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?
killme2008 · 71d ago
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 · 71d ago
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 · 71d ago
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.
atombender · 71d ago
It's less convenient because it makes the database less general-purpose. The moment you need to go beyond time-based data, you have to reach for other tools.
ClickHouse is such a wonderful database precisely it's so incredibly flexible. While most data I interact with is time-based, I also store lots of non-time-based data there to complement the time-based tables. The rich feature set of table engines, materialized views, and dictionaries means you have a lot of different tools to pick from to design your solution. For example, to optimize ETL lookup, I use a lot of dictionaries, which are not time-based.
As an example, let's say I'm ingesting logs into Greptime and some log lines have a customer_id. I would like the final table, or least a view, to be cross-referenced with the customer so that it can include the customer's name. I suppose one would have to continually ingest customer data into a Greptime table with today's date, and then join on today's date?
killme2008 · 70d ago
Fair point. Joining time-series data with business data is often necessary. While GreptimeDB currently supports external tables for Parquet and CSV files, we plan to expand this support to include datasources like MySQL and PG in the future.
qmarchi · 71d ago
Am I the only one that got, "This article smells like it was written by an AI told to 'compare these two products'"?
Something around the sentence structure just is offputting.
killme2008 · 71d ago
The author is not a native speaker; I promised it's not an AI article but with some minor reviews from AI :)
firesteelrain · 71d ago
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 · 71d ago
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 · 71d ago
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 · 71d ago
is gravwell open source?
(it seems greptime is.)
up2isomorphism · 71d ago
This space is so crowded, I think any new startup is very unlikely to survive, unless it solves its own business case first.
killme2008 · 70d ago
Yes, so many startups are trying to solve the log issue in the current stack.
In my personal observation, the vast majority of startups are still focused on the product layer and use ClickHouse directly for storage. However, ClickHouse’s tightly coupled storage and compute architecture makes it difficult to scale, and this becomes a real problem as workloads grow. GreptimeDB, on the other hand, is more focused on being an all-in-one observability database. Our log UI, however, still has quite a gap compared to products like Kibana.
This space is very crowded. I think it’s unlikely that any new startup will succeed here unless it can first solve its own business use case exceptionally well.
Would love to hear your thoughts.
chreniuc · 71d ago
How does it compare to openobserve?
atombender · 71d ago
Reading the web site, I just noticed the open-source version does not have "Log query endpoints".
Does that mean you have to use SQL (or the visual SQL builder) to query logs, and you don't get access to a log query language the way Kibana gives you KQL and Lucene syntax?
If so, I think it's a little disingenuous to write an article comparing the ELK stack, which is open source and comes with a perfectly usable query UI, to Greptime's equivalent, which is not.
killme2008 · 70d ago
In fact, we have an open-source query language, but it's still in experimental, so we don't present it on the website. The description of the enterprise feature is not precise. Sorry for the inconvenience.
GreptimeDB also open-sources the log view UI if you read the article.
I agree with you that ETL is so powerful, and GreptimeDB is so young, we still have lots of work to do. Thank you.
atombender · 70d ago
Thanks, sounds interesting. It's actually not at all clear from the article that the UI, as presented, is open source. I'm looking for an ELK replacement (in an enterprise setting), so it sounds like Greptime is something I might be able to use.
killme2008 · 69d ago
Thanks for your feedback. We fixed the descriptions of log query endpoints. Hope it's more clear.
Glad you're considering giving it a try and looking forward to your feedback.
reconnecting · 71d ago
I'm always skeptical toward software companies with an outdated year in the footer.
killme2008 · 71d ago
Thanks for pointing it out! The footer has been updated.
reconnecting · 71d ago
Thank you for your prompt attention to this matter. Until next year, then.
killme2008 · 70d ago
We'll find a way to fix it forever :D
emmanueloga_ · 71d ago
a "no brown M&Ms" razor!
reconnecting · 71d ago
From a website perspective, finding the current year can be challenging, but there's always a way to hack around it. For example, by parsing another website to get the year.
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?
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.
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?
Could you elaborate on why you find this inconvenient? I assumed logs, for example, would naturally include a timestamp.
ClickHouse is such a wonderful database precisely it's so incredibly flexible. While most data I interact with is time-based, I also store lots of non-time-based data there to complement the time-based tables. The rich feature set of table engines, materialized views, and dictionaries means you have a lot of different tools to pick from to design your solution. For example, to optimize ETL lookup, I use a lot of dictionaries, which are not time-based.
As an example, let's say I'm ingesting logs into Greptime and some log lines have a customer_id. I would like the final table, or least a view, to be cross-referenced with the customer so that it can include the customer's name. I suppose one would have to continually ingest customer data into a Greptime table with today's date, and then join on today's date?
Something around the sentence structure just is offputting.
You can predictably control costs and predict costs with these models.
(it seems greptime is.)
In my personal observation, the vast majority of startups are still focused on the product layer and use ClickHouse directly for storage. However, ClickHouse’s tightly coupled storage and compute architecture makes it difficult to scale, and this becomes a real problem as workloads grow. GreptimeDB, on the other hand, is more focused on being an all-in-one observability database. Our log UI, however, still has quite a gap compared to products like Kibana.
This space is very crowded. I think it’s unlikely that any new startup will succeed here unless it can first solve its own business use case exceptionally well.
Would love to hear your thoughts.
Does that mean you have to use SQL (or the visual SQL builder) to query logs, and you don't get access to a log query language the way Kibana gives you KQL and Lucene syntax?
If so, I think it's a little disingenuous to write an article comparing the ELK stack, which is open source and comes with a perfectly usable query UI, to Greptime's equivalent, which is not.
GreptimeDB also open-sources the log view UI if you read the article.
I agree with you that ETL is so powerful, and GreptimeDB is so young, we still have lots of work to do. Thank you.