Thank you, I would expect it to work as you write, but I am probably experiencing it working other way. But now it seems that Spark is generally trying to fit everything to RAM. I run Spark on YARN and I have wraped this to another question: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-on-Yarn-probably-trying-to-load-all-the-data-to-RAM-td17908.html ______________________________________________________________
coalesce() is a streaming operation if used without the second parameter, it does not put all the data in RAM. If used with the second parameter (shuffle = true), then it performs a shuffle, but still does not put all the data in RAM. On Sat, Nov 1, 2014 at 12:09 PM, <jan.zi...@centrum.cz <jan.zi...@centrum.cz>> wrote: Now I am getting to problems using: distData = sc.textFile(sys.argv[2]).coalesce(10) The problem is that it seems that Spark is trying to put all the data to RAM first and then perform coalesce. Do you know if there is something that would do coalesce on fly with for example fixed size of the partition? Do you think that something like this is possible? Unfortunately I am not able to find anything like this in the Spark documentation. Thank you in advance for any advices or suggestions. Best regards, Jan ______________________________________________________________ Thank you very much lot of very small json files was exactly the speed performance problem, using coalesce makes my Spark program to run on single node only twice slower (even with starting Spark) than single node Python program, which is acceptable. Jan ______________________________________________________________ Because the overhead between JVM and Python, single task will be slower than your local Python scripts, but it's very easy to scale to many CPUs. Even one CPUs, it's not common that PySpark was 100 times slower. You have many small files, each file will be processed by a task, which will have about 100ms overhead (scheduled and executed), but the small file can be processed in your single thread Python script in less than 1ms. You could pack your json files into larger ones, or you could try to merge the small tasks into larger one by coalesce(N), such as: distData = sc.textFile(sys.argv[2]).coalesce(10) # which will have 10 partitons (tasks) Davies On Sat, Oct 18, 2014 at 12:07 PM, <jan.zi...@centrum.cz <jan.zi...@centrum.cz>> wrote:
Hi, I have program that I have for single computer (in Python) exection and also implemented the same for Spark. This program basically only reads .json from which it takes one field and saves it back. Using Spark my program runs aproximately 100 times slower on 1 master and 1 slave. So I would like to ask where possibly might be the problem? My Spark program looks like: sc = SparkContext(appName="Json data preprocessor") distData = sc.textFile(sys.argv[2]) json_extractor = JsonExtractor(sys.argv[1]) cleanedData = distData.flatMap(json_extractor.extract_json) cleanedData.saveAsTextFile(sys.argv[3]) JsonExtractor only selects the data from field that is given by sys.argv[1]. My data are basically many small one json files, where is one json per line. I have tried both, reading and writing the data from/to Amazon S3, local disc on all the machines. I would like to ask if there is something that I am missing or if Spark is supposed to be so slow in comparison with the local non parallelized single node program. Thank you in advance for any suggestions or hints. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org <user-unsubscr...@spark.apache.org> For additional commands, e-mail: user-h...@spark.apache.org <user-h...@spark.apache.org>
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