Faust vs Samza

Overview

Samza

Stacks24

Followers62

Votes0

GitHub Stars832

Forks333

Faust

Stacks26

Followers80

Votes0

GitHub Stars6.8K

Forks536

Faust vs Samza: What are the differences?

Execution Model: Faust is a Python stream processing library that focuses on high-level stream processing abstractions, whereas Samza is a distributed stream processing framework that works directly with Apache Kafka. Faust provides a more lightweight and flexible execution model, while Samza offers a more robust and scalable distributed execution model.
Language Support: Faust primarily supports Python as its programming language for defining stream processing applications, making it easier for Python developers to create and deploy stream processing jobs. On the other hand, Samza supports Java and Scala, which may be more suitable for enterprises with existing Java or Scala codebases.
Fault Tolerance: Samza has built-in support for fault-tolerance mechanisms like stateful processing, checkpointing, and recovery, which are essential for handling failures in distributed systems. Faust, on the other hand, has limited built-in fault tolerance features, which may require developers to implement additional mechanisms for ensuring fault tolerance in their applications.
Scalability: Samza is designed to scale horizontally by distributing partitions of data across a cluster of machines, allowing for increased throughput and processing capacity as the data volume grows. Faust, while capable of processing data streams efficiently, may not scale as easily as Samza for handling large-scale data processing requirements.
Community and Ecosystem: Samza is part of the Apache Software Foundation, benefiting from a large and active community of developers contributing to its development and maintenance. This results in a robust ecosystem of tools, libraries, and resources that can help users leverage Samza effectively. Faust, being a Python library, may have a smaller community and ecosystem in comparison.
Ease of Deployment: Faust simplifies the deployment process by packaging stream processing applications as Python modules that can be easily distributed and run on any Python environment. In contrast, deploying Samza applications requires setting up a distributed cluster, managing dependencies, and ensuring proper configuration, which can be more complex and time-consuming.

In Summary, Faust and Samza differ in their execution models, language support, fault tolerance mechanisms, scalability, community support, and deployment ease.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Samza	Faust
It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.	It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.
HIGH PERFORMANCE; HORIZONTALLY SCALABLE; EASY TO OPERATE; WRITE ONCE, RUN ANYWHERE; PLUGGABLE ARCHITECTURE	Stream processing; Event processing; Build high performance distributed systems; Real-time data pipelines
Statistics
GitHub Stars 832	GitHub Stars 6.8K
GitHub Forks 333	GitHub Forks 536
Stacks 24	Stacks 26
Followers 62	Followers 80
Votes 0	Votes 0
Integrations
Presto Datadog Woopra	Python Flask Django Pandas PyTorch NumPy NLTK SQLAlchemy

What are some alternatives to Samza, Faust?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

Related Comparisons

Faust vs Samza: What are the differences?

Execution Model: Faust is a Python stream processing library that focuses on high-level stream processing abstractions, whereas Samza is a distributed stream processing framework that works directly with Apache Kafka. Faust provides a more lightweight and flexible execution model, while Samza offers a more robust and scalable distributed execution model.
Language Support: Faust primarily supports Python as its programming language for defining stream processing applications, making it easier for Python developers to create and deploy stream processing jobs. On the other hand, Samza supports Java and Scala, which may be more suitable for enterprises with existing Java or Scala codebases.
Fault Tolerance: Samza has built-in support for fault-tolerance mechanisms like stateful processing, checkpointing, and recovery, which are essential for handling failures in distributed systems. Faust, on the other hand, has limited built-in fault tolerance features, which may require developers to implement additional mechanisms for ensuring fault tolerance in their applications.
Scalability: Samza is designed to scale horizontally by distributing partitions of data across a cluster of machines, allowing for increased throughput and processing capacity as the data volume grows. Faust, while capable of processing data streams efficiently, may not scale as easily as Samza for handling large-scale data processing requirements.
Community and Ecosystem: Samza is part of the Apache Software Foundation, benefiting from a large and active community of developers contributing to its development and maintenance. This results in a robust ecosystem of tools, libraries, and resources that can help users leverage Samza effectively. Faust, being a Python library, may have a smaller community and ecosystem in comparison.
Ease of Deployment: Faust simplifies the deployment process by packaging stream processing applications as Python modules that can be easily distributed and run on any Python environment. In contrast, deploying Samza applications requires setting up a distributed cluster, managing dependencies, and ensuring proper configuration, which can be more complex and time-consuming.

In Summary, Faust and Samza differ in their execution models, language support, fault tolerance mechanisms, scalability, community support, and deployment ease.

Faust vs Samza

Overview