[cheddar]

What kind of ingestion numbers are you working with? The thread you link to shows that Druid can ingest ~27.5k events/sec per node, which is roughly 2.376bn events a day per node.

While you can claim bias here too, we have multiple clusters ingesting in the high hundreds of thousands of events/second and our largest cluster does close to 2m/s. That's definitely scaled horizontally across multiple nodes.

If you are suggesting there is a system out there that can ingest millions of messages a second on a single node, I'd love to hear about it :).

edit: Ah, I see from the spreadsheet that you linked that there are systems out there that claim 2.5-3.5m writes per second per node. That's really quite amazing, would be awesome if you could provide the methodology used to collect those numbers. For example, if you are sending in 500 byte events (a rather common size for what we do), if my calculations are correct, you are now sustaining 14 Gbps, which means those benchmarks were done on some beefy hardware. Can you link to a blog post that details the methodology?

[dataloopio]

Most benchmarks are given a colour for reliability and link to repeatability.

[cheddar]

Ah, cool, I chased down what you are doing and figured out that you are doing an apples to oranges comparison.

As described in your benchmark description:

https://gist.github.com/sacreman/b77eb561270e19ca973dd505527...

You are running 200 agents emitting 6000 metrics a piece using Haggar to generate load, which is at

https://github.com/dalmatinerdb/haggar

The specific thing of interest is how you are generating your data, which looks like you have a single set of dimensions and 6000 metrics dangling off of it. The loop that populates all of the "metrics" are:

https://github.com/dalmatinerdb/haggar/blob/master/main.go#L...

And the thing that actually populates the bytes are at:

https://github.com/dalmatinerdb/haggar/blob/master/util.go#L...

So, if we take this to an apples-to-apples comparison, you have 200 agents sending a single event every second with 6000 metrics in it. That means that you are successfully ingesting 200 events per second in the way that we would measure event ingestion for Druid.

Note, also, that the thread you link to is ingesting 17 independent dimensions with each and every event that flows in. From the Daltaminer docs, it looks like you put all dimension data into postgres and you don't expect any large-scale deployment to ever need more than a single postgres node:

https://gist.github.com/sacreman/9015bf466b4fa2a654486cd79b7...

Look under "Setup Postgres".

We routinely have billions of unique combinations of dimension values per day flowing into our system. Delegating the finding of the right keys to a relational database for such operations is going to be very cost-prohibitive, not to mention, you are going to have to materialize hundreds of millions of keys in order to do a simple aggregate over the day.

So, I guess this is just another case where you should never trust benchmarks that you didn't do yourself or that don't follow a standard pattern like TPC-H. It's too easy for the same words to be used with different meanings.

[dataloopio]

DalmatinerDB, InfluxDB, Prometheus and Graphite each claim their numbers based on similar benchmark methods. The results range from 500k/sec to a couple of million metrics /sec. Druid comparatively, for the same benchmark would be closer to 30k/sec. If that's factually wrong please post some details and we can update the spreadsheet.

Expanding the benchmark to cover cardinality and other aspects would indeed be comparing apples to oranges.

In terms of benchmarking DalmatinerDB with billions of unique combinations indexed in Postgres.. I think we know what will happen there :) That's what it's designed for. We can also shard in the query engine, or use any of the multi master Postgres options, but I doubt that would even be necessary.

[fangjin]

The databases listed above, to the best of my knowledge, are commonly used for dev ops metrics data and share similar terminology. Druid on the other hand, draws much of its terminology from the OLAP world. As cheddar clarified in his post above, the benchmarks for Druid are misleading as it is not an apples to apples comparison (I suspect the benchmarks for ES also suffer from this problem). A single Druid event may consist of thousands of metrics.

[gillh]

Agree with your analysis. At Netsil, one of the big factors that we considered was average query latency and fast-aggregations over high cardinality, multi-dimensional data. Few of our early customers told us that when they deployed solutions from other vendors (with storage engines such as Cassandra) at scale (800+ monitored instances), they would have to wait for several minutes to let the data render on their dashboards for a 1-day aggregation query. So, it was not just the scalable ingestion that was paramount, fast ad-hoc analytics functionality was equally important to us.

[kylequest]

and that happens to be the system they built :-)