Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
1–10 of 28 posts
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#2[deleted]
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#3Any reason to use this like in Azure over their cloud native options such as with AKS that has fluentd built into the ama-pod? It already sends logs to Azure Monitor/LogA. Azure Managed Grafana can take in Kusto queries. AMA can monitor VMs. Further you can use DCE/DCRs for custom logs. Azure provides Azure native ElasticSearch too. It seems to own this market.
You can predictably control costs and predict costs with these models.
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#4How does it compare to openobserve?
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#5How does Greptime handle dynamic schemas where you don't know most of the shape of the data upfront?
Where I work, we have maybe a hundred different sources of structured logs: Our own applications, Kubernetes, databases, CI/CD software, lots of system processes. There's no common schema other than the basics (timestamp, message, source, Kubernetes metadata). Apps produce all sorts JSON fields, and we have thousands and thousands of fields across all these apps.
It'd be okay to define a small core subset, but we'd need a sensible "catch all" rule for the rest. All fields need to be searchable, but it's of course OK if performance is a little worse for non-core fields, as long as you can go into the schema and explicitly add it in order to speed things up.
Also, how does Greptime scale with that many fields? Does it do fine with thousands of columns?
I imagine it would be a good idea to have one table per source. Is it easy/performant to search multiple tables (union ordered by time) in a single query?
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#6For logs I'd be more likely to choose https://www.gravwell.io as it's log agnostic and I've seen it crush 40Tb/s a day, whereas it looks like greptime is purpose-tuned for metrics and telemetry data.
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#7I'm always skeptical toward software companies with an outdated year in the footer.
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#8For logs I'd be more likely to choose https://www.gravwell.io as it's log agnostic and I've seen it crush 40Tb/s a day, whereas it looks like greptime is purpose-tuned for metrics and telemetry data.
is gravwell open source?
(it seems greptime is.)
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#9How does Greptime handle dynamic schemas where you don't know most of the shape of the data upfront? Where I work, we have maybe a hundred different sources of structured logs: Our own applications, Kubernetes, databases, CI/CD software, lots of system processes. There's no common schema other than the basics (timestamp, message, source, Kubernetes metadata). Apps produce all sorts JSON fields, and we have thousands…
Thanks for your question.
GreptimeDB, like MongoDB, is schemaless. When ingesting data via OTEL or its gRPC SDKs, it automatically creates tables by inferring the schema and dynamically adds new columns as needed.
Secondly, I prefer wide tables to consolidate all sources for easy management and scalability. With GreptimeDB's columnar storage based on Parquet, unused columns don't incur storage costs.
Re: Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
#10Any reason to use this like in Azure over their cloud native options such as with AKS that has fluentd built into the ama-pod? It already sends logs to Azure Monitor/LogA. Azure Managed Grafana can take in Kusto queries. AMA can monitor VMs. Further you can use DCE/DCRs for custom logs. Azure provides Azure native ElasticSearch too. It seems to own this market. You can predictably control costs and predict costs with…
Agree. Leveraging capabilities provided by cloud vendors is always a good idea. However, as the scale grows, cost inevitably becomes an issue. Third-party solutions often offer cost advantages because they support multi-cloud deployments and are optimized for specific scenarios.