Apache Flink vs Faust

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Faust

Stacks26

Followers80

Votes0

GitHub Stars6.8K

Forks536

Apache Flink vs Faust: What are the differences?

Storage and Processing Model: Apache Flink and Faust have different models for storage and processing. Apache Flink is designed for distributed stream processing and batch processing, providing support for both event time and processing time semantics. Faust, on the other hand, is a stream processing library specifically built for Kafka, allowing you to define stream processors and connect them to Kafka topics.
Programming Paradigm: Apache Flink uses a unified programming model called the DataStream API, which enables users to write stream processing jobs in a high-level language similar to SQL. Faust, on the other hand, leverages Python as its programming language, making it more accessible to Python developers.
Supported Data Sources: Apache Flink supports a wide range of data sources and connectors, including Kafka, Hadoop, Amazon S3, and more. It also provides connectors for different databases and messaging systems. In contrast, Faust is tightly integrated with Kafka and focuses on supporting Kafka topics as the primary data source.
Fault Tolerance Mechanisms: Apache Flink is designed with fault tolerance in mind and provides built-in mechanisms to handle failures. It achieves fault tolerance through its distributed snapshotting and checkpointing mechanism. Faust, being a Kafka-specific library, relies on Kafka's own fault tolerance mechanisms, such as replication and leader election.
Processing Guarantees: Apache Flink provides strong processing guarantees by ensuring exactly once semantics for both event time and processing time. It achieves this by incorporating mechanisms like event deduplication and transactional writes into its processing pipelines. Faust, on the other hand, provides at-least-once processing guarantees by leveraging Kafka's message offset tracking.
Ecosystem and Community Support: Apache Flink has a mature ecosystem and a large community backing it. It is widely used in various industries and has a rich set of libraries and tools built around it. Faust, being a relatively new library, has a smaller ecosystem and community compared to Apache Flink.

In Summary, Apache Flink and Faust differ in their storage and processing models, programming paradigms, supported data sources, fault tolerance mechanisms, processing guarantees, and ecosystem/community support.

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Advice on Apache Flink, Faust

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Comments

Detailed Comparison

Apache Flink	Faust
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.	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.
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	Stream processing; Event processing; Build high performance distributed systems; Real-time data pipelines
Statistics
GitHub Stars 25.4K	GitHub Stars 6.8K
GitHub Forks 13.7K	GitHub Forks 536
Stacks 534	Stacks 26
Followers 879	Followers 80
Votes 38	Votes 0
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	No community feedback yet
Integrations
YARN Hadoop Hadoop HBase Kafka	Python Flask Django Pandas PyTorch NumPy NLTK SQLAlchemy

What are some alternatives to Apache Flink, Faust?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

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.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Apache Flink vs Faust: What are the differences?

Storage and Processing Model: Apache Flink and Faust have different models for storage and processing. Apache Flink is designed for distributed stream processing and batch processing, providing support for both event time and processing time semantics. Faust, on the other hand, is a stream processing library specifically built for Kafka, allowing you to define stream processors and connect them to Kafka topics.
Programming Paradigm: Apache Flink uses a unified programming model called the DataStream API, which enables users to write stream processing jobs in a high-level language similar to SQL. Faust, on the other hand, leverages Python as its programming language, making it more accessible to Python developers.
Supported Data Sources: Apache Flink supports a wide range of data sources and connectors, including Kafka, Hadoop, Amazon S3, and more. It also provides connectors for different databases and messaging systems. In contrast, Faust is tightly integrated with Kafka and focuses on supporting Kafka topics as the primary data source.
Fault Tolerance Mechanisms: Apache Flink is designed with fault tolerance in mind and provides built-in mechanisms to handle failures. It achieves fault tolerance through its distributed snapshotting and checkpointing mechanism. Faust, being a Kafka-specific library, relies on Kafka's own fault tolerance mechanisms, such as replication and leader election.
Processing Guarantees: Apache Flink provides strong processing guarantees by ensuring exactly once semantics for both event time and processing time. It achieves this by incorporating mechanisms like event deduplication and transactional writes into its processing pipelines. Faust, on the other hand, provides at-least-once processing guarantees by leveraging Kafka's message offset tracking.
Ecosystem and Community Support: Apache Flink has a mature ecosystem and a large community backing it. It is widely used in various industries and has a rich set of libraries and tools built around it. Faust, being a relatively new library, has a smaller ecosystem and community compared to Apache Flink.

Apache Flink vs Faust

Overview