cortex VS Apache Pulsar

now if its more metric data you are using and want to do APM, prometheus is your man https://prometheus.io/, want to make prometheus your full time job? deploy cortex https://cortexmetrics.io/, honorable mention in the metrics space, Zabbix, https://www.zabbix.com/ I've seen use cases of zabbix going way beyond its intended use its a fantastic tool

Yes, but also no. The Prometheus ecosystem already has two FOSS time-series databases that are complementary to Prometheus itself. Thanos and Mimir. Not to mention M3db, developed at Uber, and Cortex, then ancestor of Mimir. There's a bunch of others I won't mention as it would take too long.

You can use the Remote write feature to send to a centralized location. It would have to be scalable like Cortex https://cortexmetrics.io/

Cortex is not really good for high-cardinality metrics (if you are talking about https://github.com/cortexproject/cortex)

For a homelab I think prometheus + grafana is easy to get started and scales well. There are lots of ways to set up the architecture. Prometheus can write to a directory on a filesystem, it can be set to write to a remote server, and there are other projects to integrate object storage (s3, minio, etc) or influxdb for long term storage and downsampling.

Cortex (it is renamed to Mimir recently).

Disclosure: I work for AWS, but I don't work on the Amazon Managed Service for Prometheus. I have my own very long held opinions about Free and Open Source software, and I am only speaking for myself.

To me, the AGPLv3 license isn't about forcing software users to "give changes back" to a project. It is about giving the permissions to users of software that are necessary for Software Freedom [1] when they access a program over a network. In practice, that means that changes often flow "upstream" to copyleft licensed programs one way or another. But it was never about obligating changes to be "given back" to upstream. In my personal opinion, you should be "free to fork" Free and Open Source Software (FOSS). Indeed, the Grafana folks seem to have decided to do that with Grafana Mimir.

Personally, I hope that they accept contributions under the AGPLv3 license, and hold themselves to the same obligations that others are held to with regard to providing corresponding source code of derivative works when it is made available to users over a network. In my personal opinion, too often companies use a contributor agreement that excuses them from those obligations, and also allows them to sell the software to others under licenses that do not carry copyleft obligations. See [2] for a blog post that goes into some detail about this.

If you look at the Coretex project MAINTAINERS file [3], you will see that there are two folks listed that currently work at AWS, but no other company other than Grafana Labs today. I would love to see more diversity in maintainers for a project like this, as I think too many maintainers from any one company isn't the best for long term project sustainability.

I think if you look at the Cortex Community Meeting minutes [4], you can see that AWS folks are regularly "showing up" in healthy numbers, and working collaboratively with anyone who accepts the open invitation to participate. There have been some pretty big improvements to Coretex that have merged lately, like some of the work on parallel compaction [5, 6].

TL;DR, I think it is easy to jump to some conclusions about how things are going in a FOSS project that don't hold water if you do some cursory exploration. I think best way to know what's going on in a project is to get involved!

--

[1] the rights needed to: run the program for any purpose; to study how the program works, and modify it; to redistribute copies; to distribute copies of modified versions to others

[2] https://meshedinsights.com/2021/06/14/legally-ignoring-the-l...

[3] https://github.com/cortexproject/cortex/blob/master/MAINTAIN...

[4] https://docs.google.com/document/d/1shtXSAqp3t7fiC-9uZcKkq3m...

[5] https://aws.amazon.com/blogs/opensource/scaling-cortex-with-...

[6] https://github.com/cortexproject/cortex/pull/4624

> [...] that feed into a prometheus -> cortex store, so I can then map them on Grafana.

I had to Google because I've never heard of any of those. Did I find the right ones?

https://prometheus.io/

https://cortexmetrics.io/

https://grafana.com/

Mine is much more primitive. My indoor temperature monitor is an ESP8266 that uploads the temperature to a simple PHP page that saves it in an sqlite DB. A cron job runs a Perl script every few minutes that extract the data for the last hour, 3 hours, 12 hours, 48 hours, and since the beginning of time and uses gnuplot to produce PNG graphs. There's a static page on my server that displays those graphs.

My outdoor temperature monitor uses a cheap AcuRite 433 MHz indoor/outdoor thermometer I bought. I have an RPi with an RTL-SDR attached spying on the communications between the AcuRite sensor outside and the AcuRite display inside using rtl_433. A script looks at the rtl_433 and finds the AcuRite sensor data and puts it in an sqlite DB. I haven't yet gotten around to making something to graph it.

