Kafka Streams vs. Pathway

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About Pathway

Pathway is a data processing framework that handles streaming data in a way easily accessible to Python and AI developers. It is a light, next-generation technology developed since 2020, made available for download as a Python-native package from GitHub and as a Docker image on Dockerhub. Pathway handles advanced algorithms in deep pipelines, connects to data sources like Kafka and S3, and enables real-time ML model and API integration for new AI use cases. It is powered by Rust, while maintaining the joy of interactive development with Python. Pathway’s performance enables it to process millions of data points per second, scaling to multiple workers, while staying consistent and predictable. Pathway covers a spectrum of use cases between classical streaming and data indexing for knowledge management, bringing in powerful transformations, speed, and scale.

About Kafka Streams

Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the approach of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.

Feature comparison: Pathway vs. Kafka Streams

• ⚠️2: Limited to a subset of SQL, limited JOIN complexity
• 🐌: Not scalable (e.g., local single-threaded only) or posing blocking performance issues
• 😠: Eventual consistency only.

Key Distinctions Between Pathway & Kafka Streams

Data processing & transformation

• Pathway supports both PUSH and PULL models for data pipelines, including batch processing for SQL and ML/AI use cases and streaming/live data for SQL and deep computations for ML/AI use cases. Kafka Streams supports similar functionality for SQL use cases but with limitations on JOIN complexity. It does not deliver for AI/ ML use cases. Read our 2023 WordCount and PageRank Benchmarks to learn more.
• Pathway provides basic and advanced real-time request serving capabilities, including a real-time feature store and advanced query APIs. Kafka Streams lacks support for real-time request serving.

Development & deployment effort

Pathway allows interactive development with support for both batch and streaming data. Deployment is supported through tests, CI/CD, and containerized deployment with Kubernetes/Docker. Kafka Streams lacks interactive development support and has limited deployment options.

Streaming Consistency

Both Pathway and Kafka Streams provide streaming consistency, but Pathway complies with internal consistency, while Kafka Streams is limited to eventual consistency, potentially not meeting user expectations. We strongly recommend O'Reilly 2024 edition of Streaming Databases, and specifically Chapter 6 on Streaming Consistency.

Benefits of Pathway

Pathway is used to create Python code which seamlessly combines batch processing, streaming, and real-time APIs for LLM apps. Pathway's distributed runtime (🦀-🐍) provides fresh results for your data pipelines whenever new inputs and requests are received.

Pathway was initially designed to be a life-saver (or at least a time-saver) for Python developers and ML/AI engineers faced with live data sources, where you need to react quickly to fresh data. Pathway provides a high-level programming interface in Python for defining data transformations, aggregations, and other operations on data streams. With Pathway, you can effortlessly design and deploy sophisticated data workflows that efficiently handle high volumes of data in real-time.

Pathway is interoperable with various data sources and sinks such as Kafka, CSV files, SQL/NoSQL databases, and REST APIs, allowing you to connect and process data from different storage systems. Typical use-cases of Pathway include real-time data processing, ETL (Extract, Transform, Load) pipelines, data analytics, monitoring, anomaly detection, and recommendation. Pathway can also independently provide the backbone of a light LLMops stack for real-time LLM applications.

Pathway excels in offering a comprehensive set of features for data processing and transformation, with relatively lower development and deployment effort.

Limitations of Kafka Streams

• The use of the JVM: Like any Java application, Kafka Streams relies on the Java Virtual Machine (JVM), which leads to performance overhead and resource utilization concerns. The JVM's garbage collection mechanism can introduce latency and memory management issues, impacting the overall efficiency of Kafka Streams applications, especially in high-throughput scenarios. Additionally, the JVM's memory requirements and runtime overhead may lead to higher resource consumption and increased operational costs.
• Complexity of State Management: Handling stateful operations in Kafka Streams can be complex, especially when dealing with state stores that need to be fault-tolerant and scalable.
• Limited Integration with External Systems: While Kafka Streams integrates seamlessly with Apache Kafka, its integration with external systems may not be as robust. Connecting to non-Kafka data sources or sinks might require additional workarounds or custom solutions.
• Lack of Built-in Windowing Support: Although Kafka Streams offers windowing operations for processing time-based or session-based data, its windowing capabilities may not be as advanced or flexible as some other stream processing frameworks.
• Complex Event Processing: Kafka Streams primarily focuses on stream processing tasks such as filtering, mapping, and aggregating events. It may not be well-suited for complex event processing scenarios that require sophisticated event pattern matching or temporal reasoning.

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