what I m not finding after only a few minutes of googling is

Question

what I m not finding after only a few minutes of googling is anyone who has undertaken benchmarks across jvm lt gt 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

altavir · Answer

jimn · Answer

a page about benchmarks without a benchmark hehehe nice

altavir · Answer

man shrugging

altavir · Answer

Usually java libraries tend to take quite different approach from python so direct comparison is hard

jimn · Answer

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

jimn · Answer

i dont lack the usecases but i don t have any machine killer datasets yet that aren t under NDA

jimn · Answer

the basis of my search is a small script adaptation from py to kotlin pandas or similar tool

bjonnh · Answer

what kind of data are you handling

altavir · Answer

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

bjonnh · Answer

that s what I implied by kind of data thanks for detailing

altavir · Answer

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

bjonnh · Answer

totally agreed

bjonnh · Answer

maybe we need a checklist for discussions

jimn · Answer

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

jimn · Answer

<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

jimn · Answer

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

jimn · Answer

< 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

jimn · Answer

i have also released the dataset on github as well but maybe have some last minute touches to add the munging

altavir · Answer

< 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

jimn · Answer

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

altavir · Answer

OK now I am finally understand what are you taking about And some things you ve said before making a sense

jimn · Answer

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

altavir · Answer

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

jimn · Answer

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

jimn · Answer

now i have lean single threaded minimalist cursor iterators

jimn · Answer

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

jimn · Answer

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

jimn · Answer

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

jimn · Answer

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

jimn · Answer

my current runtime uses less than 256k of RAM irregardles of the input dimensions

jimn · Answer

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

jimn · Answer

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

jimn · Answer

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

jimn · Answer

my 2 slowdown in the code was having lambdas these don t inline when i inlined every single little thing the performance improved remarkably

jimn · Answer

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

jimn · Answer

my groupby itself is actually maybe 5 seconds against 2 8 million rows but REIFYING one row can take up to a minute

jimn · Answer

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