Apache Flink is a powerful open-source framework designed for real-time stream processing and event-driven applications. While Flink excels at low-latency, high-throughput processing, it can be complex to set up and manage, particularly for smaller teams or Python-centric developers. Luckily, several alternatives offer similar capabilities with distinct advantages, from ease of use to better language support and faster deployment.
In this article, we’ll cover the Top 7 Apache Flink alternatives: GlassFlow, Spark, KsqlDB, Arroyo, RisingWave, Quix, and Bytewax focusing on solutions that offer comparable stream processing features while addressing some of Flink’s challenges.
Challenges with Apache Flink
Apache Flink has a complicated design, which makes it hard to learn and even tricky for experienced users to work with and troubleshoot. Developers often struggle with advanced topics like custom watermarks, serialization, and changing data types over time, which adds to the difficulty of using Flink.
Compared to other distributed systems, Flink can be especially challenging when it comes to setting up and managing clusters. Tuning its performance requires careful attention to hardware and job settings. Common problems include dealing with backpressure, slow-running jobs, and restoring very large states from savepoints.
As of 2024, many organizations still find it necessary to have teams of experts to build and manage Flink jobs and keep the system running smoothly. For smaller organizations, the complexity and cost of maintaining Flink can be too high, making it more practical only for larger companies with advanced streaming needs.
Comparison Table Of Apache Flink Alternatives
1. GlassFlow
Overview
GlassFlow is a modern alternative to Flink that is designed specifically for Python developers. With its serverless architecture, GlassFlow removes the complexities associated with cluster management, enabling developers to create real-time data pipelines quickly and easily. It supports out-of-the-box integrations with Python libraries like Pandas, TensorFlow, and more, allowing seamless real-time data transformation in Python.
Key Features
- Python-Centric: A fully Python-based platform that eliminates the need for JVM or complex cluster setups.
- Zero Infrastructure Setup: No need to manage shards, brokers, or compute nodes—GlassFlow runs serverless. You do not have to worry about creating Docker configurations, orchestrating multiple containers with Kubernetes, managing resources, and scaling them.
- Real-Time Data Transformation: Easily apply transformations using any Python libraries.
- Quick Pipeline Setup: Pipelines can be deployed in just minutes.
Reasons to Choose GlassFlow
- Ease of Use: GlassFlow is simple to set up and use, especially for Data Engineers, and Data Analysts.
- Low Overhead: Unlike Flink, there’s no need for complex infrastructure—GlassFlow has a built-in message broker, handles scaling and deployments automatically.
Limitations
- Python-Focused: Ideal for Python users, but not suitable for teams heavily reliant on Java or Scala.
2. Spark Structured Streaming
Overview
Spark Structured Streaming is a stream processing engine built on Apache Spark, offering both real-time and batch processing capabilities. With a high-level API, it allows developers to write streaming applications easily, and it integrates well into the broader Spark ecosystem.
Key Features
- Unified API: Use the same API for batch and stream processing.
- High Scalability: Excellent for processing large volumes of data.
- Project Lightspeed: Spark’s recent initiative includes performance improvements and enhanced support for real-time workloads.
Reasons to Choose Spark Structured Streaming
- Seamless Batch and Stream Processing: Ideal for teams already using Spark for batch processing.
- Broad Ecosystem: Integrates with many popular data sources, including Kafka and Amazon Kinesis.
Limitations
- Resource-Intensive: Requires more resources and is often more complex to manage than alternatives like GlassFlow or Bytewax.
3. KsqlDB
Overview
KsqlDB is a stream processing engine built on Apache Kafka. It enables real-time processing using SQL queries, which simplifies working with streaming data for those familiar with relational databases. It combines stream processing capabilities with a relational database model, allowing teams to query streaming data using SQL syntax.
Key Features
- SQL Interface: Uses SQL queries to process streaming data.
- Kafka Native: Built on top of Kafka Streams, making it highly integrated with the Kafka ecosystem.
- Scalable: Easily scale ksqlDB for high-throughput streaming data.
