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what I'm not finding after only a few minutes of g...
# datascience
j
what I'm not finding after only a few minutes of googling is anyone who has undertaken benchmarks across jvm<->non-jvm dataframe tools. there appears to be some articles but this looks like a very non-standardized area. i found microbenchmarks of pandas operations as well, but the machine-killing heap hazards are not in those benhcmarks.
a
j
a page about benchmarks without a benchmark hehehe nice
a
🤷‍♂️
Usually java libraries tend to take quite different approach from python, so direct comparison is hard.
j
im no connesuer of the space, but i can appreciate how pandas does one thing and keras does something else, and spcific to your link nd4j is the little engine for dl4j. comparing java libs to python definitely piles on the impedence a mile high. at this point in time im going to guess that kotlin enters well below java performance expectations and in the ballpark of optimal python tooling, but the ability to make a concise script for both looks like a much smaller gap
i dont lack the usecases, but i don't have any machine killer datasets yet that aren't under NDA.
the basis of my search is a small script adaptation from py to kotlin pandas or similar tool.
b
what kind of data are you handling?
a
I would also ask not about the data, but also about its structure. I've already said multiple times that if there are no operations performed on the whole set and it is evaluated row by row it seems absolutely meaningless to load it into memory or even use memory-mapped file instead of streaming.
b
that's what I implied by kind of data. thanks for detailing
a
The problem is that we had this discussion with the same participants several times. Kotlin does allow to implement better streaming and "flowing" capabilities without using huge buffers. But it requires more precise problem description.
b
totally agreed
maybe we need a checklist for discussions
j
I don't see the voice of experience in Alex's viewpoint, but despite his criticisms, i went and nailed it. there is a question of what super advanced planning strategy gets you On results for groupby, perhaps my straightforward attempt could be touched up.
https://github.com/jnorthrup/columnar this does in kotlin what i need from Pandas. the choice of IO and off-heap scales up where pandas and numpy will hit swap before eventually exhausting swap
I'm 99% convinced that Alex doesn't actually read code or descriptions before he judges this space based on his own straw man attempts.
@bjonnh my data per the readme, is ISAM from immutable mapped FWF format input, at this time both textually parsed fields and an intermediary NIO binary format writable using the NIO driver. Without kotlin, I might not have arrived at the design that efficienctly maps the features to the execution. With kotlin, i have something that performs better than python and can be cleanly ported to c or c++ and avail cleaner standard libraries and libc features.
i have also released the dataset on github as well, but maybe have some last minute touches to add the munging
a
@jimn It is really hard for me to follow your ideas. I intend to look through your code, but I did not found time yet. What I understood from three previous posts is that you need is grouping operation. Grouping does not require the whole data to be loaded simultaneously. Do you still insist that you must have access to the whole data? Maybe I still get your use case wrong? I am talking about map-reduce.
j
the basic idea is to take n rows of x columns and assign n/m rows of f' columns which could be a mapreduce operation, but for lstm time series you also wind up with a full pivot operation to make it x rows of n columns first. so now you may run into cascading loops of mapreduce or cartesians depending on how you do it. for 17000 columns apache arrow is untested, and i broke it and submitted the repeatable issue tracking details. i went forward with my thing at that point and didn't return. if this is ISAM, then you can do random access seek, discard, and have immutable data giving idempotent function results. FWF is an ISAM compatible layout, and if you don't have ISAM, pandas's CSV reader is pure c++ anyways, you can do that simple IO, but i digress, i wrote a jdbc streamer to stdout for fwf with meta on stderr.
a
OK, now I am finally understand what are you taking about. And some things you've said before making a sense.
j
woops, early return key. mapreduce is not inherntly a unary operator that gives you composable functions. it's a big stack of code that drops out some results as arbitrary a they please, but yes, embarrassingly parralel. except for re-reduce passes. pandas does give you that, so perhaps its a false blanket to have a dsel or at least a dataframe to arrive back at for more of the same interface. unary operators seem like a good thing for human comprehension. pandas excels at the conceptual models, but for the dreaded python overheads..
a
I still think there should be better ways than to read the whole data into memory, but at least we won't be talking abot completely different things. Sorry, but you tend to drop super-long texts, which are impossible to understand without some background.
j
my earliest attempts were indeed to tackle coroutines as flows and then i swiftly hit heap limits and went with mmap, flow, and hit heaps limits a bit later, but still too soon. for my third iteration i built the internal of a sql engine without a sql parser. it iterates rows, and cursors on x,y,z driver coordinates (fwf is y,x access) and gets the groupby job done. when i wanted to utilize coroutineContextElements as the formative glue to create maximum extensibility, i notice the threadlocal protection was lik 3/4 of my wall time.
now i have lean single threaded minimalist cursor iterators.
at this point I know there are jvm hard stop barriers that cqannot be sidestepped in the areas of kernel operations and thread affinity. to me this guarantees that any threadlocal abstractions are going to be spinning the barrel on russian roullete every single time suspend "resumes" into a thread.
so my current adequate build using mmap is still suffering some jvm related biases, like a threadlocal check (removed, now its a singleton) of the MAXINT window mapped on a file, but the unary operator features and the compasibility of the dsel as well as the coruotinecontextelements is to my liking. except corutinecontext is now not a suspension access, its just a bag
the usecases are io bound, even if we're talking about kernel disk buffers, the mmap is an io handle ultimately. but, heap is safe in the process and OS management of those mmap handles. i know that when i do a sort using gnusort, it's a million times faster. but kotlin still spanks python.
it is likely that i can added a fileio driver and it would outperform mmap in some access patterns, but the unary operator interfaces are clean enough that it makes sense to try a c++ port using namespace aliases and cpp macros to emulate the typealiased pairs i employ
my current runtime uses less than 256k of RAM irregardles of the input dimensions.
the slowdown comes from projection overheads of pivoting then groupby done without heap. it's clean, but there is certainly some datasets small enough to benefit from buffer windows to hold known repeats. On^2 groupby is not typically reading any row twidce, but it is doing an elevator from the first row to the last row for each cluster
i have considered making a zoned IO and doing re-reduce logic to combine these, the problem is that the pivots span the entire dataset so it's potentially better to do a full tablescan pivot, and break up the large intermediares into gropus before a multistage groupby, which is not far from mapreduce
using a blackboard ideology for deifining the IO access patterns and composing the IO drivers from traits, using kotlin, is also a nightmare. orthogonal maybe, but less overlap than i anticipated
my #2 slowdown in the code was having lambdas. these don't inline. when i inlined every single little thing the performance improved remarkably.
i'm sure that's my inlining is overboard at this point, but no matter how far i abuse it i don't see perofrmance degradation yet.
my groupby itself is actually maybe 5 seconds against 2.8 million rows. but REIFYING one row can take up to a minute.
2.8 million source rows for 3 years timeseries by day pivots to 1100 rows with 9800 columns in my machine killer dataset. so average groupby is summing 9800 columns on 2.8million/9800 rows, to reify each of the 1100 final rows. flow capture overhead was fatal for these kind of numbers
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