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tracing library
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README.md

tomograph

A library to help distributed applications send trace information to metrics backends like Zipkin and Statsd.

Data Model

A request to a distributed application is modeled as a trace. Each trace consists of a set of spans, and a span is a set of notes.

Each span's extent is defined by its first and last notes. Any number of additional notes can be added in between -- for example in a handler for ERROR-level logging.

The tomograph data model is basically the Dapper/Zipkin data model. For translation to statsd, we emit the length of the span as a timer metric, and each note gets emitted individually as a counter metric.

For example, here is a basic client/server interaction. It is one trace, with two spans, each with two notes -- their beginning and end:

zipkin client server

This is the same data as it would be viewed in using the statsd backend with graphite:

graphite client server

Tracing Your Application

There are a few basic ways to add tracing to your application. The lowest level one is to call start, stop, and annotate yourself:

import tomograph

tomograph.start('my service', 'a query', '127.0.0.1', 80)
(...)
tomograph.annotate('something happened')
tomograph.tag('key', 'value')
(...)
tomograph.stop('a query')

Each start/stop pair defines a span. Spans can be arbitrarily nested using this interface as long they stay on a single thread: tomograph keeps the current span stack in thread local storage.

When continuing a trace from one thread to another, you must grab the trace token from tomograph and pass it:

token = tomograph.get_trace_info()
(...)
tomograph.start('my service', 'a query', '127.0.0.1', 80, token)
(...)

That will enable tomograph to connect all of the spans into one trace.

Helpers

There are some slightly higher level interfaces to help you add tracing. For HTTP, add_trace_info_header() will add an X-Trace-Info header to a dict on the client side, and start_http() will consume that header on the server side:

def traced_http_client(url, body, headers):
    tomograph.start('client', 'http request', socket.gethostname(), 0)
    tomograph.add_trace_info_header(headers)
    http_request(url, body, headers)
    tomograph.stop('http request')


def traced_http_server(request):
    tomograph.start_http('server', 'http response', request)
    (...)
    tomograph.stop('http response')

There's no need to call start and stop yourself -- you can use the @tomograph.traced decorator:

    @tomograph.traced('My Server', 'myfunc')
    def myfunc(yadda):
        dosomething()

For WSGI pipelines, there's the class tomograph.Middleware that will consume the X-Trace-Info header. It can be added to a paste pipeline like so:

[pipeline:foo]
pipeline = tomo foo bar baz...

[filter:tomo]
paste.filter_factory = tomograph:Middleware.factory
service_name = glance-registry

If you use SQL Alchemy in your application, there are some event listeners available that will trace SQL statement execution:

_ENGINE = sqlalchemy.create_engine(FLAGS.sql_connection, **engine_args)

sqlalchemy.event.listen(_ENGINE, 'before_execute', tomograph.before_execute('my app'))
sqlalchemy.event.listen(_ENGINE, 'after_execute', tomograph.after_execute('my app'))
sqlalchemy.event.listen(_ENGINE, 'dbapi_error', tomograph.dbapi_error('my app'))

Screenshots

Here is a slightly more involved example -- a glance image list command in Openstack. It uses SQL statement tracing and the tomograph middleware:

zipkin glance image list

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