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Top Apache Flume Interview Questions and Answers

Last updated on Feb 18 2022
Sunder Rangnathan

Table of Contents

What is Flume?

A distributed service for collection, aggregating, and moving giant amounts of log knowledge, is Flume.

Why we are using Flume?

Most often Hadoop developer use this tool to get log data from social media sites. It’s developed by Cloudera for aggregating and moving very large amount of data. The primary use is gather log files from different sources and asynchronously persists in the Hadoop cluster.

What Is FlumeNG?

A real time loader for streaming your data into Hadoop. It stores data in HDFS and HBase. You’ll want to get started with FlumeNG, which improves on the original flume.

What is Apache Flume?

Apache Flume is an open-source platform to efficiently and reliably collect, aggregate and transfer massive amounts of data from one or more sources to a centralized data source. Data sources are customizable in Flume; hence it can ingest any kind of data including log data, event data, network data, social-media generated data, email messages, message queues etc.

What are the complicated steps in Flume configuration?

A . Flume can processing 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 they are connected to load data. So configuration is trigger to load streaming data. For example consumerKey, consumerSecret, accessToken and accessTokenSecret are key factors to download data from Twitter.

Can you explain Consolidation in Flume?

A . The beauty of Flume is Consolidation, it collects data from different sources even it’s different flume Agents. Flume source can collect all data flow from different sources and flows through channel and sink. Finally, send this data to HDFS or target destination.
Flume consolidation

Apache Flume support third-party plugins also?

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 of the big data analysts use this tool for streaming data.

What are Flume Core components?

 Source, Channels and Sink are core components in Apache Flume.
When Flume source receives event from external sources, it stores the event in one or multiple channels.
Flume channel is temporarily store & keeps the event until it’s consumed by the Flume sink. It acts as Flume repository.
Flume Sink removes the event from channel and put into an external repository like HDFS or Move to the next Flume agent.

How is reliability of data delivery ensured in Flume?

Flume uses a transactional approach to guarantee the delivery of data. Events or data is removed from channels only after they have been successfully stored in the terminal repository for single-hop flows, or successfully stored in the channel of next agent in the case of multi-hop flows.

How is recoverability ensured in Flume?

In Flume the events or data is staged in channels. Flume sources add events to Flume channels. Flume sinks consume events from channels and publish to terminal data stores. Channels manage recovery from failures. Flume supports different kinds of channels. In-memory channels store events in an in-memory queue, which is faster. File channels are durable which is backed by the local file system.

How do you install third-party plugins into Flume? OR Why do you need third-party plugins in Flume? OR What are the different ways you can install plugins into flume?

Flume is a plugin-based architecture. It ships with many out-of-the-box sources, channels and sinks. Many other customized components exist separately from Flume which can be plugged into Flume and used for you applications. Or you can write your own custom components and plug them into Flume.

There are two ways to add plugins to Flume.

Add the plugin jar files to FLUME_CLASSPATH variable in the flume-env.sh file.

What do you mean by consolidation in Flume? Or How do you ingest data from multiple sources into a single terminal destination?

Flume can be setup to have multiple agents process data from multiple sources and send to a single or a few intermediate destinations. Separate agents consume messages from the intermediate data source and write to a central data source.

What is Flume event?

 A unit of data with set of string attributes called Flume event. The external source like web-server 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 as an event. Each event has header and value sectors, which has header information and appropriate value that assign to the particular header.

 What are Flume Core components?

  • Source, Channels and Sink are core components in Apache Flume.
  • When Flume source receives event from external sources, it stores the event in one or multiple channels.
  • Flume channel is temporarily store & keeps the event until it’s consumed by the Flume sink. It acts as Flume repository.
  • Flume Sink removes the event from channel and put into an external repository like HDFS or Move to the next Flume agent.

 Can Flume provides 100% reliability to the data flow?

 Yes, it provide end-to-end reliability of the flow. By default Flume uses a transactional approach in the data flow. Sources and sinks encapsulated in a transactional repository provides by the channels. This channel responsible to pass reliably from end to end in the flow. So it provides 100% reliability to the data flow.

Can you explain about configuration files?

 The agent configuration is stored in local configuration file. It comprises of each agent’s source, sink and channel information. Each core component such as source, sink and channel have properties such as name, type and set of properties. For example Avro source need hostname, port number to receive data from external client. Memory channel should have maximum queue size in the form of capacity. Sink should have File System URI, Path to create files, frequency of file rotation and more configurations.

