Discuss Apache Flume ”; Previous Next Flume is a standard, simple, robust, flexible, and extensible tool for data ingestion from various data producers (webservers) into Hadoop. In this tutorial, we will be using simple and illustrative example to explain the basics of Apache Flume and how to use it in practice. Print Page Previous Next Advertisements ”;
Apache Flume – Useful Resources ”; Previous Next The following resources contain additional information on Apache Flume. Please use them to get more in-depth knowledge on this. Useful Video Courses Apache Spark Online Training Course 47 Lectures 3.5 hours Tutorialspoint More Detail Delta Lake with Apache Spark using Scala 53 Lectures 2 hours Bigdata Engineer More Detail Apache Spark with Scala for Certified Databricks Professional 78 Lectures 5.5 hours Bigdata Engineer More Detail Apache Cassandra for Beginners 28 Lectures 2 hours Navdeep Kaur More Detail NGINX, Apache, SSL Encryption – Training Course 60 Lectures 3.5 hours YouAccel More Detail Learn Advanced Apache Kafka from Scratch Featured 154 Lectures 9 hours Learnkart Technology Pvt Ltd More Detail Print Page Previous Next Advertisements ”;
Apache Flume – Quick Guide ”; Previous Next Apache Flume – Introduction What is Flume? Apache Flume is a tool/service/data ingestion mechanism for collecting aggregating and transporting large amounts of streaming data such as log files, events (etc…) from various sources to a centralized data store. Flume is a highly reliable, distributed, and configurable tool. It is principally designed to copy streaming data (log data) from various web servers to HDFS. Applications of Flume Assume an e-commerce web application wants to analyze the customer behavior from a particular region. To do so, they would need to move the available log data in to Hadoop for analysis. Here, Apache Flume comes to our rescue. Flume is used to move the log data generated by application servers into HDFS at a higher speed. Advantages of Flume Here are the advantages of using Flume − Using Apache Flume we can store the data in to any of the centralized stores (HBase, HDFS). When the rate of incoming data exceeds the rate at which data can be written to the destination, Flume acts as a mediator between data producers and the centralized stores and provides a steady flow of data between them. Flume provides the feature of contextual routing. The transactions in Flume are channel-based where two transactions (one sender and one receiver) are maintained for each message. It guarantees reliable message delivery. Flume is reliable, fault tolerant, scalable, manageable, and customizable. Features of Flume Some of the notable features of Flume are as follows − Flume ingests log data from multiple web servers into a centralized store (HDFS, HBase) efficiently. Using Flume, we can get the data from multiple servers immediately into Hadoop. Along with the log files, Flume is also used to import huge volumes of event data produced by social networking sites like Facebook and Twitter, and e-commerce websites like Amazon and Flipkart. Flume supports a large set of sources and destinations types. Flume supports multi-hop flows, fan-in fan-out flows, contextual routing, etc. Flume can be scaled horizontally. Apache Flume – Data Transfer In Hadoop Big Data, as we know, is a collection of large datasets that cannot be processed using traditional computing techniques. Big Data, when analyzed, gives valuable results. Hadoop is an open-source framework that allows to store and process Big Data in a distributed environment across clusters of computers using simple programming models. Streaming / Log Data Generally, most of the data that is to be analyzed will be produced by various data sources like applications servers, social networking sites, cloud servers, and enterprise servers. This data will be in the form of log files and events. Log file − In general, a log file is a file that lists events/actions that occur in an operating system. For example, web servers list every request made to the server in the log files. On harvesting such log data, we can get information about − the application performance and locate various software and hardware failures. the user behavior and derive better business insights. The traditional method of transferring data into the HDFS system is to use the put command. Let us see how to use the put command. HDFS put Command The main challenge in handling the log data is in moving these logs produced by multiple servers to the Hadoop environment. Hadoop File System Shell provides commands to insert data into Hadoop and read from it. You can insert data into Hadoop using the put command as shown below. $ Hadoop fs –put /path of the required file /path in HDFS where to save the file Problem with put Command We can use the put command of Hadoop to transfer data from these sources to HDFS. But, it suffers from the following drawbacks − Using put