The nice thing about that approach is that it was also easy to add support for other 433 MHz wireless sensors near me, such as the AcuRite fridge/freezer thermometer I have. I can also see a few assorted sensors of neighbors (temperature, humidity, soil moisture, tire pressure, wind speed, wind direction, rain, and a few other random things). If I wanted to it would be easy to add them to the DB.

When I made a wireless tipping range gauge recently. I used a 433 MHz transmitter module [1] and added a decoder [2] to rtl_433 that understands my data stream format. That gets my data into the rtl_433 output. No need to futz around with 433 MHz receiver modules which appear to be a pain in the ass [3]. An ATTiny85 counts the tips and runs the transmitter. The ATTiny85, the transmitter module, a battery holder, an RJ11 socket because the rain gauge has an RJ11 connector, a board to put those things on [4], and a small waterproof case is pretty much the complete parts list.

I think I'm going to standardize on this general approach. For things that do not have WiFi and only need to report data 433 MHz modules and custom decoders fro rtl_433 on the RPi. For things that do have WiFi, such as any future ESP projects I do, they will just use WiFi to talk to the RPi. If anything needs to get sent outside of my LAN the RPi will handle it.

The RPi is also currently controlling a space heater in my living room, getting connection data from my cable modem periodically and recording that in an sqlite DB, and serving a simple web page that lets me quickly change inputs and volume on my Denon receiver and so I'm already pretty much committed to keeping it running all the time.

[1] https://www.sparkfun.com/products/10534

[2] Decoders can be specified in a simple text file. Here's the one for my rain guage as an example:

  decoder {

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Edit: Here's a good example of what I'm looking for: https://github.com/apache/pulsar. It is a full application that happens to be deployed (or deployable) with Terraform, and the configuration files are available.

I am the founder of RisingWave (http://risingwave.com/), an open-source SQL streaming database. I am happy to see the launch of Warpstream! I just reviewed the project and here's my personal opinion:

* Apache Kafka is undoubtedly the leading product in the streaming platform space. It offers a simple yet effective API that has become the golden standard. All streaming/messaging vendors need to adhere to Kafka protocol.

* The original Kafka only used local storage to store data, which can be extremely expensive if the data volume is large. That's why many people are advocating for the development of Kafka Tiered Storage (KIP-405: https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A...). To my best knowledge, there are at least five vendors selling Kafka or Kafka-compatible products with tiered storage support:

-- Confluent, which builds Kora, the 10X Kafka engine: https://www.confluent.io/10x-apache-kafka/;

-- Aiven, the open-source tiered storage Kafka (source code: https://github.com/Aiven-Open/tiered-storage-for-apache-kafk...

-- Redpanda Data, which cuts your TCO by 6X (https://redpanda.com/platform-tco);

-- DataStax, which commercializes Apache Pulsar (https://pulsar.apache.org/);

-- StreamNative, which commercializes Apache Pulsar (https://pulsar.apache.org/).

* WarpStream claims to be "built directly on top of S3," which I believe is a very aggressive approach that has the potential to drastically reduce costs, even compared to tiered storage. The potential tradeoff is system performance, especially in terms of latency. As new technology, WarpStream brings novelty, and definitely it also needs to convince users that the service is robust and reliable.

* BYOC (Bring Your Own Cloud) is becoming the default option. Most of the vendors listed above offer BYOC, where data is stored in customers' cloud accounts, addressing concerns about data privacy and security.

I believe WarpStream is new technology to this market, and and would encourage the team to publish some detailed numbers to confirm its performance and efficiency!

In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ.

It's colored black on the refreshed Apache Pulsar site. https://pulsar.apache.org/

So many tools out there, its just which one do you like, I guess. I like Kafka. Works for our environment and we have a few clusters. People have brought up Cribl to replace our kafka (havent really looked into Cribl and we also run NiFi). I have even heard https://pulsar.apache.org/ , which seems to be almost another flavor of Kafka.

RisingWave is an open-source streaming database that has built-in fully-managed CDC source connectors for various databases, also it can collect data from other sources such Kafka, Pulsar, Kinesis, or Redpanda and it allows you to query real-time streams using SQL. You can get a materialized view that is always up-to-date.