Unfortunately I cannot use kudu in my projects, I would have loved to give it a try. I did not know about hudi, it seems very similar to what we do (Parquet + Avro), I will have a look. I am following the iceberg project very closely, because it appears to solve a lot of problems that we face on a regular basis. I am really excited to learn that the arrow and iceberg projects could work together and I can hope for a lot of good things coming out of these.
On Wed, Dec 19, 2018 at 2:52 PM Uwe L. Korn <uw...@xhochy.com> wrote: > This can also be solved by using a table format like > https://github.com/uber/hudi or > https://github.com/apache/incubator-iceberg where the latter has a PR > open for a basic Python implementation with pyarrow. > > These table formats support using Avro and Parquet seamlessly together > without the reader needing to take care of the storage format. > > Uwe > > > Am 19.12.2018 um 14:47 schrieb Wes McKinney <wesmck...@gmail.com>: > > > > This turns out to be a very common problem (landing incremental > > updates, dealing with compaction and small files). It's part of the > > reason that systems like Apache Kudu were developed, e.g. > > > > > https://blog.cloudera.com/blog/2015/11/how-to-ingest-and-query-fast-data-with-impala-without-kudu/ > > > > If you have to use file storage, then figuring out a scheme to compact > > Parquet files (e.g. once per hour, once per day) will definitely be > > worth it compared with using a slower file format (like Avro) > > > > - Wes > > > >> On Wed, Dec 19, 2018 at 7:37 AM Joel Pfaff <joel.pf...@gmail.com> > wrote: > >> > >> Hello, > >> > >> For my company's usecases, we have found that the number of files was a > >> critical part of the time spent doing the execution plan, so we found > the > >> idea of very regularly writing small parquet files to be rather > inefficient. > >> > >> There are some formats that support an `append` semantic (I have tested > >> successfully with avro, but there are a couple others that could be used > >> similarly). > >> So we had a few cases where we were aggregating data in a `current > table` > >> in set of avro files, and rewriting all of it in few parquet files at > the > >> end of the day. > >> This allowed us to have files that have been prepared to optimize their > >> querying performance (file size, row group size, sorting per column) by > >> maximizing the ability to benefit from the statistics. > >> And our queries were doing an UNION between "optimized for speed" > history > >> tables and "optimized for latency" current tables, when the query > timeframe > >> was crossing the boundaries of the current day. > >> > >> Regards, Joel > >> > >> On Wed, Dec 19, 2018 at 2:14 PM Francois Saint-Jacques < > >> fsaintjacq...@networkdump.com> wrote: > >> > >>> Hello Darren, > >>> > >>> what Uwe suggests is usually the way to go, your active process writes > to a > >>> new file every time. Then you have a parallel process/thread that does > >>> compaction of smaller files in the background such that you don't have > too > >>> many files. > >>> > >>>> On Wed, Dec 19, 2018 at 7:59 AM Uwe L. Korn <uw...@xhochy.com> wrote: > >>>> > >>>> Hello Darren, > >>>> > >>>> you're out of luck here. Parquet files are immutable and meant for > batch > >>>> writes. Once they're written you cannot modify them anymore. To load > >>> them, > >>>> you need to know their metadata which is in the footer. The footer is > >>>> always at the end of the file and written once you call close. > >>>> > >>>> Your use case is normally fulfilled by continously starting new files > and > >>>> reading them back in using the ParquetDataset class > >>>> > >>>> Cheers > >>>> Uwe > >>>> > >>>> Am 18.12.2018 um 21:03 schrieb Darren Gallagher <daz...