What are the important steps in the configuration?

 Configuration file is the heart of the Apache Flume’s agent.

  • Every Source must have at least one channel.
  • Every Sink must have only one channel.
  • Every Component must have a specific type.

 Apache Flume support third-party plugins also?

 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 of the bigdata analysts use this tool for streaming data.

 Can you explain Consolidation in Flume?

 The beauty of Flume is Consolidation, it collects data from different sources even it’s different flume Agents. Flume source can collect all data flow from different sources and flows through channel and sink. Finally send this data to HDFS or target destination.
Flume consolidation

Explain the core components of Flume.

There are various core components of Flume available. They are –

  1. Event-  Event is the single log entry or unit of data which we transport further.
  2. Source-  Source is the component by which data enters Flume workflows.
  3. Sink- For transporting data to the desired destination sink is responsible.
  4. Channel-Channel is nothing but a  duct between the Sink and Source.
  5. Agent- Agent is what we have known as any JVM that runs Flume.
  6. Client- Client transmits the event to the source that operates with the agent.

  Which is the reliable channel in Flume to ensure that there is no data loss?

Among the 3 channels JDBC, FILE and MEMORY, FILE Channel is the most reliable channel.

 How can Flume be used with HBase?

There are two types of HBase sinks. So, we can use Flume with HBase using one of the two HBase sinks –

  • HBaseSink (org.apache.flume.sink.hbase.HBaseSink)

supports secure HBase clusters and also the novel HBase IPC that was introduced in the version HBase 0.96.

AsyncHBaseSink (org.apache.flume.sink.hbase.AsyncHBaseSink)

It can easily make non-blocking calls to HBase, it means it has better performance than HBase sink.

What is an Agent?

In Apache Flume, an independent daemon process (JVM) is what we call an agent. At first, it receives events from clients or other agents. Afterwards, it forwards it to its next destination that is sink or agent. Note that, it is possible that Flume can have more than one agent. Also, refer the below image to understand the Flume Agent.

Apache Flume support third-party plugins also?

Yes, it has 100% plugin-based architecture. Basically, it can load and ships data from external sources to an external destination which separately from Flume. HeFileRollSinknce,  for streaming data most of the BigData analyst use this tool.

Differentiate between FileSink and FileRollSink

Basically, HDFS File Sink writes the events into the Hadoop Distributed File System – HDFS while File Roll Sink stores the events into the local file system.

Which is the Reliable Channel in Flume to ensure that there is no data loss?

 FILE Channel is the most reliable channel.

Can Flume can distribute data to multiple destinations?

Flume generally supports multiplexing flow. Here, event flows from one source to multiple channel and multiple destinations. Basically, it is achieved by defining a flow multiplexer.

How can multi-hop agent be set up in Flume?

To setup Multi-hop agent in Apache Flume we use Avro RPC Bridge mechanism.

 Why are we using Flume?

Basically, to get log data from social media sites most often Hadoop developer use this too. However, for aggregating and moving the very large amount of data it is developed by Cloudera. Majorly, we use it to gather log files from different sources and asynchronously persist in the Hadoop cluster.

What is sink processors?

We generally sink processors to invoke a particular sink from the selected group of sinks. Moreover, also to create failover paths for our sinks or load balance events across multiple sinks from a channel we use sink processors.

Explain what are the tools used in Big Data?

There are several tools available in Big Data. It includes.

  1. Hadoop
  2. Hive
  3. Pig
  4. Flume
  5. Mahout
  6. Sqoop

 Agent communicate with other Agents?

Here, each agent runs independently. As a result, there is no single point of failure.

Does Apache Flume provide support for third-party plug-ins?

 Yes it offers support for third-party plug-ins.

What are the important steps in the configuration?

Configuration file is the heart of the Apache Flume’s agents.

  • Every Source must have at least one channel.
  • Moreover, every Sink must have only one channel
  • Every component must have a specific type.

Is there any difference between FileSink and FileRollSink?

yes, there is a major difference between HDFS FileSink and FileRollSink. That HDFS File Sink writes the events into the Hadoop Distributed File System (HDFS) while File Roll Sink stores the events into the local file system.

 What is Apache Spark?

