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From Gareth Western <gar...@garethwestern.com>
Subject Re: What is the most memory-efficient technique for selecting several million records from a CSV file
Date Mon, 26 Oct 2020 09:10:29 GMT
Hi Paul,

What is the "partial fix" related to in the REST API? The API has worked fine for our needs,
except in the case I mentioned where we would like to select 12 million records all at once.
I don't think this type of query will ever work with the REST API until the API supports a
streaming protocol (e.g. gRPC or rsocket), right?

Regarding the cleaning, I found out that there is actually a small cleaning step when the
CSV is first created, so it should be possible to use this stage to convert the data to a
format such as Parquet.

Regarding the immediate solution for my problem, I got the JDBC driver working with Python
using the JayDeBeApi library, and can keep the memory usage down by using the fetchMany method
to stream batches of results from the server: https://gist.github.com/gdubya/a2489e4b9451720bb2be996725ce35bb

Mvh,
Gareth

On 23/10/2020, 22:44, "Paul Rogers" <par0328@gmail.com> wrote:

    Hi Gareth,

    The REST API is handy. We do have a partial fix queued up, but it got
    stalled a bit because of lack of reviewers for the tricky part of the code
    that is impacted. If the REST API will help your use case; perhaps you can
    help with review of the fix, or trying it out in your environment. You'd
    need a custom Drill build, but doing that is pretty easy.

    One other thing to keep in mind: Drill will ready many kinds of "raw" data.
    But, Drill does require that the data be clean. For CSV, that means
    consistent columns and consistent formatting. (A column cannot be a number
    in one place, and a string in another. If using headers, a column cannot be
    called "foo" in one file, and "bar" in another.) If your files are messy,
    it is very helpful to run an ETL step to clean up the data so you don't end
    up with random failed queries. Since the data is rewritten for cleaning,
    you might as well write the output to Parquet as Nitin suggests.

    - Paul



    On Fri, Oct 23, 2020 at 2:54 AM Gareth Western <gareth@garethwestern.com>
    wrote:

    > Thanks Paul and Nitin.
    >
    > Yes, we are currently using the REST API, so I guess that caveat is the
    > main issue. I am experimenting with JDBC and ODBC, but haven't made a
    > successfully connection with those from our Python apps yet (issues not
    > related to Drill but with the libraries I'm trying to use).
    >
    > Our use case for Drill is using it to expose some source data files
    > directly with the least amount of "preparation" possible (e.g. converting
    > to Parquet before working with the data). Read performance isn't a priority
    > yet just as long as we can actually get to the data.
    >
    > I guess I'll port the app over to Java and try again with JDBC first.
    >
    > Kind regards,
    > Gareth
    >
    > On 23/10/2020, 09:08, "Paul Rogers" <par0328@gmail.com> wrote:
    >
    >     Hi Gareth,
    >
    >     As it turns out, SELECT * by itself should use a fixed amount of memory
    >     regardless of table size. (With two caveats.) Drill, as with most query
    >     engines, reads data in batches, then returns each batch to the client.
    > So,
    >     if you do SELECT * FROM yourfile.csv, the execution engine will use
    > only
    >     enough memory for one batch of data (which is likely to be in the 10s
    > of
    >     meg in size.)
    >
    >     The first caveat is if you do a "buffering" operation, such as a sort.
    >     SELECT * FROM yourfile.csv ORDER BY someCol will need to hold all data.
    >     But, Drill spills to disk to relieve memory pressure.
    >
    >     The other caveat is if you use the REST API to fetch data. Drill's
    > REST API
    >     is not scalable. It buffers all data in memory in an extremely
    > inefficient
    >     manner. If you use the JDBC, ODBC or native APIs, then you won't have
    > this
    >     problem. (There is a pending fix we can do for a future release.) Are
    > you
    >     using the REST API?
    >
    >     Note that the above is just as true of Parquet as it is with CSV.
    > However,
    >     as Nitin notes, Parquet is more efficient to read.
    >
    >     Thanks,
    >
    >     - Paul
    >
    >
    >     On Thu, Oct 22, 2020 at 11:30 PM Nitin Pawar <nitinpawar432@gmail.com>
    >     wrote:
    >
    >     > Please convert CSV to parquet first and while doing so make sure you
    > cast
    >     > each column to correct datatype
    >     >
    >     > once you have in paraquet, your queries should be bit faster.
    >     >
    >     > On Fri, Oct 23, 2020, 11:57 AM Gareth Western <
    > gareth@garethwestern.com>
    >     > wrote:
    >     >
    >     > > I have a very large CSV file (nearly 13 million records) stored in
    > Azure
    >     > > Storage and read via the Azure Storage plugin. The drillbit
    > configuration
    >     > > has a modest 4GB heap size. Is there an effective way to select
    > all the
    >     > > records from the file without running out of resources in Drill?
    >     > >
    >     > > SELECT * … is too big
    >     > >
    >     > > SELECT * with OFFSET and LIMIT sounds like the right approach, but
    > OFFSET
    >     > > still requires scanning through the offset records, and this seems
    > to hit
    >     > > the same memory issues even with small LIMITs once the offset is
    > large
    >     > > enough.
    >     > >
    >     > > Would it help to switch the format to something other than CSV? Or
    > move
    >     > it
    >     > > to a different storage mechanism? Or something else?
    >     > >
    >     >
    >
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