I feel like we drifted away from the original topic a bit, but alright.
I don't consider it a pity we created a proprietary protocol. we know
exactly how it works and what it is capable of. It is also made exactly
for our use case, in contrast to general purpose libraries. If we ever
decide that the current implementation is lacking we can always look for
better alternatives and swap stuff out fairly easily. bonus points for
being able to swap out only part of the system, since there is a clear
distinction between *what*(how the data is serialized) and
*how*(tcp/mmap) data is exchanged, something that, in my opinion, is way
too often bundled together.
on the other hand, let's assume we went from the start with one of these
magic libraries. if you then notice it lacks something (let's say its to
slow), and can't find a different library without these faults, you are
so screwed. now you have to re-implement these magic libraries, with all
their supported features, without these faults, or otherwise you break a
a lot of user programs that built upon these.
The current implementation was a safer approach imo. It has it's faults,
and did provide me with some very nerve wrecking afternoon's, but I'd
feel really uncomfortable relying on some library that i have no control
over for the most performance-impacting component.
On 31.07.2015 11:18, Maximilian Michels wrote:
py4j looks really nice and the communication works in both ways. There is
also another Python to Java communication library called javabridge. I
think it is a pity we chose to implement a proprietary protocol for the
network communication of the Python API. This could have been abstracted
more nicely and we have already seen that you can run into problems if you
implement that yourself.
Serializers could be created dynamically if Python passed its dynamically
determined types to Java at runtime. Then everything should work on the
Java side.
On Fri, Jul 31, 2015 at 11:01 AM, Till Rohrmann <trohrm...@apache.org>
wrote:
Zeppelin uses py4j [1] to transfer data between a Python process and a JVM.
That way they can run a Python interpreter and Java interpreter and easily
share state between them. Spark also uses py4j as a bridge between Java and
Python. However, I don't know for what exactly. And I also don't know
what's the performance penalty of py4j. But programming is a lot of fun
with it :-)
Cheers,
Till
[1] https://www.py4j.org/
On Fri, Jul 31, 2015 at 10:34 AM, Stephan Ewen <se...@apache.org> wrote:
I think in short: Spark never worried about types. It is just something
arbitrary.
Flink worries about types, for memory management.
Aljoscha's suggestion is a good one: have a PythonTypeInfo that is
dynamic.
Till' also found a pretty nice way to connect Python and Java in his
Zeppelin-based demo at the meetup.
On Fri, Jul 31, 2015 at 10:30 AM, Chesnay Schepler <c.schep...@web.de>
wrote:
if its just a single array, how would you define group/sort keys?
On 31.07.2015 07:03, Aljoscha Krettek wrote:
I think then the Python part would just serialize all the tuple fields
to
a
big byte array. And all the key fields to another array, so that the
java
side can to comparisons on the whole "key blob".
Maybe it's overly simplistic, but it might work. :D
On Thu, 30 Jul 2015 at 23:35 Chesnay Schepler <c.schep...@web.de>
wrote:
I can see this working for basic types, but am unsure how it would
work
with Tuples. Wouldn't the java API still need to know the arity to
setup
serializers?
On 30.07.2015 23:02, Aljoscha Krettek wrote:
I believe it should be possible to create a special PythonTypeInfo
where
the python side is responsible for serializing data to a byte array
and
to
the java side it is just a byte array and all the comparisons are
also
performed on these byte arrays. I think partitioning and sort should
still
work, since the sorting is (in most cases) only used to group the
elements
for a groupBy(). If proper sort order would be required this would
have
to
be done on the python side.
On Thu, 30 Jul 2015 at 22:21 Chesnay Schepler <c.schep...@web.de>
wrote:
To be perfectly honest i never really managed to work my way through
Spark's python API, it's a whole bunch of magic to me; not even the
general structure is understandable.
With "pure python" do you mean doing everything in python? as in
just
having serialized data on the java side?
I believe the way to do this with Flink is to add a switch that
a) disables all type checks
b) creates serializers dynamically at runtime.
a) should be fairly straight forward, b) on the other hand....
btw., the Python API itself doesn't require the type information,
it
already does the b part.
On 30.07.2015 22:11, Gyula Fóra wrote:
That I understand, but could you please tell me how is this done
differently in Spark for instance?
What would we need to change to make this work with pure python
(as
it
seems to be possible)? This probably have large performance
implications
though.
Gyula
Chesnay Schepler <c.schep...@web.de> ezt írta (időpont: 2015.
júl.
30.,
Cs,
22:04):
because it still goes through the Java API that requires some kind
of
type information. imagine a java api program where you omit all
generic
types, it just wouldn't work as of now.
On 30.07.2015 21:17, Gyula Fóra wrote:
Hey!
Could anyone briefly tell me what exactly is the reason why we
force
the
users in the Python API to declare types for operators?
I don't really understand how this works in different systems
but
I
am
just
curious why Flink has types and why Spark doesn't for instance.
If you give me some pointers to read that would also be fine :)
Thank you,
Gyula