command, we can transfer only one file at a time while the data generators generate data at a much higher rate. Since the analysis made on older data is less accurate, we need to have a solution to transfer data in real time. If we use put command, the data is needed to be packaged and should be ready for the upload. Since the webservers generate data continuously, it is a very difficult task. What we need here is a solutions that can overcome the drawbacks of put command and transfer the “streaming data” from data generators to centralized stores (especially HDFS) with less delay. Problem with HDFS In HDFS, the file exists as a directory entry and the length of the file will be considered as zero till it is closed. For example, if a source is writing data into HDFS and the network was interrupted in the middle of the operation (without closing the file), then the data written in the file will be lost. Therefore we need a reliable, configurable, and maintainable system to transfer the log data into HDFS. Note − In POSIX file system, whenever we are accessing a file (say performing write operation), other programs can still read this file (at least the saved portion of the file). This is because the file exists on the disc before it is closed. Available Solutions To send streaming data (log files, events etc..,) from various sources to HDFS, we have the following tools available at our disposal − Facebook’s Scribe Scribe is an immensely popular tool that is used to aggregate and stream log data. It is designed to scale to a very large number of nodes and be robust to network and node failures. Apache Kafka Kafka has been developed by Apache Software Foundation. It is an open-source message broker. Using Kafka, we can handle feeds with high-throughput and low-latency. Apache Flume Apache Flume is a tool/service/data ingestion mechanism for collecting aggregating and transporting large amounts of streaming data such as log data, events (etc…) from various webserves to a centralized data store. It is a highly reliable, distributed, and configurable tool that is principally designed to transfer streaming data from various sources to
Apache Flume – Configuration ”; Previous Next After installing Flume, we need to configure it using the configuration file which is a Java property file having key-value pairs. We need to pass values to the keys in the file. In the Flume configuration file, we need to − Name the components of the current agent. Describe/Configure the source. Describe/Configure the sink. Describe/Configure the channel. Bind the source and the sink to the channel. Usually we can have multiple agents in Flume. We can differentiate each agent by using a unique name. And using this name, we have to configure each agent. Naming the Components First of all, you need to name/list the components such as sources, sinks, and the channels of the agent, as shown below. agent_name.sources = source_name agent_name.sinks = sink_name agent_name.channels = channel_name Flume supports various sources, sinks, and channels. They are listed in the table given below. Sources Channels Sinks Avro Source Thrift Source Exec Source JMS Source Spooling Directory Source Twitter 1% firehose Source Kafka Source NetCat Source Sequence Generator Source Syslog Sources Syslog TCP Source Multiport Syslog TCP Source Syslog UDP Source HTTP Source Stress Source Legacy Sources Thrift Legacy Source Custom Source Scribe Source Memory Channel JDBC Channel Kafka Channel File Channel Spillable Memory Channel Pseudo Transaction Channel HDFS Sink Hive Sink Logger Sink Avro Sink Thrift Sink IRC Sink File Roll Sink Null Sink HBaseSink AsyncHBaseSink MorphlineSolrSink ElasticSearchSink Kite Dataset Sink Kafka Sink You can use any of them. For example, if you are transferring Twitter data using Twitter source through a memory channel to an HDFS sink, and the agent name id TwitterAgent, then TwitterAgent.sources = Twitter TwitterAgent.channels = MemChannel TwitterAgent.sinks = HDFS After listing the components of the agent, you have to describe the source(s), sink(s), and channel(s) by providing values to their properties. Describing the Source Each source will have a separate list of properties. The property named “type” is common to every source, and it is used to specify the type of the source we are using. Along with the property “type”, it is needed to provide the values of all the required properties of a particular source to configure it, as shown below. agent_name.sources. source_name.type = value agent_name.sources. source_name.property2 = value agent_name.sources. source_name.property3 = value For example, if we consider the twitter source, following are the properties to which we must provide values to configure it. TwitterAgent.sources.Twitter.type = Twitter (type name) TwitterAgent.sources.Twitter.consumerKey = TwitterAgent.sources.Twitter.consumerSecret = TwitterAgent.sources.Twitter.accessToken = TwitterAgent.sources.Twitter.accessTokenSecret = Describing the Sink Just like the source, each sink will have a separate list of properties. The property named “type” is common to every sink, and it is used to specify the type of the sink we are using. Along with the property “type”, it is needed to provide values to all the required properties of a particular sink to configure it, as shown below. agent_name.sinks. sink_name.type = value agent_name.sinks. sink_name.property2 = value agent_name.sinks. sink_name.property3 = value For example, if we consider HDFS sink, following are the properties to which we must provide values to configure it. TwitterAgent.sinks.HDFS.type = hdfs (type name) TwitterAgent.sinks.HDFS.hdfs.path = HDFS directory’s Path to store the data Describing the Channel Flume provides various channels to transfer data between sources and sinks. Therefore, along with the sources and the channels, it is needed to describe the channel used in the agent. To describe each channel, you need to set the required properties, as shown below. agent_name.channels.channel_name.type = value agent_name.channels.channel_name. property2 = value agent_name.channels.channel_name. property3 = value For example, if we consider memory channel, following are the properties to which we must provide values to configure it. TwitterAgent.channels.MemChannel.type = memory (type name) Binding the Source and the Sink to the Channel Since the channels connect the sources and sinks, it is required to bind both of them to the channel, as shown below. agent_name.sources.source_name.channels = channel_name agent_name.sinks.sink_name.channels = channel_name The following example shows how to bind the sources and the sinks to a channel. Here, we consider twitter source, memory channel, and HDFS sink. TwitterAgent.sources.Twitter.channels = MemChannel TwitterAgent.sinks.HDFS.channels = MemChannel Starting a Flume Agent After configuration, we have to start the Flume agent. It is done as follows − $ bin/flume-ng agent –conf ./conf/ -f conf/twitter.conf Dflume.root.logger=DEBUG,console -n TwitterAgent where − agent − Command to start the Flume agent –conf ,-c<conf> − Use configuration file in the conf directory -f<file> − Specifies a config file path, if missing –name, -n <name> − Name of the twitter agent -D property =value − Sets a Java system property value. Print Page Previous Next Advertisements ”;
Apache Flume – Sequence Generator Source ”; Previous Next In the previous chapter, we have seen how to fetch data from twitter source to HDFS. This chapter explains how to fetch data from Sequence generator. Prerequisites To run the example provided in this chapter, you need to install HDFS along with Flume. Therefore, verify Hadoop installation and start the HDFS before proceeding further. (Refer the previous chapter to learn how to start the HDFS). Configuring Flume We have to configure the source, the channel, and the sink using the configuration file in the conf folder. The example given in this chapter uses a sequence generator source, a memory channel, and an HDFS sink. Sequence Generator Source It is the source that generates the events continuously. It maintains a counter that starts from 0 and increments by 1. It is used for testing purpose. While configuring this source, you must provide values to the following properties − Channels Source type − seq Channel We are using the memory channel. To configure the memory channel, you must provide a value to the type of the channel. Given below are the list of properties that you need to supply while configuring the memory channel − type − It holds the type of the channel. In our example the type is MemChannel. Capacity − It is the maximum number of events stored in the channel. Its default value is 100. (optional) TransactionCapacity − It is the maximum number of events the channel accepts or sends. Its default is 100. (optional). HDFS Sink This sink writes data into the HDFS. To configure this sink, you must provide the following details. Channel type − hdfs hdfs.path − the path of the directory in HDFS where data is to be stored. And we can provide some optional values based on the scenario. Given below are the optional properties of the HDFS sink that we are configuring in our application. fileType − This is the required file format of our HDFS file. SequenceFile, DataStream and CompressedStream are the three types available with this stream. In our example, we are using the DataStream. writeFormat − Could be either text or writable. batchSize − It is the number of events written to a file before it is flushed into the HDFS. Its default value is 100. rollsize − It is the file size to trigger a roll. It default value is 100. rollCount − It is the number of events written into the file before it is rolled. Its default value is 10. Example – Configuration File Given below is an example of the configuration file. Copy this content