3/1/2024 0 Comments Apache flume![]() Run the flume agent using below command:. ![]() “ng” stands for next generation.Ī = ch1Ī =spooldirĪ =/usr/kmayank/spooldir Command to start a flume agent as below.Use the command line to start a flume agent.We need to set up all the options in “.properties” file.Here the source is spooling directory and the sink is console log, Apache Flume Agent sits in between spooling directory and console log. When any log files are added to the directory, it will read those log files and push the content of the log file to the console log. This covers the very basics of setting up Flume and mitigating some of the common issues which one encounters while using it.In this tutorial, we will learn to set up an Apache Flume agent that is going to monitor a spooling directory. Can be potentially solved by Oozie Coordinator.How to trigger notification on HDFS update from flume.Did performance analysis of data ingestion directly from file after applying solutions from 8 & 9 of 20000 records.Solved by changing the rollCount(HDFSSink) and transactionCapacity(channel) so that the channel is cleared for more data.While controlling the channel capacity, encountered Memory Leaks.Also made rollSize and rollInterval as 0, so that they are not used.Solved by changing the rollCount property of the HDFSSink to the desired number of events/records per file this increased the ingestion rate, thus decreasing the ingestion time.This decreased the ingestion rate, thus increasing the ingestion time Flume could only create files to write at max 10 records in a single file by default.When ingesting directly from file, realtime updates were automatically registered, on the basis of timestamp for last modification.When using avro client, realtime update to the file was not taken into account.Directly from File System –> Total time – 00:02:44.Using Avro Client –> Total time – 00:02:48.Did performance analysis of data ingestion directly from file as well as avro client of 14000 records when only 10 records rolled up per file in HDFS.Due to memory leaks, could not write more than 12000 records from log4jappender.Solved by directly pushing the file onto avro-client (inbuilt in flume-ng).Avro’s Optimized data serialization is not expolited when using log4jappender.Solved by keeping transactionCapacity of channel low and capacity of channel high enough.Solved by making thread sleep after every 500 records, thus decreasing the load on the channel. ![]() Memory leaks in log4jappender when ingesting just 10000 records.Performance Measures, Issues and Comments: The file so created be saved as nf, and can be run as :įlume-ng agent -f nf -n source_agent It is worth noting, the above given configuration will produce separate files of 10 records each by default, taking timestamp ( an interceptor ) as the point of reference of last update of the source. ![]() Sources could be anything like an Avro Client being used by Log4JAppender as well. A basic configuration to read data taking “ local file system” as the source, keeping channel as “ memory“, and “ hdfs” as sink, could be: This task is kept alive in order to listen to any change in the source, and must be terminated manually by the user. “ Sink” usually is HDFS.Īpache Flume takes a configuration file every time it runs a task. Please note “ flume-ng” represents, “ Flume Next-Gen“įlume Agent is the one which takes care of the whole process of taking data from “ source“, putting it on to the “ channel“, and finally dumping it in the “ sink“.
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