i didn't get a clear understanding of how request to such microservice would work on NATS. What's the request-response logic?

what is the max throughput? for publisher looks like it's only 11 messages per second?

hm, I was watching this pretty sure it's already released, but no, github says v2.11 is 66% complete

When is the next video coming up ? One of the key reasons Kafka is used is for absolute ordering of messages to a unique consumer from a group. This video ended right before that topic for NATS which is really one of the missing key points for NATS to replace kafka when dealing with realtime in-order events consumption distributed across multiple consumers from the same group.

Thanks for the excellent videos. FYI, at 1:50, missing link to video.

I think the only issue now is documentation. Nats has a bunch of hidden features, I usually have to ask on slack to get help coz I can't find how to do something anywhere online.

Awesome.

This really looks like an awesome feature. Makes it much easier to expose it at the edge

This is a bit unfair comparison. To make an apple-to-apple comparison you'd have to use Kafka + ksqlDB.

Cool. Nats is a great design

4:44 server-side message filtering is nice, but I wouldn't draw a conclusion that opposite is "extremely inefficient". With kafka for example, messages are consumed in batches so there is not such a huge overhead unless IO-bound. Also, because the predicate is defined in the consumer itself it means it can be unit tested. With server side filtering I'd need to have an integration test to fully test a consumer behavior.

You talked about root keys and not using them. What root key and how are the created?

if you cancel the context. your function will return context.Canceled error, and then you do os.Exit(1) due to use of log.Fatal. So that graceful thing, doesnt work.

great video, thanks would be great to share your vim config here :)

S tier content. Extremely helpful and informative

hey thank you for the greate content. i just have a problem that i want to send message to a specific container of same workers and it seems that there's no way to do this, if i'm wrong please help me 😁

How have I not known about this for so long? Great work Jeremy keep it up. #subbed

All the very best for your future endavours Jeremy

Great work, Synadia team! Thank you, KeepApp Software Development

Thank you for another insightful video! I really appreciate the effort you put into breaking down the differences between the two systems. It’s always great to see deep dives into these technologies. However, I noticed a few points that could use some clarification. I think, you oversimplify by saying Kafka consumer groups lack state, and JetStream consumers are stateful. In reality, Kafka consumer groups do manage state, particularly offsets, which are stored in a highly available way within Kafka itself. So, consumer groups can resume processing from the correct position after failures. The second point is, you suggest that Kafka requires extensive planning for partitions and that partitioning is cumbersome. I'd agree that's true and partitioning needs careful consideration, but it is also Kafka’s primary mechanism for scaling horizontally. Proper partitioning is critical for Kafka’s ability to handle massive amounts of data efficiently. Also, Kafka provides tools to manage and rebalance partitions when necessary. Finally, you claim that Kafka offers limited options for message consumption compared to NATS JetStream, particularly in filtering and controlling replay behavior. Actually, Kafka provides significant flexibility through consumer offsets, compacted topics, and Kafka Streams, which allow for complex processing, filtering, and windowing of data. There're some other points to discuss, but these are most crucial. Cheers ;)

Thank you for the video and for such a thorough comparison. It was very insightful! However, I noticed a few technical inaccuracies regarding Kafka that I’d like to clarify. 1. You mentioned that Kafka isn’t a “proper” messaging system and is mainly a distributed log platform. Of course, Kafka did start as a distributed log, it has evolved significantly and is now widely used as a messaging system. It does handling pub-sub effectively. 2. There’s a point about Kafka topics being less flexible because they lack the hierarchical structure of NATS subjects. Well, Kafka topics are designed to be simple and efficient, with key-based partitioning that supports powerful message routing. This simplicity is key to Kafka’s scalability. 3. You suggest that Kafka requires clients to receive all messages and filter them locally, which can be inefficient. In reality, Kafka consumers can use offsets and keys to retrieve only the messages they need, especially with compacted topics or Kafka Streams. 4. You also noted that Kafka lacks CRUD operations compared to NATS JetStream. Sure, Kafka doesn’t offer traditional CRUD like a database, it has strong mechanisms like compacted topics, transactional messaging, and exactly-once semantics that handle many data management needs effectively. 5. You mentioned that Kafka isn’t “real real-time” and focuses more on throughput than latency. Kafka does use batching for throughput, and it’s also capable of low-latency processing with the right configuration. 6. Finally, you suggest that Kafka’s partitioning is a workaround for its single-consumer-per-topic design. In fact, partitioning is a deliberate design choice that enables Kafka to scale horizontally and handle massive data volumes efficiently. Thank you again for the video 👍 Cheers ;)