and save as seq_gen .conf in the conf folder of Flume. # Naming the components on the current agent SeqGenAgent.sources = SeqSource SeqGenAgent.channels = MemChannel SeqGenAgent.sinks = HDFS # Describing/Configuring the source SeqGenAgent.sources.SeqSource.type = seq # Describing/Configuring the sink SeqGenAgent.sinks.HDFS.type = hdfs SeqGenAgent.sinks.HDFS.hdfs.path = hdfs://localhost:9000/user/Hadoop/seqgen_data/ SeqGenAgent.sinks.HDFS.hdfs.filePrefix = log SeqGenAgent.sinks.HDFS.hdfs.rollInterval = 0 SeqGenAgent.sinks.HDFS.hdfs.rollCount = 10000 SeqGenAgent.sinks.HDFS.hdfs.fileType = DataStream # Describing/Configuring the channel SeqGenAgent.channels.MemChannel.type = memory SeqGenAgent.channels.MemChannel.capacity = 1000 SeqGenAgent.channels.MemChannel.transactionCapacity = 100 # Binding the source and sink to the channel SeqGenAgent.sources.SeqSource.channels = MemChannel SeqGenAgent.sinks.HDFS.channel = MemChannel Execution Browse through the Flume home directory and execute the application as shown below. $ cd $FLUME_HOME $./bin/flume-ng agent –conf $FLUME_CONF –conf-file $FLUME_CONF/seq_gen.conf –name SeqGenAgent If everything goes fine, the source starts generating sequence numbers which will be pushed into the HDFS in the form of log files. Given below is a snapshot of the command prompt window fetching the data generated by the sequence generator into the HDFS. Verifying the HDFS You can access the Hadoop Administration Web UI using the following URL − http://localhost:50070/ Click on the dropdown named Utilities on the right-hand side of the page. You can see two options as shown in the diagram given below. Click on Browse the file system and enter the path of the HDFS directory where you have stored the data generated by the sequence generator. In our example, the path will be /user/Hadoop/ seqgen_data /. Then, you can see the list of log files generated by the sequence generator, stored in the HDFS as given below. Verifying the Contents of the File All these log files contain numbers in sequential format. You can verify the contents of these file in the file system using the cat command as shown below. Print Page Previous Next Advertisements ”;
Apache Flume – Introduction ”; Previous Next What is Flume? Apache Flume is a tool/service/data ingestion mechanism for collecting aggregating and transporting large amounts of streaming data such as log files, events (etc…) from various sources to a centralized data store. Flume is a highly reliable, distributed, and configurable tool. It is principally designed to copy streaming data (log data) from various web servers to HDFS. Applications of Flume Assume an e-commerce web application wants to analyze the customer behavior from a particular region. To do so, they would need to move the available log data in to Hadoop for analysis. Here, Apache Flume comes to our rescue. Flume is used to move the log data generated by application servers into HDFS at a higher speed. Advantages of Flume Here are the advantages of using Flume − Using Apache Flume we can store the data in to any of the centralized stores (HBase, HDFS). When the rate of incoming data exceeds the rate at which data can be written to the destination, Flume acts as a mediator between data producers and the centralized stores and provides a steady flow of data between them. Flume provides the feature of contextual routing. The transactions in Flume are channel-based where two transactions (one sender and one receiver) are maintained for each message. It guarantees reliable message delivery. Flume is reliable, fault tolerant, scalable, manageable, and customizable. Features of Flume Some of the notable features of Flume are as follows − Flume ingests log data from multiple web servers into a centralized store (HDFS, HBase) efficiently. Using Flume, we can get the data from multiple servers immediately into Hadoop. Along with the log files, Flume is also used to import huge volumes of event data produced by social networking sites like Facebook and Twitter, and e-commerce websites like Amazon and Flipkart. Flume supports a large set of sources and destinations types. Flume supports multi-hop flows, fan-in fan-out flows, contextual routing, etc. Flume can be scaled horizontally. Print Page Previous Next Advertisements ”;
