Beyond Elk: Lightweight and Scalable Cloud-Native Log Monitoring
13 comments
·April 28, 2025atombender
killme2008
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.
atombender
Thanks, that seems promising. So much of the documentation is schema-oriented, I didn't see that it supported dynamic schemas.
I find it interesting that Greptime is completely time-oriented. I don't think you can create tables without a time PK? The last time I needed log storage, I ended up picking ClickHouse, because it has no such restrictions on primary keys. We use non-time-based tables all the time, as well as dictionaries. So it seems Greptime is a lot less flexible?
killme2008
Yes, GreptimeDB requires a time index column for optimized storage and querying. It's not a constraint of a primary key, but just an independent table constraint.
Could you elaborate on why you find this inconvenient? I assumed logs, for example, would naturally include a timestamp.
firesteelrain
Any 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.
killme2008
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.
client4
For 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.
dijit
is gravwell open source?
(it seems greptime is.)
ByteBard1979
What scenario would I use best?
chreniuc
How does it compare to openobserve?
reconnecting
I'm always skeptical toward software companies with an outdated year in the footer.
killme2008
Thanks for pointing it out! The footer has been updated.
null
How 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?