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A comment on M3DB, a distributed timeseries database. Parent comment

Re:M3DB, a distributed timeseries database by richieartoul on Saturday February 22, 2020 @11:19AM (#22391742)
Chronosphere, a startup founded by two of the early M3 engineers, just raised 11 million dollars to build a monitoring platform based around M3DB: https://techcrunch.com/2019/11/05/chronosphere-launches-with...

Uber also uses M3DB extensively internally and the project is nowhere near being abandoned or on life support: https://github.com/m3db/m3/commits/master

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Re:M3DB, a distributed timeseries database by StreamBright on Sunday February 23, 2020 @03:45AM (#22395817)
>> While the founders, CEO Martin Mao and CTO Rob Skillington, were working at Uber, they recognized a gap in the monitoring industry, particularly around cloud-native technologies like containers and microservices

What is the actual gap that is not addressed with one or all of the following?

- Prometheus / Grafana [1]

- Datadog [2]

- Cloudwatch [3]

1. https://docs.docker.com/config/thirdparty/prometheus/

2. https://www.datadoghq.com/blog/introducing-live-container-mo...

3. https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitori...

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Re:M3DB, a distributed timeseries database by johncolanduoni on Monday February 24, 2020 @06:05AM (#22403093)
Prometheus doesn’t have a good built in story for horizontal scaling of storage and queries, or long term storage. That’s why there’s M3DB, Cortex, etc. which let you send metrics to a set of Prometheus servers which then write to a cluster that handles broad queries and tiered storage. So these are less a competitor to Prometheus and Grafana and more an augmentation of it since it supports both emitting metrics in Prometheus form and querying them with PromQL.

Datadog gets very expensive beyond a certain point; much bigger than most startups, but much smaller than Uber.

Cloudwatch is usually used as a source of metrics rather than a destination. It doesn’t have as rich a data model as Prometheus for custom metrics, and has a lot of quite limiting restrictions (like only 10 tags per metric).

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Re:M3DB, a distributed timeseries database by StreamBright on Tuesday February 25, 2020 @11:13AM (#22414337)
Thanks for the detailed explanation. I agree with you on all points. I also think that even with the limitations you mentioned one company can find suitable monitoring for almost every scenario. I guess there is still a big enough market for M3DB where you need a huge amount of metrics, horizontal scalability and efficient long term storage.
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Re:M3DB, a distributed timeseries database by opsunit on Sunday February 23, 2020 @07:20AM (#22396507)
Wavefront: https://www.wavefront.com/
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Re:M3DB, a distributed timeseries database by nwsm on Sunday February 23, 2020 @02:18PM (#22398875)
Logging / Monitoring is the hottest market to disrupt right now.
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Re:M3DB, a distributed timeseries database by bfung on Sunday February 23, 2020 @12:25AM (#22395301)
Yep, another way to pre-market/pre-signal to investors.

A pretty common story these days:

1. I built an X to solve Unicorn U’s problem.

2. Open source it, give talks on it

3. Leave Unicorn U and start a company based on X

4. ...

5. Profit(???)

Also, debatable whether or not Unicorn U actually needed a freshly built X instead of using existing tools/tech.

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