This blog is a collection of Apache Flume interview questions and answers. Here you’ll find the most often asked Hadoop Flume interview questions, ranging from beginner to advanced. Let's start and hope this blog will help you crack an Apache Flume interview.
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Ans: Flume is a reliable distributed service for the collection and aggregation of a large amount of streaming data into HDFS. Most of the Bigdata analysts use Apache Flume to push data from different sources like Twitter, Facebook, & LinkedIn into Hadoop, Strom, Solr, Kafka & Spark.
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Ans: Most often Hadoop developers use this tool to get log data from social media sites. It’s developed by Cloudera for aggregating and moving a very large amount of data. The primary use is to gather log files from different sources and asynchronously persists in the Hadoop cluster.
Ans: A Flume agent is a JVM process that holds the Flume core components (Source, Channel, Sink) through which events flow from an external source like web servers to a destination like HDFS. The agent is the heart of the Apache Flume.
Ans: A unit of data with the set of string attributes called Flume event. The external source like the webserver sends events to the source. Internally Flume has inbuilt functionality to understand the source format. For example, Avro sends events from Avro sources to the Flume.
Each log file is considered an event. Each event has header and value sectors, which have header information and appropriate value that assign to the particular header.
Ans: The following are the core components:
Ans: Yes, it provides end-to-end reliability of the flow. By default, Flume uses a transactional approach in the data flow. Sources and sinks are encapsulated in a transactional repository provided by the channels. These channels are responsible to pass reliably from end to end in the flow. So it provides 100% reliability to the data flow.
|Related Article: Difference Between Apache Sqoop vs Apache Flume|
Ans: The agent configuration is stored in the local configuration file. It comprises each agent’s source, sinks, and channel information. Each core component such as source, sink, and channel has properties such as name, type, and set of properties.
For example, Avro source needs a hostname, the port number to receive data from an external client. The memory channel should have a maximum queue size in the form of capacity. The sink should have File System URI, Path to create files, frequency of file rotation, and more configurations.
Ans: Flume can process streaming data, so if started once, there is no stop/end to the process. asynchronously it can flows data from source to HDFS via Agent. First of all, Agent should know individual components how are connected to load data.
So the configuration is a trigger to load streaming data. For example consumer key, consumer secret, accessToken, and access token secret are key factors to download data from Twitter.
Ans: The following are the steps in the configuration:
Ans: Yes, Flume has 100% plugin-based architecture. It can load and ships data from external sources to external destinations which separately from Flume. So that most big data analysts use this tool for streaming data.
Ans: The beauty of Flume is Consolidation, it collects data from different sources even its different flume Agents. Flume sources can collect all data flow from different sources and flows through channels and sinks. Finally, send this data to HDFS or the target destination. Flume consolidation
Ans: Yes, it supports multiplexing flow. The event flows from one source to multiple channels and multiple destinations. It’s achieved by defining a flow multiplexer.
In the above example, data flows and replicated to HDFS and another sink to destination and another destination is input to another agent.
Ans: No, each agent runs independently. Flume can easily scale horizontally. As a result, there is no single point of failure.
Ans: It’s one of the most frequently asked Flume interview questions. Interceptors are used to filter the events between source and channel, channel and sink. These channels can filter unnecessary or targeted log files. Depends on the requirements you can use n number of interceptors.
Ans: Channel selectors control and separating the events and allocate them to a particular channel. There are default/ replicated channel selectors. Replicated channel selectors can replicate the data in multiple/all channels.
Multiplexing channel selectors used to separate and aggregate the data based on the event’s header information. It means based on the Sink’s destination, the event aggregate into the particular sink.
Leg example: One sink connected with Hadoop, another with S3 another with Hbase, at that time, Multiplexing channel selectors can separate the events and flow to the particular sink.
Ans: Sink processors are a mechanism by which you can create a fail-over task and load balancing.
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