We have just overcame such issue. I assume you have enabled pre-emption on YARN. After that, a little bit of Hive tuning is kind of prerequisite for all users when enabling LLAP (interactive query component). Most common problem with starting interactive server is conflicts between YARN and Hive for resource usage. There are some main parameters which are mostly effective on starting (and running stable) LLAP: hive.llap.daemon.yarn.container.mb: Suggestion on Ambari is pretty clear: Total memory used by individual LLAP daemons (YARN Container size). This includes memory
for the cache as well as for the query execution. Should be larger than the sum of
the Daemon cache size and the daemon heap size, and should leave some headroom
after this (In most cases: cache size + heap size + headroom = Memory Per Daemon) We also tried to keep it slightly below yarn.scheduler.maximum-allocation-mb value of yarn config. This way we prevent resource conflict with YARN in basic level. llap_headroom_space : Maximum headroom reserved from the YARN container running LLAP daemons.
This is an upper limit used during automatic size calculations, and the actual
value may be lower. We left this as recommended value which is very low compared to others. It is about %6 of llap_heap_size llap_heap_size: LLAP Daemon Heap Size in MB. hive.llap.io.memory.size: The amount of memory reserved for Hive's optimized in-memory cache. In memory cache size should be %20 of llap heap size And below parameters are number of executors which are set in default according to number of CPU's num_llap_nodes_per_llap_daemons: Number of Node(s) on which Hive LLAP daemon runs.<br> We set this value to number of our nodes (nodemanagers / datanodes). hive.llap.daemon.num.executors: The number of fragments that a single LLAP daemon will run concurrently.
Usually, this will be the same as the number of available CPUs<br> We set this to %40 of number of CPUs hive.llap.io.threadpool.size: Specify the number of threads to use for low-level IO thread pool.<br> We also set this one with the same value of number of executors. After updating these values, you can save configs and start interactive server. Afterwards, you may need to update queue capacities on Yarn. If you are using Ambari, Yarn Queue Manager view is ideal as it provides ability to view and adjust the values visually. Here you should check if LLAP uses more resource over default queue. If you don't have YARN Queue Manager, you can adjust below parameters and variants of the for default queue. yarn.scheduler.capacity.root.llap.capacity=12.0 yarn.scheduler.capacity.root.llap.maximum-am-resource-percent=1 yarn.scheduler.capacity.root.llap.maximum-capacity=12.0
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