5-Scatterplots.ipynb



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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">store</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">HDFStore</span><span class="p">(</span><span class="s1">&#39;/Volumes/FreshBooks/data/store.h5&#39;</span><span class="p">)</span>
<span class="n">may07</span> <span class="o">=</span> <span class="n">store</span><span class="p">[</span><span class="s1">&#39;may07&#39;</span><span class="p">]</span>
<span class="n">may08</span> <span class="o">=</span> <span class="n">store</span><span class="p">[</span><span class="s1">&#39;may08&#39;</span><span class="p">]</span>
</pre></div>

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<h1 id="Is-there-a-correlation-between-response-time-and-response-size?">Is there a correlation between response time and response size?<a class="anchor-link" href="#Is-there-a-correlation-between-response-time-and-response-size?">&#182;</a></h1><p>We can see how queries that return more data take longer to run using the pylab <code>plot()</code> method:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Elapsed (s)&#39;</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Number of rows returned&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may07</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may07</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;r.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 7&#39;</span><span class="p">)</span>
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<pre>[&lt;matplotlib.lines.Line2D at 0x112325410&gt;]</pre>
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>
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<p>But that's a little generic, as it's looking at <em>all</em> requests. We can do better! And we can compare one day to another!</p>
<p>Let's start by selecting only a subset of the data. We do deployments around 9am, so we'll just look at requests that come in after release:</p>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">import</span> <span class="nn">pytz</span>
<span class="n">toronto</span> <span class="o">=</span> <span class="n">pytz</span><span class="o">.</span><span class="n">timezone</span><span class="p">(</span><span class="s1">&#39;America/Toronto&#39;</span><span class="p">)</span>
<span class="n">may07_0900</span> <span class="o">=</span> <span class="n">toronto</span><span class="o">.</span><span class="n">localize</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2012</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">astimezone</span><span class="p">(</span><span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">tzinfo</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">may08_0900</span> <span class="o">=</span> <span class="n">toronto</span><span class="o">.</span><span class="n">localize</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2012</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">astimezone</span><span class="p">(</span><span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">tzinfo</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
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<p>I deployed some interesting changes on the 8th, with the goal of improving performance for invoice-related requests. Let's start by getting the comparison data from the previous day:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Select only the requests that came in after 9am local time</span>
<span class="n">may07_after_0900</span> <span class="o">=</span> <span class="n">may07</span><span class="p">[</span><span class="n">may07</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="n">may07_0900</span><span class="p">]</span>
<span class="c1"># Select only the requests for invoice things</span>
<span class="n">may07_invoice_after_0900</span> <span class="o">=</span> <span class="n">may07_after_0900</span><span class="p">[</span><span class="n">may07_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;invoice_InvoiceResource&#39;</span><span class="p">]</span>
<span class="c1"># Note that Pandas stores the DatetimeIndex values as naïve datetimes in UTC!</span>
<span class="n">may07_invoice_after_0900</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
DatetimeIndex: 284115 entries, 2012-05-07 13:00:00 to 2012-05-08 03:59:59
Data columns:
request_id    284115  non-null values
port          284115  non-null values
accountid     284115  non-null values
userid        284115  non-null values
contactid     284115  non-null values
level         284115  non-null values
silo          284115  non-null values
method        284115  non-null values
rows          284115  non-null values
queries       284115  non-null values
query_time    284115  non-null values
elapsed       284115  non-null values
user          284115  non-null values
sys           284115  non-null values
dtypes: float64(2), int64(10), object(2)</pre>
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<p>Now we'll get the same range of data, but from after my code went live:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">may08_after_0900</span> <span class="o">=</span> <span class="n">may08</span><span class="p">[</span><span class="n">may08</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="n">may08_0900</span><span class="p">]</span>
<span class="n">may08_invoice_after_0900</span> <span class="o">=</span> <span class="n">may08_after_0900</span><span class="p">[</span><span class="n">may08_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;invoice_InvoiceResource&#39;</span><span class="p">]</span>
<span class="n">may08_invoice_after_0900</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
DatetimeIndex: 263552 entries, 2012-05-08 13:00:00 to 2012-05-09 03:59:59
Data columns:
request_id    263552  non-null values
port          263552  non-null values
accountid     263552  non-null values
userid        263552  non-null values
contactid     263552  non-null values
level         263552  non-null values
silo          263552  non-null values
method        263552  non-null values
rows          263552  non-null values
queries       263552  non-null values
query_time    263552  non-null values
elapsed       263552  non-null values
user          263552  non-null values
sys           263552  non-null values
dtypes: float64(2), int64(10), object(2)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Response size vs request time for invoices, comparing two days&#39;</span><span class="p">)</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Elapsed (s)&#39;</span><span class="p">)</span>
<span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>  <span class="c1"># There are some uninteresting outliners </span>
<span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Number of rows returned&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may07_invoice_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may07_invoice_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;r.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 7&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may08_invoice_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may08_invoice_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;b.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 8&#39;</span><span class="p">)</span>
<span class="n">legend</span><span class="p">()</span>
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<pre>&lt;matplotlib.legend.Legend at 0x1120b0e50&gt;</pre>
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QmCC
"
>
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<p>For comparison, I didn't change much about Estimates:</p>

