In my experience, kdb+'s k and q (which is broadly speaking a legibility wrapper around k, which again broadly speaking is APL without unicode) are phenomenally fast for dense time series datasets. Though they can struggle (relatively, still pretty fast) with sparse data that's not really what they are built for. They were built for high-performance trading systems, and trading data is dense.
If you like writing dense, clever regexs (which I do) then you'll love k & q. The amount that you can get done with just a few characters is unparalleled.
Which leads to, IMHO, their main drawback: k/q (like clever regexes) are often write-only code. Picking up another's codebase or even your own after some time has passed can be very hard/impossible because of how mindbendingly dense with logic the code it. Even if they were the best choice for a given domain, I'd try to steer clear of using them for anything other then exploratory work that doesn't need to be maintained.
I program in K professionally and help maintain a fairly large codebase. It's an unusual-looking language and it takes practice to learn to "skim" K code in the way that most programmers are used to for their favorite curly-bracket language, but it can be learned.
The biggest messes I have to clean up come less from "clever" code than they do from people who try to program in K as if it were some other language. For example, somebody fond of for loops might write the following to apply a function 'f' to pairings of values and their index in a list:
result:()
i:0
do[#v
r:r,,f[v[i];i]
i:i+1
]
Ugly, complicated, but "close at hand". There is of course a much nicer and more idiomatic way to do the same thing:
result: f'[v;!#v]
Most of the time conciseness isn't the goal, but you get it as a side effect of writing good code and working "with the grain" of the language.
I work for an NYC-based company called 1010data which specializes in business analytics products. Most of our backend infrastructure is written in K3. And if anyone's curious, yes, we're hiring.
If you don't mind me asking, how does someone start with Q/Kdb+?
Can you learn it with just the free version or realistically does it require working with it in a professional setting and learning from others who already use it.
Are there any sources you recommend for a someone just starting out?
> their main drawback: k/q (like clever regexes) are often write-only code
This depends on the code reader's mentality. One line of k (or q or j or apl) would do what 10 lines of verbose languages do. For verbose languages, your expectation is spending 1 minute for 10 lines of code to fully understand it; but for terse languages, you need to change your expectation to spending 1 minute for only 1 line of code. You are not going to understand anything if you still want to spend 6 seconds per line.
On the other hand, proficiency is important. You read English articles in a slow pace in your first grade; you are not going to be a speed reader without practice, even if English is the only language you speak. No one would expect you to speed read Japanese right after you can compose simple Japanese sentences.
For me it is the opposite. I like not having to type any more than necessary. I like writing as little code as possible.
I also like being able to read through a program listing without having to wade through page upon page of long, camel case function names, and trying to follow five levels of indentation.
Not because I think that is the "wrong" approach for everyone but because I personally struggle with overcoming language and documentation verbosity in order to make any progress. It is the wrong approach for me. I wonder if perhaps this is how it feels for the typical programmer, in the OP's comment, who might struggle with a lack of verbosity.
Sometimes I streamedit (search and replace) blocks of code to remove indentation and shorten long function names to two letter labels, just so I can read without distraction.
What are the chances of finding someone who has the same preference for terseness and who is as skilled as Arthur Whitney?
q.k is about 240 lines in the version I am using. Most of the functions in .q, .Q and the other namespaces fit on a single line. This is a thing of beauty, IMO. For me, this makes studying the language manageable.
Someone in this thread posted a fizzbuzz solution last week that I thought illustrated how one can "work outward", incrementally adding primitives to the left or right to keep modifying output until the desired result is reached.
I use kdb+ daily but I'm a slow learner and still not very good with composing programs from scratch in k.
What little I have written was also done by "working outward", so the fizzbuzz example gave me some hope maybe I'm not too far off track. Thank you for that example. I hope we see some more.
I am thankful k exists, even if the intepreter is not open source and there's no BSD port.
As someone else commented somewhere, perhaps the most awkward aspect of k is getting it to interface with anything else. I think this is what keeps me from using it more frequently for more daily tasks.
But this may be more of a problem with everything else, and not necessarily with k.
I wish a more competent C programmer than I would write a lightweight replacement using linenoise or libedit to substitute for "rlwrap".
That's more on you than it is the language though. You can write obtuse write-only code in any language.
I will concede that there is a culture of trying to be a bit too clever on the k4 mailing list but it's perfectly possible to write maintainable code in kdb+
But from what I recall, the syntax is terse by design - this is not inherently bad, though. In other mainstream languages, you have to go out of your way to write obtuse code (e.g, code golf).
I'm guessing that best way to address this issue is through liberal use of explanatory comments.
Terse syntax isn't an issue, you just need to get used to reading it. Using 1 character variable names is another matter though.
There is no reason not to use camel cased variable names and indent functions, if/else blocks etc, and when written this way the code can be perfectly legible even to non q programmers.
