Best Flume Interview Questions and Answers
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Flume Interview Questions and Answers
There is a list of some prominent Flume Interview Questions. Let’s discuss all possible Flume Interview Questions
Q1: What is Apache Flume
As we know, whereas it involves efficiency and dependably collect, mixture and transfer large amounts from one or additional supply’s to a centralized data source we tend to use Apache Flume. However, it will ingest any reasonably knowledge together with log knowledge, event data, network knowledge, social-media generated knowledge, email messages, message queues etc since knowledge sources area unit customizable in Flume.
Q2: What are the Basic Features of flume?
A data collection service for Hadoop: Using Flume, we can get the data from multiple servers immediately into Hadoop.For distributed systems: 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.Open source: It is open-source software. It doesn’t require any license key for its activation. Scalable: Flume can be scaled horizontally.
Q3: What are some applications of Flume?
Assume a web application wants to analyze the customer behaviors about current activity. So this is where Flume comes in handy. It extracts data and moves the data to Hadoop for analysis. Flume is used to move the log data generated by application servers into HDFS at a higher speed.
Q4: 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.
Q5: 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.
Q6: 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.
Q7: 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 destinations which separately from Flume. SO that most of the big data analysis uses this tool for streaming data.
Q8: What’s FlumeNG?
FlumeNG is nothing however a period loader for streaming your knowledge into Hadoop. Basically, it stores knowledge in HDFS and HBase. Thus, if we wish to urge started with FlumeNG, it improves on the first flume.
Q9: How do you handle agent failures?
If the Flume agent goes down then all flows hosted on that agent are aborted. Once the agent is restarted then the flow will resume. If the channel is set up as an in-memory channel then all events that are stored in the channels when the agent went down are lost. But channels set up as a file or other stable channels will continue to process events where it lest off.
Q10 : Can Flume can distribute data to multiple destinations?
Answer: Yes. It supports multiplexing flow. The event flows from one source to multiple channels and multiple destinations, It is achieved by defining a flow multiplexer.
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Q11: What is Flume?
Flume is a reliable distributed service for the collection and aggregation of large amounts of streaming data into HDFS. Most of the Big data analysts use Apache Flume to push data from different sources like Twitter, Facebook, & LinkedIn into Hadoop, Strom, Solr, Kafka & Spark.
Q12: Why we are using Flume?
Most often Hadoop developer uses 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.
Q13: 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 the destination like HDFS. An agent is the heart of the Apache Flume.
Q14: What are Flume Core components?
Source, Channels, and Sink are core components in Apache Flume.
When the Flume source receives an event from external sources, it stores the event in one or multiple channels.
Flume channel is temporarily stored & keeps the event until it’s consumed by the Flume sink. It acts as a Flume repository.
Flume Sink removes the event from the channel and puts it into an external repository like HDFS or Move to the next Flume agent.
Q15: 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 provided by the channels. These channels were responsible to pass reliably from end to end in the flow. So it provides 100% reliability to the data flow.
Q16: Can you explain about configuration files?
The agent configuration is stored in the local configuration file. It comprises of each agent’s source, sinks, and channel information. Each core component such as source, sink, and a channel has properties such as name, type and set of properties. For example, Avro source needs 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.
Q17: 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, agents should know individual components of how they are connected to load data. So the configuration is the trigger to load streaming data. For example, consumer key, Consumer Secret, Access Token and access token secret are key factors to download data from Twitter.
Q18: 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.
Q19: Apache Flume supports 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.
Q20: 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
Q21: Can Flume distribute 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 destination and another destination is input to another agent.
Q22: Agent communicates with other Agents?
No, each agent runs independently. Flume can easily scale horizontally. As a result, there is no single point of failure.
Q23: What are interceptors?
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 un-necessary or targeted log files. Depends on requirements you can use n number of interceptors.
Q24: What are Channel selectors?
Channel selectors control and separating the events and allocate them to a particular channel. There are the 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 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.
Q25: What is sink processors?
Sink processors is a mechanism by which you can create a fail-over task and load balancing.
Q26: Which is the reliable channel in Flume to ensure that there is no data loss?
Answer: FILE Channel is the most reliable channel among the 3 channels JDBC, FILE and MEMORY.
Q 27: How can Flume be used with HBase?
Answer: Apache Flume can be used with HBase using one of the two HBase sinks –
1.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.
2.AsyncHBaseSink (org.apache.flume.sink.hbase.AsyncHBaseSink) has better performance than HBase sink as it can easily make non-blocking calls to HBase.
Q28: Is it possible to leverage real-time analysis on the big data collected by Flume directly? If yes, then explain how?
Answer: Data from Flume can be extracted, transformed and loaded in real-time into Apache Solr servers using MorphlineSolrSink.
Q29: What is a channel?
Answer: 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.
Q30: Explain the different channel types in Flume. Which channel type is faster?
Answer: The 3 different built-in channel types available in Flume are-
1.MEMORY Channel – Events are read from the source into memory and passed to the sink.
2.JDBC Channel – JDBC Channel stores the events in an embedded Derby database.
3.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.
4.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.
Q31: Explain about the replication and multiplexing selectors in Flume.
Answer: Channel Selectors are used to handling 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. The multiplexing channel selector is used when the application has to send different events to different channels.
Q32: Does Apache Flume provide support for third-party plug-ins?
Answer: 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.
Q33: Differentiate between FileSink and FileRollSink
Answer: The major difference between HDFS FileSink and FileRollSink 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.
Q34: Can Flume can distribute data to multiple destinations?
Answer: Yes. It supports multiplexing flow. The event flows from one source to multiple channels and multiple destinations, it is achieved by defining a flow multiplexer.
Q35: How multi-hop agents can be set up in Flume?
Answer: Avro RPC Bridge mechanism is used to set up the Multi-hop agent in Apache Flume.
Q36: What is FlumeNG?
Answer: 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.
Q37: Agent communicates with other Agents?
Answer: NO each agent runs independently. Flume can easily horizontally. As a result, there is no single point of failure.
Q38: What are the Data extraction tools in Hadoop?
Answer: 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.
Q39: Tell any two feature Flume?
Fume collects data efficiently, aggregate and moves large amounts of log data from many different sources to centralized data stores.
Flume is not restricted to log data aggregation and it can transport a massive quantity of event data including but not limited to network traffic data, social-media generated data, email message pretty much any data storage.
Q40: How do you handle agent failures?
If the Flume agent goes down, then all flows hosted on that agent are aborted. Once the agent is restarted then the flow will resume. If the channel is set up as an in-memory channel, then all events that are stored in the channel when the agent went down are lost. But channel set up as a file or other stable channels will continue to process events where it left off.
