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README.md

Mongotime

license CircleCI PyPI

Mongotime is a sampling-based profiler for Mongo DB. It identifies utilization patterns, including a high load of optimized queries - a class of usage that tools that focus exclusively on slow queries and operations often miss. By grouping operations Mongotime allows you to see how even very fast queries can be taking up a meaningful amount of Mongo's time and resources.

For example, imagine a scenario where your DB gets 2 500ms queries per second of one type and 5,000 10ms queries of another. Other tools highlight the slow queries, but any optimization will probably not improve the overall performance strain on Mongo; Mongotime, on the other hand, will help you identify, and maybe address, the 5k/sec of fast (probably already optimized) queries. Reducing or removing that load might have a greater overall impact on Mongo's performance.

Ex:

$ mongotime record --duration 30 recording.mtime  # take a 30 second recording of Mongo DB
...

$ mongotime analyze recording.mtime               # analyze that recording offline

%-of-active-time  %-of-time

client_host:
   50.00%  37.33% - 192.168.1.3
   25.00%  18.24% - 192.168.1.7
    0.99%   0.67% -

collection:
    49.50%  33.33% - users
    49.50%  33.33% - posts
     6.93%   4.67% - $cmd
     3.96%   2.67% -

db:
    99.01%  66.67% - prod
     3.96%   2.67% - test
     0.99%   0.67% - admin

op:
    93.07%  62.67% - update
    93.07%  62.67% - query
     8.91%   6.00% - command
     2.97%   2.00% - insert

user (custom grouping by value of a field in queries):
    14.07%   8.67% - u456
     8.91%   6.00% - u676
     2.97%   2.00% - u935
     0.99%   0.67% - u307

query_keys:
    97.03%  65.33% - {u'email': '*'}
    46.53%  31.33% - {u'_id': '*'}
     0.99%   0.67% - {}

Installing

You can use pip install mongotime, or clone the repo and install it with pip install -e ..

Using

Because mongotime needs to analyze data collected over a period of time, the usage is split into two phases: record & analyze. Recordings are saved to a file, which is analyzed separately without connecting to Mongo, so you can move that file around and run various analyses somewhere else, or at a later time.

Recording a Trace

$ mongotime record --help
Usage: mongotime record [OPTIONS] [RECORDING_FILE]

Options:
  --host TEXT             Location of Mongo host
  -i, --interval INTEGER  Sampling interval in ms
  -d, --duration INTEGER  Duration in sec to record
  --help                  Show this message and exit.

By default the recording continues until you stop it with <ctrl>+c, but you can predefine a duration like -d 30 (seconds). I recommend starting with a relatively short duration like 30 seconds, and increasing or decreasing if you want breadth or granularity.

Analyzing a Recording

$ mongotime analyze --help
Usage: mongotime analyze [OPTIONS] [RECORDING_FILE]

Options:
  --focus ASPECT              View items in just this aspect (category of
                              activity)
  --top INTEGER               View top N values in aspects, or 0 for all
  --aspect NAME PY_STATEMENT  Create an aspect from a name and a python
                              statement which when eval'd results in the
                              aspect value
  --filter PY_STATEMENT       Filter ops by this python statement returning
                              True
  --help                      Show this message and exit.

At the top of the output you'll see a summary, mainly useful to give a sense of the scope of the recording. Below that you'll see sections of "aspects" of what Mongo is spending its time on, with line items of the top things in that category taking up Mongo's time.

Aspects

Operations are grouped together into categories to show "what" the DB is doing. For example, the "collection" grouping will show you what % of time Mongo is spending on ops that are working in each collection shown. The "op" grouping shows how much time is spent on queries vs inserts vs other ops.

Understanding % Time Spent

The analysis shows how much time Mongo spends on activities by % activity, like cpu utilization. A value of 50 for % of time indicates that for half of the recorded timespan, Mongo was actively working on at least one operation in that grouping. Note: percentages may add to up to greater than 100 - this is because Mongo can simultaneously be working on different operations, and an operation can fall into multiple groupings.

Filtering (viewing intersecting aspects)

You can filter ops that go into an analysis as a way of focusing on some slice of activity. For ex, you might want to know what collections a particular client is accessing most, or how much time one collection is spending writing vs reading.

A filter can be specified on the command line as a python statement to be evaluated for each op, where the symbols available are the aspect names (shown as the groupings of rows in an analysis), and their values are the ones shown on rows under an aspect grouping. For ex, client is a variable an op has, and "127.0.0.1:5185" might be a value. Here are some examples:

  • --filter 'client == "127.0.0.1:57185"'
  • --filter 'client != "127.0.0.1:57185"'
  • --filter 'op == "update" and db != "users"'

NOTE: during analysis mongotime does not connect to MongoDB.

Custom Aspects

There's support for this, but I don't like its usability at the moment. If you want to use this feature, I'd love to hear from you.

Feedback / Future Development

Got any feedback? Wanna help? Get at me in the Issues or heewa.b@gmail.com!

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See what Mongo DB is spending time on, even if it's very fast, but frequently running operations

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