Hey Stephan,
I'd like to point out right away that the code related to your questions
is shared by both programs.
regarding your first point: i have a byte[] into which i serialize the
data first using a ByteBuffer, and then write that data to a
MappedByteBuffer.
regarding synchronization: i couldn't find a way to use elaborate things
like semaphores or similar that work between python and java alike.
the data exchange is currently completely synchronous. java writes a
record, sets an "isWritten" bit and then repeatedly checks this bit
whether it is 0. python repeatedly checks this bit whether it is 1. once
that happens, it reads the record, sets the bit to 0 which tells java
that it has read the record and can write the next one. this scheme
works the same way the other way around.
*NOW,* this may seem ... inefficient, to put it slightly. it is (or
rather should be...) way faster (5x) that what we had so far though
(asynchronous pipes).
(i also tried different schemes that all had no effect, so i decided to
stick with the easiest one)
on to your last point: I'm gonna check for that tomorrow.
On 27.8.2014 20:45, Stephan Ewen wrote:
Hi Chesnay!
That is an interesting problem, though hard to judge with the information
we have.
Can you elaborate a bit on the following points:
- When putting the objects from the Java Flink side into the shared
memory, you need to serialize them. How do you do that? Into a buffer, then
copy that into the shared memory ByteBuffer? Directly?
- Shared memory access has to be somehow controlled. The pipes give you
flow control for free (blocking write calls when the stream consumer is
busy). What do you do for the shared memory? Usually, one uses semaphores,
or, in java File(Range)Locks to coordinate access and block until memory
regions are made available. Can you check if there are some busy waiting
parts in you code?
- More general: The code is slower, but does it burn CPU cycles in its
slowness or is it waiting for locks / monitors / conditions ?
Stephan
On Wed, Aug 27, 2014 at 8:34 PM, Chesnay Schepler <
[email protected]> wrote:
Hello everyone,
This will be some kind of brainstorming question.
As some of you may know I am currently working on the Python API. The most
crucial part here is how the data is exchanged between Java and Python.
Up to this point we used pipes for this, but switched recently to memory
mapped files in hopes of increasing the (lacking) performance.
Early (simplified) prototypes (outside of Flink) showed that this would
yield a significant increase. yet when i added the code to flink and ran a
job, there was
no effect. like at all. two radically different schemes ran in /exactly/
the same time.
my conclusion was that code already in place (and not part of the
prototypes) is responsible for this.
so i went ahead and modified the prototypes to use all relevant code from
the Python API in order to narrow down the culprit. but this time, the
performance increase was there.
Now here's the question: How can the /very same code/ perform so much
worse when integrated into flink? if the code is not the problem, what
could be it?
i spent a lot of time looking for that one line of code that cripples the
performance, but I'm pretty much out of places to look.
