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temporal | nsq | |
---|---|---|
16 | 14 | |
9,739 | 24,530 | |
4.7% | 0.5% | |
9.8 | 6.3 | |
7 days ago | 2 days ago | |
Go | Go | |
MIT License | MIT License |
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.
temporal
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Rethinking Serverless with Flame
I don't know if I agree with the argument regarding durability vs elastic execution. If I can get both (with a nice API/DX) via something like Temporal (https://github.com/temporalio/temporal), what's the drawback here?
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Who's hiring developer advocates? (December 2023)
Link to GitHub -->
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temporal VS laravel-workflow - a user suggested alternative
2 projects | 23 Aug 2023
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Scaling Temporal: The Basics
However, as we mentioned, each shard needs management. Part of the management includes a cache of Workflow histories for that shard. We can see the History pods’ memory usage is rising quickly. If the pods run out of memory, Kubernetes will terminate and restart them (OOMKilled). This causes Temporal to rebalance the shards onto the remaining History pod(s), only to then rebalance again once the new History pod comes up. Each time you make a scaling change, be sure to check that all Temporal pods are still within their CPU and memory requests—pods frequently being restarted is very bad for performance! To fix this, we can bump the memory limits for the History containers. Currently, it is hard to estimate the amount of memory a History pod is going to use because the limits are not set per host, or even in MB, but rather as a number of cache entries to store. There is work to improve this: github.com/temporalio/temporal/issues/2941. For now, we’ll set the History memory limit to 8GB and keep an eye on them—we can always raise it later if we find the pod needs more.
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Temporal .NET – Deterministic Workflow Authoring in .NET
Correct, the workflow's guarantee to always complete executing independent of hardware failures is dependent on the database not losing data. You host your workflow code with Temporal's Worker library, which talks to an instance of the Temporal Server [1], which is an open-source set of services (hosted by you or by Temporal Cloud), backed by Cassandra, MySQL, or Postgres. [2] So for instance increasing Cassandra's replication factor increases your resilience to disk failure.
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Mandala: experiment data management as a built-in (Python) language feature
Re:graph frameworks - thanks for the pointers, hadn't heard about them! I'd heard of temporal which I believe provides a similar memoization capability with the purpose of not losing work in workflows that failed partway through?
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temporal VS javactrl-kafka - a user suggested alternative
2 projects | 2 Feb 2023
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Temporal PHP SDK: Scalable and resilent workflow orchestration on PHP
Documentation
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Developers and Distributed Systems and Dinosaurs, Oh MY!!!
Personally I am leveraging the knowledge and momentum of Replay to dive into the Python SDK, build out a couple of applications to deepen my knowledge around Workflows, Activities, and metrics, and continue inhaling knowledge via the monthly meetup, the application development guide, and documentation. By next year I’ll experience the conference, not as one new to Temporal, but as an expert—maybe even as one of the people helping with the architecture review or running a Birds of a Feather; if anything, I know I look forward to seeing YOU at next year’s event!
- Building financial integration with Cadence in doordash
nsq
- NSQ: Open-source realtime distributed messaging, billions of messages / day
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MQTT vs. Kafka: An IoT Advocate's Perspective
Interesting. What are you thoughts on NSQ?
Was looking at it earlier today, but haven't ever tried it out.
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Any thoughts on using Redis to extend Go's channels across application / machine boundaries?
(G)NATS can do millions of messages per second and is the right tool for the job (either that or NSQ). Redis isn't even the fastest Redis protocol implementation, KeyDB significantly outperforms it.
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FileWave: Why we moved from ZeroMQ to NATS
Bit.ly's NSQ is also an excellent message queue option.
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Infinite loop pattern to poll for a queue in a REST server app
Queue consumers are interesting because there are many solutions for them, from using Redis and persisting the data in a data store - but for fast and scalable the approach I would take is something like SQS (as I advocate AWS even free tier) or NSQ for managing your own distributed producers and consumers.
- NSQ – A realtime distributed messaging platform
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What are pros and cons of Go?
distrubition server engine ( for example websocket server multi ws gateway and worker pool,nsq.io realtime message queue and so on)
- Nsq - A realtime distributed messaging platform
- Is there any conventionally accepted repo that is representative of well designed go code ?
- NSQ: A realtime distributed messaging platform
What are some alternatives?
argo - Workflow Engine for Kubernetes
NATS - Golang client for NATS, the cloud native messaging system.
cadence - Cadence is a distributed, scalable, durable, and highly available orchestration engine to execute asynchronous long-running business logic in a scalable and resilient way.
NATS - High-Performance server for NATS.io, the cloud and edge native messaging system.
gocelery - Celery Distributed Task Queue in Go
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
Apache Kafka - Mirror of Apache Kafka
DurableTask - Durable Task Framework allows users to write long running persistent workflows in C# using the async/await capabilities.
ZeroMQ - ZeroMQ core engine in C++, implements ZMTP/3.1
Workflow Core - Lightweight workflow engine for .NET Standard
etcd - Distributed reliable key-value store for the most critical data of a distributed system