OSProfiler

OSProfiler is an OpenStack cross-project profiling library.

Background

OpenStack consists of multiple projects. Each project, in turn, has multiple services. To process some request, e.g. to boot a virtual machine, OpenStack uses multiple services from different projects. In case something works too slowly, it's extremely complicated to understand what exactly goes wrong and to locate the bottleneck.

To resolve this issue, we introduce a tiny but powerful library, osprofiler, that is going to be used by all OpenStack projects and their python clients. To be able to generate 1 trace per request, that goes through all involved services, and builds a tree of calls (see an example).

Why not cProfile and etc?

The scope of this library is quite different:

• We are interested in getting one trace of points from different service, not tracing all python calls inside one process
• This library should be easy integrable in OpenStack. It means that:
  • It shouldn't require too much changes in code bases of projects
  • We should be able to turn it off fully
  • We should be able to keep it turned on in lazy mode in production (e.g. admin should be able to "trace" on request)

OSprofiler API version 0.2.0

There are couple of things that you should know about API before learning it.

• 3 ways to add new trace point

  from osprofiler import profiler

  def some_func():

  profiler.start("point_name", {"any_info_about_point": "in_this_dict"}) # your code profiler.stop({"any_info_about_point": "in_this_dict"})

  @profiler.Trace("point_name", {"any_info_about_point": "in_this_dict"}) def some_func2(): pass

  def some_func3():

  with profiler.trace("point_name", {"any_info_about_point": "in_this_dict"}):

  # some code here

• How profiler works?

  • @profiler.Trace() and profiler.trace() are just syntax sugar, that just calls profiler.start() & profiler.stop() methods.

  • Every call of profiler.start() & profiler.stop() sends to collector 1 message. It means that every trace point crates 2 records in collector. (more about collector & records later)

  • Nested trace points are supported. Sample below, works and will produce 2 trace points:

    profiler.start("parent_point") profiler.start("child_point") profiler.stop() profiler.stop()

    Implementation in quite simple. Profiler has one stack that contains ids of all trace points. E.g.:

    profiler.start("parent_point") # trace_stack.push(<new_uuid>)

    # send to collector -> trace_stack[-2:]

    profiler.start("parent_point") # trace_stack.push(<new_uuid>)

    # send to collector -> trace_stack[-2:]

    profiler.stop() # send to collector -> trace_stack[-2:]

    # trace_stack.pop()

    profiler.stop() # send to collector -> trace_stack[-2:]

    # trace_stack.pop()

    it's simple to build a tree of nested trace points, having (pranet_id, point_id) of all trace points.

• What is actually send to to collector?

  Trace points contain 2 messages (start and stop). Messages like below are send to collector:

  { "name": <point_name>-(start|stop) "base_id": <uuid>, "parent_id": <uuid>, "trace_id": <uuid>, "info": <dict> }

  • base_id - <uuid> that is equal for all trace points that belongs

    to one trace, it is done to simplify process of retrieving all trace points related to one trace from collector

  • parent_id - <uuid> of parent trace point
  • trace_id - <uuid> of current trace point

  • info - it's dictionary that contains user information passed via calls of

    profiler start() & stop() methods.

• Setting up Collector.

  Profiler doesn't include trace points collector. End user should provide method that will send messages to collector. Let's take a look at trivial sample, where collector is just a file:

  import json

  from osprofiler import notifier

  f = open("traces", "a")

  def send_info_to_file_collector(info, context=None):

  f.write(json.dumps(info))

  notifier.set(send_info_to_file_collector)

  So now on every profiler.start() and profiler.stop() call we will write info about trace point to the end of traces file.

• Initialization of profiler.

  If profiler is not initialized, all calls of profiler.start() and profiler.stop() will be ignored.

  Initialization is quite simple procedure.

  from osprofiler import profiler

  profiler.init("SECRET_HMAC_KEY", base_id=<uuid>, parent_id=<uuid>)

  "SECRET_HMAC_KEY" - will be discussed later, cause it's related to the integration of OSprofiler & OpenStack.

  base_id and trace_id will be used to initialize stack_trace in profiler, e.g. stack_trace = [base_id, trace_id].

Integration with OpenStack

There are 4 topics related to integration OSprofiler & OpenStack:

• What we should use as a centralized collector

  We decided to use Ceilometer, because:

  • It's already integrated in OpenStack, so it's quite simple to send notifications to it from all projects.
  • There is a OpenStack API in Ceilometer that allows us to retrieve all messages related to one trace. Take a look at osprofiler.parsers.ceilometer:get_notifications

• How to setup profiler notifier

  We decided to use olso.messaging Notifier API, because:

  • oslo.messaging is integrated in all projects
  • It's the simplest way to send notification to Ceilometer, take a look at: osprofiler.notifiers.messaging.Messaging:notify method
  • We don't need to add any new CONF options in projects

• How to initialize profiler, to get one trace cross all services

  To enable cross service profiling we actually need to do send from caller to callee (base_id & trace_id). So callee will be able to init his profiler with these values.

  In case of OpenStack there are 2 kinds interaction between 2 services:

  • REST API

    It's well know that there are python clients for every projects, that generates proper HTTP request, and parses response to objects.

    These python clients are used in 2 cases:

    • User access OpenStack
    • Service from Project 1 would like to access Service from Project 2

    So what we need is to:

    • Put in python clients headers with trace info (if profiler is inited)
    • Add OSprofiler WSGI middleware to service, that will init profiler, if there are special trace headers.

    Actually the algorithm is a bit more complex. Python client sign trace info with passed to profiler.init HMAC key, and WSGI middleware checks that it's signed with HMAC that is specified in api-paste.ini. So only user that knows HMAC key in api-paste.ini can init properly profiler and send trace info that will be actually processed.

  • RPC API

    RPC calls are used for interaction between services of one project. It's well known that projects are using oslo.messaging to deal with RPC. So the best way to enable cross service tracing (inside of project). Is to add trace info to all messages (in case of inited profiler). And initialize profiler on callee side, if there is a trace info in message.

• What points should be by default tracked

  I think that for all projects we should include by default 3 kinds o points:

  • All HTTP calls
  • All RPC calls
  • All DB calls