WebFeb 26, 2024 · I was asked to diagnose and tune a long and complex ad-hoc Hive query that spent more than 4 hours on the reduce stage. The fetch from the map tasks and the merge phase completed fairly quickly (within 10 minutes) and the reducers spent most of their time iterating the input rows and performing the aggregations defined by the query – MIN, …
spark/ShuffleBlockFetcherIterator.scala at master - Github
WebSep 21, 2024 · With data loading in main process (DataLoader’s num_worker = 0) and opening hdf5 file once in __getitem__ : Batches per second: ~2. Still most of the time data is being loaded, ~90% of the profiling time. There is no overhead from opening the hdf5 file of course, that’s why larger proportion of time went to loading the data. Webthe code in TaskAttemptImpl indicate the Invalid event: TA_TOO_MANY_FETCH_FAILURE at SUCCESS_FINISHING_CONTAINER cause the job state turn into error; what i confused is what cause the appmater handle the TA_TOO_MANY_FETCH_FAILURE event on SUCCESS_FINISHING_CONTAINER,illegal event on this state. shoe stores in williamsburg va outlet mall
org.apache.tez.runtime.library.common.shuffle.orderedgrouped …
WebJVM would not be able to allocate more mem in the old-gen (~5.5 GB in this case with 8 GB JVM). This leads to the OOM. Easy option would be reduce "tez.runtime.shuffle.fetch.buffer.percent". With pipelinedshuffle this might not happen very frequently; Reason is that, with pipelined shuffle, data is sent to downstream vertex … WebAug 21, 2024 · A Fetch Failed Exception, reported in a shuffle reduce task, indicates the failure in reading of one or more shuffle blocks from the hosting executors. Debugging … WebNov 6, 2013 · Hi Chris, I'm aware of the potential problem of having a task that will consume a lot of memory. I've ran the same task on a java application, by reading the map file and running the function and it finished all records with out any memory issue. rachel salisbury diary