Debugging ========= Django Debug Toolbar (DDT) ---------------- This is a useful tool for examining SQL queries, performance, headers, requests, signals, cache, logging, and more. To enable DDT, you need to set your `INTERNAL_IPS` to the IP address of your load balancer. This can be overriden in `local_settings`. This IP address can be found by making a GET to any page on the browsable API and looking for a like this in the standard output: ``` awx_1 | 14:42:08 uwsgi.1 | 172.18.0.1 GET /api/v2/tokens/ - HTTP/1.1 200 ``` Allow this IP address by adding it to the `INTERNAL_IPS` variable in `local_settings`, then navigate to the API and you should see DDT on the right side. If you don't see it, make sure to set `DEBUG=True`. > Note that enabling DDT is detrimental to the performance of AWX and adds overhead to every API request. It is recommended to keep this turned off when you are not using it. SQL Debugging ------------- AWX includes a powerful tool for tracking slow queries across all of its Python processes. As the AWX user, run: ``` $ awx-manage profile_sql --threshold 2 --minutes 5 ``` ...where threshold is the max query time in seconds, and minutes it the number of minutes to record. For the next five minutes (in this example), any AWX Python process that generates a SQL query that runs for >2s will be recorded in a `.sqlite` database in `/var/log/tower/profile`. This is a useful tool for logging all queries at a per-process level, or filtering and searching for queries within a certain code branch. For example, if you observed that certain HTTP requests were particularly slow, you could enable profiling, perform the slow request, and then search the log: ``` $ sqlite3 -column -header /var/log/tower/profile/uwsgi.sqlite sqlite> .schema queries CREATE TABLE queries ( id INTEGER PRIMARY KEY, version TEXT, # the AWX version pid INTEGER, # the pid of the process stamp DATETIME DEFAULT CURRENT_TIMESTAMP, argv REAL, # argv of the process time REAL, # time to run the query (in seconds) sql TEXT, # the actual query explain TEXT, # EXPLAIN VERBOSE ... of the query bt TEXT # python stack trace that led to the query running ); sqlite> SELECT time, sql FROM queries ORDER BY time DESC LIMIT 1; time sql ---------- --------------------------------------------------------------------------------------------- 0.046 UPDATE "django_session" SET "session_data" = 'XYZ', "expire_date" = '2019-02-15T21:56:45.693290+00:00'::timestamptz WHERE "django_session"."session_key" = 'we9dumywgju4fulaxz3oki58zpxgmd6t' ``` Remote Debugging ---------------- Python processes in Tower's development environment are kept running in the background via supervisord. As such, interacting with them via Python's standard `pdb.set_trace()` isn't possible. Bundled in our container environment is a remote debugging tool, `sdb`. You can use it to set remote breakpoints in Tower code and debug interactively over a telnet session: ```python # awx/main/tasks.py class SomeTask(awx.main.tasks.BaseTask): def run(self, pk, **kwargs): # This will set a breakpoint and open an interactive Python # debugger exposed on a random port between 7899-7999. The chosen # port will be reported as a warning in the Tower logs, e.g., # # [2017-01-30 22:26:04,366: WARNING/Worker-11] Remote Debugger:7900: Please telnet into 0.0.0.0 7900. # # You can access it from your host machine using telnet: # # $ telnet localhost import sdb sdb.set_trace() ``` Keep in mind that when you interactively debug in this way, any process that encounters a breakpoint will wait until an active client is established (it won't handle additional tasks) and concludes the debugging session with a `continue` command. To simplify remote debugging session management, Tower's development environment comes with tooling that can automatically discover open remote debugging sessions and automatically connect to them. From your *host* machine (*i.e.