Apache Spark is a general-purpose & lightning fast cluster computing system. It provides high-level API. For example, Java, Scala, Python and R. Apache Spark is a tool for Running Spark Applications. Spark is 100 times faster than Big Data Hadoop and 10 times faster than accessing data from disk.

Types of Data Flow in Flume?

  • Multi-hop Flow

Learn Hadoop from Industry Experts
Basically, before reaching the final destination there can be multiple agents and an event may travel through more than one agent, within Flume. This is what we call as multi-hop Data flow in Flume.

  • Fan-out Flow

In very simple language when data transfers or the data flow from one source to multiple channels that is what we call fan-out flow. Basically, in Flume Data flow, it is of two categories −
1. Replicating
It is the data flow where the data will be replicated in all the configured channels.
2. Multiplexing
On defining Multiplexing we can say the data flow where the data will be sent to a selected channel which is mentioned in the header of the event.

  • Fan-in Flow

While it comes to fan-in flow it is known as the data flow in which the data will be transferred from many sources to one channel.

What is flume agent?

However, in Apache Flume, an independent daemon process (JVM) is what we call a Flume Agent. At first, it receives events from clients or other agents. Afterwards, it forwards it to its next destination that is sink or agent. Note that, it is possible that Flume can have more than one agent.

What is Flume event?

The basic unit of the data which is transported inside Flume is what we call a Flume Events. Generally, it contains a payload of the byte array. Basically, we can transport it from the source to the destination accompanied by optional headers.

Why Flume?

Apart from collecting logs from distributed systems, it is also capable of performing other use cases. like

  1. It Collects readings from array of sensors
  2. Also, it collects impressions from custom apps for an ad network
  3. Moreover, it collects it readings from network devices in order to monitor their performance.
  4. Also, preserves the reliability, scalability, manageability, and extensibility while it serves maximum number of clients with higher QoS

What is Flume Client?

 Those which generates events and then sent it to one or more agents is what we call Flume Client.

What are possible types of Channel Selectors?

Channel Selectors are generally of two types −

  • Default channel selectors − Replicating channel selectors which replicate all the events in each channel is what we call Default channel selectors.
  • Multiplexing channel selectors − The Channel selectors which decide the channel to send an event based on the address in the header of that event are Multiplexing channel selectors.

Can you define what is Event Serializer in Flume?

 While it comes to convert a Flume event into another format for output, we use Apache Flume event serializer mechanism.
Learn it in detail.  Flume – Event Serializers

What is Streaming / Log Data?

While it comes to Streaming / Log Data, it is the data produced by various data sources and usually require to be analyzed. Data Sources like, applications servers, social networking sites, cloud servers and enterprise servers. So, that data is generally in the form of log files or events.

What are Tools available to send the streaming data to HDFS?

There are several Tools available to send the streaming data to HDFS.  They are:

  • Facebook’s Scribe
  • Apache Kafka
  • Apache Flume

How to Use HDFS put Command for Data Transfer from Flume to HDFS?

Basically, in handling the log data, the main challenge is to move the logs produced by multiple servers to Hadoop environment.
In order to insert data into Hadoop and read from it, Hadoop File System Shell offers commands. So, by using put command we can insert the data.
$ Hadoop fs –put /path of the required file /path in HDFS where to save the file

What are use cases of Apache Flume?

 There are several use cases.

  1. While we want to acquire data from a variety of source and store into Hadoop system, we use Apache Flume.
  2. Whenever we need to handle high-velocity and high-volume data into Hadoop system, we go for Apache Flume.
  3. It also helps in Reliable delivery of data to the destination.
  4. When the velocity and volume of data increases, Flume turned as a scalable solution that can run quite easily just by adding more machine to it.
  5. Without incurring any downtime Flume dynamically configures the various components of the architecture.

What is Apache Flume?

Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data source. Review this Flume use case to learn how Mozilla collects and Analyze the Logs using Flume and Hive.

Flume is a framework for populating Hadoop with data. Agents are populated throughout ones IT infrastructure – inside web servers, application servers and mobile devices, for example – to collect data and integrate it into Hadoop.

Which is the reliable channel in Flume to ensure that there is no data loss?

FILE Channel is the most reliable channel among the 3 channels JDBC, FILE and MEMORY.

What is an Agent?

A process that hosts flume components such as sources, channels and sinks, and thus has the ability to receive, store and forward events to their destination.

Is it possible to leverage real time analysis on the big data collected by Flume directly? If yes, then explain how?

