Apache Flink vs Samza

Overview

Samza

Stacks24

Followers62

Votes0

GitHub Stars832

Forks333

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Samza: What are the differences?

Introduction

Apache Flink and Samza are both stream processing systems that provide support for real-time data processing. While they share similarities in terms of their purpose, there are several key differences between the two.

Integration with Ecosystem: Apache Flink has a broader integration with various data sources and sinks, including Hadoop Distributed File System (HDFS), Apache Kafka, and others. Samza, on the other hand, has a more specific focus on integrating with Apache Kafka, making it a suitable choice for Kafka-based architectures.
Processing Model: Flink supports both batch processing and stream processing, offering a unified processing model. It provides a rich set of operators and an event time processing model, allowing for complex event-driven data processing. Samza, on the contrary, is primarily designed for stream processing and does not inherently support batch processing.
State Management: Flink provides built-in support for maintaining and managing state in stream processing applications. It includes features like stateful stream processing, fault-tolerant state checkpoints, and state recovery. Samza, on the other hand, does not have built-in state management capabilities and relies on external systems like Apache Kafka or Apache HBase for storing and managing the state.
Fault Tolerance: Flink offers robust fault-tolerance mechanisms, including exactly-once processing guarantees. It achieves this by maintaining consistent checkpoints of the operator states and providing recovery mechanisms in case of failures. Samza, on the other hand, focuses on at-least-once processing guarantees. It relies on Apache Kafka's offset-tracking mechanism for handling failures and ensuring data integrity.
Programming Model: Flink provides a high-level programming model with a SQL-like language called Flink SQL, as well as APIs in Java and Scala. It also supports complex event processing using CEP libraries and graph-based data processing using the Gelly library. Samza, on the other hand, primarily emphasizes a simple and lightweight programming model using the Apache Kafka Streams API.
Community and Maturity: Flink has a larger and more active community compared to Samza, resulting in a wider range of documentation, community support, and ecosystem integrations. Flink is also more mature and has been widely adopted in various industries. Samza, although still actively maintained, has a smaller community and is relatively less mature.

In summary, Apache Flink offers broader ecosystem integration, support for batch processing, built-in state management, and exactly-once processing guarantees. On the other hand, Samza focuses on integration with Apache Kafka, provides a lightweight programming model, relies on external systems for state management, and offers at-least-once processing guarantees.

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Advice on Samza, Apache Flink

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

Samza	Apache Flink
It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.	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.
HIGH PERFORMANCE; HORIZONTALLY SCALABLE; EASY TO OPERATE; WRITE ONCE, RUN ANYWHERE; PLUGGABLE ARCHITECTURE	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
Statistics
GitHub Stars 832	GitHub Stars 25.4K
GitHub Forks 333	GitHub Forks 13.7K
Stacks 24	Stacks 534
Followers 62	Followers 879
Votes 0	Votes 38
Pros & Cons
No community feedback yet	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
Integrations
Presto Datadog Woopra	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Samza, Apache Flink?

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.

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.

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.

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.

Related Comparisons

Apache Flink vs Samza: What are the differences?

Introduction

Integration with Ecosystem: Apache Flink has a broader integration with various data sources and sinks, including Hadoop Distributed File System (HDFS), Apache Kafka, and others. Samza, on the other hand, has a more specific focus on integrating with Apache Kafka, making it a suitable choice for Kafka-based architectures.
Processing Model: Flink supports both batch processing and stream processing, offering a unified processing model. It provides a rich set of operators and an event time processing model, allowing for complex event-driven data processing. Samza, on the contrary, is primarily designed for stream processing and does not inherently support batch processing.
State Management: Flink provides built-in support for maintaining and managing state in stream processing applications. It includes features like stateful stream processing, fault-tolerant state checkpoints, and state recovery. Samza, on the other hand, does not have built-in state management capabilities and relies on external systems like Apache Kafka or Apache HBase for storing and managing the state.
Fault Tolerance: Flink offers robust fault-tolerance mechanisms, including exactly-once processing guarantees. It achieves this by maintaining consistent checkpoints of the operator states and providing recovery mechanisms in case of failures. Samza, on the other hand, focuses on at-least-once processing guarantees. It relies on Apache Kafka's offset-tracking mechanism for handling failures and ensuring data integrity.
Programming Model: Flink provides a high-level programming model with a SQL-like language called Flink SQL, as well as APIs in Java and Scala. It also supports complex event processing using CEP libraries and graph-based data processing using the Gelly library. Samza, on the other hand, primarily emphasizes a simple and lightweight programming model using the Apache Kafka Streams API.
Community and Maturity: Flink has a larger and more active community compared to Samza, resulting in a wider range of documentation, community support, and ecosystem integrations. Flink is also more mature and has been widely adopted in various industries. Samza, although still actively maintained, has a smaller community and is relatively less mature.

Apache Flink vs Samza

Overview

Apache Flink vs Samza: What are the differences?