Faust

Features

Real-Time Data Streaming - Provides a platform for processing and delivering event-driven data streams in real-time.
Distributed Key-Value Stores - Maintains a consistent and replicated key-value state store across multiple processing nodes.
Streaming Processors - Operates as a distributed processing engine that handles continuous event sequences across multiple worker nodes.
Stream Processing - Provides a framework for the continuous ingestion, transformation, and analysis of high-velocity Kafka data streams.
Distributed Sharding Architectures - Distributes stream workloads across multiple worker nodes by routing events with the same key to the same processor.
Stream Processing Libraries - Provides a comprehensive Python library for building distributed stream processing applications integrated with Kafka.
Local State Stores - Maintains a persistent key-value database on each node to store large amounts of state locally.
Real-Time Event Processing - Executes asynchronous computation and application logic for every single event received from a messaging queue.
Distributed State Management - Maintains consistent application state and replicated key-value data across distributed worker nodes for high availability.
Asynchronous Event Loops - Uses a non-blocking event loop to handle high-throughput data streams and concurrent network requests.
Event-Driven Frameworks - Provides a framework for building high-concurrency applications that transform data from messaging topics using asynchronous functions.
Asynchronous Stream Processors - Uses an asynchronous event loop and non-blocking functions to handle high-throughput event streams and real-time analysis.
Data Joins - Combines multiple real-time streams or tables using join operations to correlate event data.
Data Serialization - Implements mechanisms for encoding, parsing, and serializing data structures using type annotations.
Data Serialization Formats - Defines structured data models and serialization formats to ensure consistent formatting for stream keys and values.
Time-Window Aggregations - Tracks data summaries over sliding or tumbling time intervals to analyze trends within continuous streams.
Horizontal Scaling - Provides mechanisms to increase processing capacity by distributing partitioned topics across multiple worker nodes.
Horizontal Scaling Strategies - Implements architectural methods to expand system throughput by sharding partitioned topics across multiple worker instances.
Log Replication Strategies - Ensures high availability by treating state updates as a transaction log that can be replicated and recovered.
Serialization Models - Uses Python type hints to define data models for automatic serialization and deserialization of stream messages.
Streaming Engines - Python stream processing library inspired by Kafka Streams.

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Star history

Open-source alternatives to Faust

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