4. Socorro
Very Large Array at Socorro, New Mexico, USA. Photo taken by Hajor, 08.Aug.2004. Released under cc.by.sa
and/or GFDL. Source: http://en.wikipedia.org/wiki/File:USA.NM.VeryLargeArray.02.jpg
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9. “Socorro has a lot of moving parts”
...
“I prefer to think of them as dancing parts”
11. Lifetime of a crash
• Raw crash submitted by user via POST (metadata json +
minidump)
• Collected to disk by collector (web.py WSGI app)
• Moved to HBase by crashmover
• Noticed in queue by monitor and assigned for processing
12. Processing
• Processor spins off minidumpstackwalk (MDSW)
• MDSW re-unites raw crash with symbols to generate a stack
• Processor generates a signature and pulls out other data
• Processor writes processed crash back to HBase and to
PostgreSQL
13. Back end processing
Large number of cron jobs, e.g.:
• Calculate aggregates: Top crashers by signature, URL,
domain
• Process incoming builds from ftp server
• Match known crashes to bugzilla bugs
• Duplicate detection
• Match up pairs of dumps (OOPP, content crashes, etc)
• Generate extracts (CSV) for engineers to analyze
14. Middleware
• Moving all data access to be through REST API (by end of
year)
• (Still some queries in webapp)
• Enable other front ends to data and us to rewrite webapp
using Django in 2012
• In upcoming version (late 2011, 2012) each component will
have its own API for status and health checks
15. Webapp
• Hard part here: how to visualize some of this data
• Example: nightly builds, moving to reporting in build time
rather than clock time
• Code a bit crufty: rewrite in 2012
• Currently KohanaPHP, will be (likely) Django
16. Implementation details
• Python 2.6 mostly (except PHP for the webapp)
• PostgreSQL9.1, some stored procs in pgpl/sql
• memcache for the webapp
• Thrift for HBase access
• HBase we use CDH3
18. A different type of scaling:
• Typical webapp: scale to millions of users without
degradation of response time
• Socorro: less than a hundred users, terabytes of data.
19. Basic law of scale still applies:
The bigger you get, the more spectacularly you fail
20. Some numbers
• At peak we receive 2300 crashes per minute
• 2.5 million per day
• Median crash size 150k, max size 20MB (reject bigger)
• ~110TB stored in HDFS (3x replication, ~40TB of HBase data)
21. What can we do?
• Does betaN have more (null signature) crashes than other
betas?
• Analyze differences between Flash versions x and y crashes
• Detect duplicate crashes
• Detect explosive crashes
• Find “frankeninstalls”
• Email victims of a malware-related crash
22. Implementation scale
• > 115 physical boxes (not cloud)
• Now up to 8 developers + sysadmins + QA + Hadoop ops/
analysts
• (Yes, hiring. mozilla.org/careers)
• Deploy approximately weekly but could do continuous if
needed
24. Development process
• Fork
• Hard to install: use a VM (more in a moment)
• Pull request with bugfix/feature
• Code review
• Lands on master
25. Development process -2
• Jenkins polls github master, picks up changes
• Jenkins runs tests, builds a package
• Package automatically picked up and pushed to dev
• Wanted changes merged to release branch
• Jenkins builds release branch, manual push to stage
• QA runs acceptance on stage (Selenium/Jenkins + manual)
• Manual push same build to production
26. Note “Manual” deployment =
• Run a single script with the build as
parameter
• Pushes it out to all machines and restarts
where needed
27. ABSOLUTELY CRITICAL
All the machinery for continuous deployment
even if you don’t want to deploy continuously
28. Configuration management
• Some releases involve a configuration change
• These are controlled and managed through Puppet
• Again, a single line to change config the same way every
time
• Config controlled the same way on dev and stage; tested the
same way; deployed the same way
29. Virtualization
• You don’t want to install HBase
• Use Vagrant to set up a virtual machine
• Use Jenkins to build a new Vagrant VM with each code build
• Use Puppet to configure the VM the same way as production
• Tricky part is still getting the right data
30. Virtualization - 2
• VM work at
github.com/rhelmer/socorro-vagrant
• Also pulled in as a submodule to socorro
31. Upcoming
• ElasticSearch implemented for better search including
faceted search, waiting on hardware to pref it on
• More analytics: automatic detection of explosive crashes,
malware, etc
• Better queueing: looking at Sagrada queue
• Grand Unified Configuration System
32. Everything is open (source)
Fork: https://github.com/mozilla/socorro
Read/file/fix bugs: https://bugzilla.mozilla.org/
Docs: http://www.readthedocs.org/docs/socorro
Mailing list: https://lists.mozilla.org/listinfo/tools-socorro
Join us in IRC: irc.mozilla.org #breakpad
