Good lead, pandas on Spark concat() is worth trying. It looks like it uses a join, but not 100% sure from the source. The SQL concat() function is indeed a different thing.
On Wed, Apr 20, 2022 at 3:24 PM Bjørn Jørgensen <bjornjorgen...@gmail.com> wrote: > 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 >
