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async-web-server.html
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async-web-server.html
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Basic Examples
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Dask Arrays
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Xarray with Dask Arrays
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DataFrames: Read and Write Data
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DataFrames: Groupby
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Gotcha’s from Pandas to Dask
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DataFrames: Reading in messy data
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Machine Learning
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Blockwise Ensemble Methods
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Scale Scikit-Learn for Small Data Problems
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Score and Predict Large Datasets
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Batch Prediction with PyTorch
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Train Models on Large Datasets
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Incrementally Train Large Datasets
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Text Vectorization Pipeline
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Hyperparameter optimization with Dask
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Scale XGBoost
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Use Voting Classifiers
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Automate Machine Learning with TPOT
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Generalized Linear Models
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Singular Value Decomposition
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Applications
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Analyze web-hosted JSON data
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Async/Await and Non-Blocking Execution
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Asynchronous Computation: Web Servers + Dask
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Embarrassingly parallel Workloads
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Handle Evolving Workflows
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Image Processing
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ETL Pipelines with Prefect
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Stencil Computations with Numba
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Time Series Forecasting
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User Surveys
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2021 Dask User Survey Results
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2020 Dask User Survey Results
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2019 Dask User Survey Results
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Speed
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Timings
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Blocking async
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Discussion
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Asynchronous off-process computation with Dask
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Performance changes
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Parallel computing
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Asynchronous computing
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<h1>Asynchronous Computation: Web Servers + Dask</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#Speed">
Speed
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#Timings">
Timings
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#Blocking-async">
Blocking async
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Discussion
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<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#Asynchronous-off-process-computation-with-Dask">
Asynchronous off-process computation with Dask
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<a class="reference internal nav-link" href="#Performance-changes">
Performance changes
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<a class="reference internal nav-link" href="#Parallel-computing">
Parallel computing
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<a class="reference internal nav-link" href="#Asynchronous-computing">
Asynchronous computing
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<p class="admonition-title">Live Notebook</p>
<p>You can run this notebook in a <a class="reference external" href="https://mybinder.org/v2/gh/dask/dask-examples/main?urlpath=lab/tree/applications/async-web-server.ipynb">live session</a> <a class="reference external" href="https://mybinder.org/v2/gh/dask/dask-examples/main?urlpath=lab/tree/applications/async-web-server.ipynb"><img alt="Binder" src="https://static.mybinder.org/badge_logo.svg" /></a> or view it <a class="reference external" href="https://github.com/dask/dask-examples/blob/main/applications/async-web-server.ipynb">on Github</a>.</p>
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<p><img alt="b31daa0c7cd64076b553c0869e7eeb5e" class="no-scaled-link" src="http://dask.pydata.org/en/latest/_images/dask_horizontal.svg" width="30%" /></p>
<div class="section" id="Asynchronous-Computation:-Web-Servers-+-Dask">
<h1>Asynchronous Computation: Web Servers + Dask<a class="headerlink" href="#Asynchronous-Computation:-Web-Servers-+-Dask" title="Permalink to this headline">¶</a></h1>
<p>Lets imagine a simple web server that serves both fast-loading pages and also performs some computation on slower loading pages. In our case this will be a simple Fibonnaci serving application, but you can imagine replacing the <code class="docutils literal notranslate"><span class="pre">fib</span></code> function for running a machine learning model on some input data, fetching results from a database, etc..</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tornado.ioloop</span>
<span class="kn">import</span> <span class="nn">tornado.web</span>
<span class="k">def</span> <span class="nf">fib</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">return</span> <span class="n">n</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">fib</span><span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">fib</span><span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">FibHandler</span><span class="p">(</span><span class="n">tornado</span><span class="o">.</span><span class="n">web</span><span class="o">.</span><span class="n">RequestHandler</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">fib</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
