streamparse
thanos
streamparse | thanos | |
---|---|---|
1 | 66 | |
1,490 | 12,585 | |
0.0% | 0.3% | |
2.7 | 9.6 | |
10 days ago | 4 days ago | |
Python | Go | |
Apache License 2.0 | Apache License 2.0 |
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.
streamparse
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Apache Heron: A realtime, distributed, fault-tolerant stream processing engine
Wonder why this is getting posted today in particular?
The quick summary here is that this was a clean-house rewrite of Apache Storm done by an internal team at Twitter. As an open source project history refresher, Apache Storm was originally built by a startup called Backtype, and the project was led by Nathan Marz, the technical founder of Backtype. Then, Backtype was acquired by Twitter, and Storm became a major component for large-scale stream processing (of tweets, tweet analytics, and other things) at Twitter.
I wrote a summary of the "interesting bits" of Apache Storm here:
https://blog.parse.ly/storm/
However, at a certain point, Nathan Marz left Twitter, and a different group of engineers tried to rethink Storm inside Twitter. There was also a lot of work going on around Apache Mesos at the time. Heron is kind of a merger of their "rethinking" of Storm while also making it possible to manage Storm-like Heron clusters using Mesos.
But, I don't think Heron really took off. Meanwhile, Storm got very, very stable in the 1.x series, and then had a clean-house rewrite from Clojure to Java in the 2.x series. The last stable/major Storm release was in 2020.
Storm provides a stream processing programming API, a multi-lang wire protocol, and a cluster management approach. But certain cluster computing problems can probably be better solved at the infrastructure layer today. That said, it's still a very powerful system; on my team, we process 75K+ events per second across hundreds of vCPU cores and thousands of Python processes by combining Storm and Kafka with our open source project, streamparse.
https://github.com/Parsely/streamparse
(Also, I'd be remiss if I didn't mention -- if you're interested in stream processing and distributed computing, we are hiring Python Data Engineers to work on a stack involving Storm, Spark, Kafka, Cassandra, etc.) -- https://www.parse.ly/careers/python_data_engineer
thanos
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Looking for a way to remote in to K's of raspberry pi's...
Monitoring = netdata on each RPi https://www.netdata.cloud/ binded to the vpn interface being scraped into a prometeus thaons https://thanos.io/ setup with grafana to give management the Green all is good screens (very important).
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thanos VS openobserve - a user suggested alternative
2 projects | 30 Aug 2023
- FLaNK Stack Weekly for 24 July 2023
- FLaNK Stack Weekly for 10 July 2023
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Monitoring multiple kubernetes cluster with single Prometheus operator
Sounds like you want something like Thanos
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Is anyone frustrated with anything about Prometheus?
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.
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Thousandeyes Pricing Model
Long term storage all depends on your needs and sophistication. I use Thanos for our system since it has an extremely flexible scaling system. But there is also Grafana Mimir. They're both similar in that they use Prometheus TSDB format as part of the underlying storage. One nice Thanos advantage is that it does do downsampling in addition to being able to store raw metric data for a long time. It will auto-select downsampled data to make requests faster.
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Monitoring many cluster k8s
You can aggregate all your clusters Prometheus metrics together with a wonderful tool called Thanos. This will allow you to use just a single Grafana instance against Thanos and using a label select which cluster you wish to see metrics from. The downside of this, is that none of the Grafana dashboards from the internet will work as-is. You'll need to customize all of them for Thanos support. The other downside is, you have a single point of failure, and (see next item) you can't customize who can access what in regards to your dev vs production data/metrics/access.
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Best unicorn monitoring system?
Depending on how you want to set things up, you can use Thanos or Mimir to create the single-pane-of-glass view of your data.
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Prometheus vs EFS: I don't know who to believe
You could look at something like Thanos and store your data in S3: https://thanos.io/