Flume is a standard, simple, robust, versatile, and extendable tool for ingesting data into Hadoop from a variety of data providers (webservers).
Apache Flume is a dependable and distributed log data collection, aggregation, and distribution system. It's a highly available, dependable service with adjustable recovery methods.
The Flume's main goal is to capture streaming data from various web servers and store it in HDFS. Its architecture is basic and adaptable, based on streaming data flows. It is fault-tolerant and provides a fault tolerance and failure recovery mechanism.
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Ques. 1): What does Apache Flume stand for?
Apache Flume is an open source platform for collecting, aggregating, and transferring huge amounts of data from one or more sources to a centralised data source effectively and reliably. Flume's data sources can be customised, so it can injest any type of data, such as log data, event data, network data, social media produced data, email messages, message queues, and so on.
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Apart from collecting logs from distributed systems, it is also capable of performing other use cases. like
It Collects readings from array of sensors
Also, it collects impressions from custom apps for an ad network
Moreover, it collects it readings from network devices in order to monitor their performance.
Also, preserves the reliability, scalability, manageability, and extensibility while it serves maximum number of clients with higher QoS.
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Apache Flume is an open source platform for collecting, aggregating, and transferring huge amounts of data from one or more sources to a centralised data source effectively and reliably. Flume's data sources can be customised, so it can injest any type of data, such as log data, event data, network data, social media produced data, email messages, message queues, and so on.
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Apart from collecting logs from distributed systems, it is also capable of performing other use cases. like
It Collects readings from array of sensors
Also, it collects impressions from custom apps for an ad network
Moreover, it collects it readings from network devices in order to monitor their performance.
Also, preserves the reliability, scalability, manageability, and extensibility while it serves maximum number of clients with higher QoS.
Flume is a dependable distributed service for aggregating and collecting massive amounts of streaming data into HDFS. Most big data analysts utilise Apache Flume to deliver data into Hadoop, Strom, Solr, Kafka, and Spark from various sources such as Twitter, Facebook, and LinkedIn.
When it comes to Flume, it uses Sinks to send messages to their destinations. However, with Kafka, you must use a Kafka Consumer API to accept messages from the Kafka Broker.
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Ques. 5): What is flume agent, exactly?
A Flume agent is a Java virtual machine (JVM) process that hosts the components that allow events to flow from an external source to the central repository or to the next destination.
For each flume data flow, the Flume agent connects the external sources, Flume sources, Flume Channels, Flume sinks, and external destinations. Flume agent accomplishes this by mapping sources, channels, sinks, and other components, as well as defining characteristics for each component, in a configuration file.
A Flume agent is a Java virtual machine (JVM) process that hosts the components that allow events to flow from an external source to the central repository or to the next destination.
For each flume data flow, the Flume agent connects the external sources, Flume sources, Flume Channels, Flume sinks, and external destinations. Flume agent accomplishes this by mapping sources, channels, sinks, and other components, as well as defining characteristics for each component, in a configuration file.
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Ques. 6): How do you deal with agent errors?
If a Flume agent fails, all flows hosted on that agent are terminated.
Flow will resume once the agent is restarted. All events stored in the chavvels when the agent went down are lost if the channel is set up as an in-memory channel. Channels configured as file or other stable channels, on the other hand, will continue to handle events where they left off.
If a Flume agent fails, all flows hosted on that agent are terminated.
Flow will resume once the agent is restarted. All events stored in the chavvels when the agent went down are lost if the channel is set up as an in-memory channel. Channels configured as file or other stable channels, on the other hand, will continue to handle events where they left off.
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Ques. 7): In Flume, how is recoverability ensured?
Flume organises events and data into channels. Flume sources populate Flume channels with events. Flume sinks consume channel events and publish them to terminal data storage. Failure recovery is handled by channels. Flume supports a variety of channels. In-memory channels save events in an in-memory queue for speedier processing. The local file system backs up file channels, making them durable.
Flume organises events and data into channels. Flume sources populate Flume channels with events. Flume sinks consume channel events and publish them to terminal data storage. Failure recovery is handled by channels. Flume supports a variety of channels. In-memory channels save events in an in-memory queue for speedier processing. The local file system backs up file channels, making them durable.
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Ques. 8): What are the Flume's Basic Characteristics?
A Hadoop data gathering service: We can quickly pull data from numerous servers into Hadoop using Flume. For distributed systems, use the following formula: Flume is also used to import massive amounts of event data from social networking sites such as Facebook and Twitter, as well as e-commerce sites such as Amazon and Flipkart. Source code: It is an open-source programme. It can be activated without the use of a licence key. Flume may be resized vertically and horizontally.
1. A flume transports data from sources to sinks. This data collection might be planned or event-driven. Flume features its own query processing engine, which makes it simple to alter each fresh batch of data before sending it to its destination.
2. Apache Flume is horizontally scalable.
3. Apache Flume provides support for large sets of sources, channels, and sinks.
4. With Flume, we can collect data from different web servers in real-time as well as in batch mode.
5. Flume provides the feature of contextual routing.
6. If the read rate exceeds the write rate, Flume provides a steady flow of data between read and write operations.
A Hadoop data gathering service: We can quickly pull data from numerous servers into Hadoop using Flume. For distributed systems, use the following formula: Flume is also used to import massive amounts of event data from social networking sites such as Facebook and Twitter, as well as e-commerce sites such as Amazon and Flipkart. Source code: It is an open-source programme. It can be activated without the use of a licence key. Flume may be resized vertically and horizontally.
