Apache Flink Versus Apache Kafka — A Deep Dive into Real-Time Processing Powerhouses

Squaring Off in the Streaming Arena: Analyzing Performance, Use Cases, Pros & Cons and Future Trends

Fig.1 Flink Logo

Hello, data lovers! Today, we are going to deal with the wide concept of distributed data processing. Apache Flink has been established as a framework useful to tackle among the execution of demanding stateful computations over both bounded and unbounded streams of data.
Spend some time with us to understand how Flink’s robust engine works impeccably in various cluster environments and conducts efficient operations at remarkable speed, regardless of the scale.

One of the Flink’s strengths is that it can be used as a toolkit by developers. It allows users to build incredible applications using Java and SQL programming languages. This ability allows developers to solve a wide range of practical issues related to different fields of industries as financial industry, e-commerce, telecommunication and beyond.

Ultimately, Apache Flink stands out from its competitors with the support of a fundamentally different feature bridging a gap between batch and stream processing. Unlike traditional systems that force developers to choose between one or the other, Flink provides an opportunity to fully utilize both paradigms within a single framework. Therefore, it not only facilitates the development but also opens up brand new possibilities in data-driven decision-making and real-time analysis.

Here, we will discover some of the remarkable features of Apache Flink and find out its key features and use cases. Additionally, we will build up a comparison between Apache Flink and Apache Kafka in order to identify the main differences between the two systems.

Flink Ecosystem

Fig.2 Flink Ecosystem

Let’s delve into some key components of the Apache Flink ecosystem:

DataSet API: DataSet API of Apache Flink enables developers to do batch operations on static data sets. It is designed to operate a familiar programming model like Apache Spark’s Resilient Distributed Datasets (RDDs) for processing data in bulk with the use of operators such as map, filter, reduce, etc.,

DataStream API: In contrast to DataSet API, DataStream API processes continuous data flows in the real time model. Thus, developers can be able to build event-driven applications by giving them tools for windowing, time based operations, watermarks and stateful computations.

Table API & SQL: The Table API and SQL give a more abstract level over the DataSet and DataStream APIs, enabling users to express their data processing tasks in a more declarative and SQL-like manner. This abstraction makes it easier to write and maintain queries as well as for them to be integrated with the already existing SQL-based tools and components.

Gelly: Gelly is the graph processing library of Apache Flink platform. It offers a set of graph algorithms and operators for analyzing and processing graph data efficiently. Thanks to Gelly, it can be possible to attempt tasks like graph traversal, pattern matching, and community detection.

FlinkML: The FlinkML is a machine learning library that delivers scalable implementation of machine learning algorithms, which can be provided to make predictions using large-scale datasets. It consists of algorithms such as classification, regression, clustering and collaborative filter, etc. FlinkML benefits from Flink’s distributed processing capabilities to train models in parallel and handle large volumes of data efficiently.

Why Apache Flink?

Flink has been proven to scale to thousands of cores and terabytes of application state, delivers high throughput and low latency, and powers some of the world’s most demanding stream processing applications.

Fig.3 Analytical Applications

1. Unified Processing: Apache Flink is a single platform that combines batch and stream processing excluding the need for different systems. This greatly accelerates processing of data in sourcing and maintenance of the pipelines.
2. Stateful Computations: Flink accommodates for stateful computations, adding, modifying and updating continuously the state as the system runs. Besides that, this feature of the process can effectively be suited for complex data processing such as fraud detection and real-time analytics.
3. Fault Tolerance: Flink offers a strong fault-tolerance feature, so your data processing jobs continue to run smoothly even in case of any failures. It accomplishes this through methods like distributed checkpoints and automatic recovery, minimizing downtime and data loss.
4. Scalability: Whether you’re processing data on a tiny cluster or a large-scale distributed system, Apache Flink can grow horizontally. This scalability means that your applications may grow with your data demands while maintaining performance.
5. High Performance: Flink has been engineered for in-memory processing, so it has a faster high throughput and low latency for data processing tasks. This makes it well-suited for use cases requiring real-time insights and rapid decision-making.
6. Rich APIs: Flink benefits of many libraries and frameworks for creating data-processing applications including support for programming languages such as Java, Scala and SQL APIs. This utility enables developers to choose the technological facilities that best fit their needs.
7. Ecosystem Integration: Apache Flink flawlessly integrates with components of data ecosystem like Apache Kafka, Apache Hadoop, and other storage systems.

In conclusion, Apache Flink is discovered as a strong and flexible tool for data processing, providing its users a combination of features, performance and scalability, designed to handle all kinds of data processing tasks.