Something else that leads people into the write-only trap is that the usual way of working with the language is to use the REPL loop while working, where you can tend to be doing multiple things on 1 line. It's just laziness not to reformat and clean up the code afterwards though.
While I agree that it's possible to write maintainable in kdb+, I'd argue that it takes noticeably more effort; or, as in inverse, if you drop your coding standards things get worse faster. I'd say the same for regex as well (though in a more limited sense).
The "characters to clever/unmaintainable" ratios you achieve with k (which most kdb+ platforms end up reaching for at some point) are almost unparalleled. A famous example of how awesome/powerful/ridiculous k can be is the "4 lines of K" text editor: http://www.kparc.com/$/edit.k
I guess my point is that letting your guard down, for even a single line, can be orders of magnitude more trouble than it would be in most languages and for that reason I wouldn't base a new stack on it. Also, the developers for it are rare-ish and expensive + it has a serious learning curve for those unfamiliar with FP or Lisp.
> Not knowing k, seems like the worst part of the edit.k examples is every variable has a one character name. Or do those single character tokens have special meanings in k? Which would be even worse.
It can seem that way, but there is value in dense programs: They take up less room on the screen. This means you can see where you repeat yourself, and can often find bugs by just reading your code.
I can't count the number of times I scrolled over some code and missed a bug.
Yes, the terseness of the language itself lends itself to terseness everywhere - even in variable namings. It's not necessary and I usually avoid that when I write k or q. Purposefully obfuscating code like that lends to the "bad rep" that the language gets.
The main problem with Q is the users. Most users are math grads first, programmers second. They don't use version control. They don't comment their code.
Yes Q is dense but it can be annotated with comments.
I also have to maintain some java code written by this lot and it's nearly as unintelligible.
I've build columnar OLAP databases and database engines in C++ for work. Now I'm doing it in my free time. Based on my experience the Phi and it's architecture is very exciting for OLAP databases workloads.
Reasons:
- Even in a OLAP database you end up with quite a few places that have very branchy code. Research on GPU friendly algorithms on things like (complex) JOINS and GROUP BY is pretty new. Additionally complex queries will functions and operations that you might not have a good GPU implementation for (like regex matching)
- Compression. You can use input data that compressed in anyway that there is a x86_64 library for. So you can now use LZ4, ZHUFF, GZIP, XZ. You can have 70+ independent threads decompressing input data (it's OLAP so it's pre-partitioned anyways). (Technically branching, again)
- Indexing techniques that cannot efficient implemented on the GPU can be used again. (Again branching)
- If you handle your own processing scheduling well, you will end up with near optimal IO / memory pattern (make sure to schedule the work on the core with local memory) and you not bound PCIe speed of the GPU. With enough PCIe lanes and lots of SSD drives you process as near memory speeds (esp. when we'll have Xpoint memory)
So the bottom line is if can intelligently farm out work in correct size chunks (it's OLAP so it's prob partitioned anyways) the the Phi is fantastic processor.
I'm primarily talking about the bootable package with Omni-Path interconnect (for multiple).
I've been using kdb+/q for a long time (7 years now) - and can attest to its speed. Objects are placed in memory with the intention that you will run vectorized operations over them. So both the memory-model and the language are designed to work together.
Lots of people complain about the conciseness of the language and that it is "write-once" code. I tend to disagree. While it might take a while to understand code you didn't write (or even code you wrote a while ago), focusing on writing in q rather than the terser k can improve readability tremendously.
My only wish is that someone would write a free/open-source 64-bit interpreter for q - with similar performance and speed to the closed version. Kona (for k) gets close https://github.com/kevinlawler/kona
If he made the layout a bit uglier, and made the language more esoteric and generally difficult to understand, this would make a fantastic academic paper.
But seriously, what a wonderful world it would be if all papers were this well written.
For a columnar database, that's a continuous chunk of memory. Assuming 32bit q defaults to 32bit int, 1.1 billion integers across four machines means each 64-core (with 4 threads/core) KNL chip is averaging over 275M elements of int array, or 1.1M 32bit int operations per thread. Now think again whether that's amazing or not.
is it ? those things are trivial enough to be entirely bandwidth limited.
total_amount is 4 byte, passenger_count is 1 and those are tightly packed in a column layout.
streaming through that in 150ms is almost within the reach of a single normal chip with dual channel DDR3 ram.
Of course, not quite, and that's discounting the (small) sync overhead but still, no need to shell out 4 big servers, overpriced phi chips and fancy wide bus memory.
