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Re: (Ab)using parquet files on S3 storage for a huge logging database


There's been a bunch of work on adding page indices to parquet: https://github.com/apache/parquet-format/blob/master/PageIndex.md <https://github.com/apache/parquet-format/blob/master/PageIndex.md>
I haven't followed progress in detail but I think the Java implementation supports this now.

Looked pretty similar to what kdb or clickhouse do, so with fitting storage underneath it should enable very fast point lookups.

Andreas

> Am 19.09.2018 um 20:30 schrieb Brian Bowman <Brian.Bowman@xxxxxxx>:
> 
> Gerlando,
> 
> AFAIK Parquet does not yet support indexing.  I believe it does store min/max values at the row batch (or maybe it's page) level which may help eliminate large "swaths" of data depending on how actual data values corresponding to a search predicate are distributed across large Parquet files.
> 
> I have an interest in the future of indexing within the native Parquet structure as well.  It will be interesting to see where this discussion goes from here.
> 
> -Brian 
> 
> On 9/19/18, 3:21 PM, "Gerlando Falauto" <gerlando.falauto@xxxxxxxxx> wrote:
> 
>    EXTERNAL
> 
>    Thank you all guys, you've been extremely helpful with your ideas.
>    I'll definitely have a look at all your suggestions to see what others have
>    been doing in this respect.
> 
>    What I forgot to mention was that while the service uses the S3 API, it's
>    not provided by AWS so any solution should be based on a cloud offering
>    from a different big player (it's the three-letter big-blue one, in case
>    you're wondering).
> 
>    However, I'm still not clear as to how Drill (or pyarrow) would be able to
>    gather data with random access. In any database, you just build an index on
>    the fields you're going to run most of your queries over, and then the
>    database takes care of everything else.
> 
>    With Parquet, as I understand, you can do folder-based partitioning (is
>    that called "hive" partitioning?) so that you can get random access over
>    let's say
>    source=source1/date=20180918/*.parquet.
>    I assume drill could be instructed into doing this or even figure it out by
>    itself, by just looking at the folder structure.
>    What I still don't get though, is how to "index" the parquet file(s), so
>    that random (rather than sequential) access can be performed over the whole
>    file.
>    Brian mentioned metadata, I had a quick look at the parquet specification
>    and I sortof understand it somehow resembles an index.
>    Yet I fail to understand how such an index could be built (if at all
>    possible), for instance using pyarrow (or any other tool, for that matter)
>    for reading and/or writing.
> 
>    Thank you!
>    Gerlando
> 
>    On Wed, Sep 19, 2018 at 7:55 PM Ted Dunning <ted.dunning@xxxxxxxxx> wrote:
> 
>> The effect of rename can be had by handling a small inventory file that is
>> updated atomically.
>> 
>> Having real file semantics is sooo much nicer, though.
>> 
>> 
>> 
>> On Wed, Sep 19, 2018 at 1:51 PM Bill Glennon <wglennon@xxxxxxxxx> wrote:
>> 
>>> Also, may want to take a look at https://aws.amazon.com/athena/.
>>> 
>>> Thanks,
>>> Bill
>>> 
>>> On Wed, Sep 19, 2018 at 1:43 PM Paul Rogers <par0328@xxxxxxxxx.invalid>
>>> wrote:
>>> 
>>>> Hi Gerlando,
>>>> 
>>>> I believe AWS has entire logging pipeline they offer. If you want
>>>> something quick, perhaps look into that offering.
>>>> 
>>>> What you describe is pretty much the classic approach to log
>> aggregation:
>>>> partition data, gather data incrementally, then later consolidate. A
>>> while
>>>> back, someone invented the term "lambda architecture" for this idea.
>> You
>>>> should be able to find examples of how others have done something
>>> similar.
>>>> 
>>>> Drill can scan directories of files. So, in your buckets (source-date)
>>>> directories, you can have multiple files. If you receive data, say,
>>> every 5
>>>> or 10 minutes, you can just create a separate file for each new drop of
>>>> data. You'll end up with many files, but you can query the data as it
>>>> arrives.
>>>> 
>>>> Then, later, say once per day, you can consolidate the files into a few
>>>> big files. The only trick is the race condition of doing the
>>> consolidation
>>>> while running queries. Not sure how to do that on S3, since you can't
>>>> exploit rename operations as you can on Linux. Anyone have suggestions
>>> for
>>>> this step?
>>>> 
>>>> Thanks,
>>>> - Paul
>>>> 
>>>> 
>>>> 
>>>>    On Wednesday, September 19, 2018, 6:23:13 AM PDT, Gerlando Falauto
>> <
>>>> gerlando.falauto@xxxxxxxxx> wrote:
>>>> 
>>>> Hi,
>>>> 
>>>> I'm looking for a way to store huge amounts of logging data in the
>> cloud
>>>> from about 100 different data sources, each producing about 50MB/day
>> (so
>>>> it's something like 5GB/day).
>>>> The target storage would be an S3 object storage for cost-efficiency
>>>> reasons.
>>>> I would like to be able to store (i.e. append-like) data in realtime,
>> and
>>>> retrieve data based on time frame and data source with fast access. I
>> was
>>>> thinking of partitioning data based on datasource and calendar day, so
>> to
>>>> have one file per day, per data source, each 50MB.
>>>> 
>>>> I played around with pyarrow and parquet (using s3fs), and came across
>>> the
>>>> following limitations:
>>>> 
>>>> 1) I found no way to append to existing files. I believe that's some
>>>> limitation with S3, but it could be worked around by using datasets
>>>> instead. In principle, I believe I could also trigger some daily job
>>> which
>>>> coalesces, today's data into a single file, if having too much
>>>> fragmentation causes any disturbance. Would that make any sense?
>>>> 
>>>> 2) When reading, if I'm only interested in a small portion of the data
>>> (for
>>>> instance, based on a timestamp field), I obviously wouldn't want to
>> have
>>> to
>>>> read (i.e. download) the whole file. I believe Parquet was designed to
>>>> handle huge amounts of data with relatively fast access. Yet I fail to
>>>> understand if there's some way to allow for random access, particularly
>>>> when dealing with a file stored within S3.
>>>> The following code snippet refers to a 150MB dataset composed of 1000
>>>> rowgroups of 150KB each. I was expecting it to run very fast, yet it
>>>> apparently downloads the whole file (pyarrow 0.9.0):
>>>> 
>>>> fs = s3fs.S3FileSystem(key=access_key, secret=secret_key,
>>>> client_kwargs=client_kwargs)
>>>> with fs.open(bucket_uri) as f:
>>>>    pf = pq.ParquetFile(f)
>>>>    print(pf.num_row_groups) # yields 1000
>>>>    pf.read_row_group(1)
>>>> 
>>>> 3) I was also expecting to be able to perform some sort of query, but
>> I'm
>>>> also failing to see how to specify index columns or such.
>>>> 
>>>> What am I missing? Did I get it all wrong?
>>>> 
>>>> Thank you!
>>>> Gerlando
>>>> 
>>> 
>> 
> 
>