Profiling OpenStruct, Eager Loading, Method Missing, and Lazy Loading

I was recently working on a gem that involved marshaling data from a remote API. I really wanted the gem to behave like a native Ruby object, but they methods would vary depending on the response. Since the data was dynamic, it would have been counter-productive and not scalable to define each of the methods individually.

As such, I thought of a few different ways to get the result I wanted, but that just raised more questions, mainly performance. How would each of these methods perform? This was especially important given the large number of queries I would be making. In this blog post, I detail the exploration of these different methods and arrive at some pretty cool conclusions.

The Scenario

In this scenario, we will be using the result of a Github API call to my profile. The hash (as follows) will be stored and referenced in the @store variable:

  'has_wiki' => false,
  'has_issues' => false,
  'forks' => 0,
  'open_issues' => 0,
  'language' => 'Ruby',
  'description' => 'A distributed build system for the open source community.',
  'svn_url' => '',
  'pushed_at' => '2012-06-16T17:32:35Z',
  'full_name' => 'sethvargo/travis-ci',
  'git_url' => 'git://',
  'created_at' => '2012-06-16T17:00:29Z',
  'url' => '',
  'has_downloads' => true,
  'watchers' => 1,
  'size' => 188,
  'homepage' => '',
  'clone_url' => '',
  'ssh_url' => '',
  'html_url' => '',
  'updated_at' => '2012-06-16T17:32:35Z',
  'owner' => {
    'avatar_url' => '',
    'login' => 'sethvargo',
    'gravatar_id' => '87f282c6c2cdad13100dffe8c1daf77d',
    'url' => '',
    'id' => 408570
  'name' => 'travis-ci'

The Metrics

Because of the nature of my curiosity, we will measure on three different metrics:

Pure Hash

The Pure Hash is our baseline. This is without any magic. We are just using the straight-forward bracket-notation for accessing a value in the hash:



The OpenStruct is a Ruby library that allows builds methods from a given hash.

OpenStruct certainly has advantages, being only two lines of code. It also returns nil instead of raising an exception when calling a "method" that doesn't exist.

require 'ostruct'
class FiddleOpenStruct < OpenStruct; end

Eager Loading

With Eager Loading, we parse the data on initial object creation and define methods for each of the objects in the hash. This is obviously expensive on the object creation, but should be faster for subsequent calls.

Eager Loading will raise an exception when calling a method that does not exist, although this could be overridden with method_missing.

class FiddleEagerLoading
  def initialize(hash)
    hash.each do |key,value|
      define_singleton_method(key.to_s){ hash[key] }


Using method_missing, we "proxy" the requests to their associated keys in the hash store. The biggest problem with method_missing is performance, and that fact is very well-documented. Every call must go through the entire object stack before it hits method_missing, so we can except this method to not perform well.

With method_missing, we can either call super (raise an exception) or end with nil and any non-existent methods will return nil, just like OpenStruct.

class FiddleMethodMissing
  def initialize(hash)
    @hash = hash

  def method_missing(m, *args, &block)
    @hash[m.to_s].nil? ? super : @hash[m.to_s]

Lazy Loading

A hybrid of Eager Loading and method_missing, in Lazy Loading, we dynamically create methods as they are requested in method_missing. On an initial request, no methods exist, so we hit method_missing. However, method_missing defines a method. On a subsequent call, we don't execute the entire stack and call the newly created method (meta-programming for the win)!

class FiddleLazyLoading
  def initialize(hash)
    @hash = hash

  def method_missing(m, *args, &block)
    unless @hash[m.to_s].nil?
      value = @hash[m.to_s]
      define_singleton_method(m.to_s){ value }
      return value

The Test Suite

This test suite is very simplistic, coming in under 50 lines of code. You can see we are testing the three different metrics with appropriate puts statements to differentiate the output:

@methods = @store.keys
@n = 10000

puts "\n\n"
puts 'Creating the Object' do |x|'pure hash:')      { @n.times{ @store } }'open struct:')    { @n.times{ } }'eager loading:')  { @n.times{ } }'method_missing:') { @n.times{ } }'lazy loading:')   { @n.times{ } }

puts "\n\n"
puts 'First method call' do |x|'pure hash:')      { @n.times{ @methods.each{|m| @store[m] } } }'open struct:')    { @n.times{ @methods.each{|m| } } }'eager loading:')  { @n.times{ @methods.each{|m| } } }'method_missing:') { @n.times{ @methods.each{|m| } } }'lazy loading:')   { @n.times{ @methods.each{|m| } } }

puts "\n\n"
puts "[2..#{@n}] times"
@fiddle_open_struct =
@fiddle_eager_loading =
@fiddle_method_missing =
@fiddle_lazy_loading =

@methods.each do |m|
end do |x|'pure hash:')      { @n.times{ @methods.each{|m| @store[m] } } }'open struct:')    { @n.times{ @methods.each{|m| @fiddle_open_struct.send(m.to_sym) } } }'eager loading:')  { @n.times{ @methods.each{|m| @fiddle_eager_loading.send(m.to_sym) } } }'method_missing:') { @n.times{ @methods.each{|m| @fiddle_method_missing.send(m.to_sym) } } }'lazy loading:')   { @n.times{ @methods.each{|m| @fiddle_lazy_loading.send(m.to_sym) } } }


