I am running a Pig Script which is running fine for small data. But when I scale the data, I am getting the following error at my map stage. Please refer to the map logs as below.
My Pig script is doing a group by first, followed by a join on the grouped data. Any clues to understand where I should look at or how shall I deal with this situation. I don't want to just go by just increasing the heap space. My map jvm heap space is already 3 GB with io.sort.mb = 768 MB.
2014-02-06 19:15:12,243 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2014-02-06 19:15:15,025 INFO org.apache.hadoop.util.ProcessTree: setsid exited with exit code 0 2014-02-06 19:15:15,123 INFO org.apache.hadoop.mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@2bd9e282 2014-02-06 19:15:15,546 INFO org.apache.hadoop.mapred.MapTask: io.sort.mb = 768 2014-02-06 19:15:19,846 INFO org.apache.hadoop.mapred.MapTask: data buffer = 612032832/644245088 2014-02-06 19:15:19,846 INFO org.apache.hadoop.mapred.MapTask: record buffer = 9563013/10066330 2014-02-06 19:15:20,037 INFO org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor 2014-02-06 19:15:21,083 INFO org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigRecordReader: Created input record counter: Input records from _1_tmp1327641329 2014-02-06 19:15:52,894 INFO org.apache.hadoop.mapred.MapTask: Spilling map output: buffer full= true 2014-02-06 19:15:52,895 INFO org.apache.hadoop.mapred.MapTask: bufstart = 0; bufend = 611949600; bufvoid = 644245088 2014-02-06 19:15:52,895 INFO org.apache.hadoop.mapred.MapTask: kvstart = 0; kvend = 576; length = 10066330 2014-02-06 19:16:06,182 INFO org.apache.hadoop.mapred.MapTask: Finished spill 0 2014-02-06 19:16:16,169 INFO org.apache.pig.impl.util.SpillableMemoryManager: first memory handler call - Collection threshold init = 328728576(321024K) used = 1175055104(1147514K) committed = 1770848256(1729344K) max = 2097152000(2048000K) 2014-02-06 19:16:20,446 INFO org.apache.pig.impl.util.SpillableMemoryManager: Spilled an estimate of 308540402 bytes from 1 objects. init = 328728576(321024K) used = 1175055104(1147514K) committed = 1770848256(1729344K) max = 2097152000(2048000K) 2014-02-06 19:17:22,246 INFO org.apache.pig.impl.util.SpillableMemoryManager: first memory handler call- Usage threshold init = 328728576(321024K) used = 1768466512(1727018K) committed = 1770848256(1729344K) max = 2097152000(2048000K) 2014-02-06 19:17:35,597 INFO org.apache.pig.impl.util.SpillableMemoryManager: Spilled an estimate of 1073462600 bytes from 1 objects. init = 328728576(321024K) used = 1768466512(1727018K) committed = 1770848256(1729344K) max = 2097152000(2048000K) 2014-02-06 19:18:01,276 INFO org.apache.hadoop.mapred.MapTask: Spilling map output: buffer full= true 2014-02-06 19:18:01,288 INFO org.apache.hadoop.mapred.MapTask: bufstart = 611949600; bufend = 52332788; bufvoid = 644245088 2014-02-06 19:18:01,288 INFO org.apache.hadoop.mapred.MapTask: kvstart = 576; kvend = 777; length = 10066330 2014-02-06 19:18:03,377 INFO org.apache.hadoop.mapred.MapTask: Finished spill 1 2014-02-06 19:18:05,494 INFO org.apache.hadoop.mapred.MapTask: Record too large for in-memory buffer: 644246693 bytes 2014-02-06 19:18:36,008 INFO org.apache.pig.impl.util.SpillableMemoryManager: Spilled an estimate of 306271368 bytes from 1 objects. init = 328728576(321024K) used = 1449267128(1415299K) committed = 2097152000(2048000K) max = 2097152000(2048000K) 2014-02-06 19:18:44,448 INFO org.apache.hadoop.mapred.TaskLogsTruncater: Initializing logs' truncater with mapRetainSize=-1 and reduceRetainSize=-1 2014-02-06 19:18:44,780 FATAL org.apache.hadoop.mapred.Child: Error running child : java.lang.OutOfMemoryError: Java heap space at java.util.Arrays.copyOf(Arrays.java:2786) at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:94) at java.io.DataOutputStream.write(DataOutputStream.java:90) at java.io.DataOutputStream.writeUTF(DataOutputStream.java:384) at java.io.DataOutputStream.writeUTF(DataOutputStream.java:306) at org.apache.pig.data.BinInterSedes.writeDatum(BinInterSedes.java:454) at org.apache.pig.data.BinInterSedes.writeTuple(BinInterSedes.java:542) at org.apache.pig.data.BinInterSedes.writeBag(BinInterSedes.java:523) at org.apache.pig.data.BinInterSedes.writeDatum(BinInterSedes.java:361) at org.apache.pig.data.BinInterSedes.writeTuple(BinInterSedes.java:542) at org.apache.pig.data.BinInterSedes.writeDatum(BinInterSedes.java:357) at org.apache.pig.data.BinSedesTuple.write(BinSedesTuple.java:57) at org.apache.pig.impl.io.PigNullableWritable.write(PigNullableWritable.java:123) at