Hi BalaCan you share details of the profiles itself? It might be that the MongoDB storage plugin is translating the query into 100 mongo queries because of some (100?) specific filter criteria in the Drill query?JVM Heap usage fluctuation would indicate frequent object creation and garbage collection, probably by the Mongo storage plugin itself. Are you running the query through the WebUI or via JDBC? As long as you are not seeing any GC logs indicating a leak in the heap memory, the heap usage fluctuating is normal for your 6GB heap allocation.If you are using the WebUI or REST API, it is possible that there is overhead in Drill rendering the resultset that can cause higher heap and CPU usage.
On 10/18/2018 1:10:25 PM, Balasubramanian Naganathan <firstname.lastname@example.org> wrote:Hello,
We have tableau BI tool which is getting data from MongoDB using Apache
We are running drill on 5 nodes each having 8 core and 16 GB RAM, but they
are not running as a cluster. Each node is an individual instance. We have
a Load Balancer to load balance across these 5 nodes.
-Xms6G -Xmx6G -XX:MaxDirectMemorySize=13G -XX:ReservedCodeCacheSize=1024m
-Ddrill.exec.enable-epoll=false -Dproperty=value -Duser.timezone=UTC
-XX:+UseStringDeduplication -Dproperty=value -Duser.timezone=UTC
We see that apache drill is reaching 100% CPU when we run 30 queries per
second. All the queries are very simple queries without any aggregation.
Also each query in Apache drill is getting converted to 100 queries in
Mongo. 97% of the queries are find(1) and count() mongoDB Queries. Not sure
why they are triggered.
Also we tried adding "-XX:+UseStringDeduplication", JVM parameter, We saw
the load become uneven after adding this parameter. Are there any other JVM
parameters that we can add to improve drill performance?
Even though there are no calls to drill, the heap memory usage is
fluctuating. It goes up to around 70% (~4GB) and comes back to around 1GB.