great updates and good luck to you Jeremy

For IdP integration it would be great to get some guidance to integrate to KeyCloak

I have a need for this feature. Currently we use Redis and we have a User cache. Each User is placed into the cache with a TTL of 10 minutes. This allows the User data to not be looked up from the datastore again for 10 minutes which is the average time users stay around. However, each new read updates the TTL to 10 minutes from now. Only once they have been inactive for 10 minutes OR they change their User data are they evicted. In the case of an update the Update is placed immediately into the Cache. This keeps our cache relatively small and self cleaning while allowing users that stick around to bypass the lookup overhead for each request.

Great work guys!

looking forward to the v2.11 NATS release! #teamNATS #JetStream

Are you in investment for a sml deposits...please confirm....thank you .

When did you create the basic chat app before the auth stuff? I can't find the video

Hi. What IDE do you use?

I don't understand this. I thought it was a message broker running in-memory. Where did the services part come in? Does nats have some 'nats functions' that run inside of nats core? Or can we use everything you said with nats js? I'm using nest js currently for my monorepo. I'm curious to know if i can integrate everything you described here.

can anyone tell me how to install the nats plugin on benthos?

Nice

To answer the first question: the current latest version (i.e. top of main) of the `nats` CLI tool let's you view messages inside a working queue stream using `nats stream view`

thanks for the video! gonna try it out now

Does NATS offer a way to order queues? E.g. start processing messages from queue B only after queue A is empty.

Excellent demonstration! Will explained!

Hey I know this is super old now, but wouldn’t securing this be challenging? How are you going to securely connect to nats in the front end without exposing the details? Thanks for any feedback.

You can run it on Google and hetzner where nested virtualisation is supported

Thanks for helping with my initial heartburn of seeing a similar leadership election (using Redis) in a codebase that I recently inherited (for which no original dev is available). I’m hoping to be able to move to NATS and I think I *might* be able to remove its particular use, but good to see that it wasn’t totally astronaut engineered (even though it’s just a web app backend 😅).

Another great overview. Thanks!

nice editor, could you share your configs?

Some nsc commands used: nsc add operator --generate-signing-key --sys --name university nsc edit operator --require-signing-keys --account-jwt-server-url "nats://0.0.0.0:4222" nsc edit operator --require-signing-keys --account-jwt-server-url "nats://0.0.0.0:4222" nsc add account college nsc edit account college --sk generate nsc add user --account college student_1 nsc list keys -A nsc generate config --nats-resolver --sys-account SYS > resolver.conf nsc generate config --nats-resolver --sys-account college > sens-resolver.conf nsc push -A nsc generate creds -n student_1 > student_1.creds

NATS is so refreshing. It can do and be lots of things

I would be very curious to know the NeoVim configuration you have setup, do you have it public? 🙂

very cool!

Love the CP ux :)

i don't like how kafka is the first thing that shows up in my searches when it's so limited in terms of usecase

Do you happen to have the code running on the Pi some where handy? I need to do something similar to this :)

This is huge. I'm not ready to use it yet, but will definitely use it when my system is in production. I have to figure most self-hosted nats clusters will do the same! I wonder if you'd consider some sort of free or cheap dev license, where we can get it connected to get it integrated into our workflows while developing our system, but it has severely limited message quotas or something like that so that we aren't freeloading off of it on a production system?

Good idea ! That’s what we spoke about recently…