@gmail.com>: > >>>> > >>>>>> [Cross posted from https://github.com/apache/arrow/issues/3203] > >>>>>> > >>>>>> I'm adding new data to a parquet file every 60 seconds using this > >>> code: > >>>>>> > >>>>>> import os > >>>>>> import json > >>>>>> import time > >>>>>> import requests > >>>>>> import pandas as pd > >>>>>> import numpy as np > >>>>>> import pyarrow as pa > >>>>>> import pyarrow.parquet as pq > >>>>>> > >>>>>> api_url = 'https://opensky-network.org/api/states/all' > >>>>>> > >>>>>> cols = ['icao24', 'callsign', 'origin', 'time_position', > >>>>>> 'last_contact', 'longitude', 'latitude', > >>>>>> 'baro_altitude', 'on_ground', 'velocity', 'true_track', > >>>>>> 'vertical_rate', 'sensors', 'geo_altitude', 'squawk', > >>>>>> 'spi', 'position_source'] > >>>>>> > >>>>>> def get_new_flight_info(writer): > >>>>>> print("Requesting new data") > >>>>>> req = requests.get(api_url) > >>>>>> content = req.json() > >>>>>> > >>>>>> states = content['states'] > >>>>>> df = pd.DataFrame(states, columns = cols) > >>>>>> df['timestamp'] = content['time'] > >>>>>> print("Found {} new items".format(len(df))) > >>>>>> > >>>>>> table = pa.Table.from_pandas(df) > >>>>>> if writer is None: > >>>>>> writer = pq.ParquetWriter('openskyflights.parquet', > >>> table.schema) > >>>>>> writer.write_table(table=table) > >>>>>> return writer > >>>>>> > >>>>>> if __name__ == '__main__': > >>>>>> writer = None > >>>>>> while (not os.path.exists('opensky.STOP')): > >>>>>> writer = get_new_flight_info(writer) > >>>>>> time.sleep(60) > >>>>>> > >>>>>> if writer: > >>>>>> writer.close() > >>>>>> > >>>>>> This is working fine and the file grows every 60 seconds. > >>>>>> However unless I force the loop to exit I am unable to use the > parquet > >>>>>> file. In a separate terminal I try to access the parquet file using > >>> this > >>>>>> code: > >>>>>> > >>>>>> import pandas as pd > >>>>>> import pyarrow.parquet as pq > >>>>>> > >>>>>> table = pq.read_table("openskyflights.parquet") > >>>>>> df = table.to_pandas() > >>>>>> print(len(df)) > >>>>>> > >>>>>> which results in this error: > >>>>>> > >>>>>> Traceback (most recent call last): > >>>>>> File "checkdownloadsize.py", line 7, in <module> > >>>>>> table = pq.read_table("openskyflights.parquet") > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 1074, in read_table > >>>>>> use_pandas_metadata=use_pandas_metadata) > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/filesystem.py", > >>>> line 182, in read_parquet > >>>>>> filesystem=self) > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 882, in __init__ > >>>>>> self.validate_schemas() > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 895, in validate_schemas > >>>>>> self.schema = self.pieces[0].get_metadata(open_file).schema > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 453, in get_metadata > >>>>>> return self._open(open_file_func).metadata > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 459, in _open > >>>>>> reader = open_file_func(self.path) > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 984, in open_file > >>>>>> common_metadata=self.common_metadata) > >>>>>> File > >>>> > >>> > "/home/xxxx/.local/share/virtualenvs/opensky-WcPvsoLj/lib/python3.5/site-packages/pyarrow/parquet.py", > >>>> line 102, in __init__ > >>>>>> self.reader.open(source, metadata=metadata) > >>>>>> File "pyarrow/_parquet.pyx", line 639, in > >>>> pyarrow._parquet.ParquetReader.open > >>>>>> File "pyarrow/error.pxi", line 83, in pyarrow.lib.check_status > >>>>>> pyarrow.lib.ArrowIOError: Invalid parquet file. Corrupt footer. > >>>>>> > >>>>>> Is there a way to achieve this? > >>>>>> I'm assuming that if I call writer.close() in the while loop then it > >>>> will > >>>>>> prevent any further data being written to the file? Is there some > kind > >>>> of > >>>>>> "flush" operation that can be used to ensure all data is written to > >>> disk > >>>>>> and available to other processes or threads that want to read the > >>> data? > >>>>>> > >>>>>> Thanks > >>>>>> > >>>> > >>>> > >>> > >>> -- > >>> Sent from my jetpack. > >>> >
