Apache Flink vs Apache Spark: Top Differences

Apache Flink and Apache Spark are two well-liked competitors in the rapidly growing field of big data, where information flows like a roaring torrent. These distributed processing frameworks are available as open-source software and can handle large datasets with unparalleled speed and effectiveness. But for your particular need, which one is the best?

In-depth coverage of the main features, advantages, and disadvantages of Flink and Spark is provided in this guide, enabling you to make well-informed choices for your upcoming data-driven victory. We’ll investigate the differences between their processing methods (batch and streaming), discover the mysteries of fault tolerance, and present the leading windowing tool.

What is Apache Flink?

Apache Flink represents an open-source, distributed engine crafted for stateful processing across unbounded (streams) and bounded (batches) datasets. Stream processing applications operate seamlessly, ensuring minimal downtime while efficiently handling data ingestion in real-time. Flink prioritizes low latency processing, executing computations in memory, and maintaining high availability by eliminating single points of failure and facilitating horizontal scaling.

Apache Flink boasts advanced state management abilities, providing exactly-once consistency guarantees, and utilizes event-time processing semantics, handling out-of-order and late data with finesse. Designed with a streaming-first approach, Apache Flink provides a suitable programming interface for both stream and batch processing.

Key Features of Apache Flink:

• State Management: Delivers advanced state management with exact-once consistency confirmations, ensuring data integrity in stream processing applications.
• High Throughput and Low Latency: is Qualified for processing high volumes of data with low latency, making it suited for real-time analytics and decision-making.
• Event-Time Processing: Implements event-time processing semantics, allowing refined handling of out-of-order and late-arriving data for correct analysis
• Rich Set of Operators and APIs: Delivers a complete set of operators and APIs for creating complex data processing pipelines, supporting different data transformations and analytics tasks.
• Streaming-First Design: Developed with a streaming-first approach, prioritizing real-time data processing and analysis over batch processing.

What is Apache Spark?

Apache Spark is an open-source distributed processing system, that is best in handling large-scale big-data workloads with its in-memory caching and optimized query performance capabilities. Its support for different development APIs including Java, Scala, Python, and R facilitates code reuse across multiple workloads, from batch processing to real-time analytics and machine learning. Also, Spark offers fault-tolerance mechanisms ensuring data reliability, and its optimized performance engine improves speed and efficiency for demanding data processing tasks.

Furthermore, Spark integrates seamlessly with a rich ecosystem of tools and libraries, developing its capabilities and providing users with a complete set for data storage, processing, and analysis.

Key Features of Apache Spark:

• In-Memory Processing: Apache Spark operates in-memory caching to speed up data processing, decreasing the need for disk I/O functions and improving overall performance.
• Distributed Computing: Spark distributes data processing tasks across a set of machines, helping similar performance and scalable data processing for large-scale workloads.
• Unified Platform: Spark delivers a suitable platform for different data processing tasks, including batch processing, interactive queries, real-time analytics, and machine learning, simplifying development and reducing the requirement for multiple systems.
• Versatile Development APIs: Spark delivers product APIs in multiple languages including Java, Scala, Python, and R, helping ease of use and code reuse across different programming standards.
• Rich Ecosystem: Spark integrates smoothly with a broad range of tools and libraries including Hadoop, Apache Hive, Apache HBase, and more, delivering users with a complete ecosystem for data storage, processing, and analysis.

Apache Flink vs Apache Spark

As we differentiate these frameworks i.e. Apache Flink and Apache Spark you’ll discover the perfect tool to transform your raw data into actionable insights and conquer the ever-growing mountain of information.

1. Iterative Processing

Apache Flink:

Distinct data processing systems usually lack native support for iterative processing, a crucial capability for different machine learning and graph algorithm systems. Flink addresses this need with two dedicated iterative operations: iterate and delta iterate. In contrast, Spark does not offer built-in support for iterative processing. Developers using Spark must manually implement such operations, typically resorting to conventional loop statements.