Reasons to Choose KsqlDB
- Familiarity: If your team is comfortable with SQL, ksqlDB provides a simpler way to work with streaming data.
- Kafka-Centric: Ideal for teams already using Kafka for their data streams.
Limitations
- Tied to Kafka: While ksqlDB is a great choice for Kafka users, it may not be suitable for teams using other messaging systems.
4. Arroyo
Overview
Arroyo is a new stream processing engine written in Rust that offers high performance and scalability. With Arroyo, you can process streams of data efficiently, making it suitable for organizations dealing with high-volume, real-time data.
Key Features
- Rust-Based: Provides high-performance and low-latency stream processing.
- SQL and Rust Pipelines: Supports both SQL-based and Rust-based processing pipelines.
- Sub-Second Results: Processes high-volume real-time data with minimal delay.
Reasons to Choose Arroyo
- High Performance: Arroyo’s Rust-based engine offers low-latency data processing.
- Scalability: Suitable for high-throughput, real-time analytics at scale.
Limitations
- Newer Solution: While powerful, Arroyo is newer and less mature compared to established frameworks like Flink or Spark.
5. RisingWave
Overview
RisingWave is a Postgres-compatible SQL streaming database that is designed to reduce the complexity of building real-time applications. It supports incremental computations, making it ideal for real-time data analysis without the need for manual stream processing pipelines.
Key Features
- SQL Interface: Allows developers to query streaming data using SQL.
- Incremental Computations: Performs real-time calculations and updates dynamically as new data arrives.
- Cloud-Native: Designed to run seamlessly in the cloud with easy scalability.
Reasons to Choose RisingWave
- SQL-Based: Perfect for teams that want to query real-time data without writing complex stream processing logic.
- Low Latency: Delivers low-latency results by performing incremental computations.
Limitations
- Primarily Cloud-Based: Best suited for teams running cloud-native applications.
6. Quix
Overview
Quix is a stream processing platform and open-source library designed for Python and C# developers. Quix specializes in handling time-series data and telemetry, offering a developer-friendly environment for real-time data processing without the complexity of managing separate clusters.
Key Features
- Python and C# Support: Designed for developers in these languages to handle real-time streaming data.
- Fully Managed Platform: The Quix platform handles infrastructure, scaling, and deployment.
- Time-Series Focus: Excellent for handling telemetry and time-series data.
Reasons to Choose Quix
- Ease of Use: Quix simplifies data streaming and processing for Python and C# developers.
- Low Complexity: The fully managed SaaS platform eliminates the need for DevOps resources to manage infrastructure.
Limitations
- Newer Ecosystem: While promising, Quix’s ecosystem is still growing compared to more established platforms.
7. Bytewax
Overview
Bytewax is an open-source Python framework for building real-time and batch dataflows. It’s built on the Timely Dataflow library, which is known for its efficiency in stream processing. Bytewax provides a Python-friendly alternative to Flink, making it easier for teams already working in the Python ecosystem.
Key Features
- Python-Centric: Built entirely in Python, Bytewax offers a familiar development environment for Python users.
- Stateful Processing: Supports windowing and stateful operations for advanced real-time processing.
- Scalable: Efficiently scales to handle large volumes of streaming data.
Reasons to Choose Bytewax
- Python-Friendly: Perfect for teams that are focused on using Python for data processing.
- Low Latency: Bytewax provides low-latency processing, making it ideal for time-sensitive data flows.
Limitations
- Less Mature: Bytewax is a newer platform with fewer integrations and a smaller community compared to Flink or Spark.
Conclusion
Each alternative comes with its strengths, from simplified deployment models to powerful real-time analytics—allowing you to choose the one that best fits your team’s needs. GlassFlow provides a Python-centric, serverless alternative that makes real-time data processing easier for developers who do not have experience with Kafka. Spark Structured Streaming offers scalability and flexibility for teams already using Spark, while KsqlDB and Arroyo provide specialized solutions for Kafka and high-performance data processing, respectively. RisingWave provides additional flexibility for Postgres-style SQL-based querying, Quix and Bytewax round out the list, offering Python-friendly solutions with real-time data processing capabilities.