#Apache_Flink

Question count - 0     Last updated    - V2 (29-May-2024)

What is Apache Flink?

What are the differences between the DataStream API and the DataSet API in Flink?

What are windows in Apache Flink, and why are they important?

What is event time processing, and how does it differ from processing time?

What are watermarks in Flink, and how are they used?

What are some common configuration parameters in Flink, and how do they affect performance?

What are Flink connectors, and why are they important?

What is backpressure in Flink, and how can it be managed?

What is Flink SQL, and how can it be used in stream processing?

What is the difference between a savepoint and a checkpoint in Flink?

What is a side output in Flink, and when would you use it?

What is a state backend in Flink, and how do you configure it?

What are incremental checkpoints, and why are they important?

What are the steps to implement a custom Flink connector?

What are task slots in Flink, and how do they relate to resource management?

What is Flink's process function, and when would you use it?

What is the difference between Flink?s at-least-once and exactly-once semantics?

What considerations would you take into account for deploying Flink on Kubernetes?

What is the role of the keyBy function in Flink?

What are operator state and keyed state in Flink?

What are the challenges of processing out-of-order events in Flink?

What are the common bottlenecks in a Flink job, and how can they be mitigated?

What are the best practices for deploying Flink jobs in a production environment?

What are some common issues that can occur with checkpointing, and how do you resolve them?

What are the considerations for running Flink in a multi-tenant environment?

What are the steps you would take to troubleshoot and fix a failed Flink job?

What is the type of Lambda Expression?

Explain the difference between the KeyedStream and non-KeyedStream in Flink.

Explain the purpose and functionality of Flink's Table API and SQL.

How does Flink handle stateful stream processing?

How does Flink ensure fault tolerance in stream processing?

How do you deploy a Flink job on a cluster?

How do you optimize the performance of a Flink application?

How does Flink handle complex event processing (CEP)?

How would you handle late-arriving data in Flink?

How does Flink ensure data consistency and exactly-once processing semantics?

How does Flink handle data partitioning and parallelism?

How does Flink ensure state consistency during failures?

How do you handle watermarks in scenarios with varying event delays?

How do you achieve exactly-once semantics with Apache Kafka and Flink?

How do you implement a custom window function in Flink?

How does Apache Flink support Machine Learning tasks?

How would you handle dynamic reconfiguration of a Flink job?

How do you perform debugging and troubleshooting of Flink jobs?

How do you implement custom serialization in Flink?

How does Flink's state abstraction work?

How does Flink guarantee exactly-once processing semantics?

How do you handle schema evolution in Flink when integrating with Kafka?

How would you perform a rolling upgrade of a Flink application?

How does Flink's checkpointing mechanism work under the hood?

How does Flink support distributed data processing and consistency in the context of exactly-once semantics?

How would you handle a scenario where a Flink job's state grows significantly over time?

Apache Flink	Apache Flink is a powerful framework and distributed processing engine for stateful computations over unbounded and bounded data streams It is widely used for big data processing and real-time analytics due to its high throughput, low latency, and ability to handle complex event processing. JobManager TaskManager Distributed execution engine
DataStream API	Unbounded Streams: How to handle continuous data streams with the DataStream API. Transformation Operations: Map, filter, keyBy, reduce, window, etc. Windowing: Different types of windows (time-based, count-based, session windows) and their applications.
DataSet API	Bounded Data Processing: Working with finite datasets. Batch Processing: Using the DataSet API for batch processing tasks, transformations, and optimizations.
State Management	Stateful Stream Processing: Managing state in streaming applications. State Backends: Various state backends like memory, RocksDB, and their use cases. Checkpointing and Savepoints: Mechanisms for fault tolerance and state recovery.
Event Time and Watermarks	Event Time Processing: Handling events based on their timestamps. Watermarks: Generating and using watermarks to handle out-of-order events.
Deployment and Configuration	Cluster Setup: Setting up and managing a Flink cluster. Job Submission: Deploying Flink jobs on clusters. Configuration Parameters: Tuning Flink?s performance through configuration.
Fault Tolerance	Checkpointing: Ensuring fault tolerance through periodic state snapshots. Savepoints: Manual snapshots for job upgrades and maintenance. Failure Recovery: Mechanisms to recover from failures and resume job execution.
Integration with Other Systems	Connectors: Integrating Flink with various data sources and sinks like Kafka, HDFS, Cassandra, etc. Flink SQL: Using SQL queries for stream and batch processing. CEP (Complex Event Processing): Pattern matching and complex event processing capabilities.
Performance Optimization	Parallelism: Configuring task parallelism for optimal performance. Resource Management: Efficiently managing CPU, memory, and other resources. Tuning Tips: Practical tips and best practices for optimizing Flink jobs.
Use Cases and Examples	Real-Time Analytics: Implementing real-time data analytics and dashboards. Event-Driven Applications: Building applications that respond to events in real-time. Batch Processing: Large-scale batch processing applications.
Advanced Topics	Streaming SQL: Leveraging SQL queries for stream processing. Machine Learning: Integrating machine learning algorithms with Flink. Graph Processing: Using Flink for processing graph data.

Flink_SQL         FlinkML