![]() ![]() Val targetSchemaList = flatten(targetRawData.schema).map(r => r.dataType.toString).toList Val sourceSchemaList = flatten(sourceDataFrame.schema).map(r => r.dataType.toString).toList val sourceDataFrame = sourceDataFrame.toDF(lumns map(_.toLowerCase): _*) ![]() Val sourceDataFrame = spark.createDataFrame(sourceData,schema) Val sourceData = sourceRawCsvData.mapPartitionsWithIndex((index, element) => if (index = 0) element.drop(1) else element) Val headerColumns = sourceRawCsvData.first().split(",").to Println("Validating the table structure.") Val targetRawData = hc.sql("Select datetime,load_datetime,trim(source_bank) as source_bank,trim(emp_name) as emp_name,header_row_count, emp_hours from " + targetTableName) Println("Extracting target data from hive table " + targetTableName) Val sourceRawCsvData = sc.textFile(sourceDataLocation) Println("Extracting SAS source data from csv file location " + sourceDataLocation) Val sourceDataLocation = "s3a://rbspoc-sas/sas_valid_large.txt" val sourceDataLocation = "hdfs://localhost:9000/sourcec.txt" ![]() Val spark: SparkSession = SparkSession.builder().appName("Simple Application").config("spark.master", "local").getOrCreate() val conf = new SparkConf().setAppName("Simple Application") ![]() How can I debug and resolve the same? import .hive._ I am not sure if there is any memory leak or not. GC Overhead Limit 2209 Closed XiozTzu opened this issue on 13 comments XiozTzu commented on serge-rider added the question label on serge-rider added this to the 4.2. The error comes at Test 3 step sourceDataFrame.except(targetRawData).count > 0 I am not sure why I am getting this error. When I run the code below, I am getting GC overhead limit exceeded error. My data file is about 1.5GB and about 0.2 billion rows. If the Hive table is too large to fit into memory, the query can fail. As a proof of concept, consider the following Class, which reproduces the OutOfMemoryError: GC overhead limit exceeded with JDK 1.I am running the below code in spark to compare the data stored in a csv file and a hive table. The default behavior for Apache Hive joins is to load the entire contents of a table into memory so that a join can be performed without having to perform a Map/Reduce step. The throughput goal for the G1 GC is 90 percent application time and 10 percent garbage collection time. for example: java -Xm圆gģ) Also, if you don’t have memory leaks in your application, it is recommended to upgrade to a newer version of JDK which uses the G1GC algorithm. That should be also evident by clicking on the See Stracktrace link, which will print the stack trace of that Thread.Ģ) If you cannot find any memory leak, increase the heap size if current heap is not enough. If you click on the “Details” link you will see some more info about where the instances reside and why they are so big. Then, open the Heap Dump with Eclipse Mat and generate a Leak Suspect report as in this example:Īs you can see, MAT has found one leak suspect, which occupies 71% of application’s memory, taken by instances of class. To do that, include the following option in your JVM, so that an Heap dump will be created upon an Out of Memory error: -XX:+HeapDumpOnOutOfMemoryError for xlpath in excels : csvpath xlpath split join yadayda try: exception handling since we don't know the number of sheets for i in range ( 15 ): dynamic number of sheets df ( spark. This is meant to prevent applications from running for an extended period of time while making little or no progress reclaiming objects.īefore talking about the possible solution, it is worth to know that this feature can be disabled with the following option: java -XX:-UseGCOverheadLimitĭisabling this throttle however, will just postpone the memory issue that will turn soon into a “: Java heap space.”ġ) Check for Memory leaks with a memory profiling tool like Eclipse MAT ( ), Visual VM etc and fix any memory leaks. I only read in one excel at a time with a for loop. This exception is typically thrown because the amount of live data barely fits into the Java heap having little free space for new allocations. After a garbage collection, if the Java process is spending more than approximately 98% of its time doing garbage collection and if it is recovering less than 2% of the heap and has been doing so far the last 5 consecutive garbage collections, then a thrown. Let’see how to solve it.Īccording to the JDK Troubleshooting guide, the “ : GC overhead” limit exceeded indicates that the garbage collector is running all the time and Java program is making very slow progress. The error “ : GC overhead limit exceeded” is fairly common for old JDK (mostly JDK 1.6 and JDK 1.7). ![]()
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