Apache Flume – Fetching Twitter Data ”; Previous Next Using Flume, we can fetch data from various services and transport it to centralized stores (HDFS and HBase). This chapter explains how to fetch data from Twitter service and store it in HDFS using Apache Flume. As discussed in Flume Architecture, a webserver generates log data and this data is collected by an agent in Flume. The channel buffers this data to a sink, which finally pushes it to centralized stores. In the example provided in this chapter, we will create an application and get the tweets from it using the experimental twitter source provided by Apache Flume. We will use the memory channel to buffer these tweets and HDFS sink to push these tweets into the HDFS. To fetch Twitter data, we will have to follow the steps given below − Create a twitter Application Install / Start HDFS Configure Flume Creating a Twitter Application In order to get the tweets from Twitter, it is needed to create a Twitter application. Follow the steps given below to create a Twitter application. Step 1 To create a Twitter application, click on the following link https://apps.twitter.com/. Sign in to your Twitter account. You will have a Twitter Application Management window where you can create, delete, and manage Twitter Apps. Step 2 Click on the Create New App button. You will be redirected to a window where you will get an application form in which you have to fill in your details in order to create the App. While filling the website address, give the complete URL pattern, for example, http://example.com. Step 3 Fill in the details, accept the Developer Agreement when finished, click on the Create your Twitter application button which is at the bottom of the page. If everything goes fine, an App will be created with the given details as shown below. Step 4 Under keys and Access Tokens tab at the bottom of the page, you can observe a button named Create my access token. Click on it to generate the access token. Step 5 Finally, click on the Test OAuth button which is on the right side top of the page. This will lead to a page which displays your Consumer key, Consumer secret, Access token, and Access token secret. Copy these details. These are useful to configure the agent in Flume. Starting HDFS Since we are storing the data in HDFS, we need to install / verify Hadoop. Start Hadoop and create a folder in it to store Flume data. Follow the steps given below before configuring Flume. Step 1: Install / Verify Hadoop Install Hadoop. If Hadoop is already installed in your system, verify the installation using Hadoop version command, as shown below. $ hadoop version If your system contains Hadoop, and if you have set the path variable, then you will get the following output − Hadoop 2.6.0 Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r e3496499ecb8d220fba99dc5ed4c99c8f9e33bb1 Compiled by jenkins on 2014-11-13T21:10Z Compiled with protoc 2.5.0 From source with checksum 18e43357c8f927c0695f1e9522859d6a This command was run using /home/Hadoop/hadoop/share/hadoop/common/hadoop-common-2.6.0.jar Step 2: Starting Hadoop Browse through the sbin directory of Hadoop and start yarn and Hadoop dfs (distributed file system) as shown below. cd /$Hadoop_Home/sbin/ $ start-dfs.sh localhost: starting namenode, logging to /home/Hadoop/hadoop/logs/hadoop-Hadoop-namenode-localhost.localdomain.out localhost: starting datanode, logging to /home/Hadoop/hadoop/logs/hadoop-Hadoop-datanode-localhost.localdomain.out Starting secondary namenodes [0.0.0.0] starting secondarynamenode, logging to /home/Hadoop/hadoop/logs/hadoop-Hadoop-secondarynamenode-localhost.localdomain.out $ start-yarn.sh starting yarn daemons starting resourcemanager, logging to /home/Hadoop/hadoop/logs/yarn-Hadoop-resourcemanager-localhost.localdomain.out localhost: starting nodemanager, logging to /home/Hadoop/hadoop/logs/yarn-Hadoop-nodemanager-localhost.localdomain.out Step 3: Create a Directory in HDFS In Hadoop DFS, you can create directories using the command mkdir. Browse through it and create a directory with the name twitter_data in the required path as shown below. $cd /$Hadoop_Home/bin/ $ hdfs dfs -mkdir hdfs://localhost:9000/user/Hadoop/twitter_data Configuring Flume We have to configure the source, the channel, and the sink using the configuration file in the conf folder. The example given in this chapter uses an experimental source provided by Apache Flume named Twitter 1% Firehose Memory channel and HDFS sink. Twitter 1% Firehose Source This source is highly experimental. It connects to the 1% sample Twitter Firehose using streaming API and continuously downloads tweets, converts them to Avro format, and sends Avro events to a downstream Flume sink. We will get this source by default along with the installation of Flume. The jar files corresponding to this source can be located in the lib folder as shown below. Setting the classpath Set the classpath variable to the lib folder of Flume in Flume-env.sh file as shown below. export CLASSPATH=$CLASSPATH:/FLUME_HOME/lib/* This source needs the details such as Consumer key, Consumer secret, Access token, and Access token secret of a Twitter application. While configuring this source, you have to provide values to the following properties − Channels Source type : org.apache.flume.source.twitter.TwitterSource consumerKey − The OAuth consumer key consumerSecret − OAuth consumer secret accessToken − OAuth access token accessTokenSecret − OAuth token secret maxBatchSize − Maximum number of twitter messages that should be in a twitter batch. The