21–30 of 39 posts
Uber -> Chronosphere Google -> Lightstep Facebook -> Honeycomb Twitter -> Buoyant (and Zipkin, OSS)
It just seems another clone of datadog
Actually I think my real question is, why are there such a proliferation of these monitoring/logging/visualization -AAS startups? Who are the target customers, in terms of spends?
This isn't a problem that you can build a business around.
Edit: Ah, I get it. This is like a Mesosphere play--they're shepherding the M3 technology in the open source ecosystem and offering a commercial version. That makes more sense.
> Chronoshere, a startup from two ex-Uber engineers, who helped create the open source M3 monitoring project to handle Uber-level scale, officially launched today with the goal of building a commercial company on top of the open source project. I recall a thread here from 2-3 weeks ago about how “Uber-scale” wasn’t really Uber scale, and that most of these publicized “Uber-scale” projects ended up getting canned inte…
Rob, co-founder and M3DB creator here, Uber collected billions of metric samples and we had tens of billions of metrics in M3 at Uber. Netflix for reference has not published any numbers higher than single digit billions of time series. The system has run in production for several years now at Uber now. That's my thoughts on the matter, hah.
Earlier quoted context omitted.
Rob, co-founder and M3DB creator here, Uber collected billions of metric samples and we had tens of billions of metrics in M3 at Uber. Netflix for reference has not published any numbers higher than single digit billions of time series. The system has run in production for several years now at Uber now. That's my thoughts on the matter, hah.
New Relic touts collecting trillions of data points per day.
> Released in 2015, M3 now houses over 6.6 billion time series. M3 aggregates 500 million metrics per second and persists 20 million resulting metrics-per-second to storage globally (with M3DB), using a quorum write to persist each metric to three replicas in a region.
So, if that's accurate, they're collecting one trillion data points every two seconds.
This looks like a competitor to Cortex (https://www.cncf.io/blog/2018/12/18/cortex-a-multi-tenant-ho...).
The technical details of their software are described in https://eng.uber.com/m3/ This looks like a competitor to Cortex ( https://www.cncf.io/blog/2018/12/18/cortex-a-multi-tenant-ho... ).
Earlier quoted context omitted.
I think you'd be very hard pressed to scale an Elasticsearch cluster to 10s of millions of writes/s without breaking the bank (and even if you had a pile of money to light on fire I don't think an Elasticsearch cluster with the number of nodes you'd require to support that would work very well). Elasticsearch is a great piece of technology and its very versatile which makes it a great fit for a lot of problems (Uber,…
Es is very efficient with metrics especially in recent releases.
What I am not getting from my superficial knowledge is that why is Prometheus getting so much traction over elastic search. Elastic search claims to be as good for metrics and events. The ES database itself is more advanced with eventual consistency and search capability. It can do log analytics and it can be backend to tracing tool like Jaeger. Why so much investment in Prometheus. Disclaimer: I have not used Promet…
At their core these systems are basically specialized column stores, they have complete different read/write patterns to something like ES. The basic query unit for example is always going to be the scan, I'm not even aware of any monitoring system with some kind of secondary index capability. ES supports a bunch of nice result aggregation stuff on top of Lucene, whereas these systems are primarily /built for/ this u…