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From Steve Loughran <ste...@cloudera.com.INVALID>
Subject Re: DataFrameReader bottleneck in DataSource#checkAndGlobPathIfNecessary when reading S3 files
Date Sat, 07 Sep 2019 13:22:34 GMT
On Fri, Sep 6, 2019 at 10:56 PM Arwin Tio <arwin.tio@hotmail.com> wrote:

> I think the problem is calling globStatus to expand all 300K files.
>
> In my particular case I did not use any glob patterns, so my bottleneck
> came from the FileSystem#exists specifically. I do concur that the
> globStatus expansion could also be problematic.
>
> But you might
> consider, if possible, running a lot of .csv jobs in parallel to query
> subsets of all the files, and union the results. At least there you
> parallelize the reading from the object store.
>
> That is a great solution! I think that's what I will do as a workaround
> for the moment. Right now I'm thinking that a potential improvement here is
> to parallelize the SparkHadoopUtil#globPathIfNecessary and
> FileSystem#exists calls whenever possible (i.e. when multiple paths are
> specified), so that the client doesn't have to.
>
>
The other tactic though it'd go through a lot more of the code would be to
postpone the exists check until the work is scheduled, which is implicitly
in open() on the workers, or explicit when the RDD does the split
calculation and calls getFileBlockLocations(). If you are confident that
that always happens (and you will have to trace back from those calls in
things like org.apache.spark.streaming.util.HdfsUtils and
ParallelizedWithLocalityRDD)
then you get those scans in the driver ... but I fear regression handling
there gets harder.

* have SparkHadoopUtils differentiate between files returned
> by globStatus(), and which therefore exist, and those which it didn't glob
> for -it will only need to check those.
> * then worry about parallel execution of the scan, again
>
> Okay sounds good, I will take a crack at this and open a ticket. Any
> thoughts on the parallelism; should it be configurable?
>

For file input formats (parquet, orc, ...) there is an option, default ==
8. Though its also off by default...maybe i should change that.


> Another possible QoL improvement here is to show progress log messages -
> something that indicates to the user that the cluster is stuck while the
> driver is listing S3 files, maybe even including the FS
> getStorageStatistics?
>

yeah. If you want some examples of this, take a look at
https://github.com/steveloughran/cloudstore . the locatedfilestatus command
replicates what happens during FileInputFormat scans, so is how I'm going
to tune IOPs there. It might also be good to have those bits of the hadoop
MR classes which spark uses to log internally @ debug, so everything gets
this logging if they ask for it.