default value is 1000 (optional). maxBatchDurationMillis − Maximum number of milliseconds to wait before closing a batch. The default value is 1000 (optional). Channel We are using the memory channel. To configure the memory channel, you must provide value to the type of the channel. type − It holds the type of the channel. In our example, the type is MemChannel. Capacity − It is the maximum number of events stored in the channel. Its default value is 100 (optional). TransactionCapacity − It is the maximum number of events the channel accepts or sends. Its default value is 100 (optional). HDFS Sink This sink writes data into the HDFS. To configure this sink, you must provide the following details. Channel type − hdfs hdfs.path − the path of the directory in HDFS where data is to be stored. And we can provide some optional values based on the scenario. Given below are the optional properties of the HDFS sink that we are configuring in our application. fileType − This is the required file
Apache Flume – NetCat Source ”; Previous Next This chapter takes an example to explain how you can generate events and subsequently log them into the console. For this, we are using the NetCat source and the logger sink. Prerequisites To run the example provided in this chapter, you need to install Flume. Configuring Flume We have to configure the source, the channel, and the sink using the configuration file in the conf folder. The example given in this chapter uses a NetCat Source, Memory channel, and a logger sink. NetCat Source While configuring the NetCat source, we have to specify a port while configuring the source. Now the source (NetCat source) listens to the given port and receives each line we entered in that port as an individual event and transfers it to the sink through the specified channel. While configuring this source, you have to provide values to the following properties − channels Source type − netcat bind − Host name or IP address to bind. port − Port number to which we want the source to listen. Channel We are using the memory channel. To configure the memory channel, you must provide a value to the type of the channel. Given below are the list of properties that you need to supply while configuring the memory channel − type − It holds the type of the channel. In our example, the type is MemChannel. Capacity − It is the maximum number of events stored in the channel. Its default value is 100. (optional) TransactionCapacity − It is the maximum number of events the channel accepts or sends. Its default value is 100. (optional). Logger Sink This sink logs all the events passed to it. Generally, it is used for testing or debugging purpose. To configure this sink, you must provide the following details. Channel type − logger Example Configuration File Given below is an example of the configuration file. Copy this content and save as netcat.conf in the conf folder of Flume. # Naming the components on the current agent NetcatAgent.sources = Netcat NetcatAgent.channels = MemChannel NetcatAgent.sinks = LoggerSink # Describing/Configuring the source NetcatAgent.sources.Netcat.type = netcat NetcatAgent.sources.Netcat.bind = localhost NetcatAgent.sources.Netcat.port = 56565 # Describing/Configuring the sink NetcatAgent.sinks.LoggerSink.type = logger # Describing/Configuring the channel NetcatAgent.channels.MemChannel.type = memory NetcatAgent.channels.MemChannel.capacity = 1000 NetcatAgent.channels.MemChannel.transactionCapacity = 100 # Bind the source and sink to the channel NetcatAgent.sources.Netcat.channels = MemChannel NetcatAgent.sinks.LoggerSink.channel = MemChannel Execution Browse through the Flume home directory and execute the application as shown below. $ cd $FLUME_HOME $ ./bin/flume-ng agent –conf $FLUME_CONF –conf-file $FLUME_CONF/netcat.conf –name NetcatAgent -Dflume.root.logger=INFO,console If everything goes fine, the source starts listening to the given port. In this case, it is 56565. Given below is the snapshot of the command prompt window of a NetCat source which has started and listening to the port 56565. Passing Data to the Source To pass data to NetCat source, you have to open the port given in the configuration file. Open a separate terminal and connect to the source (56565) using the curl command. When the connection is successful, you will get a message “connected” as shown below. $ curl telnet://localhost:56565 connected Now you can enter your data line by line (after each line, you have to press Enter). The NetCat source receives each line as an individual event and you will get a received message “OK”. Whenever you are done with passing data, you can exit the console by pressing (Ctrl+C). Given below is the snapshot of the console where we have connected to the source using the curl command. Each line that is entered in the above console will be received as an individual event by the source. Since we have used the Logger sink, these events will be logged on to the console (source console) through the specified channel (memory channel in this case). The following snapshot shows the NetCat console where the events are logged. Print Page Previous Next Advertisements ”;