Data from Flume can be extracted, transformed and loaded in real-time into  Apache Solr servers using Morphline Solr Sink.

What is a channel?

It stores events, events are delivered to the channel via sources operating within the agent. An event stays in the channel until a sink removes it for further transport.

Explain about the different channel types in Flume. Which channel type is faster?

The 3 different built-in channel types available in Flume are-

MEMORY Channel – Events are read from the source into memory and passed to the sink.

JDBC Channel – JDBC Channel stores the events in an embedded Derby database.

FILE Channel –File Channel writes the contents to a file on the file system after reading the event from a source. The file is deleted only after the contents are successfully delivered to the sink.

MEMORY Channel is the fastest channel among the three however has the risk of data loss. The channel that you choose completely depends on the nature of the big data application and the value of each event.

Why we are using Flume?

Most often Hadoop developer use this too to get data from social media sites. It’s developed by Cloudera for aggregating and moving very large amount of data. The primary use is to gather log files from different sources and asynchronously persist in the Hadoop cluster.

What is FlumeNG?

A real time loader for streaming your data into Hadoop. It stores data in HDFS and HBase. You’ll want to get started with FlumeNG, which improves on the original flume.

Explain what are the tools used in Big Data?

Tools used in Big Data includes:

  • Hadoop
  • Hive
  • Pig
  • Flume
  • Mahout
  • Sqoop

Does Apache Flume provide support for third party plug-ins?

Most of the data analysts use Apache Flume has plug-in-based architecture as it can load data from external sources and transfer it to external destinations.

What is Flume event?

A unit of data with set of string attributes called Flume event. The external source like web-server 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 as an event. Each event has header and value sectors, which has header information and appropriate value that assign to the particular header.

 What Is Apache Flume?

Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data source. Review this Flume use case to learn how Mozilla collects and Analyse the Logs using Flume and Hive.

Flume is a framework for populating Hadoop with data. Agents are populated throughout one’s IT infrastructure – inside web servers, application servers and mobile devices, for example – to collect data and integrate it into Hadoop.

Will Apache Flume give support for third-party plug-ins?

Apache Flume has plug-in primarily based design. Basically, it will load knowledge from external sources and transfer it to external destinations most of the information analysts use it.

 What is that the reliable channel in Flume to confirm that there’s no knowledge loss?

A. Among the three channels JDBC, FILE and MEMORY, FILE Channel is that the most reliable channel.

What are interceptors?

A . Interceptors are used to filter the events between source and channel, channel and sink. These channels can filter un-necessary or targeted log files. Depends on requirements you can use n number of interceptors.

 What is sink processors?

Sink processors is a mechanism by which you can create a fail-over task and load balancing.

Can Flume can distributes data to multiple destinations?

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 a destination and another destination is input to another agent.

Can Flume provide 100% reliability to the data flow?

Yes, it provides end-to-end reliability of the flow. By default, Flume uses a transactional approach in the data flow. Sources and sinks encapsulated in a transactional repository provide 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.

What are the important steps in the configuration?

The configuration file is the heart of the Apache Flume’s agent.
Every Source must have at least one channel.
Every Sink must have only one channel.
Every Component must have a specific type.

Agent communicate with other Agents?

No, each agent runs independently. Flume can easily scale horizontally. As a result, there is no single point of failure.

What are Channel selectors?

Channel selectors control and separating the events and allocate to a particular channel. There are default/ replicated channel selectors. Replicated channel selectors can replicated 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 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.

What are the key components used in Flume data flow?

Flume flow has three main components – Source, Channel and Sink

Source:  Flume source is a Flume component that consumes events and data from sources such as a web server or message queue. Flume sources are customized to handle data from specific sources. For example, an Avro Flume source is used to ingest data from Avro clients and a Thrift flume source is used to ingest data from Thrift clients. You can write custom Flume sources to inject custom data. For example, you can write a Twitter Flume source to inject Tweets.

Channel:  Flume sources ingest data and store them into one or more channels. Channels are temporary stores, that keep the data until it is consumed by Flume sinks.

Sink:   Flume sinks removes the data stored in channels and puts it into a central repository such as HDFS or Hive.

What is flume Agent?

A Flume agent is a JVM process that hosts the components through which events flow from an external source to either the central repository or to the next destination. Flume agent wires together the external sources, Flume sources, flume Channels, Flume sinks, and external destinations for each flume data flow. Flume agent does this through a configuration file in which it maps the sources, channels, sinks, etc. and defines the properties for each component.