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<div class="prompt input_prompt">In&nbsp;[7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">may07_estimate_after_0900</span> <span class="o">=</span> <span class="n">may07_after_0900</span><span class="p">[</span><span class="n">may07_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;estimate_EstimateResource&#39;</span><span class="p">]</span>
<span class="n">may08_estimate_after_0900</span> <span class="o">=</span> <span class="n">may08_after_0900</span><span class="p">[</span><span class="n">may08_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;estimate_EstimateResource&#39;</span><span class="p">]</span>
<span class="n">title</span><span class="p">(</span><span class="s1">&#39;Response size vs request time for estimates, comparing two days&#39;</span><span class="p">)</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Elapsed (s)&#39;</span><span class="p">)</span>
<span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Number of rows returned&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may07_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may07_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;r.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 7&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may08_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may08_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;b.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 8&#39;</span><span class="p">)</span>
<span class="n">legend</span><span class="p">()</span>
</pre></div>

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<pre>&lt;matplotlib.legend.Legend at 0x139680e50&gt;</pre>
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>
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<p>But I did change estimates week later! Let's see how they fare:</p>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">may14</span> <span class="o">=</span> <span class="n">store</span><span class="p">[</span><span class="s1">&#39;may14&#39;</span><span class="p">]</span>
<span class="n">may14_0900</span> <span class="o">=</span> <span class="n">toronto</span><span class="o">.</span><span class="n">localize</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2012</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">astimezone</span><span class="p">(</span><span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">tzinfo</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">may14_after_0900</span> <span class="o">=</span> <span class="n">may14</span><span class="p">[</span><span class="n">may14</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="n">may14_0900</span><span class="p">]</span>
<span class="n">may14_estimate_after_0900</span> <span class="o">=</span> <span class="n">may14_after_0900</span><span class="p">[</span><span class="n">may14_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;estimate_EstimateResource&#39;</span><span class="p">]</span>

<span class="n">may15</span> <span class="o">=</span> <span class="n">store</span><span class="p">[</span><span class="s1">&#39;may15&#39;</span><span class="p">]</span>
<span class="n">may15_0900</span> <span class="o">=</span> <span class="n">toronto</span><span class="o">.</span><span class="n">localize</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2012</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">astimezone</span><span class="p">(</span><span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">tzinfo</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">may15_after_0900</span> <span class="o">=</span> <span class="n">may15</span><span class="p">[</span><span class="n">may15</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="n">may15_0900</span><span class="p">]</span>
<span class="n">may15_estimate_after_0900</span> <span class="o">=</span> <span class="n">may15_after_0900</span><span class="p">[</span><span class="n">may15_after_0900</span><span class="p">[</span><span class="s1">&#39;silo&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;estimate_EstimateResource&#39;</span><span class="p">]</span>

<span class="n">title</span><span class="p">(</span><span class="s1">&#39;Response size vs request time for estimates, comparing two days&#39;</span><span class="p">)</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Elapsed (s)&#39;</span><span class="p">)</span>
<span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Number of rows returned&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may14_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may14_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;r.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 14&#39;</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">may15_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;elapsed&#39;</span><span class="p">],</span> <span class="n">may15_estimate_after_0900</span><span class="p">[</span><span class="s1">&#39;rows&#39;</span><span class="p">],</span> <span class="s1">&#39;b.&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;May 15&#39;</span><span class="p">)</span>
<span class="n">legend</span><span class="p">()</span>
</pre></div>

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<pre>&lt;matplotlib.legend.Legend at 0x1396cd750&gt;</pre>
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