Apache Flume Tutorial PDF Version Quick Guide Resources Job Search Discussion Flume is a standard, simple, robust, flexible, and extensible tool for data ingestion from various data producers (webservers) into Hadoop. In this tutorial, we will be using simple and illustrative example to explain the basics of Apache Flume and how to use it in practice. Audience This tutorial is meant for all those professionals who would like to learn the process of transferring log and streaming data from various webservers to HDFS or HBase using Apache Flume. Prerequisites To make the most of this tutorial, you should have a good understanding of the basics of Hadoop and HDFS commands. Print Page Previous Next Advertisements ”;
Apache Flume – Data Transfer In Hadoop ”; Previous Next Big Data, as we know, is a collection of large datasets that cannot be processed using traditional computing techniques. Big Data, when analyzed, gives valuable results. Hadoop is an open-source framework that allows to store and process Big Data in a distributed environment across clusters of computers using simple programming models. Streaming / Log Data Generally, most of the data that is to be analyzed will be produced by various data sources like applications servers, social networking sites, cloud servers, and enterprise servers. This data will be in the form of log files and events. Log file − In general, a log file is a file that lists events/actions that occur in an operating system. For example, web servers list every request made to the server in the log files. On harvesting such log data, we can get information about − the application performance and locate various software and hardware failures. the user behavior and derive better business insights. The traditional method of transferring data into the HDFS system is to use the put command. Let us see how to use the put command. HDFS put Command The main challenge in handling the log data is in moving these logs produced by multiple servers to the Hadoop environment. Hadoop File System Shell provides commands to insert data into Hadoop and read from it. You can insert data into Hadoop using the put command as shown below. $ Hadoop fs –put /path of the required file /path in HDFS where to save the file Problem with put Command We can use the put command of Hadoop to transfer data from these sources to HDFS. But, it suffers from the following drawbacks − Using put command, we can transfer only one file at a time while the data generators generate data at a much higher rate. Since the analysis made on older data is less accurate, we need to have a solution to transfer data in real time. If we use put command, the data is needed to be packaged and should be ready for the upload. Since the webservers generate data continuously, it is a very difficult task. What we need here is a solutions that can overcome the drawbacks of put command and transfer the “streaming data” from data generators to centralized stores (especially HDFS) with less delay. Problem with HDFS In HDFS, the file exists as a directory entry and the length of the file will be considered as zero till it is closed. For example, if a source is writing data into HDFS and the network was interrupted in the middle of the operation (without closing the file), then the data written in the file will be lost. Therefore we need a reliable, configurable, and maintainable system to transfer the log data into HDFS. Note − In POSIX file system, whenever we are accessing a file (say performing write operation), other programs can still read this file (at least the saved portion of the file). This is because the file exists on the disc before it is closed. Available Solutions To send streaming data (log files, events etc..,) from various sources to HDFS, we have the following tools available at our disposal − Facebook’s Scribe Scribe is an immensely popular tool that is used to aggregate and stream log data. It is designed to scale to a very large number of nodes and be robust to network and node failures. Apache Kafka Kafka has been developed by Apache Software Foundation. It is an open-source message broker. Using Kafka, we can handle feeds with high-throughput and low-latency. Apache Flume Apache Flume is a tool/service/data ingestion mechanism for collecting aggregating and transporting large amounts of streaming data such as log data, events (etc…) from various webserves to a centralized data store. It is a highly reliable, distributed, and configurable tool that is principally designed to transfer streaming data from various sources to HDFS. In this tutorial, we will discuss in detail how to use Flume with some examples. Print Page Previous Next Advertisements ”;