BigW Consortium Gitlab

  1. 16 Nov, 2017 1 commit
  2. 02 Nov, 2017 11 commits
  3. 23 Feb, 2017 1 commit
  4. 05 Jul, 2016 2 commits
  5. 03 Jun, 2016 2 commits
  6. 15 May, 2016 1 commit
  7. 18 Apr, 2016 1 commit
  8. 08 Apr, 2016 1 commit
  9. 12 Jan, 2016 1 commit
    • Stop tracking call stacks for instrumented views · 355c341f
      Yorick Peterse authored
      Where a vew is called from doesn't matter as much. We already know what
      action they belong to and this is more than enough information. By
      removing the file/line number from the list of tags we should also be
      able to reduce the number of series stored in InfluxDB.
  10. 07 Jan, 2016 1 commit
  11. 06 Jan, 2016 1 commit
  12. 04 Jan, 2016 1 commit
  13. 31 Dec, 2015 1 commit
    • Removed tracking of raw SQL queries · a6c60127
      Yorick Peterse authored
      This particular setup had 3 problems:
      
      1. Storing SQL queries as tags is very inefficient as InfluxDB ends up
         indexing every query (and they can get pretty large). Storing these
         as values instead means we can't always display the SQL as easily.
      2. We already instrument ActiveRecord query methods, thus we already
         have timing information about database queries.
      3. SQL obfuscation is difficult to get right and I'd rather not expose
         sensitive data by accident.
  14. 17 Dec, 2015 4 commits
    • Track location information as tags · 9f95ff0d
      Yorick Peterse authored
      This allows the information to be displayed when using certain functions
      (e.g. top()) as well as making it easier to aggregate on a per file
      basis.
    • Use custom code for instrumenting method calls · 1b077d2d
      Yorick Peterse authored
      The use of ActiveSupport would slow down instrumented method calls by
      about 180x due to:
      
      1. ActiveSupport itself not being the fastest thing on the planet
      2. caller_locations() having quite some overhead
      
      The use of caller_locations() has been removed because it's not _that_
      useful since we already know the full namespace of receivers and the
      names of the called methods.
      
      The use of ActiveSupport has been replaced with some custom code that's
      generated using eval() (which can be quite a bit faster than using
      define_method).
      
      This new setup results in instrumented methods only being about 35-40x
      slower (compared to non instrumented methods).
    • Use string evaluation for method instrumentation · b66a16c8
      Yorick Peterse authored
      This is faster than using define_method since we don't have to keep
      block bindings around.
    • Storing of application metrics in InfluxDB · 141e946c
      Yorick Peterse authored
      This adds the ability to write application metrics (e.g. SQL timings) to
      InfluxDB. These metrics can in turn be visualized using Grafana, or
      really anything else that can read from InfluxDB. These metrics can be
      used to track application performance over time, between different Ruby
      versions, different GitLab versions, etc.
      
      == Transaction Metrics
      
      Currently the following is tracked on a per transaction basis (a
      transaction is a Rails request or a single Sidekiq job):
      
      * Timings per query along with the raw (obfuscated) SQL and information
        about what file the query originated from.
      * Timings per view along with the path of the view and information about
        what file triggered the rendering process.
      * The duration of a request itself along with the controller/worker
        class and method name.
      * The duration of any instrumented method calls (more below).
      
      == Sampled Metrics
      
      Certain metrics can't be directly associated with a transaction. For
      example, a process' total memory usage is unrelated to any running
      transactions. While a transaction can result in the memory usage going
      up there's no accurate way to determine what transaction is to blame,
      this becomes especially problematic in multi-threaded environments.
      
      To solve this problem there's a separate thread that takes samples at a
      fixed interval. This thread (using the class Gitlab::Metrics::Sampler)
      currently tracks the following:
      
      * The process' total memory usage.
      * The number of file descriptors opened by the process.
      * The amount of Ruby objects (using ObjectSpace.count_objects).
      * GC statistics such as timings, heap slots, etc.
      
      The default/current interval is 15 seconds, any smaller interval might
      put too much pressure on InfluxDB (especially when running dozens of
      processes).
      
      == Method Instrumentation
      
      While currently not yet used methods can be instrumented to track how
      long they take to run. Unlike the likes of New Relic this doesn't
      require modifying the source code (e.g. including modules), it all
      happens from the outside. For example, to track `User.by_login` we'd add
      the following code somewhere in an initializer:
      
          Gitlab::Metrics::Instrumentation.
            instrument_method(User, :by_login)
      
      to instead instrument an instance method:
      
          Gitlab::Metrics::Instrumentation.
            instrument_instance_method(User, :save)
      
      Instrumentation for either all public model methods or a few crucial
      ones will be added in the near future, I simply haven't gotten to doing
      so just yet.
      
      == Configuration
      
      By default metrics are disabled. This means users don't have to bother
      setting anything up if they don't want to. Metrics can be enabled by
      editing one's gitlab.yml configuration file (see
      config/gitlab.yml.example for example settings).
      
      == Writing Data To InfluxDB
      
      Because InfluxDB is still a fairly young product I expect the worse.
      Data loss, unexpected reboots, the database not responding, you name it.
      Because of this data is _not_ written to InfluxDB directly, instead it's
      queued and processed by Sidekiq. This ensures that users won't notice
      anything when InfluxDB is giving trouble.
      
      The metrics worker can be started in a standalone manner as following:
      
          bundle exec sidekiq -q metrics
      
      The corresponding class is called MetricsWorker.