1. What is the primary purpose of Apache Flume?
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Explanation:
Apache Flume is primarily used for efficiently collecting, aggregating, and moving large amounts of log data into the Hadoop Distributed File System (HDFS).
2. What are Flume Agents?
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Explanation:
Flume Agents are JVM processes that host the components through which data flows from an external source to the destination (like HDFS). An agent can have sources, channels, and sinks.
3. In Flume, what is a 'Source'?
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Explanation:
In Apache Flume, a 'Source' is the component responsible for ingesting data into the system from external sources like log files, network traffic, social media streams, etc.
4. What is a 'Sink' in Apache Flume?
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In Flume, a 'Sink' is the component that delivers data to the desired destination, such as HDFS, HBase, or other data stores and analytics platforms.
5. What are 'Channels' in Flume?
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Channels in Flume act as the conduit between the Sources and Sinks, temporarily storing the incoming data before it is consumed by the Sink.
6. How does Flume provide reliability to data flow?
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Explanation:
Flume ensures reliable data flow by using a transactional approach. If a transaction (data transfer) fails, Flume will attempt to replay the transaction, ensuring no data loss.
7. What type of data model does Flume use for data transportation?
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Flume is designed for streaming data flows, allowing for continuous data ingestion and movement in real-time.
8. What is the role of a Flume 'Interceptor'?
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Explanation:
Interceptors in Flume allow events to be intercepted and modified in-flight as they move from the source to the channel, enabling data enrichment or filtering.
9. Can Flume handle multiple sources and multiple sinks in a single agent?
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Explanation:
A single Flume agent can be configured to have multiple sources and multiple sinks, enabling complex data flow architectures.
10. What is a Flume 'Event'?
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In Flume, an event is the fundamental unit of data that flows through the system, typically comprising a payload (the data) and optional headers.
11. How does Flume support failover and load balancing?
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Explanation:
Flume supports failover and load balancing by configuring multiple agents and sinks, thus ensuring continuous data flow even if a component fails or gets overloaded.
12. What is the function of a Flume 'Serializer'?
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Explanation:
A Flume Serializer is used to convert data into a specific format required by the sink. This is important for ensuring compatibility with different types of data stores and systems.
13. In Flume, what is a 'Fan-out flow'?
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A 'Fan-out flow' in Flume refers to the configuration where data from a single source is sent to multiple sinks, useful for replicating data or performing different actions simultaneously.
14. What are 'File Channel' and 'Memory Channel' in Flume?
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Explanation:
File Channel and Memory Channel are two types of channels in Flume used for buffering data. File Channel stores events in a file system for higher reliability, while Memory Channel uses in-memory storage for faster performance.
15. Can Flume be integrated with systems other than Hadoop?
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Explanation:
Apache Flume can be integrated with a variety of systems besides Hadoop, including data stores like HBase, analytics platforms, and cloud storage services.
16. What is Flume's 'Pollable Source'?
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Explanation:
A Pollable Source in Flume is a type of source that actively pulls or fetches data from its origin at configured intervals, as opposed to waiting for data to be pushed to it.
17. How is data reliability achieved in Flume's File Channel?
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Explanation:
In Flume's File Channel, data reliability is achieved by persisting data to the local file system, ensuring that the data is not lost even if the system crashes or restarts.
18. What is the role of a 'Sink Processor' in Flume?
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Explanation:
A Sink Processor in Flume is used to determine which sink or set of sinks should be used for each event or batch of events, enabling dynamic routing and load balancing.
19. Can Flume be used for complex event processing (CEP)?
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Explanation:
While Flume is primarily used for data ingestion, it can be part of a complex event processing system when combined with additional tools like Apache Storm or Apache Spark for real-time data processing.
20. What is the advantage of using Flume's Avro Source and Sink?
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Explanation:
Flume's Avro Source and Sink are used for inter-agent communication in distributed environments, facilitating efficient and reliable data transfer between different Flume agents.
21. How does Flume handle backpressure?
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Flume handles backpressure by throttling the data flow at the source, preventing the overwhelming of sinks and channels and ensuring stable data processing.
22. What are 'Flume Agents' in the context of a distributed Flume deployment?
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In a distributed Flume deployment, 'Flume Agents' refer to independent Flume instances that operate in different physical locations or servers, each capable of running sources, channels, and sinks.
23. What is the primary benefit of using Flume's Morphline Sink?
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Explanation:
Flume's Morphline Sink allows for real-time data transformation and enrichment, enabling on-the-fly modifications and processing of data before it reaches the final destination.
24. Can Flume sources initiate the transfer of data from web servers?
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Explanation:
Flume's HTTP Source can initiate the transfer of data from web servers by fetching data over HTTP, enabling integration with web-based data sources and services.
25. What mechanism does Flume use to ensure exactly-once processing semantics?
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Explanation:
Flume ensures exactly-once processing semantics by using transactional channels. Each event is committed to the channel in a transaction, and if a failure occurs, the transaction is rolled back, preventing data loss or duplication.