beam
RabbitMQ
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beam | RabbitMQ | |
---|---|---|
30 | 92 | |
7,508 | 11,590 | |
1.5% | 1.8% | |
10.0 | 10.0 | |
3 days ago | 1 day ago | |
Java | Starlark | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
beam
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Ask HN: Does (or why does) anyone use MapReduce anymore?
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.).
As for the framework called MapReduce, it isn't used much, but its descendant https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.
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beam VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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How do Streaming Aggregation Pipelines work?
Apache Beam is one of many tools that you can use
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Releasing Temporian, a Python library for processing temporal data, built together with Google
Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.
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Kafka cluster loses or duplicates messages
To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).
- Apache Beam
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Real Time Data Infra Stack
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow
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Google Cloud Reference
Apache Beam: Batch/streaming data processing 🔗Link
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Composer out of resources - "INFO Task exited with return code Negsignal.SIGKILL"
What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all.
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Pub/Sub parallel processing best practices
That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.
RabbitMQ
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Building Llama as a Service (LaaS)
Although they did not make it into production, I experimented with the RabbitMQ message broker, Python (Django, Flask), Kubernetes + minikube, JWT, and NGINX. This was a hobby project, but I intended to learn about microservices along the way.
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A Developer's Journal: Simplifying the Twelve-Factor App
Messaging/Queueing Systems (Amazon SQS, RabbitMQ, Beanstalkd)
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FastStream: Python's framework for Efficient Message Queue Handling
Later, we discovered Propan, a library created by Nikita Pastukhov, which solved similar problems but for RabbitMQ. Recognizing the potential for collaboration, we joined forces with Nikita to build a unified library that could work seamlessly with both Kafka and RabbitMQ. And that's how FastStream came to be—a solution born out of the need for simplicity and efficiency in microservices development.
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The Complete Microservices Guide
Inter-Service Communication: Middleware provides communication channels and protocols that enable microservices to communicate with each other. This can include message brokers like RabbitMQ, Apache Kafka, RPC frameworks like gRPC, or RESTful APIs.
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Project Structure Review [.Net] [Console]
This is an implementation of pub/sub. The publisher is on a separate project. The message broker is Azure Service Bus. We use NServiceBus for code implementation. I use rabbitMQ broker for local tests. Nothing I can do about the tech stack. This is more of a high level single project structure review 😅
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The Role of Queues in Building Efficient Distributed Applications
RabbitMQ is a robust and highly configurable open-source message broker that implements the Advanced Message Queuing Protocol (AMQP).
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Should I chain calls in backend?
When using third-party services, especially within a "transaction", it's often a good idea to use a persistent Message Queue (MQ) system like RabbitMQ. Go through all their tutorials to get a really good understanding of how message queues work and how they can be used to solve your problem.
- Node still seems better than python after all this time for web server speed but..
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Delayed events pattern, no more crons
The best technical solution to provide the event queues is to use a message-broker technology like RabbitMQ.
- RabbitMQ 3.12.0 Released
What are some alternatives?
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
NATS - High-Performance server for NATS.io, the cloud and edge native messaging system.
Apache Hadoop - Apache Hadoop
mosquitto - Eclipse Mosquitto - An open source MQTT broker
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
MediatR - Simple, unambitious mediator implementation in .NET
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
nsq - A realtime distributed messaging platform
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
BeanstalkD - Beanstalk is a simple, fast work queue.
Apache Hive - Apache Hive
rq - Simple job queues for Python