Flink vs. Pathway
Explore Pathway, a source-available Stream Processing Framework, as an alternative to Flink.
Compare their features, and more to understand their distinctions and benefits.
Check the comparison spreadsheet
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 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 clustered environments, performing computations in-memory at speed and at any scale. With its long history and active community support, Flink remains a top choice for organizations seeking to unlock insights from their streaming data sources.
Feature comparison: Pathway vs. Flink
| Feature | Pathway | Apache Flink |
|---|---|---|
| General | ||
| Processing Type | Stream and batch (with the same engine). Guarantees of same results returned whether running in batch or streaming. Capacity for asynchronous stream processing and API integration. | Stream and batch (with different engines). |
| Programming language APIs | Python, SQL | JVM (Java, Kotlin, Scala), SQL, Python |
| Programming API | Table API | DataStream API and Table API, with partial compatibility |
| Software integration ecosystems/plugin formats. | Python, C binary interface (C, C++, Rust), REST API. | JVM |
| Ease of development | ||
| How to QuickStart | Get Python. Do `pip install pathway`. Run directly. | Get Java. Download and unpack Flink packages. Start a local Flink Cluster with `./bin/start-local.sh`. Use netcat to start a local server. Submit your program to the server for running. |
| Running local experiments with data | Use Pathway locally in VS Code, Jupyter, etc. | Based on local Flink clusters |
| CI/CD and Testing | Usual CI/CD setup for Python (use GitHub Actions, Jenkins etc.) Simulated stream library for easy stream testing from file sources. | Based on local Flink cluster integration into CI/CD pipelines |
| Interactive work possible? | Yes, data manipulation routines can be interactively created in notebooks and the Python REPL | Compilation is necessary, breaking data-scientist's flow of work |
| Performance | ||
| Scalability | Horizontal* and vertical scaling. Scales to thousands of cores and terabytes of application state. Standard and custom libraries (including ML library) are scalable. | Horizontal and vertical scaling. Scales to thousands of cores and terabytes of application state. Most standard libraries (including ML library) do not parallelize in streaming mode. |
| Performance for basic tasks (groupby, filter, single join) | Delivers high throughput and low latency. | Slower than Pathway in benchmarks. |
| Transformation chain length in batch computing | 1000+ transformations possible, iteration loops possible | Max. 40 transformations recommended (in both batch and streaming mode). |
| Fast advanced data transformation (iterative graph algorithms, machine learning) | In batch and streaming mode. | No; restricted subset possible in batch mode only. |
| Parameter tuning required | Instance sizing only. Possibility to set window cut-off times for late data. | Considerable tuning required for streaming jobs. |
| Architecture and deployment | ||
| Distributed Deployment (for Kubernetes or bare metal clusters) | Pool of identical workers (pods).* Sharded by data. | Includes a JobManager and pool of TaskManagers. Work divided by operation and/or sharded by data. |
| Dataflow handling and communication | Entire dataflow handled by each worker on a data shard, with asynchronous communication when data needs routing between workers. Backpressure built-in. | Multiple communication mechanisms depending on configuration. Backpressure handling mechanisms needed across multiple workers. |
| Internal Incremental Processing Paradigm | Commutative (based on record count deltas) | Idempotent (upsert) |
| Primary data structure for state | Multi-temporal Log-structured merge-tree (shared arrangements). In-memory state. | Log-structured merge-tree. In-memory state. |
| State Management | Integrated with computation. Cold-storage persistence layer optional. Low checkpointing overhead.* | Integrated with computation. Cold-storage persistence layer optional. |
| Semantics of stream connectors | Insert / Upsert | Insert / Upsert |
| Message Delivery Guarantees | Ensures exactly-once delivery guarantees for state and outputs (if enabled) | Ensures exactly-once delivery guarantees for state and outputs (if enabled) |
| Consistency | Consistent, with exact progress tracking. Outputs reflect all data contained in a prefix of the source streams. All messages are atomically processed, if downstream systems have a notion of transaction no intermediate states are sent out of the system. | Eventually consistent, with approximate progress tracking using watermarks. Outputs may reflect partially processed messages and transient inconsistent outputs may be sent out of the system. |
| Processing out-of-order data | Supported by default. Outputs of built-in operations do not depend on data arrival order (unless they are configured to ignore very late data). Event times used for windowing and temporal operations. | Supported or fragile, depending on the scenario. Event time processing supported in addition to arrival time and approximate watermarking semantics. |
| Fault tolerance | Rewind-to-snapshot. Partial failover handled transparently in hot replica setups.* | Rewind-to-snapshot. Support for partial failover present or not depending on scheduler. |
| Monitoring system | Prometheus-compatible endpoint on each pod | |
| Logging system | Integrates with Docker and Kubernetes Container logs | |
| Machine Learning support | ||
| Language of ML library implementation | Python / Pathway | JVM / Flink |
| Parallelism support by ML libraries | ML libraries scale vertically and horizontally | Most ML libraries are not built for parallelization |
| Supported modes of ML inference | CPU Inference on worker nodes. Asynchronous Inference (GPU/CPU). Alerting of results updates after model change. | CPU Inference on worker nodes. |
| Supported modes of ML learning | Add data to the training set. Update or delete data in the training set. Revise past classification decisions. | Add data to the training set. |
