signalfx-instrumentation-tornado 1.2.0

OpenTracing support for Tornado applications

This package enables distributed tracing in Tornado projects via The OpenTracing Project. Once a production system contends with real concurrency or splits into many services, crucial (and formerly easy) tasks become difficult: user-facing latency optimization, root-cause analysis of backend errors, communication about distinct pieces of a now-distributed system, etc. Distributed tracing follows a request on its journey from inception to completion from mobile/browser all the way to the microservices.

As core services and libraries adopt OpenTracing, the application builder is no longer burdened with the task of adding basic tracing instrumentation to their own code. In this way, developers can build their applications with the tools they prefer and benefit from built-in tracing instrumentation. OpenTracing implementations exist for major distributed tracing systems and can be bound or swapped with a one-line configuration change.

If you want to learn more about the underlying python API, visit the python source code.

Installation

Run the following command:

$ pip install signalfx-instrumentation-tornado

Setting up Tracing for All Requests

In order to implement tracing in your system (for all the requests), add the following lines of code to your site’s Application constructor to enable tracing:

import tornado_opentracing
from tornado_opentracing.scope_managers import TornadoScopeManager

# Create your opentracing tracer using TornadoScopeManager for active Span handling.
tracer = SomeOpenTracingTracer(scope_manager=TornadoScopeManager())

# Initialize tracing before creating the Application object
tornado_opentracing.init_tracing()

# And either ONE of these possible values:

# 1. Specify a TornadoTracing object.
app = Application(
    ''' Other parameters here '''
    opentracing_tracing=tornado_opentracing.TornadoTracing(tracer),
)

# 2. Pass a module-level callable, invoked once,
# returning an opentracing compliant Tracer with optional parameters.
app = Application(
    ''' Other parameters here '''
    opentracing_tracer_callable='opentracing.mocktracer.MockTracer',
    opentracing_tracer_parameters={
        'scope_manager': tornado_opentracing.scope_managers.TornadoScopeManager(),
    },
)

It is possible to set additional settings, for advanced usage:

app = Application(
    ''' Other parameters here '''
    opentracing_tracing=tornado_opentracing.TornadoTracing(tracer),
    opentracing_trace_all=True, # defaults to True.
    opentracing_trace_client=True, # AsyncHTTPClient tracing, defaults to True
    opentracing_traced_attributes=['method'], # only valid if trace_all==True
    opentracing_start_span_cb=my_start_span_cb, # optional start Span callback.
)

Note: Valid request attributes to trace are listed here. When you trace an attribute, this means that created spans will have tags with the attribute name and the request’s value.

Tracing All Requests

In order to trace all requests, set opentracing_trace_all=True when creating Application (this is the default value). If you want to record any attributes (as tags) for all requests, then add them to opentracing_traced_attributes. For example, if you wanted to trace the uri and method, then set opentracing_traced_attributes = ['uri', 'method'].

opentracing_start_span_cb is a callback invoked after a new Span has been created, and it must have two parameters: the new Span and the request object.

Tracing requires init_tracing() to be called before Application is created (which will patch the RequestHandler, Application and other Tornado components).

Tracing Individual Requests

If you don’t want to trace all requests to your site, then you can use function decorators to trace individual functions. This can be done by managing a globally unique TornadoTracing object yourself, and adding the following lines of code to any get/post/put/delete function of your RequestHandler sub-classes:

tracing = TornadoTracing(some_opentracing_tracer)

class MyRequestHandler(tornado.web.RequestHandler):
    # put the decorator before @tornado.gen.coroutine, if used
    @tracing.trace(['uri', 'method']) # optionally pass a list of traced attributes
    def get(self):
        ... # do some stuff

This tracing usage doesn’t consume any opentracing_* setting defined in Application, and there is not need to call init_tracing.

The optional arguments allow for tracing of request attributes.

Tracing HTTP Client Requests

When tracing all requests, tracing for AsyncHTTPClient is enabled by default, but this can be disabled by setting opentracing_trace_client=False.

For applications tracing individual requests, or using only the http client (no tornado.web usage), client tracing can be enabled like this:

tornado_opentracing.init_client_tracing(some_opentracing_tracer)

init_client_tracing takes an OpenTracing-compatible tracer, and can optionally take a start_span_cb parameter as callback. Observe this call is not required when required when using trace_all with the init_tracing initialization.

Note: A current limitation of TornadoScopeManager prevents scheduling more than one coroutine with active Span at a time (see the Active Span Handling section below). And since it’s a common pattern to use AsyncHTTPClient to fetch multiple urls at a time, newly created Span for client requests will not be set as active through ScopeManager.

Active Span handling

For active Span handling and propagation, your Tracer should use tornado_opentracing.scope_managers.TornadoScopeManager. Tracing both all requests and individual requests will set up a proper stack context automatically, and the active Span will be propagated from parent coroutines to their children. In any other case, code needs to be run under tracer_stack_context() explicitly:

from tornado_opentracing.scope_managers import tracer_stack_context

with tracer_stack_context():
    ioloop.IOLoop.current().run_sync(main_func)

Note: Currently TornadoScopeManager does not support scheduling more than one coroutine setting the active Span at a time, as the given context is shared, and thus can be messed up:

@tornado.gen.coroutine
def child_coroutine(name, input_data):
    # Cannot set Span as active.
    # However, the parent active Span will still be set,
    # thus no need to specify it with child_of=
    with tracer.start_span('child-%s' % name) as span:
        ...

@tornado.gen.corotuine
def parent_coroutine():
    with tracer.start_active_span('parent'):
      a = child_coroutine('A', input_a)
      b = child_coroutine('B', input_b)
      yield [a, b]

Examples

Here is a simple example of a Tornado application that log all requests:

Other examples are included under the examples directrory.

Further Information

If you’re interested in learning more about the OpenTracing standard, please visit opentracing.io or join the mailing list. If you would like to implement OpenTracing in your project and need help, feel free to send us a note at [e-mail].