Spark Streaming vs. Pathway
Explore Pathway, a source-available Stream Processing Framework, as an alternative to Spark Streaming.
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 Spark Streaming
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms that can be expressed with high-level functions like map, reduce, join and window. Finally, processed data can be pushed out to filesystems, databases, and live dashboards.
Feature comparison: Pathway vs. Spark Streaming
Key Distinctions
| Stream Processing Frameworks | ||
|---|---|---|
| Pathway | Spark / Databricks | |
| Data processing & transformation | ||
| PUSH - data pipelines | ||
| Batch - for SQL use cases | ✅ | ✅ |
| Batch - for ML/AI use cases | ✅ | ✅ |
| Streaming / live data for SQL use cases | ✅ | ⚠️2 |
| Streaming / live data for ML/AI use cases | ✅ | ❌ |
| PULL - real-time request serving | ||
| Basic (Real-time feature store) | ✅ | ✅ |
| Advanced (Query API / on-demand API) | ✅ | ❌ |
| Development & deployment effort | ||
| INTERACTIVE DEVELOPMENT - notebooks, data experimentation | ||
| Batch / local data files | ✅ | ✅ |
| Streaming | ✅ | ❌ |
| DEPLOYMENT | ||
| Tests and CI/CD: Local - in process, without cluster | ✅ | ✅🐌 |
| Job management directly through containerized deployment (Kubernetes / Docker) | ✅ | ❌ |
| Horizontal + vertical scaling | ✅ | ✅ |
| Streaming Consistency | ||
| STREAMING CONSISTENCY | ✅ | 😠 |
- ⚠️2: Limited to a subset of SQL, limited JOIN complexity
- 🐌: Not scalable (e.g., local single-threaded only) or posing blocking performance issues
- 😠: Does not comply with user expectation.
Data processing & transformation
- Pathway and Spark both support data processing and transformation for various use cases. They both offer good batch processing for both SQL and ML/AI use cases. Pathway does so equally for streaming/ live data. However, Spark Streaming has limitations regarding JOIN complexity and extensiveness (Limited to a subset of SQL use cases) compared to Pathway. In Spark, running batch jobs requires switching to a different Spark engine. In Pathway, all jobs are handled with the same engine.
- Pathway provides real-time request serving capabilities, including a real-time feature store. Spark Streaming does not support real-time request serving but can be extended to have request serving capabilities by querying Delta Tables, and relying on tools such as Presto. However, Spark Streaming may not support advanced features like query APIs or on-demand API as comprehensively as Pathway.
Development & deployment effort
Pathway supports interactive development with Jupyter notebooks and data experimentation for both batch and streaming data. Deployment is facilitated through tests, CI/CD, and containerized deployment with Kubernetes/Docker. In contrast, Spark Streaming has limited support for interactive development and limited deployment options.
Streaming Consistency
Both Pathway and Spark Streaming provide streaming consistency, but Pathway complies with internal consistency while Spark Streaming is limited to eventual consistency. We strongly recommend O'Reilly 2024 edition of Streaming Databases, and specifically Chapter 6 on Streaming Consistency.
Usability
The native development stack for Spark Streaming is based on the Java Virtual Machine, so it provides excellent support for code developed in Java or Scala. The Support for Python in Spark Streaming is based on a number of wrapper API's, which provides a moderately integrated Python environment, allowing limited possibility of integrating external Python libraries. Spark Streaming 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 Spark Streaming 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 Spark Streaming
- The use of the JVM: The use of the Java Virtual Machine (JVM) in Spark presents drawbacks compared to Rust-based frameworks due to performance overhead, memory management issues, and higher resource utilization associated with the JVM's garbage collection mechanism.
- Limited State Management: Spark Streaming's state management capabilities are limited compared to other stream processing frameworks like Apache Flink or Kafka Streams. Managing and updating state across multiple processing stages can be challenging, especially for complex stateful operations.
- Complexity of Windowing Operations: While Spark Streaming supports windowing operations for aggregating data over time or other criteria, its windowing capabilities may not be as flexible or efficient as some other stream processing frameworks. Handling late data or out-of-order events within windows can be complex.
- Integration with External Systems: While Spark Streaming integrates well with the broader Spark ecosystem, its integration with external systems may be limited. Connecting to non-Spark data sources or sinks may require additional effort or custom development.
FAQs
What would you say is the main differentiation between Pathway and Spark Streaming?
Running machine learning (ML) models in a streaming environment presents a myriad of challenges that can quickly turn into headaches for data scientists and ML engineers. Spark Streaming is not optimized for streaming ML/AI workloads, leading to bottlenecks and inefficiencies. Spark Streaming stack components fail to keep up with the high velocity of incoming data, resulting in lagging processing times and increased latency compared to other stream processing systems. Handling multiple joins, transformations, and model updates in real-time can quickly overwhelm the system, leading to resource contention and degraded performance. For data scientists and ML engineers accustomed to interactive development environments and Python-based ML tooling, transitioning to a streaming environment can be a jarring experience. Debugging pipelines becomes a painstaking process, exacerbated by the lack of real-time feedback and visibility into the streaming data flow. The journey from development to scaling is fraught with challenges, often resulting in unpredictable results and consistency issues. Without years of experience with a particular analytics engine such as Spark Streaming, predicting the running speed and resource utilization of ML workloads in a streaming context is difficult. Unforeseen bottlenecks and performance quirks can derail even the most carefully crafted ML pipelines, leading to frustration and delays in deployment. Additionally, Spark's dependence on the Java ecosystem may limit flexibility and introduce compatibility challenges.
Pathway, as a high-throughput, low-latency data processing framework, solves those problems for Python & ML/AI developers.