<span class="k">class</span> <span class="nc">FastHandler</span><span class="p">(</span><span class="n">tornado</span><span class="o">.</span><span class="n">web</span><span class="o">.</span><span class="n">RequestHandler</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"Hello!"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">make_app</span><span class="p">():</span>
<span class="k">return</span> <span class="n">tornado</span><span class="o">.</span><span class="n">web</span><span class="o">.</span><span class="n">Application</span><span class="p">([</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"/fast"</span><span class="p">,</span> <span class="n">FastHandler</span><span class="p">),</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"/fib/(\d+)"</span><span class="p">,</span> <span class="n">FibHandler</span><span class="p">),</span>
<span class="p">])</span>
<span class="n">app</span> <span class="o">=</span> <span class="n">make_app</span><span class="p">()</span>
<span class="n">app</span><span class="o">.</span><span class="n">listen</span><span class="p">(</span><span class="mi">8000</span><span class="p">)</span>
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<tornado.httpserver.HTTPServer at 0x7f82ccab3040>
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<div class="section" id="Speed">
<h2>Speed<a class="headerlink" href="#Speed" title="Permalink to this headline">¶</a></h2>
<p>We know that users associate fast response time to authoritative content and trust, so we want to measure how fast our pages load. We’re particularly interested in doing this during many simultaneous loads, simulating how our web server will respond when serving many users</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">asyncio</span>
<span class="kn">import</span> <span class="nn">tornado.httpclient</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">tornado</span><span class="o">.</span><span class="n">httpclient</span><span class="o">.</span><span class="n">AsyncHTTPClient</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="k">async</span> <span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="sd">""" Get url n times concurrently. Print duration. """</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">futures</span> <span class="o">=</span> <span class="p">[</span><span class="n">client</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
<span class="n">results</span> <span class="o">=</span> <span class="k">await</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="o">*</span><span class="n">futures</span><span class="p">)</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="s1">', </span><span class="si">%d</span><span class="s1"> simultaneous requests, '</span> <span class="o">%</span> <span class="n">n</span><span class="p">,</span> <span class="s1">'total time: '</span><span class="p">,</span> <span class="p">(</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="p">))</span>
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</div>
</div>
</div>
<div class="section" id="Timings">
<h2>Timings<a class="headerlink" href="#Timings" title="Permalink to this headline">¶</a></h2>
<p>We see that</p>
<ol class="arabic simple">
<li><p>Tornado has about a 3-5ms roundtrip time</p></li>
<li><p>It can run 100 such queries in around 100ms, so there is some nice concurrency happening</p></li>
<li><p>Calling fib takes a while</p></li>
<li><p>Calling fib 100 times takes around 100 times as long, there is not as much parallelism</p></li>
</ol>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">await</span> <span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fast'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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http://localhost:8000/fast , 1 simultaneous requests, total time: 0.005836009979248047
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">await</span> <span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fast'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
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http://localhost:8000/fast , 100 simultaneous requests, total time: 0.13756465911865234
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">await</span> <span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fib/28'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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http://localhost:8000/fib/28 , 1 simultaneous requests, total time: 0.11277055740356445
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">await</span> <span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fib/28'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
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http://localhost:8000/fib/28 , 100 simultaneous requests, total time: 11.173585653305054
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<div class="section" id="Blocking-async">
<h2>Blocking async<a class="headerlink" href="#Blocking-async" title="Permalink to this headline">¶</a></h2>
<p>In the example below we see that one call to the slow <code class="docutils literal notranslate"><span class="pre">fib/</span></code> route will unfortunately block other much faster requests:</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">ensure_future</span><span class="p">(</span><span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fib/35'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">ensure_future</span><span class="p">(</span><span class="n">measure</span><span class="p">(</span><span class="s1">'http://localhost:8000/fast'</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="k">await</span> <span class="n">b</span>
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http://localhost:8000/fib/35 , 1 simultaneous requests, total time: 3.2470662593841553
http://localhost:8000/fast , 1 simultaneous requests, total time: 3.246363639831543
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<div class="section" id="Discussion">
<h2>Discussion<a class="headerlink" href="#Discussion" title="Permalink to this headline">¶</a></h2>
<p>There are two problems/opportunities here:</p>
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<li><p>All of our <code class="docutils literal notranslate"><span class="pre">fib</span></code> calls are independent, we would like to run these computations in parallel with multiple cores or a nearby cluster.</p></li>
<li><p>Our slow computationally intense <code class="docutils literal notranslate"><span class="pre">fib</span></code> requests can get in the way of our fast requests. One slow user can affect everyone else.</p></li>
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<div class="section" id="Asynchronous-off-process-computation-with-Dask">
<h3>Asynchronous off-process computation with Dask<a class="headerlink" href="#Asynchronous-off-process-computation-with-Dask" title="Permalink to this headline">¶</a></h3>
<p>To resolve both of these problems we will offload computation to other processes or computers using Dask. Because Dask is an async framework it can integrate nicely with Tornado or Asyncio.</p>
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