How do you check the integrity of file channels?

Fluid platform provides a File Channel Integrity tool which verifies the integrity of individual events in the File channel and removes corrupted events.

How do you handle agent failures?

If Flume agent goes down then all flows hosted on that agent are aborted. Once the agent is restarted then flow will resume. If the channel is set up as in-memory channel then all events that are stored in the channels when the agent went down are lost. But channels setup as file or other stable channels will continue to process events where it left off.

Why we are using Flume?

 Most often Hadoop developer use this tool to get log data from social media sites. It’s developed by Cloudera for aggregating and moving very large amount of data. The primary use is gather log files from different sources and asynchronously persists in the Hadoop cluster.

What is Flume Agent?

 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 destination like HDFS. Agent is heart of the Apache Flume.

What are the complicated steps in Flume configuration?

 Flume can process streaming data, so if started once, there is no stop/end to the process. asynchronously it can flow data from source to HDFS via Agent. First of all Agent should know individual components how they are connected to load data. So configuration is trigger to load streaming data. For example consumerKey, consumerSecret, accessToken and accessTokenSecret are key factors to download data from Twitter.

Can Flume can distributes data to multiple destinations?

 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 above example, data flows and replicated to HDFS and another sink to destination and another destination is input to another agent.

 Agent communicate with other Agents?

 No, each agent runs independently. Flume can easily scale horizontally. As a result, there is no single point of failure.

 What are interceptors?

 It’s one of the most frequently asked Flume interview question. Interceptors are used to filter the events between source and channel, channel and sink. These channels can filter un-necessary or targeted log files. Depends on requirements you can use n number of interceptors.

 What are Channel selectors?

 Channel selectors control and separating the events and allocate to a particular channel. There are default/ replicated channel selectors. Replicated channel selectors can replicated 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 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.

What is sink processors?

 Sink processors is a mechanism by which you can create a fail-over task and load balancing.

  What is Flume?

 A distributed service for collecting, aggregating, and moving large amounts of log data, is Flume.

Is it possible to leverage real-time analysis of the big data collected by Flume directly? If yes, then explain how?

By using MorphlineSolrSink we can extract, transform and load Data from Flume in real-time into Apache Solr servers.

 What is a channel?

 A transient store that receives the events from the source also buffers them till they are consumed by sinks is what we call a Flume channel. To be very specific it acts as a bridge between the sources and the sinks in Flume.
Basically, these channels can work with any number of sources and sinks are they are fully transactional.
Like − JDBC channel, File system channel, Memory channel, etc.

Explain about the different channel types in Flume. Which channel type is faster?

 There are 3 types of different built-in channel in Flume. they are-

  1. MEMORY Channel – Through this MEMORY Channel Events are read from the source into memory and passed to the sink.
  2. JDBC Channel – It stores the events in an embedded Derby database.
  3. FILE Channel –It writes the contents to a file on the file system after reading the event from a source. The file is deleted only after the contents are successfully delivered to the sink.

While we come to the fastest channel, it is the MEMORY Channel. It is the fastest channel among the three. Although, make sure it has the risk of data loss.

What is Interceptor?

 To alter/inspect flume events which are transferred between source and channel, we use Flume Interceptors.

 Explain about the replication and multiplexing selectors in Flume.

Basically, to handle multiple channels, we use Channel selectors. Moreover, an event can be written just to a single channel or to multiple channels, on the basis of Flume header value. By default, it is the Replicating selector, if a channel selector is not specified to the source. Although, the same event is written to all the channels in the source’s channels list, by using the replicating selector.  However, when the application has to send different events to different channels, we use Multiplexing channel selector.

 Does Apache Flume provide support for third-party plug-ins?

Apache Flume has plug-in-based architecture. Basically, it can load data from external sources and transfer it to external destinations most of the data analysts use it.

What is FlumeNG?

FlumeNG is nothing but a real-time loader for streaming your data into Hadoop. Basically, it stores data in HDFS and HBase. Thus, if we want to get started with FlumeNG, it improves on the original flume.

Can flume provide 100% reliability to the data flow?

Flume generally offers the end-to-end reliability of the flow. Also, it uses a transactional approach to the data flow, by default.
In addition, Source and sink encapsulate in a transactional repository provides the channels. Moreover, to pass reliably from end to end flow these channels are responsible. Hence, it offers 100% reliability to the data flow.

