Re: Python UDF performance at large scale
I'm thinking that the batched synchronous version will be too slow (with small batch size) or easy to OOM with large (batch size). If it's not that hard, you can give it a try. On Wed, Jun 24, 2015 at 4:39 PM, Justin Uang justin.u...@gmail.com wrote: Correct, I was running with a batch size of about 100 when I did the tests, because I was worried about deadlocks. Do you have any concerns regarding the batched synchronous version of communication between the Java and Python processes, and if not, should I file a ticket and starting writing it? On Wed, Jun 24, 2015 at 7:27 PM Davies Liu dav...@databricks.com wrote: From you comment, the 2x improvement only happens when you have the batch size as 1, right? On Wed, Jun 24, 2015 at 12:11 PM, Justin Uang justin.u...@gmail.com wrote: FYI, just submitted a PR to Pyrolite to remove their StopException. https://github.com/irmen/Pyrolite/pull/30 With my benchmark, removing it basically made it about 2x faster. On Wed, Jun 24, 2015 at 8:33 AM Punyashloka Biswal punya.bis...@gmail.com wrote: 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 dav...@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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
Sweet, filed here: https://issues.apache.org/jira/browse/SPARK-8632 On Thu, Jun 25, 2015 at 3:05 AM Davies Liu dav...@databricks.com wrote: I'm thinking that the batched synchronous version will be too slow (with small batch size) or easy to OOM with large (batch size). If it's not that hard, you can give it a try. On Wed, Jun 24, 2015 at 4:39 PM, Justin Uang justin.u...@gmail.com wrote: Correct, I was running with a batch size of about 100 when I did the tests, because I was worried about deadlocks. Do you have any concerns regarding the batched synchronous version of communication between the Java and Python processes, and if not, should I file a ticket and starting writing it? On Wed, Jun 24, 2015 at 7:27 PM Davies Liu dav...@databricks.com wrote: From you comment, the 2x improvement only happens when you have the batch size as 1, right? On Wed, Jun 24, 2015 at 12:11 PM, Justin Uang justin.u...@gmail.com wrote: FYI, just submitted a PR to Pyrolite to remove their StopException. https://github.com/irmen/Pyrolite/pull/30 With my benchmark, removing it basically made it about 2x faster. On Wed, Jun 24, 2015 at 8:33 AM Punyashloka Biswal punya.bis...@gmail.com wrote: 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 dav...@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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
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 dav...@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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
Correct, I was running with a batch size of about 100 when I did the tests, because I was worried about deadlocks. Do you have any concerns regarding the batched synchronous version of communication between the Java and Python processes, and if not, should I file a ticket and starting writing it? On Wed, Jun 24, 2015 at 7:27 PM Davies Liu dav...@databricks.com wrote: From you comment, the 2x improvement only happens when you have the batch size as 1, right? On Wed, Jun 24, 2015 at 12:11 PM, Justin Uang justin.u...@gmail.com wrote: FYI, just submitted a PR to Pyrolite to remove their StopException. https://github.com/irmen/Pyrolite/pull/30 With my benchmark, removing it basically made it about 2x faster. On Wed, Jun 24, 2015 at 8:33 AM Punyashloka Biswal punya.bis...@gmail.com wrote: 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 dav...@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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
From you comment, the 2x improvement only happens when you have the batch size as 1, right? On Wed, Jun 24, 2015 at 12:11 PM, Justin Uang justin.u...@gmail.com wrote: FYI, just submitted a PR to Pyrolite to remove their StopException. https://github.com/irmen/Pyrolite/pull/30 With my benchmark, removing it basically made it about 2x faster. On Wed, Jun 24, 2015 at 8:33 AM Punyashloka Biswal punya.bis...@gmail.com wrote: 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 dav...@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.u...@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 dav...@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.u...@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
Re: Python UDF performance at large scale
// + 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 dav...@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.u...@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
Python UDF performance at large scale
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
Re: Python UDF performance at large scale
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.u...@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 - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