Apache Flink Tames Real-Time Data

Apache Flink is helpful in solving problems that can frequently get on your way when working with streams.
Here are some of the main ways Flink helps out:

1. Support for Unbounded and Bounded Data Streams: Flink supports the notion of both unbounded (never-ending) and bounded (finite or batch) data streams. With bounded streams, the tabulating is done incrementally as the data is being processed, which is similar to batch and stream processing. Flink handles bounded data streams as a special case of streaming and provides a variety of enhancements to make pre- and post-processing considerably more efficient.
   Apache Flink offers state and context for unbounded data streams, such as time frames, allowing for continuous real-time processing.
2. Event-Time and Processing-Time Semantics: Time is an important concept in stream processing applications. Flink allows event-time data processing using timestamps. Data can be ordered by time in the event-time mode, and late-data handling is possible. In contrast, processing time is determined by the time the data arrives to the Flink server, which might be better in low-latency context.
3. Watermarks for Late-Data Handling: Developers can define watermarks for unbounded streaming data, which define a minimum amount of data that must be collected before processing can start. Watermarks have extensions that allow developers to declare methods for processing data that arrives late, after the watermark has been already reached.
4. Exactly-Once Guarantees: When things go wrong in a stream processing application, it is possible to have either lost, or duplicated results. With Flink, depending on the choices you make for your application and the cluster you run it on, any of these outcomes is possible:
   -Flink makes no effort to recover from failures (at most once)
   -Nothing is lost, but you may experience duplicated results (at least once)
   -Nothing is lost or duplicated (exactly once)

Flink Empowers Stream Processing at Scale

In contrast to other stream processors, Flink has become a prevalent choice for the most complex real-time systems, due to its capacity to deal with huge amounts of data in real time. High-profile examples are Uber, Netflix, Stripe, and Reddit, who make Flink to work for them in any tasks ranging from payment processing and safety measures that prevent spam and harassment.

Key features of Flink that facilitate stream processing include:

1. Parallel, Distributed Processing: Thousands of Flink workers (or TaskManagers) can run in parallel, coordinated by the Flink cluster’s JobManager, and can be scaled up or down to handle data spikes or user-base growth in a cost-effective way.
2. Fault Tolerance: Flink’s fault tolerance mechanism restores programs in the event of a failure and continues to run them. These problems include machine hardware faults, network failures, transitory software failures, and so on. Flink can only ensure exactly-once state changes to user-defined states if the source participates in the snapshotting process. To guarantee end-to-end exactly-once record delivery (in addition to exactly-once state semantics), the data sink needs to take part in the checkpointing mechanism.
3. Administering Systems without Data Loss: Flink uses the feature of the savepoints to remain consistent and saves the processing state at the point when the savepoint was initiated. This feature can prevent any loss of data during system upgrades, scheduled system maintenance and spontaneous freeze.
4. System Monitoring: One of the most important features of Apache Flink is being able to monitor and debug real-time systems which can be “scalable” anytime you like. Monitoring goes hand-in-hand with observability, which is a prerequisite for troubleshooting and performance tuning. Nowadays, with the complexity of modern enterprise applications and the speed of delivery increasing, an engineering team must understand and have a complete overview of its applications’ status at any given point in time.

Comparison between Apache Kafka and Apache Flink

Fig.4 Kafka & Flink

We have already spoken about main features of Apache Flink, now let’s take a look on a quick comparison between Apache Kafka Streams and Apache Flink. It is important to take in account that Flink is a running engine on which processing jobs run, while Kafka Streams is a Java library that enables client applications to run streaming jobs without the need for extra distributed systems besides a running Kafka cluster. This implies that if users want to leverage Flink for stream processing, they will need to work with two systems. In addition, both Apache Flink and Kafka Streams offer high-level APIs (Flink DataStream APIs, Kafka Streams DSL) as well as advanced APIs for more complex implementations, such as the Kafka Streams Processor APIs.

Apache Kafka

Fig.5 Apache Kafka

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

Use cases

• Real-time data ingestion and analytics: Kafka manages accurately processing, storing and reporting information in real-time from multiple sources, data aggregation and triggering event-based actions including sensors, web-based applications, and databases.
• Log aggregation: It becomes central hub for acquiring and bundling log files generated by distributed systems. In this way log aggregation provides uniform framework to analyze website access logs, monitor service performance or detect significant events from system logs.
• Stream processing integration: Kafka is designed to operate with streaming processing systems like Flink and Spark in order to transform, filter, aggregate or enrich input data and produce output data in real-time.
• Microservices Architectures: particularly leveraged for the implementations of event-driven patterns like event sourcing or CQRS.
• Machine Learning: train and apply machine learning models on streaming data for real-time scoring.

Pros

• Seamless Integration: Apache Kafka Streams integrates seamlessly with the Apache Kafka ecosystem, allowing for easy setup and integration with other Kafka components.
• Fault tolerance: Kafka Streams employs a fault-tolerant architecture, as a practical process even when brokers are down and ensure high availability and reliability of stream processing applications.
• Scalability: Kafka will smooth the vertical scalability by increasing the amount of broker within the server group and therefore meet the ever-growing data volume demands.
• Exactly-Once Semantics: Kafka Streams provides exactly-once processing semantics, ensuring that each message is processed exactly once, even in case of failures or restarts.
• Flexibility: Kafka Streams offers flexibility in terms of application development, allowing developers to write stream processing logic using familiar Java or Scala programming languages.