*, _outside_ of the development container), you can run: ``` sdb-listen ``` This will open a Python process that listens for new debugger sessions and automatically connects to them for you. Graph Jobs ---------- The `awx-manage graph_jobs` can be used to visualize how Jobs progress from pending to waiting to running. ``` awx-manage graph_jobs --help usage: awx-manage graph_jobs [-h] [--refresh REFRESH] [--width WIDTH] [--height HEIGHT] [--version] [-v {0,1,2,3}] [--settings SETTINGS] [--pythonpath PYTHONPATH] [--traceback] [--no-color] [--force-color] Plot pending, waiting, running jobs over time on the terminal optional arguments: -h, --help show this help message and exit --refresh REFRESH Time between refreshes of the graph and data in seconds (defaults to 1.0) --width WIDTH Width of the graph (defaults to 100) --height HEIGHT Height of the graph (defaults to 30) ``` Below is an example run with 200 Jobs flowing through the system. [![asciicast](https://asciinema.org/a/xnfzMQ30xWPdhwORiISz0wcEw.svg)](https://asciinema.org/a/xnfzMQ30xWPdhwORiISz0wcEw) ======= Code Instrumentation Profiling ------------------------------ Decorate a function to generate profiling data that will tell you the percentage of time spent in branches of a code path. This comes at an absolute performance cost. However, the relative numbers are still very helpful. Requirements for `dot_enabled=True` **Note:** The profiling code will run as if `dot_enabled=False` when `gprof2dot` package is not found ``` /var/lib/awx/venv/awx/bin/pip3 install gprof2dot ``` Below is the signature of the `@profile` decorator. ``` @profile(name, dest='/var/log/tower/profile', dot_enabled=True) ``` ``` from awx.main.utils.profiling import profile @profile(name="task_manager_profile") def task_manager(): ... ``` Now, invoke the function being profiled. Each run of the profiled function will result in a file output to `dest` containing the profile data summary as well as a dot graph if enabled. The profile data summary can be viewed in a text editor. The dot graph can be viewed using `xdot`. ``` bash-4.4$ ls -aln /var/log/tower/profile/ total 24 drwxr-xr-x 2 awx root 4096 Oct 15 13:23 . drwxrwxr-x 1 root root 4096 Oct 15 13:23 .. -rw-r--r-- 1 awx root 635 Oct 15 13:23 2.001s-task_manager_profile-2303-272858af-3bda-45ec-af9e-7067aa86e4f3.dot -rw-r--r-- 1 awx root 587 Oct 15 13:23 2.001s-task_manager_profile-2303-272858af-3bda-45ec-af9e-7067aa86e4f3.pstats -rw-r--r-- 1 awx root 632 Oct 15 13:23 2.002s-task_manager_profile-2303-4cdf4660-3ef4-4238-8164-33611822d9e3.dot -rw-r--r-- 1 awx root 587 Oct 15 13:23 2.002s-task_manager_profile-2303-4cdf4660-3ef4-4238-8164-33611822d9e3.pstats ``` ``` xdot /var/log/tower/profile/2.001s-task_manager_profile-2303-272858af-3bda-45ec-af9e-7067aa86e4f3.dot ``` Code Instrumentation Timing --------------------------- Similar to the profiling decorator, there is a timing decorator. This is useful when you do not want to incur the overhead of profiling and want to know the accurate absolute timing of a code path. Below is the signature of the `@timing` decorator. ``` @timing(name, dest='/var/log/tower/timing') ``` ``` from awx.main.utils.profiling import timing @timing(name="my_task_manager_timing") def task_manager(): ... ``` Now, invoke the function being timed. Each run of the timed function will result in a file output to `dest`. The timing data will be in the file name. ``` bash-4.4# ls -aln total 16 drwxr-xr-x 2 0 0 4096 Oct 20 12:43 . drwxrwxr-x 1 0 0 4096 Oct 20 12:43 .. -rw-r--r-- 1 0 0 61 Oct 20 12:43 2.002178-seconds-my_task_manager-ab720a2f-4624-47d0-b897-8549fe7e8c99.time -rw-r--r-- 1 0 0 60 Oct 20 12:43 2.00228-seconds-my_task_manager-e8a901be-9cdb-4ffc-a34a-a6bcb4266e7c.time ``` The class behind the decorator can also be used for profiling. ``` from awx.main.utils.profiling import AWXProfiler prof = AWXProfiler("hello_world") prof.start() ''' code to profile here ''' prof.stop() # Note that start() and stop() can be reused. An new profile file will be output. prof.start() ''' more code to profile ''' prof.stop() ```