Apache Spark vs Samza

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Samza

Stacks24

Followers62

Votes0

GitHub Stars832

Forks333

Apache Spark vs Samza: What are the differences?

Introduction

Apache Spark and Samza are both powerful distributed computing frameworks commonly used in big data processing. They differ in various aspects that cater to different use cases and requirements.

Processing Model: Apache Spark is primarily designed for in-memory processing of data, making it suitable for iterative algorithms and interactive data analysis. On the other hand, Samza focuses on stream processing, enabling real-time processing of large volumes of data with low latency.
Fault Tolerance: Spark provides fault tolerance through resilient distributed datasets (RDDs), offering fault recovery at the granularity of RDDs. Samza, on the other hand, provides fault tolerance through checkpoints, allowing it to resume processing from the last checkpoint in case of failures.
Batch vs. Streaming: While Spark can handle both batch and streaming data processing, it is more optimized for batch processing compared to streaming. Samza, on the other hand, is specifically designed for stream processing, making it more efficient for real-time data processing requirements.
Programming Language Support: Apache Spark supports multiple programming languages like Scala, Java, Python, and R, making it versatile for developers with different preferences. Samza is heavily focused on Java and allows for seamless integration with existing Java-based applications and infrastructure.
State Management: Spark does not provide built-in state management capabilities for stream processing, requiring users to integrate external systems for state handling. In contrast, Samza comes with built-in state management capabilities, making it easier for users to manage and store state information within the framework.
Use Cases: Apache Spark is suitable for a wide range of use cases, including machine learning, interactive data analysis, and batch processing, making it a versatile choice for various data processing requirements. Samza, on the other hand, is more specialized for stream processing use cases that require low-latency real-time processing of data streams.

In Summary, Apache Spark and Samza differ in their processing models, fault tolerance mechanisms, focus on batch vs. streaming processing, programming language support, state management capabilities, and specific use cases they cater to in the realm of distributed computing.

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Advice on Apache Spark, Samza

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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Detailed Comparison

Apache Spark	Samza
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.	It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	HIGH PERFORMANCE; HORIZONTALLY SCALABLE; EASY TO OPERATE; WRITE ONCE, RUN ANYWHERE; PLUGGABLE ARCHITECTURE
Statistics
GitHub Stars 42.2K	GitHub Stars 832
GitHub Forks 28.9K	GitHub Forks 333
Stacks 3.1K	Stacks 24
Followers 3.5K	Followers 62
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	No community feedback yet
Integrations
No integrations available	Presto Datadog Woopra

What are some alternatives to Apache Spark, Samza?

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.

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.

Related Comparisons

Apache Spark vs Samza: What are the differences?

Introduction

Apache Spark and Samza are both powerful distributed computing frameworks commonly used in big data processing. They differ in various aspects that cater to different use cases and requirements.

Processing Model: Apache Spark is primarily designed for in-memory processing of data, making it suitable for iterative algorithms and interactive data analysis. On the other hand, Samza focuses on stream processing, enabling real-time processing of large volumes of data with low latency.
Fault Tolerance: Spark provides fault tolerance through resilient distributed datasets (RDDs), offering fault recovery at the granularity of RDDs. Samza, on the other hand, provides fault tolerance through checkpoints, allowing it to resume processing from the last checkpoint in case of failures.
Batch vs. Streaming: While Spark can handle both batch and streaming data processing, it is more optimized for batch processing compared to streaming. Samza, on the other hand, is specifically designed for stream processing, making it more efficient for real-time data processing requirements.
Programming Language Support: Apache Spark supports multiple programming languages like Scala, Java, Python, and R, making it versatile for developers with different preferences. Samza is heavily focused on Java and allows for seamless integration with existing Java-based applications and infrastructure.
State Management: Spark does not provide built-in state management capabilities for stream processing, requiring users to integrate external systems for state handling. In contrast, Samza comes with built-in state management capabilities, making it easier for users to manage and store state information within the framework.
Use Cases: Apache Spark is suitable for a wide range of use cases, including machine learning, interactive data analysis, and batch processing, making it a versatile choice for various data processing requirements. Samza, on the other hand, is more specialized for stream processing use cases that require low-latency real-time processing of data streams.

Apache Spark vs Samza

Overview

Apache Spark vs Samza: What are the differences?