And the moment you have been waiting for - the results:

Creating the Object
                      user     system      total        real
pure hash:        0.000000   0.000000   0.000000 (  0.000515)
open struct:      1.440000   0.000000   1.440000 (  1.440354)
eager loading:    0.470000   0.000000   0.470000 (  0.468859)
method_missing:   0.000000   0.000000   0.000000 (  0.003673)
lazy loading:     0.000000   0.000000   0.000000 (  0.002815)

First method call
                      user     system      total        real
pure hash:        0.030000   0.000000   0.030000 (  0.021060)
open struct:     32.720000   0.010000  32.730000 ( 32.740407)
eager loading:   10.470000   0.030000  10.500000 ( 10.496159)
method_missing:   0.270000   0.000000   0.270000 (  0.261547)
lazy loading:     0.970000   0.000000   0.970000 (  0.976031)

[2..10000] times
                      user     system      total        real
pure hash:        0.020000   0.000000   0.020000 (  0.021658)
open struct:      0.080000   0.000000   0.080000 (  0.077467)
eager loading:    0.080000   0.000000   0.080000 (  0.081599)
method_missing:   0.190000   0.000000   0.190000 (  0.191151)
lazy loading:     0.080000   0.000000   0.080000 (  0.071562)

As expected, the pure hash (base) out-performed all other methods. In creating the initial object, the OpenStruct performed very poorly at over a second. Furthermore, as you might expect, eager loading took about half a second. The rest of the methods had negligible results. On the first method call, the OpenStruct took over 30 seconds, and eager loading took about 10 seconds. We also see that lazy loading began pulling ahead of method_missing. Finally, in subsequent requests (after initial object creation and first method call), method_missing performed the poorest.

Here are some observations to help you visualize the results:

Creating Initial Object

                       0        10       100      1000     10000
pure hash       0.000007  0.000003  0.000008  0.000052  0.000514
open struct     0.000002  0.001660  0.014470  0.147943  1.507701
eager loading   0.000002  0.000406  0.004800  0.046868  0.482254
method_missing  0.000003  0.000007  0.000029  0.000269  0.003883
lazy loading    0.000002  0.000007  0.000029  0.000535  0.002656

Clearly the OpenStruct took a significant amount of time, measuring over 4x its closest competitor. Ignoring the OpenStruct outlier, eager loading, as expected, took longer than all other methods. This is because it generates all those methods on each object creation. Comparing the other results, we can conclude the following:

First Method Call

                       0        10       100      1000      10000
pure hash       0.000008  0.000026  0.000236  0.002248  0.0210930
open struct     0.000002  0.032629  0.328859  3.340419  33.446652
eager loading   0.000003  0.011754  0.103952  1.072228  10.856142
method_missing  0.000002  0.000329  0.003961  0.027261  0.2574720
lazy loading    0.000002  0.000968  0.009337  0.100895  1.0136820

Again we see that OpenStruct performs poorly, taking over 30 seconds to process 10,000 first method calls. method_missing surprisingly performed very well. Lazy loading doesn't have a chance to shine here because we are only calling the method once (thus essentially using method_missing). The extra time is from actually defining the method. With this data, we can conclude the following:

Subsequent Method Calls

                       0        10       100      1000     10000    100000    1000000    10000000
pure hash       0.000008  0.000027  0.000229  0.002292  0.021919  0.210670  2.1555960   21.669286
open struct     0.000003  0.000090  0.000825  0.008313  0.078974  0.781603  7.9774830   76.648551
eager loading   0.000002  0.000093  0.000878  0.008558  0.084123  0.839965  8.5130510   80.623932
method_missing  0.000002  0.000217  0.001972  0.020136  0.186405  1.916927  19.288969  188.095461
lazy loading    0.000002  0.000081  0.000754  0.007333  0.072564  0.737112  7.4007320   72.202056

In drawing conclusions from this data, it's more important to look at how the data is scaling, rather than the factors themselves. In the long run, lazy loading performed the best, but OpenStruct is a close second. It turns out OpenStruct isn't that bad when making a lot of queries. method_missing performed very poorly.

Final Conclusions

Each of the methods we analyzed provide different benefits under different circumstances. It's impossible to say "use method x, because it's better". One of the reasons I chose to analyze multiple use cases is because it illustrates the fact that there is no "silver bullet" answer to this common problem.

If you're in irb and need to marshall a hash into an object, OpenStruct is the easiest and fastest route. Similarly, if you are creating an object that receives thousands of requests, you may want to implement lazy loading. There is not easy answer, but hopefully these statistics and benchmarks help in your next project.

Known Caveats

These methods are purely for example and fails many edge cases.

  1. These methods require special cases when your object returns data that is actually nil or null. It could raise an exception instead of actually returning nil as expected.
  2. Allowing methods to by dynamically called opens you up to a world of hurt - especially if your data's keys correspond to existing Ruby methods. As an example, when writing this blog post, my original hash had a key named fork. When I was actually sending fork, it was calling the Ruby Kernel.fork method, which really threw me for a loop.
  3. I neglected many qualitative metrics, such as ease of use, implementation time, and readability, when conducting this study. Those metrics, by definition, are extremely difficult to measure.

by Seth Vargo