Increase for more reducers. What is best value to keep memory size setting, so that i dont come across : Java heap space, Java Out of Memory problem. can any one suggest me TEz Calculates mappers and reducers. Entrance reducers should be removed when you see a bottleneck of bees at the hive entrance. Goal: Hive on Tez : How to identify the reused YARN containers Env: Hive 2.1 Tez 0.8 Solution: Tez can reuse YARN containers to improve the performance, because it saves the time to allocate a new YARN container. Hive.exec.max.dynamic.partitions: Maximum number of dynamic partitions allowed to be … In order to manually set the number of mappers in a Hive query when TEZ is the execution engine, the configuration `tez.grouping.split-count` can be used by either: Setting it when logged into the HIVE CLI. : MapR 4.1 Hbase 0.98 Redhat 5.5 Note: It’s also good to indicate details like: MapR 4.1 (reported) and MapR 4.0 (unreported but likely) Hive provides an alternative, SORT BY, that orders the data only within each reducer and performs a local ordering where each reducer’s output will be sorted. Decrease for fewer reducers. Environment E.g. So to put it all together Hive/ Tez estimates number of reducers using the following formula and then schedules the Tez DAG. The final parameter that determines the initial number of reducers is hive.exec.reducers.byte.per.reducer. Query ID… 0.25: tez.max.partition.factor: Increase for more reducers. If the number of dead bees inside the hive has increased, that might be a sign that their numbers are down, and they may be vulnerable to attack. 2.0: tez.shuffle-vertex-manager.min-task-parallelism: Set a value if reducer counts are too low, even if the tez.shuffle-vertex-manager.min-src-fraction property is already adjusted. Consider using a different execution engine (i.e. ORDER BY takes only single reducer to process the data which may take an unacceptably long time to execute for longer data sets. At which point, adding an entrance reducer may be the right choice. By default hive.exec.reducers.byte.per.reducer is set to 256MB, specifically 258998272 bytes. SET hive.exec.dynamic.partition.mode = nonstrict; Some other things are to be configured when using dynamic partitioning, like. If you meet performance issues or OOM issues on Tez, you may need to change the number of Map/Reduce tasks. According to the result, MR is quicker than Tez on creation, but slower than Tez on query, along with query condition’s increase, MR’s query performance became worse. Initially there will be no way for the user to set different numbers of reducers for each of the separate reduce stages. Better performance is traded for total ordering. If hive.input.format is set to “org.apache.hadoop.hive.ql.io.CombineHiveInputFormat” which is the default in newer version of Hive, Hive will also combine small files whose file size are smaller than mapreduce.input.fileinputformat.split.minsize, so the number of mappers will be reduced to reduce overhead of starting too many mappers. spark, tez) or using Hive 1.X releases. In other words, `set tez.grouping.split-count=4` will create four mappers; An entry in the `hive-site.xml` can be added Hive.exec.max.dynamic.partitions.pernode: Maximum number of partitions to be created in each mapper/reducer node. A colony lives or dies by efficiency. In most cases hive will determine the number of reducers by looking at the input size of a particular MR job. After I installed Tez, it's ok to run hive jobs via Tez, but when I changed engine to MR, I got below error: WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. The FORMULA. There is already a ticket (HIVE-3946) to address this shortcoming which can be used for both Tez and MR. My assumption is we cant set number of Mapper and reducer like MR 1.0, It is based on Settings like Yarn container size, Mapper minimum memory and maximum memory . Decrease for fewer reducers.