[jnordwick]

I still can't figure out why people can't even come close to KDB+. It is a real conundrum. I've been waiting patiently for something to show up, but the gap seems to keep getting bigger instead of smaller.

Is it that people want to make the problem more complex that it needs to be? Is it that those who know most about these issues don't share their secrets so implemented from the outside often don't have a good understanding of how to do things properly? If you were to asked the guy behind Prometheus if he's looked at the commercial offerings and what he's learned from them, would even be able to speak about them intelligently?

There seems to be a huge skills gap on these things that I can't put my finger on. I'd love to be able to use a real TSDB, even at only half the speed and usefulness. It would be great for these smaller firms that cant or wont pay the license fees for a commercial offering until they get larger.

[jandrewrogers]

This observation applies to database engines generally. It is straightforward to explain: almost everyone with deep expertise in sophisticated database engine internals are contractually prohibited from disclosing anything about the design of such things. It is an industry steeped in trade secrets. Sophisticated database engines are littered with novel algorithms and designs that have never been published. Because basic performance superiority is a key market differentiator, database companies have invested heavily in computer science R&D for decades to get an edge they are loath to share. Sadly, academia is increasingly in the role of independently re-discovering what has been known for 10-20 years but treated as a secret.

The other big factor is that almost all open source database engines are the product of someone who is basically designing their first database engine. Prometheus is now on its third(?) redesign, and while you can see the growth of the designers' skill it is still a relatively naive design. This is not a jab at the designers, it just takes many creative iterations over many years to discover all the tricks that the experts know but nobody publishes. I probably spent a decade producing database engines that in hindsight were pretty mediocre but at the time I thought they were sophisticated -- I didn't know what I didn't know.

As a last observation, the other issue is that building a genuinely sophisticated database engine requires a commitment to writing an enormous amount of code before the barest database kernel can even bootstrap, on the order of 20kLoC of dense C++, never mind provide any database functionality. I've noticed that most open source projects need to see the payoff of code running and doing something minimally useful with much lower levels of investment. For database engines, getting to running code as quickly as possible compromises the design but the pressure to get to running code is understandable. The amount of time and effort required to design and build a state-of-the-art database engine goes far beyond what most people are willing to invest in what is essentially a hobby.

[wickawic]

> Sadly, academia is increasingly in the role of independently re-discovering what has been known for 10-20 years but treated as a secret.

This has been true of network programming for a long time as well. Private companies find novel ways of switching and routing packets, reducing latency, etc. and academia is left to pick up the pieces.

[zeveb]

> This observation applies to database engines generally. It is straightforward to explain: almost everyone with deep expertise in sophisticated database engine internals are contractually prohibited from disclosing anything about the design of such things. It is an industry steeped in trade secrets. Sophisticated database engines are littered with novel algorithms and designs that have never been published. Because basic performance superiority is a key market differentiator, database companies have invested heavily in computer science R&D for decades to get an edge they are loath to share. Sadly, academia is increasingly in the role of independently re-discovering what has been known for 10-20 years but treated as a secret.

This makes an interesting case for software patents: what if database companies patented those insights instead of keeping them secret, and thus could keep others from using them for, say, 3-4 years? At the end of that time anyone could use those techniques.

This seems to me better than either trade secrets are the current absurdly-long patent lifetimes.

[geocar]

KDB+ is not 20kLoC of dense C++.

[aksyn]

It's 1VLLoC of C, right? ;)

[stingraycharles]

You should look into proprietary technologies, there are some very compelling offerings there. I have personally used QuasarDB ( https://www.quasardb.net/ ), and can say that it's on par with kdb+ in terms of performance, scales horizontally and has support for transactions and secondary indexes. They have a single node free edition as well.

Typically these solutions tend to follow the money, but if you ignore the list price, you can usually get a decent deal for a startup.

[jnordwick]

I know there are other proprietary dbs out there. I was speaking of FOSS/open databases any cant they come even without an order of magnitude of commercial offerings. Quasar would be included in that commercial group.

I'm only slightly familiar with Quasar, but I believe it is just a key-value store with document characterists, not a general database. The comparisons on their website are mostly to other KV stores and document systems.