Happy to take contribs there as Hadoop JIRAs & PRs

>
> Thanks,
>
> Arwin
> ------------------------------
> *From:* Steve Loughran <stevel@cloudera.com>
> *Sent:* September 6, 2019 4:15 PM
> *To:* Sean Owen <srowen@gmail.com>
> *Cc:* Arwin Tio <arwin.tio@hotmail.com>; dev@spark.apache.org <
> dev@spark.apache.org>
> *Subject:* Re: DataFrameReader bottleneck in
> DataSource#checkAndGlobPathIfNecessary when reading S3 files
>
>
>
> On Fri, Sep 6, 2019 at 2:50 PM Sean Owen <srowen@gmail.com> wrote:
>
> I think the problem is calling globStatus to expand all 300K files.
> This is a general problem for object stores and huge numbers of files.
> Steve L. may have better thoughts on real solutions. But you might
> consider, if possible, running a lot of .csv jobs in parallel to query
> subsets of all the files, and union the results. At least there you
> parallelize the reading from the object store.
>
>
> yeah, avoid globs and small files, especially small files in deep trees.
>
>
> I think it's hard to optimize this case from the Spark side as it's
> not clear how big a glob like s3://foo/* is going to be. I think it
> would take reimplementing some logic to expand the glob incrementally
> or something. Or maybe I am overlooking optimizations that have gone
> into Spark 3.
>
>
> A long time ago I actually tried to move Filesystem.globFiles off its own
> recursive treewalk into supporting the option of flat-list-chlldren +
> filter. But while you can get some great speedups in some layouts, you can
> get pathological collapses in perf elsewhere, which makes the people
> running those queries very sad. So I gave up.
>
> Parallelized scans can do speedup; look at the code in
> org.apache.hadoop.mapred.LocatedFileStatusFetcher to see what it does
> there. I've only just started exploring what we can do to tune that, with
> HADOOP-16458, HADOOP-16465
> <https://issues.apache.org/jira/browse/HADOOP-16465>, which should speed
> up ORC/Parquet scans) . These are designed to cut 1-2 HEAD requests off per
> directory list, which may seem small but from my early measurements, can be
> significant.
>
> That's why cutting things like an exists check makes a big difference,
> especially if you are going to call some list() or open() operation
> straight after -just call the operation and rely on the
> FileNotFoundException to tell you when it's not there.
>
> Now, looking at the code, if the list has already come from a real call to
> globPath, then yes, the existsCall is wasteful, where waste = 500+ mills
> per file:
> http://steveloughran.blogspot.com/2016/12/how-long-does-filesystemexists-take.html
>
> For speedup then
> * have SparkHadoopUtils differentiate between files returned
> by globStatus(), and which therefore exist, and those which it didn't glob
> for -it will only need to check those.
> * then worry about parallel execution of the scan, again
>
> Why not file a JIRA on the spark work; send me a ref so I can look at your
> patch.
>
> One thing to know here is that not only does the S3A FS class have
> counters for all operations you can get from getStorageStatistics, if you
> call toString() on it it will print out the current stats. So you can just
> log the fs string value before and after an operation and see what's gone
> on. We track FS API calls (op_*) and actual http requests of the store
> (object_*); both are interesting. object_ to see what is expensive (and in
> the S3A FS code, what we should cut), the op_ values what API calls are
> used a lot and should somehow be eliminated or, if you have insights,
> optimised better. Removal is usually the best, as it speeds up everything.
>
> Long term, relying on directory trees to list your source data, commit
> algorithms which move/instantiate changes isn't sustainable. Things like
> Apache Iceberg are where data should go ... things for which S3 can be
> viewed as a fault-injecting test infrastructure. It's the Chaos Monkey of
> object storage.
>
>
>
> On Fri, Sep 6, 2019 at 7:09 AM Arwin Tio <arwin.tio@hotmail.com> wrote:
> >
> > Hello,
> >
> > On Spark 2.4.4, I am using DataFrameReader#csv to read about 300000
> files on S3, and I've noticed that it takes about an hour for it to load
> the data on the Driver. You can see the timestamp difference when the log
> from InMemoryFileIndex occurs from 7:45 to 8:54:
> >
> > 19/09/06 07:44:42 INFO SparkContext: Running Spark version 2.4.4
> > 19/09/06 07:44:42 INFO SparkContext: Submitted application:
> LoglineParquetGenerator
> > ...
> > 19/09/06 07:45:40 INFO StateStoreCoordinatorRef: Registered
> StateStoreCoordinator endpoint
> > 19/09/06 08:54:57 INFO InMemoryFileIndex: Listing leaf files and
> directories in parallel under: [300K files...]
> >
> >
> > I believe that the issue comes from
> DataSource#checkAndGlobPathIfNecessary [0], specifically from when it is
> calling FileSystem#exists. Unlike bulkListLeafFiles, the existence check
> here happens in a single-threaded flatMap, which is a blocking network call
> if your files are stored on S3.
> >
> > I believe that there is a fairly straightforward opportunity for
> improvement here, which is to parallelize the existence check perhaps with
> a configurable number of threads. If that seems reasonable, I would like to
> create a JIRA ticket and submit a patch. Please let me know!
> >
> > Cheers,
> >
> > Arwin
> >
> > [0]
> https://github.com/apache/spark/blob/branch-2.4/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala#L557
>
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