Visibility for web developers

Transcript

1. Visibility for Web Developers Bruno Renié Django Island 2014

2. Working with ops & support staff Large infrastructure, lots of

   moving parts whoami: lead dev @ exoscale

3. People need to know what's going on, anytime Not everyone

   is a developer SSH, grep and tail not acceptable

4. Goal: visibility on infrastructure & apps

5. Errors Events Metrics

6. Errors

7. Easy: use sentry

8. Great tool for developers, but… People also need to know

   about things that are not broken

9. Events

10. Event == log call Timestamped information about something worth knowing

11. Easy to emit logs Searching on remote machines, not so

    much

12. Centralized logging Agent forwards logs to an aggregator Aggregator stores

    logs and provides a search interface

13. Aggregator Hosts logstash+elasticsearch kibana log shippers

14. Collecting logs rsyslog / syslog-ng logstash-forwarder (lumberjack) logstash

15. In Python logging.handlers.SyslogHandler

16. Centralizing isn't enough

17. A common way to log things import logging logger =

    logging.getLogger(__name__) logger.info("user {} logged in".format(user.email))

18. Not much information No structure

19. Structured logging Record = set of key-value pairs event="user logged

    in" user="test@example.com" ip="1.2.3.4"

20. In Python: pip install structlog Hynek Schlawack logger = structlog.get_logger()

    logger.info("user logged in", user="test@example.com" ip="1.2.3.4") {"event": "user.login", "user": "test@example.com", "host": "1.2.3.4"}

21. Contextual loggers log = logger.bind(user=request.user.email) log.info("payment", amount=120, currency="CHF") {"event": "payment",

    "user": "test@example.com", "amount": 120, "currency": "CHF"}

22. structlog JSON elasticsearch Rich data for building dashboards

23. Demo

24. Marketing: "Who signed up today?" Finance: "How much income in

    the past week?" Support: "What did this user do in the past 24 hours?"

25. Events Centralize logs Add structure Profit logstash, elasticsearch structlog kibana

26. Metrics

27. Time series data Continuous, regular intervals Big player: graphite ecosystem

    carbon whisper graphite

28. <identifier> <value> <timestamp> db1.load.midterm 4.6 1399842778 Measure at regular intervals,

    send using the carbon line protocol echo " … " | nc host port

29. Traditional metrics stack agents collectd, apps, scripts carbon whisper graphite

    ingests metrics stores metrics renders graphs

30. Counting and timing: statsd In-memory buffer Aggregates and flushes to

    carbon at regular intervals

31. In Python pystatsd django-statsd-mozilla from stastd import StastClient statsd =

    StatsClient() @statsd.timer("something") def expensive_computation(): # …

32. Graphing Graphite: rendering API (json / PNG / etc) Countless

    dashboard apps Grafana Graph-explorer etc

33. Demo

34. What about alerts? Riemann: metrics hub/proxy App server overloaded, full

    disk, etc Analyzes metrics computes aggregates notifies

35. Demo

36. we are hiring! Questions! @brutasse Image credits: http://en.wikipedia.org/wiki/Explosion

37. Resources Events Metrics structlog.org rsyslog logstash elasticsearch kibana carbon /

    whisper graphite-api grafana collectd statsd riemann.io bernhard

38. Hosted services (not exhaustive) Logging Metrics Loggly Papertrail Hosted Graphite

    Librato JSON / structured logging support?