What are the complicated steps in Flume configurations?

we can process streaming data, by using Flume. Hence, if started once, there is no stop/end of the process. asynchronously it can flows data from source to HDFS via the agent. First of all, the agent should know individual components how they are connected to load data. Thus, to load streaming data configuration is the trigger. for example, consumerkey, consumersecret accessToken, and accessTokenSecret are key factors to download data from Twitter.

Which is the reliable channel in Flume to ensure that there is no data loss?

The most reliable channel is FILE Channel among the 3 channels JDBC, FILE, and MEMORY.

What are Flume core components?

 Source, Channels, and sink are core components in Apache Flume.

What are the Data extraction tools in Hadoop?

Sqoop can be used to transfer data between RDBMS and HDFS. Flume can be used to extract the streaming data from social media, weblog etc and store it on HDFS.

Explain data flow in Flume?

Basically, we use a framework Flume to transfer log data into HDFS. However, we can say events and log data are generated by the log servers. Also, these servers have Flume agents running on them. Moreover, these agents receive the data from the data generators.
To be more specific, in Flume there is an intermediate node which collects the data in these agents, that nodes are what we call as Collector. As same as agents, in Flume, there can be multiple collectors.
Afterwards, from all these collectors the data will be aggregated and pushed to a centralized store. Such as HBase or HDFS.  To understand better, refer the following Flume Data Flow diagram, it explains Flume Data Flow model.

can you explain about configuration files?

Basically, in the local configuration file, the agent configuration stores. Moreover, it comprises of each agent’s source, sinks, and channel information.
In addition, each core components such as source, sink, and channel have properties such as name, type, and set properties.

Tell any two feature Flume?

Any two features of Flume.

  • Data Flow

In Hadoop environments, Flume works with streaming data sources. Especially which generates continuously. Such as log files.

  • Routing

Generally, Flume looks at the payload such as stream data or event. Also, construct a routing which is apt.

Any two Limitations of Flume?

 Any two limitations of  Flume.

  • Weak Ordering Guarantee

While it comes to ordering guarantee, Apache flume is very weak in it.

  • Duplicacy

In many scenarios, Flume does not guarantee that message reaching is unique. However, it is a possibility that duplicate messages might pop in at times.

What are the similarities and differences between Apache Flume and Apache Kafka?

While it comes to Flume, it pushes messages to their destination via its Sinks. However, With Kafka, you need to consume messages from Kafka Broker using a Kafka Consumer API.

Explain Reliability and Failure Handling in Apache Flume?

To guarantee reliable message delivery Flume NG, it uses channel-based transactions. Moreover, while a message moves from one agent to another, two transactions are started, one on the agent that delivers the event and the other to the agent that receives the event. However, it must receive success indication from the receiving agent in order for the sending agent to commit its transaction.
Basically, the receiving agent only returns a success indication if its own transaction commits properly first. This ensures guaranteed delivery semantics between the hops that the flow makes.

What are Channel Selectors?

 To determine which channel we should select to transfer the data in case of multiple channels, we use Channel Selector.
Learn it in detail, follow the link.  Channel Selectors

What is Flume?

Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many fail over and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.

How can Flume be used with HBase?

Apache Flume can be used with HBase using one of the two HBase sinks –

HBaseSink (org.apache.flume.sink.hbase.HBaseSink) supports secure HBase clusters and also the novel HBase IPC that was introduced in the version HBase 0.96.

AsyncHBaseSink (org.apache.flume.sink.hbase.AsyncHBaseSink) has better performance than HBase sink as it can easily make non-blocking calls to HBase.

Working of the HBaseSink –

In HBaseSink, a Flume Event is converted into HBase Increments or Puts. Serializer implements the HBaseEventSerializer which is then instantiated when the sink starts. For every event, sink calls the initialize method in the serializer which then translates the Flume Event into HBase increments and puts to be sent to HBase cluster.

Working of the AsyncHBaseSink-

AsyncHBaseSink implements the AsyncHBaseEventSerializer. The initialize method is called only once by the sink when it starts. Sink invokes the setEvent method and then makes calls to the getIncrements and getActions methods just similar to HBase sink. When the sink stops, the cleanUp method is called by the serializer.

Explain about the replication and multiplexing selectors in Flume.