This was a good idea for a test. I'll definitely check out the author's other stuff. Commenting briefly on cost: while the article mentions the free 32 bit version early on, the actual benchmarks were done using the commercial version. I've had the impression the comercial version was cost prohibitive for us poor folks. For those interested in experimenting with Xeon Phi though, it looks like you can get started for ~$5k: http://dap.xeonphi.com/
If you just want to meddle with a Xeon Phi, you can get some as cheap as $300: https://www.amazon.com/Intel-BC31S1P-Xeon-31S1P-Coprocessor/... , though that's from the 3100 rather than 7200 family, and so won't perform as fast. That kind of money puts it more into the hobby territory, though.
With the new generation there is now a difference. This is because this generation has Phis in both addin card (PCIe) and a bootable CPU package (when you run Linux or windows or whatever on the Phi).
Generally with the PCIe one you're running something like OpenCL and with system CPU package you run threads and processes like you normally would.
Technically you could run software directly on the old addin cards since they boot to Linux but you had handle the distribution, running and communication of your software with the host. (you could run any x86_64 binary)
Both gcc & llvm can compile binaries targeting the Phi. Since it runs in Linux (as addin PCIe card or host Linux CPU) it can run any kind of ELF x86_64 binary. They also support generating AVX-512 instruction of the Phi.
I don't see how these results provide much useful information in terms of being able to say x is faster than y.
The hardware doesn't seem consistent across different benchmarks. He says it's fast for a "cpu system", but for practical purposes Phi competes more with GPGPUs.
Would this be just as fast with one redis system with 512GB ram? I don't know too many apples to oranges here.
All conclusions are only valid for similar workloads, but each of MapD and GPUs, Q/kdb+ and Xeon Phi, Redshift, Athena, Big Query, Presto, and Elasticsearch claim to be fast, inexpensive, easy to work with, and otherwise great for Big Data. Which ones really are fast? How fast is fast? How much is this going to cost? Do I need 5 nodes or 50?
A few examples of some useful conclusions:
- Just because a relatively well-optimized PostgreSQL database on a regular workstation takes 5 minutes to run a query doesn't mean you can't get special hardware to run that query faster than you can type.
- Spark + S3 + Amazon Elastic Map Reduce look like an ideal tool to work with large data, but they're pretty slow compared to better tools, and even compared to plain PostgreSQL.
- HDFS really is a lot faster than S3.
- Performance of an Xeon Phi 64-core CPU is within an order of magnitude to an NVidia Titan X.
- Loading 104 GB of compressed data into Q/kdb+ expands to 125 GB with and takes about 30 minutes, but on Redshift expands to 2 TB and takes many hours to upload on a normal connection, plus 4 hours to actually import!
- It might cost $5000 to custom-build a GPU-based supercomputer that can do these queries in under a second, but you can run similar queries if you're willing to wait for 5 minutes each by spinning up instances for a few dollars an hour plus a few more dollars an hour for storage, or by just running PostgreSQL on your workstation.
Also, not a conclusion, but it's incredibly useful to have a simple example exactly how to configure the tool and import some CSV data
These conclusions don't seem very useful because either they are already well established or are not valid. Some examples:
Just because a relatively well-optimized PostgreSQL database on a regular workstation takes 5 minutes to run a query doesn't mean you can't get special hardware to run that query faster than you can type.
Already well established for years with systems like redis, and more recently with gpu databases, and other techniques posted on HN regularly.
Spark + S3 + Amazon Elastic Map Reduce...is pretty slow compared to better tools, and even compared to plain PostgreSQL.
Not valid because it doesn't generalize. It so much depends on type of work being done, system architecture, etc, that you can only say it may or may not be true.
Performance of an Xeon Phi 64-core CPU is within an order of magnitude to an NVidia Titan X.
Not precise enough to matter because getting within 10x difference is not close to being competitive.*
Loading 104 GB of compressed data into Q/kdb+ expands to 125 GB with and takes about 30 minutes, but on Redshift expands to 2 TB and takes many hours to upload on a normal connection, plus 4 hours to actually import!
I don't see how it's possible for 104GB of csv text data to decompress into only 125GB. For cvs to compress only ~20%...doesn't make sense.
It might cost $5000 to custom-build a GPU-based supercomputer that can do these queries in under a second
No, two problems here. The hardware in question could have used 1 cheap CPU instead of two expensive Xeons and been much less expensive. Bigger problem: The MapD software itself will be $50,000.
The speed comparisons may be well known to you, but as someone only really using trivial desktop app SQLite databases, they weren't known to me. Thanks for pointing out my errors!
> I don't see how it's possible for 104GB of csv text data to decompress into only 125GB. For cvs to compress only ~20%...doesn't make sense.
The CSV file itself is around 500 GB. The internal representation, which might use binary formats for numbers, or compress text, uses 125 GB. Redshift expands it to 2TB for all the indexing and mapping.
> Bigger problem: The MapD software itself will be $50,000.
Ouch. That's a rather large oversight. Is the author affiliated with MapD, perhaps?
