ActiveMQ vs Apache Spark

Overview

ActiveMQ

Stacks880

Followers1.3K

Votes77

GitHub Stars2.4K

Forks1.5K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

ActiveMQ vs Apache Spark: What are the differences?

Introduction:

ActiveMQ and Apache Spark are two popular tools in the field of distributed computing and data processing. While both are essential in handling large-scale data and enabling real-time analytics, they serve different purposes and come with distinct features. Here are the key differences between ActiveMQ and Apache Spark:

1. Messaging vs. Processing: ActiveMQ is a message broker system that facilitates communication between different software systems by enabling the transmission of data asynchronously. On the other hand, Apache Spark is a distributed computing framework primarily used for processing and analyzing large datasets in a parallel and fault-tolerant manner.

2. Data Streaming vs. Batch Processing: ActiveMQ excels in supporting data streaming, where messages are processed in real-time as they arrive. In contrast, Apache Spark is more focused on batch processing, where data is processed in large blocks at scheduled intervals, providing the ability to perform complex analytics on historical data.

3. Programming Model: ActiveMQ primarily uses messaging protocols such as JMS (Java Message Service) for communication, making it suitable for building scalable and reliable messaging systems. Apache Spark, on the other hand, offers a wide range of APIs and libraries (e.g., Spark Core, Spark SQL, MLlib) that support various programming languages like Java, Scala, Python, and R for processing big data.

4. Scalability and Fault Tolerance: Apache Spark is designed to handle massive datasets and can scale horizontally by adding more nodes to the cluster. It also provides fault tolerance by replicating data partitions across nodes, ensuring the system's resilience. While ActiveMQ supports clustering to achieve high availability, it may not be as streamlined for handling complex analytical workloads on a large scale.

5. In-Memory Processing: One of the key strengths of Apache Spark is its in-memory processing capabilities. It stores intermediate data in memory, allowing for significantly faster data processing compared to systems that rely heavily on disk I/O, such as ActiveMQ. This feature enables Spark to deliver near real-time analytics and iterative processing for machine learning algorithms.

6. Use Cases: ActiveMQ is commonly used for building reliable messaging systems, handling communication between microservices, and ensuring smooth integration between different components of a distributed application. Apache Spark, on the other hand, is widely employed for tasks like data ETL (Extract, Transform, Load), interactive data querying, machine learning, and stream processing.

In Summary, ActiveMQ focuses on messaging and real-time communication, while Apache Spark prioritizes distributed data processing, scalability, and in-memory computing for fast analytics and machine learning.

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Advice on ActiveMQ, Apache Spark

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

ActiveMQ	Apache Spark
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.	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.
Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment	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 2.4K	GitHub Stars 42.2K
GitHub Forks 1.5K	GitHub Forks 28.9K
Stacks 880	Stacks 3.1K
Followers 1.3K	Followers 3.5K
Votes 77	Votes 140
Pros & Cons
Pros 18 Easy to use 14 Open source 13 Efficient 10 JMS compliant 6 High Availability Cons 1 Difficult to scale 1 ONLY Vertically Scalable 1 Support 1 Low resilience to exceptions and interruptions	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 ActiveMQ, 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.

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.

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

ActiveMQ vs Apache Spark: What are the differences?

Introduction:

ActiveMQ vs Apache Spark

Overview