Weston, I'm interested in following up.
________________________________ De: Weston Pace <[email protected]> Enviado: quinta-feira, 4 de agosto de 2022 12:15 Para: [email protected] <[email protected]> Assunto: Re: Issue filtering partitioned Parquet files on partition keys using PyArrow Você não costuma receber emails de [email protected]. Saiba por que isso é importante<https://aka.ms/LearnAboutSenderIdentification> [EXTERNAL EMAIL] There is a lot of room for improvement here. In the datasets API the call that you have described (read_parquet) is broken into two steps: * dataset discovery During dataset discovery we don't use any partition filter. The goal is to create the "total dataset" of all the files. So in your case this means listing out all 150,120 directories. For every file we discover we capture a partition expression for this file. This is probably where the bulk of time is being spent (listing the directories). * dataset read During the dataset read we apply the partition filter. So we are going to iterate through all ~150k files and compare the filter expression with the previously captured partition expression, eliminating files that don't match. In this phase we don't have any idea of the original directory structure. So instead of performing 27 top-level comparisons + 5560 second-level comparisons we end up having to calculate all 150k comparisons. Both of these steps are considerably longer than they need to be. If I were to guess I would guess that a majority of the time is spent in that first step but I don't think the time spent in that second step is negligible. One fairly straightforward solution would be to allow the partition filter to be used during dataset discovery. This would yield a much smaller dataset so step 2 would be much faster but it could also allow the discovery process to skip entire directories. If anyone is interested in working on a fix for this I'd be happy to point them at the files that will need to be changed and go into a more detailed discussion of potential solutions. On Thu, Aug 4, 2022 at 5:53 AM Tomaz Maia Suller <[email protected]<mailto:[email protected]>> wrote: Hi David, I wonder if the problem with the attachments has to do with the files not having extensions... I'm trying to send them with .prof this time. Anyway: 1. I'm writing to a local filesystem; I've mounted a NFTS partition which is on a HDD. Since the dataset is only ~1.5 GB, I'll try to move it to the SSD I have available and see if I get lower access times. 2. I'm using trying to use ParquetDataset; though I'm using it directly most of the time, i.e. I'm using Pandas which then itself uses (if I understood it correctly) ParquetDataset. I've tried accessing with both the legacy and new versions of the API, according to that use_legacy_dataset parameter. The legacy API is significantly faster, with access time of about 1 second, though still ridiculously slow compared to accessing the path straight away. If the attachments still don't work for some reason, I'll write up what I ran: >>> pq_command_new = "pq.ParquetDataset('.', filters=[('state_code', '==', 31), >>> ('city_code', '==', 6200)], use_legacy_dataset=False)" >>> pq_command_old = "pq.ParquetDataset('.', filters=[('state_code', '==', 31), >>> ('city_code', '==', 6200)], use_legacy_dataset=True)" >>> pq_baseline = "pq.ParquetDataset('./state_code=31/city_code=6200')" >>> cProfile.run(pq_command_new, '/tmp/pq_legacy_false.prof') This took about 17 seconds. >>> cProfile.run(pq_command_old, '/tmp/pq_legacy_true.prof') This took about 1 second. >>> cProfile.run(pq_baseline, '/tmp/pq_legacy_true.prof') This took 0.0075 second. These runs were all after the first run after booting up the computer, which took over 500 seconds as I've said. I'm starting to think I should send this to the development mailing list rather than the user one, since the obvious solution is specifying the paths directly rather than trying to use the API. ________________________________ De: Lee, David <[email protected]<mailto:[email protected]>> Enviado: quarta-feira, 3 de agosto de 2022 19:49 Para: [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> Assunto: RE: Issue filtering partitioned Parquet files on partition keys using PyArrow Você não costuma receber emails de [email protected]<mailto:[email protected]>. Saiba por que isso é importante<https://aka.ms/LearnAboutSenderIdentification> [EXTERNAL EMAIL] The attachments didn’t come through properly.. I’ve got additional questions. 