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From Punyashloka Biswal <punya.bis...@gmail.com>
Subject Re: Python UDF performance at large scale
Date Wed, 24 Jun 2015 12:33:03 GMT
Hi Davies,

In general, do we expect people to use CPython only for "heavyweight" UDFs
that invoke an external library? Are there any examples of using Jython,
especially performance comparisons to Java/Scala and CPython? When using
Jython, do you expect the driver to send code to the executor as a string,
or is there a good way to serialized Jython lambdas?

(For context, I was unable to serialize Nashorn lambdas when I tried to use
them in Spark.)

Punya
On Wed, Jun 24, 2015 at 2:26 AM Davies Liu <davies@databricks.com> wrote:

> Fare points, I also like simpler solutions.
>
> The overhead of Python task could be a few of milliseconds, which
> means we also should eval them as batches (one Python task per batch).
>
> Decreasing the batch size for UDF sounds reasonable to me, together
> with other tricks to reduce the data in socket/pipe buffer.
>
> BTW, what do your UDF looks like? How about to use Jython to run
> simple Python UDF (without some external libraries).
>
> On Tue, Jun 23, 2015 at 8:21 PM, Justin Uang <justin.uang@gmail.com>
> wrote:
> > // + punya
> >
> > Thanks for your quick response!
> >
> > I'm not sure that using an unbounded buffer is a good solution to the
> > locking problem. For example, in the situation where I had 500 columns,
> I am
> > in fact storing 499 extra columns on the java side, which might make me
> OOM
> > if I have to store many rows. In addition, if I am using an
> > AutoBatchedSerializer, the java side might have to write 1 << 16 == 65536
> > rows before python starts outputting elements, in which case, the Java
> side
> > has to buffer 65536 complete rows. In general it seems fragile to rely on
> > blocking behavior in the Python coprocess. By contrast, it's very easy to
> > verify the correctness and performance characteristics of the synchronous
> > blocking solution.
> >
> >
> > On Tue, Jun 23, 2015 at 7:21 PM Davies Liu <davies@databricks.com>
> wrote:
> >>
> >> Thanks for looking into it, I'd like the idea of having
> >> ForkingIterator. If we have unlimited buffer in it, then will not have
> >> the problem of deadlock, I think. The writing thread will be blocked
> >> by Python process, so there will be not much rows be buffered(still be
> >> a reason to OOM). At least, this approach is better than current one.
> >>
> >> Could you create a JIRA and sending out the PR?
> >>
> >> On Tue, Jun 23, 2015 at 3:27 PM, Justin Uang <justin.uang@gmail.com>
> >> wrote:
> >> > BLUF: BatchPythonEvaluation's implementation is unusable at large
> scale,
> >> > but
> >> > I have a proof-of-concept implementation that avoids caching the
> entire
> >> > dataset.
> >> >
> >> > Hi,
> >> >
> >> > We have been running into performance problems using Python UDFs with
> >> > DataFrames at large scale.
> >> >
> >> > From the implementation of BatchPythonEvaluation, it looks like the
> goal
> >> > was
> >> > to reuse the PythonRDD code. It caches the entire child RDD so that it
> >> > can
> >> > do two passes over the data. One to give to the PythonRDD, then one to
> >> > join
> >> > the python lambda results with the original row (which may have java
> >> > objects
> >> > that should be passed through).
> >> >
> >> > In addition, it caches all the columns, even the ones that don't need
> to
> >> > be
> >> > processed by the Python UDF. In the cases I was working with, I had a
> >> > 500
> >> > column table, and i wanted to use a python UDF for one column, and it
> >> > ended
> >> > up caching all 500 columns.
> >> >
> >> > I have a working solution over here that does it in one pass over the
> >> > data,
> >> > avoiding caching
> >> >
> >> > (
> https://github.com/justinuang/spark/commit/c1a415a18d31226ac580f1a9df7985571d03199b
> ).
> >> > With this patch, I go from a job that takes 20 minutes then OOMs, to a
> >> > job
> >> > that finishes completely in 3 minutes. It is indeed quite hacky and
> >> > prone to
> >> > deadlocks since there is buffering in many locations:
> >> >
> >> >     - NEW: the ForkingIterator LinkedBlockingDeque
> >> >     - batching the rows before pickling them
> >> >     - os buffers on both sides
> >> >     - pyspark.serializers.BatchedSerializer
> >> >
> >> > We can avoid deadlock by being very disciplined. For example, we can
> >> > have
> >> > the ForkingIterator instead always do a check of whether the
> >> > LinkedBlockingDeque is full and if so:
> >> >
> >> > Java
> >> >     - flush the java pickling buffer
> >> >     - send a flush command to the python process
> >> >     - os.flush the java side
> >> >
> >> > Python
> >> >     - flush BatchedSerializer
> >> >     - os.flush()
> >> >
> >> > I haven't added this yet. This is getting very complex however.
> Another
> >> > model would just be to change the protocol between the java side and
> the
> >> > worker to be a synchronous request/response. This has the disadvantage
> >> > that
> >> > the CPU isn't doing anything when the batch is being sent across, but
> it
> >> > has
> >> > the huge advantage of simplicity. In addition, I imagine that the
> actual
> >> > IO
> >> > between the processes isn't that slow, but rather the serialization of
> >> > java
> >> > objects into pickled bytes, and the deserialization/serialization +
> >> > python
> >> > loops on the python side. Another advantage is that we won't be taking
> >> > more
> >> > than 100% CPU since only one thread is doing CPU work at a time
> between
> >> > the
> >> > executor and the python interpreter.
> >> >
> >> > Any thoughts would be much appreciated =)
> >> >
> >> > Other improvements:
> >> >     - extract some code of the worker out of PythonRDD so that we can
> do
> >> > a
> >> > mapPartitions directly in BatchedPythonEvaluation without resorting to
> >> > the
> >> > hackery in ForkedRDD.compute(), which uses a cache to ensure that the
> >> > other
> >> > RDD can get a handle to the same iterator.
> >> >     - read elements and use a size estimator to create the
> BlockingQueue
> >> > to
> >> > make sure that we don't store too many things in memory when batching
> >> >     - patch Unpickler to not use StopException for control flow, which
> >> > is
> >> > slowing down the java side
> >> >
> >> >
>

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