# Integration

There are 4 topics related to integration OSprofiler & OpenStack:

## What we should use as a centralized collector?

We primarily 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 an OpenStack API in Ceilometer that allows us to retrieve all messages related to one trace. Take a look at osprofiler.drivers.ceilometer.Ceilometer:get_report

In OSProfiler starting with 1.4.0 version other options (MongoDB driver in 1.4.0 release, Elasticsearch driver added later, etc.) are also available.

## How to setup profiler notifier?

We primarily decided to use oslo.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.drivers.messaging.Messaging:notify method

- We don’t need to add any new CONF options in projects

In OSProfiler starting with 1.4.0 version other options (MongoDB driver in 1.4.0 release, Elasticsearch driver added later, etc.) are also available.

## How to initialize profiler, to get one trace across 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 its profiler with these values.

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

- REST API

  It’s well known that there are python clients for every project, that generate proper HTTP requests, and parse responses 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 your service, this initializes the profiler, if and only if there are special trace headers, that are signed by one of the HMAC keys from api-paste.ini (if multiple keys exist the signing process will continue to use the key that was accepted during validation).

    - The common items that are used to configure the middleware are the following (these can be provided when initializing the middleware object or when setting up the api-paste.ini file):

          hmac_keys = KEY1, KEY2 (can be a single key as well)

  Actually the algorithm is a bit more complex. The Python client will also sign the trace info with a HMAC key (lets call that key `A`) passed to profiler.init, and on reception the WSGI middleware will check that it’s signed with one of the HMAC keys (the wsgi server should have key `A` as well, but may also have keys `B` and `C`) that are specified in api-paste.ini. This ensures that only the user that knows the HMAC key `A` in api-paste.ini can init a profiler properly and send trace info that will be actually processed. This ensures that trace info that is sent in that does not pass the HMAC validation will be discarded. NOTE: The application of many possible validation keys makes it possible to roll out a key upgrade in a non-impactful manner (by adding a key into the list and rolling out that change and then removing the older key at some time in the future).

  - Optionally you can enable client tracing using requests, Currently only supported by OTLP driver, this will add client call tracing. see profiler/trace_requests’s option.

- 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. It’s very good, because projects deal with RPC in similar way.

  So there are 2 required changes:

  - On callee side put in request context trace info (if profiler was initialized)

  - On caller side initialize profiler, if there is trace info in request context.

  - Trace all methods of callee API (can be done via profiler.trace_cls).

## What points should be tracked by default?

I think that for all projects we should include by default 5 kinds of points:

- All HTTP calls - helps to get information about: what HTTP requests were done, duration of calls (latency of service), information about projects involved in request.

- All RPC calls - helps to understand duration of parts of request related to different services in one project. This information is essential to understand which service produce the bottleneck.

- All DB API calls - in some cases slow DB query can produce bottleneck. So it’s quite useful to track how much time request spend in DB layer.

- All driver calls - in case of nova, cinder and others we have vendor drivers. Duration

- ALL SQL requests (turned off by default, because it produce a lot of traffic)

this page last updated: 2026-03-27 00:00:00

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