CloudServices/Sagrada/Metlog: Difference between revisions

The '''Metrics''' project is part of [[Services/Sagrada|Project Sagrada]], providing a service for applications to capture and inject arbitrary data into a back end storage suitable for out-of-band analytics and processing.

The first version of the Metrics system will focus on providing an easy mechanism for the [[Services/Sync| Sync]] and [https://browserid.org/ BrowserID] projects (and any other internal Mozilla services) to efficiently send profiling data and any other arbitrary metrics information that may be desired into one or more backend storage locations.  Once the data has made it to its final destination, there should be available to those w/ appropriate access the ability to do analytics queries and report generation on the accumulated data.

The proposed Services Metrics architecture will consist of 3 layers:

; router : The router is what will be listening for the messages sent out by the provided libraries.  It will deserialize these messages and examine the metadata to determine the appropriate back end(s) to which the message should be delivered.  The format and protocol for delivering these messages to the endpoints will vary from back end to back end.  We plan on using [http://logstash.net/ logstash] as the message router, because it is already planned to be installed on every services server machine, and it is built specifically for this type of event-based messager routing.

* [https://github.com/etsy/statsd statsd]: '''(Phase 1)''' statsd is already in the pipeline to be running on every Services machine

* [https://github.com/mozilla-metrics/bagheera Bagheera]: '''(Phase 1)''' Bagheera is a REST service provided by the Mozilla Metrics team that will insert data into the Metrics team's Hadoop infrastructure, available for later processing.

* [https://github.com/dcramer/django-sentry Sentry]: '''(Phase 1)''' Sentry is an exception logging infrastructure that provides useful debugging tools to service app developers.  Sentry is not yet planned on being provided by any Mozilla operations team, using it would require buy-in from and coordination with a Mozilla internal service provider (probably the Services Ops team).

* [http://esper.codehaus.org/ Esper]: '''(Phase 2)''' System for "complex event processing", i.e. one which will watch various statistic streams in real time looking for anomalous behavior.

* [http://opentsdb.net/ OpenTSDB] '''(Phase 2)''' A "Time Series Database" providing fine grained real time monitoring and graphing.

* [http://www.arcsight.com/products/products-esm/ ArcSight ESM] '''(Phase 2)''' A "security correlation engine" already in use throughout the Mozilla organization.

== Proposed API ==

The atomic unit for the Services Metrics system is the "message". The structure of a message is inspired by that of the well known syslog message standard, with some slight extensions to allow for more rich metadata. Each message will consist of the following fields:

* ''env_version'': API version number of the "message envelope", i.e. any changes to the message data structure (exclusive of message-type-specific changes that may be embedded within the fields or the payload) must increment the env_version value. The structure described in this document is envelope version 0.8.

We will provide a "metlog" library that will both ease generation of these messages and that will handle packaging them up and delivering them into the message processing infrastructure.  Implementations of this library will likely be available in both Python and Javascript, but the Python library will be available first and this document will, for now, only describe the Python API. The Javascript API will be similar, modulo syntactic sugar that is available in Python but not in JS (e.g. decorators, context managers), and will be documented in detail in the future.  The proposed Python API is as follows:

 

; '''MetlogClient(sender, logger="", severity=6)''' : Primary metlog client class which can accept metlog messages, format and serialize them, and pass them along for delivery to the message processor.

 

* ''sender'': A "sender" object, i.e. any object that exposes a ''send_message'' method accepting a single ''msg'' argument.  All actual message transport and delivery is the responsibility of the sender.  We will provide a ZeroMQ Publisher sender implementation.

* ''logger'': Default for all subsequent ''metlog'' calls which do not explicitly pass this value.

* ''severity'': Default for all subsequent ''metlog'' calls which do not explicitly pass this value.

 

; '''MetlogClient.metlog(type, timestamp=None, logger=None, severity=None, message="", fields=None)''' : Sends a single log message along to the metlog listener(s). Most of the arguments correspond to the message fields described above.  Only ''type'' is strictly required, the rest will be populated by reasonable defaults if they aren't provided:

 

 

 

* ''name'': A required string label for the timer that will be added to the message fields

* ''rate'' Represents what fraction of these invocations should actually be timed; a value of 0.3 would mean that the code would be timed and the results sent off approximately 30% of the time it was executed

 

; '''MetlogClient.incr(name, timestamp=None, logger=None, severity=None, fields=None)''' : Sends an "increment counter" message to metlog.  ''name'' is a required string label for the counter that will be added to the message metadata.

 

The Python framework that underlies the Services Apps will be annotated w/ ''timer'' calls to automatically generate performance metrics for such key activities as authentication and execution of the actual view callable.  The sample rate of these calls will be able to be specified in the app configuration, where a value of 0 can be entered to turn off the timers altogether.  These will ultimately feed into a [https://github.com/etsy/statsd statsd] / [http://graphite.wikidot.com/ graphite] back end provided by Services Ops, where app owners will be able to see graphs of the captured data.

Any service app will have the ability to easily generate arbitrary message data and metadata for delivery into the services metrics system.  Any messages not specifically recognized as being intended for statsd or sentry will be delivered to a Hadoop cluster provided by the Metrics team, allowing for later analysis via custom map-reduce jobs or [https://hive.apache.org/ Hive] queries.