1. What filesystem are these files stored on? I’ve seen issues using S3 if HEAD operations aren’t prioritized. I’m assuming that without HEAD operations you can’t effectively scan a parquet file’s footer and reading the entire file isn’t efficient. available (eventual consistency for HEAD operations) Behaves the same as the “read-after-new-write” consistency level, but only provides eventual consistency for HEAD operations. Offers higher availability for HEAD operations than “read-after-new-write” if Storage Nodes are unavailable. Differs from AWS S3 consistency guarantees for HEAD operations only. 2. Are you using pyarrow.parquet.ParquetDataset or pyarrow.dataset? https://arrow.apache.org/docs/python/parquet.html Note The ParquetDataset is being reimplemented based on the new generic Dataset API (see the Tabular Datasets<https://arrow.apache.org/docs/python/dataset.html#dataset> docs for an overview). This is not yet the default, but can already be enabled by passing the use_legacy_dataset=False keyword to ParquetDataset or read_table(): pq.ParquetDataset('dataset_name/', use_legacy_dataset=False) Enabling this gives the following new features: * Filtering on all columns (using row group statistics) instead of only on the partition keys. * More fine-grained partitioning: support for a directory partitioning scheme in addition to the Hive-like partitioning (e.g. “/2019/11/15/” instead of “/year=2019/month=11/day=15/”), and the ability to specify a schema for the partition keys. * General performance improvement and bug fixes. It also has the following changes in behaviour: * The partition keys need to be explicitly included in the columns keyword when you want to include them in the result while reading a subset of the columns This new implementation is already enabled in read_table, and in the future, this will be turned on by default for ParquetDataset. The new implementation does not yet cover all existing ParquetDataset features (e.g. specifying the metadata, or the pieces property API). Feedback is very welcome. From: Tomaz Maia Suller <[email protected]<mailto:[email protected]>> Sent: Wednesday, August 3, 2022 2:54 PM To: [email protected]<mailto:[email protected]> Subject: Issue filtering partitioned Parquet files on partition keys using PyArrow External Email: Use caution with links and attachments Hi, I'm trying to load a dataset I created consisting of Parquet files partitioned on two columns, but reading from a single partition takes over 10 minutes on the first try and still over 15 seconds on any subsequent one while specifying the path to the partition directly takes 50 milliseconds. Am I doing something wrong? The data is arranged in the following way: $ tree -d . ├── state_code=11 │ ├── city_code=1005 │ ├── city_code=106 │ ├── city_code=1104 │ ├── city_code=114 │ ├── city_code=1203 │ ├── city_code=122 │ ├── city_code=130 │ ├── city_code=1302 ... There are 27 state codes and 5560 city codes, so 27 "first level" partitions and 5560 "second level" partitions in total. Each partition often contains only a few kBs worth of Parquet files and nome is greates than ~5MB. These files were written using PySpark and I have full control of how they're generated, in case you think there's a better way to arrange them. I chose this partitioning since I wish to analyse one city at a time; I have also experimented with having only a single level partitioning with 5560 partitions, but didn't see any increase in performance. I'm using Pandas to read the files, and have tried using PyArrow directly as well. Regardless, I've profiled the reading of a single partition using cProfile, and the results clearly show PyArrow is taking the longest to run. I've attached the results of two runs I did using IPython: one right after rebooting my computer, which took well over 500 seconds; and one executed right after that, which took about 15 seconds, with the following command: >>> command = "pd.read_parquet('.', engine='pyarrow', filters=[('state_code', >>> '==', 31), ('city_code', '==', 6200)])" >>> cProfile.run(command, '/path/to/stats') It was much better the second time around but still terrible compared to specifying the path manually, which took around 50 milliseconds according to %timeit. I have absolutely no idea why the filesystem scan is taking so long. I have seen this issue https://issues.apache.org/jira/browse/ARROW-11781<https://urldefense.com/v3/__https:/issues.apache.org/jira/browse/ARROW-11781__;!!KSjYCgUGsB4!a08YdpWy7XPfHdgrfg8lMkMTia4epHAyid9h4ZrOEkINTFDbNphftopUuNpDDeL2-ZUSQttF_62sxBUHeVVk_g$> related to the same problem, but it mentions there should be no performance issue as of July 2021, whereas I'm having problems right now. I think I'll stick to specifying the partitions to read "by hand", but I was really really curious on whether I messed something up, or if (Py)Arrow really is inefficient in a task which seems so trivial at first sight. Thanks, Tomaz. P.S.: It's my first time sending an email to a mailing list, so I hope sending attachments is okay, and sorry if it isn't. Importante: As informações deste e-mail são confidenciais. O uso não autorizado é proibido por lei. Por favor, considere o ambiente antes de imprimir. Important: The information on this e-mail is confidential. 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