, since this is a part of the Apache Hadoop umbrella, we can get good support and always have great options to utilise all the features this tool provides.A Flume source consumes events delivered to it by an external source like a web conf/ -f conf/nf =DEBUG,console -n TwitterAgentĪpache Flume is extensively used these days due to the reliable transmission (one sender and one receiver) it provides and also due to its recovery features, which rapidly help us in the event of a crash. To start Flume, type: bin/flume-ng agent -conf. The value can be modified to get the tweets for some other topic like football, movies, etc. path should point to the NameNode and the location in HDFS where the tweets will go to. The consumerKey, consumerSecret, accessToken and accessTokenSecret have to be replaced with those obtained from. path = hdfs://localhost:9000/user/flume/tweets/ = opensource, OSFY, opensourceforyou -> Here you could add multiple keywords into the source engine = MemChannel To configure the Flume agent, type: TwitterAgent.sources = Twitter Set JAVA_HOME and FLUME_CLASSPATH in the flume-env.shįLUME_CLASSPATH = “/usr/lib/apache-flume-1.4.0-bin/lib/”Ĭonf/nf should have all the agents (Flume, memory and HDFS) defined as below. Sudo cp conf/flume-env.sh.template conf/flume-env.sh Put the apache-flume-1.4.0-bin directory inside the /usr/lib/ directory: sudo mv apache-flume-1.4.0-bin /usr/lib/ĭownload the and add it to the Flume class path (the JAR contains the Java classes to pull the tweets and save them into HDFS): sudo mv Downloads/ /usr/lib/apache-flume-1.4.0-bin/lib/Ĭheck whether the Flume snapshot has moved to the lib folder of Apache Flume: ls /usr/lib/apache-flume-1.4.0-bin/lib/flume*Ĭopy flume-env.sh.template content to flume-env.sh: cd /usr/lib/apache-flume-1.4.0-bin/ Here, we are using CENT OS 6.7 in a virtual machine where we are going to deploy it.ĭownload Flume using the following command: wget Įxtract the file from the Flume tar file, as follows: tar -xvf apache-flume-1.4. By the time we’re finished, you should be able to configure and launch a Flume agent and understand how various data flows are easily constructed from multiple agents. This article illustrates how quickly you can set up Flume agents to collect fast-moving data streams and to push the data into Hadoop’s file system from Twitter. Has the ability to take in new data streams and additional storage volume, on the fly. Also, it provides contextual routing.įlume uses channel-based transactions to guarantee reliable message delivery. It uses channel-based delivery, where there is always one sender and one receiver contained for every message. This feature helps it act as a mediator when the incoming data rate increases, and provides a steady flow of data. This feature allows Flume to take in streams from multiple sources and then push them onto HDFS for storage and analysisĬontextual routing and it acts as a buffer Some of the features of Apache Flume are given in Table 1. It also provides a simple extensible data model that can be used for any analytic application. Multiple Flume agents can be configured to collect high volumes of data. That’s where the evolution began and, currently, there are many solutions in place and the most widely used one being Apache Flume.įlume is an extremely reliable service, which is used to efficiently collect, aggregate and store large amounts of data. Hence, the need arose for creating a standard on how all the data streams could be directed into the HDFS more efficiently. For example, system or application logs, geolocation data and monitoring, as well as social media trends and updates - all constitute fast-moving streams requesting for storage in the Hadoop Distributed File System (HDFS). Once the data gets into the Hadoop framework, you have won half the battle but the real struggle begins when new, fast-moving data streams starts flowing in. Challenges with data load in the Hadoop framework
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