Apache Spark:

Spark does offer a caching operation, allowing applications to cache a dataset explicitly and access it from memory during iterative computations. However, due to Spark’s batch-wise iteration process with an external loop, it needs to schedule and execute each iteration individually, potentially impacting performance. In contrast, Flink utilizes native loop operators, which can lead to arguably better performance for machine learning and graph processing algorithms compared to Spark.

2. Performance

Apache Flink:

Apache Flink is best in low-latency, high-throughput stream processing. It designs real-time analytics, making it ideal for systems where data needs to be processed rapidly as it arrives. Flink Is designed to handle backpressure, ensuring system stability even under high loads. This is achieved through built-in flow control mechanisms that prevent data processing bottlenecks.

Flink Utilizes operator chaining and pipelined execution to optimize data processing performance. This approach enables efficient parallelism and resource utilization during data processing tasks.

Apache Spark:

Apache Spark, on the other hand, is renowned for its fast batch-processing capabilities. It focuses primarily on efficiently handling large volumes of data in batch processing tasks, making it suitable for scenarios where data can be processed in discrete batches. Spark Streaming may struggle to handle backpressure, potentially leading to performance degradation.

Apache Spark Employs RDDs and data partitioning strategies like Hash and Range partitioning to enhance parallelism and optimize resource utilization during data processing tasks.

3. Fault Tolerance

Apache Flink:

Flink works as a fault-tolerant processing engine using a variant of the Chandy-Lamport algorithm to charge distributed snapshots. This algorithm, being lightweight and non-blocking, enables the system to maintain higher throughput and consistency guarantees. Regular intervals are set for check-pointing data sources, sinks, and application states, including window and user-defined states, facilitating failure recovery. Flink demonstrates resilience by sustaining numerous jobs over extended periods, and it offers configuration options for developers to tailor responses to various types of losses.

Apache Spark:

Spark features automatic recovery from failures without requiring additional code or manual configuration from developers. Data is initially written to Write-Ahead Logs (WAL), ensuring recovery even in the event of a crash before processing. With RDDs (Resilient Distributed Datasets) as the abstraction, Spark transparently recomputes partitions on failed nodes, seamlessly managing failures for end-users.

4. Optimization

Apache Flink:

Flink features a cost-based optimizer specifically designed for batch-processing tasks. This optimizer meticulously examines the data flow, analyzing available resources and data characteristics to select the most efficient execution plan. Moreover, Flink’s stream processing capabilities are further enhanced by pipeline-based execution and low-latency scheduling, ensuring swift and efficient data processing

Apache Spark:

Spark utilizes the Catalyst optimizer, renowned for its extensibility in optimizing data transformation and processing queries. Additionally, Spark integrates the Tungsten execution engine, enhancing the physical execution of operations to achieve superior performance.

Moreover, the Catalyst optimizer in Spark offers a flexible framework for query optimization, allowing developers to easily extend its capabilities to suit specific use cases.

5. Windowing

Apache Flink:

Flink’s windowing operations are exclusively applied to keyed streams. A keyed stream involves partitioning the stream into multiple segments based on a user-provided key. This enables Flink to execute these segmented streams concurrently across the distributed infrastructure beneath.

Flink offers extensive capabilities for windowing, encompassing event-time and processing-time-based windows, session windows, and adaptable custom window functions. Flink’s windowing functionality excels in efficiency and accuracy for stream processing, being purpose-built for continuous data streams.

Apache Spark:

Spark offers windowing functions for processing streaming data within fixed or sliding time windows. However, Spark’s windowing capabilities are limited to time-based implementations and do not extend beyond temporal constraints. Compared to Flink, Spark’s windowing functionality is less versatile and efficient, primarily due to its dependence on micro-batching.

6. Language Support

Apache Flink:

Flink backs multiple programming languages like Java, Scala, and Python. However, Flink’s Python support is not as advanced as Spark’s, potentially constraining its appeal to teams focused on Python for data science.

Using Flink, developers have the flexibility to craft applications using Java, Scala, Python, and SQL. The Flink runtime automates the compilation and optimization of these programs into dataflow programs, ready for execution on the Flink cluster.