Channel Selectors are used to handle multiple channels. Based on the Flume header value, an event can be written just to a single channel or to multiple channels. If a channel selector is not specified to the source then by default it is the Replicating selector. Using the replicating selector, the same event is written to all the channels in the source’s channels list. Multiplexing channel selector is used when the application has to send different events to different channels.

Does Apache Flume provide support for third party plug-ins?

Most of the data analysts use Apache Flume has plug-in-based architecture as it can load data from external sources and transfer it to external destinations.

Does Apache Flume support third-party plugins?

Yes, Flume has 100% plugin-based architecture, it can load and ships data from external sources to external destination which separately from Flume. SO that most of the bigdata analysis use this tool for screaming data.

Differentiate between File Sink and File Roll Sink

The major difference between HDFS File Sink and File Roll Sink is that HDFS File Sink writes the events into the Hadoop Distributed File System (HDFS) whereas File Roll Sink stores the events into the local file system.

What are the complicated steps in Flume configurations?

Flume can processing streaming data. so, if started once, there is no stop/end to the process. asynchronously it can flow data from source to HDFS via agent. First of all, agent should know individual components how they are connected to load data. So, configuration is trigger to load streaming data. for example, consumerkey, consumersecret accessToken and accessTokenSecret are key factor to download data from twitter.

What are Flume core components?

Cource, Channels and sink are core components in Apache Flume. When Flume source receives event from external source, it stores the event in one or multiple channels. Flume channel is temporarily store and keep the event until’s consumed by the Flume sink. It acts as Flume repository. Flume Sink removes the event from channel and put into an external repository like HDFS or Move to the next flume.

 What are the Data extraction tools in Hadoop?

Sqoop can be used to transfer data between RDBMS and HDFS. Flume can be used to extract the streaming data from social media, web log etc. and store it on HDFS.

Does Flume provide 100% reliability to the data flow?

Yes, Apache Flume provides end to end reliability because of its transactional approach in data flow.

Tell any two features of Flume?

Fume collects data efficiently, aggregate and moves large amount of log data from many different sources to centralized data store.

Flume is not restricted to log data aggregation and it can transport massive quantity of event data including but not limited to network traffic data, social-media generated data, email message an pretty much any data storage.

What are interceptors?

Interceptors are used to filter the events between source and channel, channel and sink. These channels can filter un-necessary or targeted log files. Depends on requirements you can use n number of interceptors.

 

So, this brings us to the end of the Apache Flume Interview Questions blog.This Tecklearn ‘Top Apache Flume Interview Questions and Answers’ helps you with commonly asked questions if you are looking out for a job in Apache Flume or Big Data Domain. If you wish to learn Apache Flume and build a career in Big Data domain, then check out our interactive, Big Data Spark and Hadoop Developer Training, that comes with 24*7 support to guide you throughout your learning period.

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Introduction to Hadoop and the Hadoop Ecosystem

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Hadoop Architecture and HDFS

  • Distributed Processing on a Cluster
  • Storage: HDFS Architecture • Storage: Using HDFS
  • Resource Management: YARN Architecture
  • Resource Management: Working with YARN

Importing Relational Data with Apache Sqoop

  • Sqoop Overview
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  • Limiting Results
  • Improving Sqoop’s Performance
  • Sqoop 2

Introduction to Impala and Hive

  • Introduction to Impala and Hive
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  • Comparing Hive to Traditional Databases
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Modelling and Managing Data with Impala and Hive

  • Data Storage Overview
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  • HCatalog
  • Impala Metadata Caching

Data Formats

  • Selecting a File Format
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  • Compression

Data Partitioning

  • Partitioning Overview
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Capturing Data with Apache Flume

  • What is Apache Flume?
  • Basic Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Flume Configuration

Spark Basics

  • What is Apache Spark?
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  • Functional Programming in Spark

Working with RDDs in Spark

  • A Closer Look at RDDs
  • Key-Value Pair RDDs
  • MapReduce
  • Other Pair RDD Operations

Writing and Deploying Spark Applications

  • Spark Applications vs. Spark Shell
  • Creating the SparkContext
  • Building a Spark Application (Scala and Java)
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  • The Spark Application Web UI
  • Configuring Spark Properties
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Parallel Programming with Spark

  • Review: Spark on a Cluster
  • RDD Partitions
  • Partitioning of File-based RDDs
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Spark Caching and Persistence

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