| Representative real-time Machine Learning libraries. | Classification (including kNN), Clusterings, graph clustering, graph algorithms, vector indexes, signal processing. Geospatial libraries, spatio-temporal data, GPS and trajectories.* Possibility to integrate external Python real-time ML libraries. | Classification (including kNN), Clusterings, vector indexes. |
| Support for iterative algorithms (iterate until convergence, gradient descent, etc.) | Yes | No |
| API Integration with external Machine Learning models and LLMs | Yes | No / fragile |
| Typical Analytics and Machine Learning use cases | Data fusion Monitoring and alerting (rule-based or ML-powered) IoT and logs data observability (rule-based or ML-powered) Trajectory mining* Graph learning Recommender systems Ontologies and dynamic knowledge graphs. Real-time data indexing (vector indexes). LLM-enabled data pipelines and RAG services. Low-latency feature stores. | Monitoring and alerting (rule-based) IoT and logs data observability (rule-based) |
| API and HTTP microservices | ||
| REST/HTTP API integration | Non-blocking (Asynchronous API calls) supported in addition to Synchronous calls. | Blocking (Synchronous calls) |
| Acting as microservice host | Provides API endpoint mechanism for user queries. Supports registered queries (API session mechanism, alerting). | No |
| Use as low-latency feature store | Yes, standalone. From 1ms latency. | Possible in combination with Key-value store like Redis. From 5ms latency. Requires manual versioning/consistency checks. |
Key Distinctions Between Pathway & Flink
Data processing & transformation
- Data Pipelines: Pathway offers both batch and streaming/live data processing capabilities, for SQL use cases and ML/AI use cases. While Flink provides robust data processing and transformation functionalities for SQL use cases, it is slow on ML/AI use cases in batch and hardly delivers on streaming ML/AI use cases. Read our 2023 WordCount and PageRank Benchmarks to learn more.
- Pathway supports real-time feature store functionalities, whereas Flink alone does not. Flink can be integrated with other tools such as Redis or Druid to obtain such functionality. Pathway enables Query API and on-demand API with minimal effort, while Flink alone does not. Further combining Flink with Druid would allow for External ML integration (SQL-first processing).
Development & deployment effort
- Interactive Development: Pathway supports interactive development through notebooks and data experimentation with ease, while Flink's development and deployment is good for batch / local data files but is lacking in this area for streaming.
- Deployment: Tests and CI/CD can be done with Pathway in a local Python environment, without launching a cluster. Job management in Pathway can be done directly through containerized deployment (with Kubernetes or Docker). Flink, either as a standalone stream processing framework or combined with Druid or Redis, requires the launching of a Flink cluster to which jobs are sent.
- Both frameworks support horizontal and vertical scaling effectively.
Streaming Consistency
Flink does not support data consistency. As written in the great 2024 O'Reilly Streaming Databases book, and more specifically in Chapter 6 - “classical stream processors [like Flink] (...) guarantee only a weaker form of consistency called eventual consistency.”
Pathway, on the other side, supports a “stronger form of consistency where every output is the correct output for a subset of the inputs” - also called internal consistency.
Usability
The native development stack for Flink is based on the Java Virtual Machine, so it provides excellent support for code developed in Java or Scala. The Support for Python in Flink is based on a wrapper API's which is not considered a roadmap priority, is often incomplete and lags in features compared to the Java version. Flink has very limited schema and type validation, and provides very limited syntax help in Visual Studio Code and other development environments. Pathway is natively Python and provides advanced Python library integration, full schema and type validation at the time of job preparation, and a python-native integration experience for syntax help with Visual Studio Code and other development environments. Both Pathway and Flink provide a layer for expressing data transformations in SQL.
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 Flink
Flink presents strong data processing capabilities but lags in deployment ease and interactive development support.
Developing applications in Flink can be more complex compared to some other stream processing frameworks due to its focus on low-level APIs and concepts like state management. This complexity may require developers to invest more time in understanding Flink's architecture and APIs.
Flink's support for interactive development, such as interactive notebooks or REPL (Read-Eval-Print Loop) environments, is not as mature as some other frameworks. This can make it challenging for developers to rapidly prototype and experiment with their code.
Although Flink can be used for machine learning and AI tasks, its support for these use cases may not be as extensive as dedicated ML/AI frameworks. The support for ML/AI with streaming data is extremely slow and limited. Integrating Flink with ML/AI libraries and tools may require additional effort and customization.
FAQs
What would you say is the main differentiation between Pathway and Flink?
Pathway's strongest long-term differentiation lies in the use cases that Pathway opens up, compared to incumbent streaming technologies. Pathway enables real-time machine learning, unlearning, graph algorithms, and other advanced transformations, all on live data. It is a fundamental shift compared to what was possible until now. In fact, the logistics and moving assets use cases are great examples of this, so are RAG pipelines for document streams and data intelligence. Pathway effectively pushes the AI field forward.