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How does this compare to Parseable?
https://github.com/parseablehq/parseable
First guess is that the underlying storage / query layer is pretty similar (Parquet + Datafusion), but OpenObserve has more built in use cases?
As an aside, itโs awesome that Datafusionโs existence and maturity makes launching a product with scalable analytical reads 10x easier than before and cool to see so many projects integrating it
Helm chart is available for HA mode installation that allows you to use minio/s3/gcs etc - https://openobserve.ai/docs/ha_deployment/ .
For single node installations, you can use base k8s manifests that is available using instructions at - https://openobserve.ai/docs/quickstart/#self-hosted-installa... .
Mike created a helm chart for s ingle node installation too - https://github.com/mshade/openobserve-chart/
That's traces? I was wondering if I could use https://github.com/open-telemetry/opentelemetry-js/tree/main...
Please give the benefit of the doubt on HN.
This company created ZincSearch:
https://github.com/zincsearch/zincsearch
Prabhat is one of the core contributors/maintainers:
https://github.com/zincsearch/zincsearch/graphs/contributors
https://github.com/prabhatsharma
Also the negative insinuation of using โcheapโ labor out of India to build the product is unnecessary. If youโre concerned about code quality, look at the code.
Assuming everyone working with devs in India is doing so cynically is not charitable.
I dont know why the headquarters was set as india versus SF but does it actually even matter?
I am a maintainer at SigNoz. Nice to see OpenObserve's belief that the future of Observability should be OpenSource. We chose clickhouse rather than building a database as it takes multi-year effort to move a db to maturity and clickhouse has been battle-tested at Yandex, Uber and Cloudflare. Clickhouse also provides native integration with s3 and other blob storages. Our users have been using disk as hot storage for a week and moving the data to s3 after that. Tiered storage is really cool in terms of query performance.
We have also seen logs data of our users at a compression ratio of 30x/40x. We have published a logs benchmark (https://github.com/SigNoz/logs-benchmark) where the data is very high-cardinal (causing a compression factor of only 2.5x). Would love to see how does OpenObserve perform in that dataset someday.
Wishing you best for the journey ahead.
Sounds interesting!
Will you compare with qryn? Self-hosted sentry?
qryn.metrico.in/
https://develop.sentry.dev/self-hosted/
I'd be curious to hear how this compares to
https://qryn.metrico.in
and
https://github.com/highlight/highlight
(There are some interesting comparisons/comments vs signoiz in sibling threads).
Check it out here: https://github.com/keyval-dev/odigos
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