Apache Spark vs ZeroMQ

Overview

ZeroMQ

Stacks258

Followers586

Votes71

GitHub Stars10.6K

Forks2.5K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs ZeroMQ: What are the differences?

Apache Spark is a powerful open-source data processing engine while ZeroMQ is a high-performance messaging library.

**1. Programming Model**: Apache Spark provides a high-level API for parallel data processing with built-in libraries for various tasks, while ZeroMQ focuses on message queuing and distribution with low-latency communication.
**2. Use Case**: Apache Spark is primarily used for big data analytics, machine learning, and data processing workflows, whereas ZeroMQ is ideal for building distributed applications and microservices with a focus on messaging patterns.
**3. Fault Tolerance**: Apache Spark provides built-in fault tolerance mechanisms like lineage tracking and RDDs for handling data losses, while ZeroMQ relies on the application layer for handling message delivery and recovery in case of failures.
**4. Scalability**: Apache Spark offers scalability through distributed computing with support for large clusters for processing massive datasets, whereas ZeroMQ scales well for distributed messaging tasks but does not inherently support large-scale data processing.
**5. Flexibility**: Apache Spark is more opinionated in terms of its execution model and data handling approaches, while ZeroMQ offers more flexibility in designing custom messaging patterns and workflows tailored to specific use cases.
**6. Performance**: Apache Spark is optimized for in-memory processing and can deliver high throughput for data-intensive tasks, whereas ZeroMQ provides low-latency messaging capabilities but may not be as performant for data processing operations.

In Summary, Apache Spark and ZeroMQ differ in their programming models, use cases, fault tolerance mechanisms, scalability, flexibility, and performance characteristics.

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Advice on ZeroMQ, Apache Spark

Meili

Software engineer at Digital Science

Sep 24, 2020

Needs adviceon

ZeroMQ

RabbitMQ

Amazon SQS

Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to:

Not loose messages in services outages
Safely restart service without losing messages (@{ZeroMQ}|tool:1064| seems to need to close the socket in the receiver before restart manually)

Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?

Thank you for your time

500k views500k

Comments

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.

576k views576k

Comments

Detailed Comparison

ZeroMQ	Apache Spark
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.	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.
Connect your code in any language, on any platform.;Carries messages across inproc, IPC, TCP, TPIC, multicast.;Smart patterns like pub-sub, push-pull, and router-dealer.;High-speed asynchronous I/O engines, in a tiny library.;Backed by a large and active open source community.;Supports every modern language and platform.;Build any architecture: centralized, distributed, small, or large.;Free software with full commercial support.	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
Statistics
GitHub Stars 10.6K	GitHub Stars 42.2K
GitHub Forks 2.5K	GitHub Forks 28.9K
Stacks 258	Stacks 3.1K
Followers 586	Followers 3.5K
Votes 71	Votes 140
Pros & Cons
Pros 23 Fast 20 Lightweight 11 Transport agnostic 7 No broker required 4 Low level APIs are in C Cons 5 No message durability 3 Not a very reliable system - message delivery wise 1 M x N problem with M producers and N consumers	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

What are some alternatives to ZeroMQ, Apache Spark?

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.

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.

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.

Related Comparisons

Apache Spark vs ZeroMQ: What are the differences?

Apache Spark is a powerful open-source data processing engine while ZeroMQ is a high-performance messaging library.

**1. Programming Model**: Apache Spark provides a high-level API for parallel data processing with built-in libraries for various tasks, while ZeroMQ focuses on message queuing and distribution with low-latency communication.
**2. Use Case**: Apache Spark is primarily used for big data analytics, machine learning, and data processing workflows, whereas ZeroMQ is ideal for building distributed applications and microservices with a focus on messaging patterns.
**3. Fault Tolerance**: Apache Spark provides built-in fault tolerance mechanisms like lineage tracking and RDDs for handling data losses, while ZeroMQ relies on the application layer for handling message delivery and recovery in case of failures.
**4. Scalability**: Apache Spark offers scalability through distributed computing with support for large clusters for processing massive datasets, whereas ZeroMQ scales well for distributed messaging tasks but does not inherently support large-scale data processing.
**5. Flexibility**: Apache Spark is more opinionated in terms of its execution model and data handling approaches, while ZeroMQ offers more flexibility in designing custom messaging patterns and workflows tailored to specific use cases.
**6. Performance**: Apache Spark is optimized for in-memory processing and can deliver high throughput for data-intensive tasks, whereas ZeroMQ provides low-latency messaging capabilities but may not be as performant for data processing operations.

In Summary, Apache Spark and ZeroMQ differ in their programming models, use cases, fault tolerance mechanisms, scalability, flexibility, and performance characteristics.

Apache Spark vs ZeroMQ

Overview