Sorry for asking. But why does`t concat work? Pandas on spark have ps.concat <https://github.com/apache/spark/blob/1cc2d1641c23f028b5f175f80a695891ff13a6e2/python/pyspark/pandas/namespace.py#L2299> which takes 2 dataframes and concat them to 1 dataframe. It seems <https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.functions.concat.html#pyspark.sql.functions.concat> like the pyspark version takes 2 columns and concat it to one column.
ons. 20. apr. 2022 kl. 21:04 skrev Sean Owen <sro...@gmail.com>: > cbind? yeah though the answer is typically a join. I don't know if there's > a better option in a SQL engine, as SQL doesn't have anything to offer > except join and pivot either (? right?) > Certainly, the dominant data storage paradigm is wide tables, whereas > you're starting with effectively a huge number of tiny slim tables, which > is the impedance mismatch here. > > On Wed, Apr 20, 2022 at 1:51 PM Andrew Davidson <aedav...@ucsc.edu> wrote: > >> Thanks Sean >> >> >> >> I imagine this is a fairly common problem in data science. Any idea how >> other solve? For example I wonder if running join something like BigQuery >> might work better? I do not know much about the implementation. >> >> >> >> No one tool will solve all problems. Once I get the matrix I think it >> spark will work well for our need >> >> >> >> Kind regards >> >> >> >> Andy >> >> >> >> *From: *Sean Owen <sro...@gmail.com> >> *Date: *Monday, April 18, 2022 at 6:58 PM >> *To: *Andrew Davidson <aedav...@ucsc.edu> >> *Cc: *"user @spark" <user@spark.apache.org> >> *Subject: *Re: How is union() implemented? Need to implement column bind >> >> >> >> A join is the natural answer, but this is a 10114-way join, which >> probably chokes readily just to even plan it, let alone all the shuffling >> and shuffling of huge data. You could tune your way out of it maybe, but >> not optimistic. It's just huge. >> >> >> >> You could go off-road and lower-level to take advantage of the structure >> of the data. You effectively want "column bind". There is no such operation >> in Spark. (union is 'row bind'.) You could do this with zipPartition, which >> is in the RDD API, and to my surprise, not in the Python API but exists in >> Scala. And R (!). If you can read several RDDs of data, you can use this >> method to pair all their corresponding values and ultimately get rows of >> 10114 values out. In fact that is how sparklyr implements cbind on Spark, >> FWIW: https://rdrr.io/cran/sparklyr/man/sdf_fast_bind_cols.html >> >> >> >> The issue I see is that you can only zip a few at a time; you don't want >> to zip 10114 of them. Perhaps you have to do that iteratively, and I don't >> know if that is going to face the same issues with huge huge plans. >> >> >> >> I like the pivot idea. If you can read the individual files as data rows >> (maybe list all the file names, parallelize with Spark, write a UDF that >> reads the data for that file to generate the rows). If you can emit (file, >> index, value) and groupBy index, pivot on file (I think?) that should be >> about it? I think it doesn't need additional hashing or whatever. Not sure >> how fast it is but that seems more direct than the join, as well. >> >> >> >> On Mon, Apr 18, 2022 at 8:27 PM Andrew Davidson <aedav...@ucsc.edu.invalid> >> wrote: >> >> Hi have a hard problem >> >> >> >> I have 10114 column vectors each in a separate file. The file has 2 >> columns, the row id, and numeric values. The row ids are identical and in >> sort order. All the column vectors have the same number of rows. There are >> over 5 million rows. I need to combine them into a single table. The row >> ids are very long strings. The column names are about 20 chars long. >> >> >> >> My current implementation uses join. This takes a long time on a cluster >> with 2 works totaling 192 vcpu and 2.8 tb of memory. It often crashes. I >> mean totally dead start over. Checkpoints do not seem help, It still >> crashes and need to be restarted from scratch. What is really surprising >> is the final file size is only 213G ! The way got the file was to copy >> all the column vectors to a single BIG IRON machine and used unix cut and >> paste. Took about 44 min to run once I got all the data moved around. It >> was very tedious and error prone. I had to move a lot data around. Not a >> particularly reproducible process. I will need to rerun this three more >> times on different data sets of about the same size >> >> >> >> I noticed that spark has a union function(). It implements row bind. Any >> idea how it is implemented? Is it just map reduce under the covers? >> >> >> >> My thought was >> >> 1. load each col vector >> >> 2. maybe I need to replace the really long row id strings with >> integers >> >> 3. convert column vectors into row vectors using piviot (Ie matrix >> transpose.) >> >> 4. union all the row vectors into a single table >> >> 5. piviot the table back so I have the correct column vectors >> >> >> >> I could replace the row ids and column name with integers if needed, and >> restore them later >> >> >> >> Maybe I would be better off using many small machines? I assume memory is >> the limiting resource not cpu. I notice that memory usage will reach 100%. >> I added several TB’s of local ssd. I am not convinced that spark is using >> the local disk >> >> >> >> >> >> will this perform better than join? >> >> >> >> · The rows before the final pivot will be very very wide (over 5 >> million columns) >> >> · There will only be 10114 rows before the pivot >> >> >> >> I assume the pivots will shuffle all the data. I assume the Colum vectors >> are trivial. The file table pivot will be expensive however will only need >> to be done once >> >> >> >> >> >> >> >> Comments and suggestions appreciated >> >> >> >> Andy >> >> >> >> >> >> -- Bjørn Jørgensen Vestre Aspehaug 4, 6010 Ålesund Norge +47 480 94 297