Apache Spark:

Spark helps different programming languages, including Scala, Java, Python, and R. This comprehensive language support improves Spark’s inclusivity, appealing to a various community of developers and data scientists. Moreover, it enables seamless collaboration and integration within versatile teams, enabling innovation and knowledge sharing.

7. APIs and Libraries

Apache Flink:

Provides a comprehensive set of APIs in Java, Scala, and Python for crafting data processing applications. Flink’s libraries encompass FlinkML for machine learning, FlinkCEP for complex event processing, and Gelly for graph processing.

Apache Spark:

Spark Provides a complete set of Java, Scala, Python, and R APIs, and improves availability to a wider developer. Spark also increased comprehensive libraries, including MLlib for machine learning, GraphX for graph practices, and Spark Streaming for real-time data practices.

8. Ecosystem and Community

Apache Flink:

Although Flink is achieving traction, its ecosystem presently lags behind that of Spark. However, Flink is in a state of continuous growth, regularly including new features, therefore solidifying its standing as a challenging player in the realm of big data processing.

Apache Spark:

Spark boasts a comprehensive and well-developed ecosystem, full of a diverse array of connectors, libraries, and tools at your disposal. This extensive framework enables the accessibility of resources, support, and third-party integrations for your project, streamlining your development journey.

When To Use Apache Flink

• Real-time Analytics: When you need to process constant streams of data in real time and derive insights or perform analytics on the fly, Flink’s stream processing capabilities excel.
• Complex Event Processing (CEP): If your application involves detecting difficult patterns or series of events within a stream, Flink’s CEP library provides effective tools for event pattern matching and detection.
• Low-Latency Requirements: When your services demand low-latency processing, Flink’s architecture is designed to minimize processing overhead and perform millisecond-level latencies

When To Use Apache Spark

• Real-time Stream Processing: Spark streaming allows the process of real-time streaming data, making it suitable for applications like real-time analytics and monitoring.
• Batch Processing: Spark is well known for batch practice tasks, such as ETL (Extract, Transform, Load) jobs, data cleaning, and data practice. It delivers high-level APIs in languages like Scala, Java, Python, and R, making them open to a broad range of users and use cases.
• Machine Learning: Spark’s MLlib library delivers scalable machine learning algorithms for structuring, training, and open models at scale, covering a broad range of machine learning tasks.

Apache Flink vs Apache Spark: Difference Table

Aspects	Apache Flink	Apache Spark
Processing Style	Primarily stream processing, with batch processing capabilities	Primarily batch processing, with real-time stream processing through Spark Streaming
Focus	Low-latency, real-time analytics	High-throughput, large-scale data processing
State Management	Advanced state management with exactly-once consistency guarantees	Resilient Distributed Datasets (RDDs) for fault tolerance
Windowing	Extensive capabilities for event-time and processing-time-based windows, session windows, and custom window functions (designed for streams)	Limited to time-based windows (less versatile for streams)
Language Support	Java, Scala, Python (Python support less mature)	Scala, Java, Python, R
Ecosystem & Community	Growing ecosystem, but less extensive than Spark’s	Comprehensive and well-developed ecosystem with a wide range of connectors, libraries, and tools
Strengths	Real-time analytics, complex event processing (CEP), low-latency requirements	Batch processing, machine learning (MLlib library), diverse language support
Ideal Use Cases	Real-time fraud detection, sensor data analysis, stock price analysis	ETL (Extract, Transform, Load) jobs, data cleaning, large-scale batch analytics

Conclusion

In conclusion, Apache Spark and Apache Flink stand out as effective distributed data processing frameworks with different strengths. Spark is best in batch processing and helps multiple languages, catering to various use cases. Conversely, Flink shows prowess in stream processing, offering real-time analytics with minimal latency. Deciding between Spark and Flink on specific project needs, including processing requirements, latency sensitivity, language support, and team ability. A detailed evaluation, considering factors like ecosystem and learning curve, alongside proof-of-concept tests, is essential for making an informed decision and managing big data processing challenges effectively.