[StreamBright]

>> I still can't figure out why people can't even come close to KDB+

You can have a look what happend with extremely simple code bases over time. Imagine the same effect on larger code.

https://www.computer.org/cms/Computer.org/ComputingNow/issue...

[zeptomu]

I have to admit that I did not know time-series databases are a thing and just recently realized that because they came up more often. Unfortunately I do not have an answer for you, but I know traditional DB systems are hard to build, as we expect more reliability and guarantees from them compared to other (daemon) software.

However I am interested to know why this kind of data and/or problems require specific software and why it can't be handled by traditional RDBMs? Obviously you could model the domain with a classic database, but seemingly there exist important queries that can't be satisfied (at least timely) through classic systems - what kind of queries are these, and why do they fail using a general-purpose DB approach?

[nickpeterson]

I'm no expert, but I believe the Crux of the issue is how data is naturally organized and stored. In a row oriented database, most data is stored in pages that contain rows. There are often indexes with ordering, but unless a secondary index contains all the values needed (often called a covering index), the entire row must be retrieved to answer any query that uses that information.

Most Time-series databases are columnar in nature, and often have the concept of time baked into the ordering of values (think vectors not sets). Because they are columnar, they are more trivial to retrieve just the data needed by a query. Suddenly instead of loading a billion rows and averaging the value in one column, you're just accesing the column itself to answer the question. From an IO perspective that's a huge savings.

Now imagine you have special, dark arts for working on compressed data, and a query optimizer you've been tuning for a decade for demanding clients. It does not surprise me that kdb is much faster than the open source competitors. And to be fair, even with excellent traditional databases like postgres, I bet Oracle, db2, and Ms SQL are still generally faster in most queries.

[bbrazil]

> Suddenly instead of loading a billion rows and averaging the value in one column, you're just accesing the column itself to answer the question. From an IO perspective that's a huge savings.

The other side of this is that writing out data in that form would naively be an iop per sample, as you're usually appending one sample to 100s of time series in one request.

A significant part of monitoring TSDB design is buffering up samples and batching writes in order reduce that iop rate to something sane.

For example in the right circumstances the 1.x Prometheus design can ingest 250k samples/s on a hard disk which provides ~100 iops/s.

[nunb]

There is also timescale which is built atop Postgres. Or rather, modifies Postgres' query engine etc., this is not some rails app!

https://blog.timescale.com/when-boring-is-awesome-building-a...

[ash]

> ... why this kind of data and/or problems require specific software and why it can't be handled by traditional RDBMs?

There's Tgres, that stores time series in Postgres, efficiently (using Postgres arrays and views):

https://github.com/tgres/tgres

https://grisha.org/blog/2017/01/21/storing-time-seris-in-pos...

[shaklee3]

What about extremedb? It's not free, but I believe is on par. Or did you mean only free?

[jnordwick]

I was talking mostly about FOSS.

I've actually never heard of ExtremeDB. It's only in-memory though, so it can it even handle the huge datasets that timeseries/column dbs handle? I don't see any performance tests for it either.

[shaklee3]

It does in-memory, hybrid, and disk. It's typically benchmarked versus kdb+ only:

http://financial.mcobject.com/downloads/white-papers-collate...

[rodionos]

In-memory, K/Q, expensive, old, closed source?

[angersock]

> It would be great for these smaller firms that cant or wont pay the license fees for a commercial offering until they get larger.

Why should we give that work away for free, especially if it's not that hard to roll your own for most small-scale needs?

[derefr]

Small company doesn't mean small scale, if the data is purchased/licensed instead of primary data. There are one-man startups trying to do build their business on analysis of Facebook-sized data-sets.

[angersock]

> There are one-man startups trying to do build their business on analysis of Facebook-sized data-sets.

Then maybe they should learn how to write their own databases, or pay people for their expertise. :)

[branchless]

If someone could get somewhere near kdb and have it be easier to write / debug I'm sure banks would be interested as there are very few kdb heroes out there. The amount of time wasted by strats writing code once then another one writing the same thing some time later must be stupendous.