Profiling Java application in Google Appengine Flex

There are a lot of ways java applications can be profiled for memory and CPU to find memory leaks or any culprit block of code where the app spends most of the time. In Google AppEngine Flex the application code (jar or war) runs inside a docker container which in turn runs inside a VM dedicated a service/module. While Google Cloud Profile can be used for analysing live applications it only supports CPU profiling when it comes to Java in AppEngine flex. Memory profiling is supported for Java only in AppEngine standard and python and go in AppEngine flex. So as of now, one has to profile the memory in JVM manually.

SSH to the AppEngine instance

If you have Editor role you can ssh to the specific appengine flex instance using gcloud utility. You can connect to the instance by clicking on the SSH button against the instance in the instances page in the appengine GCP console.
  1.  SSH to the instance
    $ gcloud --project "demoneil" app instances ssh "aef-default-20190225t133842-tlmz" --service "default" --version "20190225t133842"
  2. Find the docker processes.

    $ docker ps
            CONTAINER ID        IMAGE                                                                                                                            CONTAINER ID        IMAGE                                                                                                                            COMMAND                  CREATED             STATUS              PORTS                                                                  NAMES
    d388874ebe58   "/docker-entrypoin..."   41 minutes ago      Up 41 minutes>8080/tcp                                              gaeapp
    f01675bb33c8                                                                                                "/proxy"                 41 minutes ago      Up 41 minutes                                                                              api
    8ef572057a10                                                                                              "/var/lib/nginx/bi..."   42 minutes ago      Up 42 minutes       8080/tcp,>8443/tcp, 8090/tcp,>10402/tcp   nginx_proxy
    69fce23569a2                                                                                              "./start_iap_watch..."   42 minutes ago      Up 42 minutes                                                                              iap_watcher
    aca67e06d5ed                                                                                           "/opt/google-fluen..."   42 minutes ago      Up 42 minutes                                                                              fluentd_logger
  3. Get a shell for the docker container running your code. Usually, it has an image URL from and name as "gaeapp", like the first one here.
    $ docker exec -it gaeapp /bin/bash
  4. Find the process and change the user to the user running the jetty server, which is usually "jetty"
    root@d388874ebe58:/var/lib/jetty# ps -aux
    jetty        1  0.1 11.0 2509224 113236 ?      Ssl  08:13   0:05 java -showversion -agentpath:/opt/cdbg/,--alsologtostderr=tru
    root       187  0.0  0.3  18140  3268 ?        Ss   09:01   0:00 /bin/bash
    root       191  0.0  0.2  36636  2816 ?        R+   09:02   0:00 ps -aux
    $ su jetty
  5. Create a directory to write the heap dump as the current directory may not have write access.
    mkdir dump_dir.
    chmod 777 dump_dir
  6. Generate the heap dump for the process id
    jmap -dump:live,file=dump_dir/ 50
  7. Exit the container and copy the dump file from within the container to the VM
    docker cp gaeapp:/var/lib/jetty/dump_dir/ .
  8. Exit the VM and copy the file from the VM to the local machine
    gcloud app instances scp --service default --version 20190225t133842 aef-default-20190225t133842-tlmz:/home/neil/ .

Analyse the dump

There are several tools like VisualVM and Yorkit to analyse the heap dump file to find out where are the memory issues. I used something which is publicly hosted and good for small dump files.

Here is a sample report for the getting started the app.


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