1. A flume transports data from sources to sinks. This data collection might be planned or event-driven. Flume features its own query processing engine, which makes it simple to alter each fresh batch of data before sending it to its destination.
2. Apache Flume is horizontally scalable.
3. Apache Flume provides support for large sets of sources, channels, and sinks.
4. With Flume, we can collect data from different web servers in real-time as well as in batch mode.
5. Flume provides the feature of contextual routing.
6. If the read rate exceeds the write rate, Flume provides a steady flow of data between read and write operations.
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Ques. 9): What exactly is the Flume event?
Flume event is a data unit containing a set of string properties. The source receives events from an external source, such as a web server. Flume contains built-in capabilities to recognise the source format. Avro, for example, delivers events to the Flume from Avro sources.
Each log file is treated as an individual event. Each event has header and value sectors, which contain header information as well as the proper value for each header.
Flume event is a data unit containing a set of string properties. The source receives events from an external source, such as a web server. Flume contains built-in capabilities to recognise the source format. Avro, for example, delivers events to the Flume from Avro sources.
Each log file is treated as an individual event. Each event has header and value sectors, which contain header information as well as the proper value for each header.
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Ques. 10): In Flume, explain the replication and multiplexing
selections.
Answer: Channel selectors are used to handle many channels. Furthermore, based on the Flume header value, an event can be written to a single channel or numerous channels. If no channel selector is supplied for the source, it defaults to the Replicating selector.
Answer: Channel selectors are used to handle many channels. Furthermore, based on the Flume header value, an event can be written to a single channel or numerous channels. If no channel selector is supplied for the source, it defaults to the Replicating selector.
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Ques. 11): What exactly is FlumeNG?
FlumeNG is nothing more than a real-time loader for streaming data into Hadoop. It basically uses HDFS and HBase to store data. As a result, if we wish to start with FlumeNG, we should know that it improves on the original flume.
Using the replicating selection, the same event is written to all of the channels in the source's channels list. We use the Multiplexing channel selection when the application has to broadcast distinct events to multiple channels.
FlumeNG is nothing more than a real-time loader for streaming data into Hadoop. It basically uses HDFS and HBase to store data. As a result, if we wish to start with FlumeNG, we should know that it improves on the original flume.
Using the replicating selection, the same event is written to all of the channels in the source's channels list. We use the Multiplexing channel selection when the application has to broadcast distinct events to multiple channels.
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Ques. 12): Could you please clarify what configuration files
are?
The configuration of the agent is saved in a local configuration file. It contains information about each agent's source, sink, and channel. Name, type, and set of properties are all properties of each fundamental component, such as source, sink, and channel. To accept data from an external client, an Avro source, for example, requires the hostname and port number. In terms of capacity, the memory channel should have a maximum queue size. Sink should have File System URI, Path to Create Files, File Rotation Frequency, and other settings.
The configuration of the agent is saved in a local configuration file. It contains information about each agent's source, sink, and channel. Name, type, and set of properties are all properties of each fundamental component, such as source, sink, and channel. To accept data from an external client, an Avro source, for example, requires the hostname and port number. In terms of capacity, the memory channel should have a maximum queue size. Sink should have File System URI, Path to Create Files, File Rotation Frequency, and other settings.
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Ques. 13): What is topology design in Apache Flume?
The initial step in Apache Flume is to verify all data sources and sinks, after which we may determine whether we need event aggregation or rerouting. When gathering data from multiple sources, aggregation and rerouting are required to redirect those events to a different place.
The initial step in Apache Flume is to verify all data sources and sinks, after which we may determine whether we need event aggregation or rerouting. When gathering data from multiple sources, aggregation and rerouting are required to redirect those events to a different place.
The core components of Flume are –
Event- The single log entry or unit of data that is transported.
Source- This is the component through which data enters Flume workflows.
Sink-It is responsible for transporting data to the desired destination.
Channel- it is the duct between the Sink and Source.
Agent- Any JVM that runs Flume.
Client- The component that transmits event to the source that operates with the agent.
To transport log data into HDFS, we use the Flume framework. The log servers, on the other hand, generate events and log data. Flume agents are also running on these servers. Furthermore, the data generators provide the data to these agents.
To be more explicit, there is an intermediate node in Flume that collects data from these agents; these nodes are referred to as Collectors. There can be several collectors in Flume, just like there can be multiple agents.
After that, data from all of these collectors will be gathered and transferred to a central location. For example, HBase or HDFS. Refer to the Flume Data Flow diagram below for a better understanding of the Flume Data Flow paradigm.
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.
Ans: The data is "streamed" off the hard disc by keeping the drive's maximum I/O rate for these huge blocks of data constant. The write-once, read-many-times pattern is the most efficient data processing pattern, according to HDFS.
To deliver the event to the Hbase system, Apache Flume HBaseSink and AsyncHBaseSink are both employed. The HTable API is used to transfer data to HBase in the case of HBaseSink, while the asynchbase API is used to send stream data to HBase in the case of AsyncHBaseSink. The callbacks are responsible for handling any failures.
Ans:
It's another Apache Software Foundation top-level project designed to provide continuous data injection in Hadoop HDFS. The data can be any type of data, however Flume is best suited for handling log data, such as web server log data.
Ans: Streaming simply means that you can get a continuous bitrate over a specific threshold when sending data, rather than having it come in bursts or waves. If HDFS is set up for streaming, it will very certainly enable seek, albeit with the added overhead of caching data for a steady stream.
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