>Just because a relatively well-optimized PostgreSQL database on a regular workstation takes 5 minutes to run a query doesn't mean you can't get special hardware to run that query faster than you can type.
This is actually my biggest complaint with the article. He used cstore_fdw with Postgres, which doesn't allow much real indexing, and as far as I can tell (knowing only a little bit about it) he didn't really use any of the benefits of cstore_fdw.
I'd be interested to see how plain Postgres, possibly on a compressed filesystem, with properly-indexed tables stacks up.
Why not? Sounds like the initial data load and indexing are done up front. Once you get past that to run the benchmark it's not clear that a quad channel ddr4 system would be saturated.
A current-generation Xeon E7 has a memory bandwidth of up to 102 GB/s. The article says 90, which is probably a realistic estimate of achievable throughput. But the data is 500GB. So the problem is not with loading it from disk (that can be done beforehand), but getting it from CPU to RAM fast enough.
Inaccurate; the very point of columnar stores is that if I'm only interested in a column I only expend the memory bandwidth required for that very column and nothing else. Hence typical queries to a columnar store would never stream the whole 500 GB for processing.
Right, a columnar store would touch only a couple (1.1bn * three numerical columns) of those 500 GB. But the question was about Redis :)
The author has an overview of the benchmarks with various systems at http://tech.marksblogg.com/benchmarks.html – but one can't just compare rows as many factors vary (esp. hardware).
This is the comparison to the titan X / mapD article I was looking for. Still looks like the gpu is very competitive.
Sort of meta, but Mark's job seems awesome. Gets all these toys and writes about configuring them. (The actual configuring is probably a pain but still)
According to wikipedia, the fastest GDDR5 can do 256 Gbit per chip[0]. I don't know how many chips are typically used. MCDRAM in the article does 400 GB/s, or 3,200 Gb/s. That would require 12.5 of those GDDR5 chips, assuming they scale linearly.
Speed and size are somewhat independent. Speed is set by the bus frequency and how many chips you use - every chip has a fixed data bus width, typically 32 bits, while overall size is then how many chips you have times how big each chip is.
Unlike typical computer memory architectures, where the memory bus connects multiple chips or modules to one controller, GDDR doesn't do that; every slice of the memory controller only speaks with a single chip, strictly point-to-point. (Reducing bus load and layout issues and thus allowing higher clock rates).
That's why, with GPUs, it's usually sufficient to say how wide the bus is (often 64 - 128 - 256 - 384 - 512 bits) to get a rough idea of it's performance, since memory clock frequencies occupy a rather narrow range. (However, narrow-bus, lower-end GPUs often don't use the same technology as higher-end GPUs, eg. DDR3 instead of GDDR5)
GDDR5 can typically do 240GB/s access time on a typical GPU, and there are multiple chips on many cards (Tesla K80). The newer cards use HBM2 and can do 732GB/s (http://www.nvidia.com/object/tesla-p100.html).
Kind of. Nvidia lowered the voltage on their P100 so it does not hit those rates. Theoretically it can go that high, but the power draw was too large. Next gen we'll likely see that.
The interpreted Jd is fast, but you need the compiled, commercial license Jd, or you used to, for the speed test.
I love J compared with K, but that is because I found it first, and the differences between J and K are minimal, but a different enough to keep me using J.
I think jd is mostly plain old interpreted J. There are a few shared libraries to add functionality to core J, but it's mostly just J. You can probably get a non-commercial license if you ask nicely.
If the combination of such languages, high-performance hardware, and large scale compute problems is interesting.. the startup I work for in Mountain View is hiring...
It looks like year is extracted from pickup_datetime at ETL, so hence its not a fair comparison against the other databases that do this at runtime in Q3 and Q4. In something like MapD Q3 would be nearly as fast as Q1 (~20ms) without the extract function, which involves relatively complicated math.
All lots of fun, but kdb has an eye watering cost of 200k dollars per year per server.
Here's hoping some combo of Apache Arrow (also cache aware, much more language stack flexibilty), Aerospike (lua built in), Impala, and others, can finally take on this overpriced product, which has had a lack of serious competitors for 20 years, owing to its (price inelastic) finance client base.
The binary file of the database (all-inclusive and statically linked) is around 300 kilobytes (!), which makes is probably the most expensive (non-custom) software per kilobyte.
If you like writing dense, clever regexs (which I do) then you'll love k & q. The amount that you can get done with just a few characters is unparalleled.
Which leads to, IMHO, their main drawback: k/q (like clever regexes) are often write-only code. Picking up another's codebase or even your own after some time has passed can be very hard/impossible because of how mindbendingly dense with logic the code it. Even if they were the best choice for a given domain, I'd try to steer clear of using them for anything other then exploratory work that doesn't need to be maintained.