org.apache.hadoop.io.serializer.WritableSerialization$WritableSerializer.serialize(WritableSerialization.java:90) at org.apache.hadoop.io.serializer.WritableSerialization$WritableSerializer.serialize(WritableSerialization.java:77) at org.apache.hadoop.mapred.IFile$Writer.append(IFile.java:179) at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.spillSingleRecord(MapTask.java:1501) at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1091) at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:691) at org.apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapReduce$Map.collect(PigGenericMapReduce.java:128) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapBase.runPipeline(PigGenericMapBase.java:269) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapBase.map(PigGenericMapBase.java:262) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapBase.map(PigGenericMapBase.java:64) at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:76
2) Can't you increase the parallelism of mappers so that each mapper has to handle a smaller size of data? Pig determines the number of mappers by total input size / pig.maxCombinedSplitSize (128MB by default). So you can try to lower pig.maxCombinedSplitSize.
But I admit Pig internal data types are not memory-efficient, and that is an optimization opportunity. Contribute!
On Thu, Feb 6, 2014 at 2:54 PM, praveenesh kumar <[EMAIL PROTECTED]>wrote:
But I am wondering if the above config changes would make any huge difference in my case. As per my logs, I am very worried about this line -
INFO org.apache.hadoop.mapred.MapTask: Record too large for in-memory buffer: 644245358 bytes
If I am understanding it properly, my 1 record is very large to fit into the memory, which is causing the issue. Any of the above changes wouldn't make any huge impact, please correct me if I am taking it totally wrong.
- Adding hadoop user group here as well, to throw some valuable inputs to understand the above question. Since I am doing a join on a grouped bag, do you think that might be the case ?
But if that is the issue, as far as I understand Bags in Pig are spillable, it shouldn't have given this issue.
I can't get rid of group by, Grouping by first should idealing improve my join. But if this is the root cause, if I am understanding it correctly,
do you think I should get rid of group-by.
But my question in that case would be what would happen if I do group by later after join, if will result in much bigger bag (because it would have more records after join)
Am I thinking here correctly ?
On Fri, Feb 7, 2014 at 3:11 AM, Cheolsoo Park <[EMAIL PROTECTED]> wrote:
You're thinking correctly, and it's true that Pig bags are spillable.
However, spilling is no magic, meaning you can still run into OOM with huge bags like you have here. Pig runs Spillable Memory Manager (SMM) in a separate thread. When spilling is triggered, SMM locks bags that it's trying to spill to disk. After the spilling is finished, GC frees up memory. The problem is that it's possible that more bags are loaded into memory while the spilling is in progress. Now JVM triggers GC, but GC cannot free up memory because SMM is locking the bags, resulting in OOM error. This happens quite often.
Sounds like you do group-by to reduce the number of rows before join and don't immediately run any aggregation function on the grouped bags. If that's the case, can you compress those bags? For eg, you could add a foreach after group-by and run a UDF that compresses a bag and returns it as bytearray. From there, you're moving around small blobs rather than big bags. Of course, you will need to decompress them when you restore data out of those bags at some point. This trick saved me several times in the past particularly when I dealt with bags of large chararrays.
Just a thought. Hope this is helpful.
Thanks, Cheolsoo On Fri, Feb 7, 2014 at 7:37 AM, praveenesh kumar <[EMAIL PROTECTED]>wrote:
Your explanation makes perfect sense in my case. Thanks for explaining what is happening behind the scenes. I am wondering you used normal java compression/decompression or is there a UDF already available to do this stuff or some kind of property that we need to enable to say to PIG that compress bags before spilling.
Regards Prav On Fri, Feb 7, 2014 at 4:37 PM, Cheolsoo Park <[EMAIL PROTECTED]> wrote:
It was actually zlib compression in Python. But you can certainly use any other compression lib. Unfortunately, this is not a built-in feature in Pig, but I agree that would be useful. On Fri, Feb 7, 2014 at 11:45 AM, praveenesh kumar <[EMAIL PROTECTED]>wrote:
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