Cons

• Complex setup and management: Building and managing Kafka Streams applications can be complex, requiring understanding of distributed systems, fault tolerance, and stream processing concepts.
• Learning Curve: Apache Kafka Streams has a steep learning curve, especially for users who are not familiar with the Kafka ecosystem and stream processing concepts.
• Limited Functionality: Kafka offers a basic streaming processing functionality, it may lack some advanced features compared to other stream processing frameworks, anyway you will need the other tools to do the complex streaming processing.

Apache Flink

Fig.6 Apache Flink

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

Use Cases

• Real-time analytics: Flink is able to do computations and complex analytics to data in real time but also performance monitoring, anomaly detection, and IoT sensor data processing.
• Continuous ETL (Extract, Transform, Load): Flink was created for complex event processing (process large amount of data streams) as fast as data streams arrive for any transformation or enrichment tasks like detecting sequences of events or managing time windows.
• Event-driven applications: Flink delivers event-driven applications like fraud business, recommendations systems and real-time IoT analytics.
• Machine Learning: Machine learning apply and train on streming data, enabling real-time processing of machine learning outcomes and predictions.

Pros

• Advanced stream processing capabilities: Flink is an advanced processing tool that can deal with stateful processing, event-time processing and windowing, it can be applied in complex stream processing tasks.
• Low latency and high throughput: Flink has high scalability and can handle large volumes of data and high throughput requirements with its distributed architecture, allowing for horizontal scaling across multiple nodes.
• Fault Tolerance: Flink provides strong fault tolerance abilities, ensuring that data processing tasks continue without any interruption even in the event of failures or system crashes.
• Unified batch and stream processing: Flink supports a unified processing model for both batch and stream processing, reducing in this way the need to have separate systems for different processing models.
• Rich API Support: Flink offers rich APIs for programming in Java, Scala, and Python, making it accessible to a wide range of developers with different programming language preferences.

Cons

• Learning curve: Flink has a steep learning curve, its cutting edge traits and capabilities could make it quite hard to be used, specifically requiring developers to understand distributed systems concepts, streaming semantics, and Flink-specific APIs and features.
• Resource-intensive: Flink applications can be resource-intensive, requiring computational and memory resources.
• State Management: Managing state in Apache Flink applications can be challenging, especially for applications with large state requirements, requiring consideration of state storage and management strategies.
• Limited Tooling and Ecosystem: Apache Flink has a growing ecosystem and community, it may lack the extensive tooling and ecosystem of other stream processing frameworks, leading to potential gaps in integration and support for certain use cases and industries.

Conclusion

Finally, in a nutshell, Apache Kafka and Apache Flink are just like two real-life superheroes in the Data Processing world. Both can perform the same tasks but in different ways. For managing data, providing storage and exchanging messages between systems, your best choice would for sure be Kafka. Whereas Flink provides advanced stateful processing capabilities, allowing creation of complex stateful transformations and aggregations, which may be necessary for certain kinds of applications. Moreover, Flink’s optimized execution engine ensuring high performance and efficiency, making it suitable for processing large volumes of data at scale. Overall, Flink complements Kafka by offering a more comprehensive solution for data processing, especially for use cases that require both batch and stream processing capabilities. Take into account that you need to choose between Kafka and Flink solutions: do you need a data collector or data-analyzer? It depends, you can start identifying your unique case, by measuring the amount of data you have and see how much it is possible to withstand the different technologies.

Apache Flink latest release: Features, improvements and more

According to its ongoing effort to enhance performance and usability, Apache Flink has released version 1.9 with a slew of new and powerful features. Among them standard out the support for process state sharing, enabling developers to build more sophisticated real-time data processing applications. Additionally, a new API for state control has been introduced, simplifying the monitoring and management of application states. Flink 1.9 also boasts significant performance improvements, thanks to optimizations in resource management and parallel task execution. New connectors for integration have been added with various data sources and cloud services making Flink even more flexible and interoperable. Finally, the introduction of new tools for application debugging and monitoring allows developers to quickly identify and resolve any issues, enhancing overall system reliability. Thanks to these new features, Apache Flink once again positioned as one of the leader platforms for real-time data processing, suitable for a wide range of use cases and application scenarios.
For more information about this new release please check the official documentation (https://nightlies.apache.org/flink/flink-docs-release-1.19/release-notes/flink-1.19/)

Resources

https://www.confluent.io/blog/exploring-apache-flink-1-19/

https://flink.apache.org/ (official Doc)

https://bitrock.it/blog/technology/apache-flink-and-kafka-stream-a-comparative-analysis.html

https://medium.com/@mitch_datorios/harnessing-deduplication-in-apache-flink-bfa39da8f8ee

https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/learn-flink

https://en.wikipedia.org/wiki/Apache_Flink

https://quix.io/blog/kafka-vs-flink-comparison

https://thenewstack.io/apache-flink-2023-retrospective-and-glimpse-into-the-future/