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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.ExtensionScalar.html">pyarrow.ExtensionScalar</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.Tensor.html">pyarrow.Tensor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2 has-children"><a class="reference internal" href="api/compute.html">Compute Functions</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.negate_checked.html">pyarrow.compute.negate_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sqrt.html">pyarrow.compute.sqrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sqrt_checked.html">pyarrow.compute.sqrt_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.subtract_checked.html">pyarrow.compute.subtract_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_and.html">pyarrow.compute.bit_wise_and</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_not.html">pyarrow.compute.bit_wise_not</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_or.html">pyarrow.compute.bit_wise_or</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_xor.html">pyarrow.compute.bit_wise_xor</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.shift_left_checked.html">pyarrow.compute.shift_left_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.shift_right_checked.html">pyarrow.compute.shift_right_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ceil.html">pyarrow.compute.ceil</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.floor.html">pyarrow.compute.floor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.round.html">pyarrow.compute.round</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.round_to_multiple.html">pyarrow.compute.round_to_multiple</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.trunc.html">pyarrow.compute.trunc</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ln.html">pyarrow.compute.ln</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ln_checked.html">pyarrow.compute.ln_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log10.html">pyarrow.compute.log10</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log10_checked.html">pyarrow.compute.log10_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log1p.html">pyarrow.compute.log1p</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log1p_checked.html">pyarrow.compute.log1p_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log2.html">pyarrow.compute.log2</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log2_checked.html">pyarrow.compute.log2_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.logb.html">pyarrow.compute.logb</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.logb_checked.html">pyarrow.compute.logb_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.acos_checked.html">pyarrow.compute.acos_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.asin.html">pyarrow.compute.asin</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.asin_checked.html">pyarrow.compute.asin_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.atan.html">pyarrow.compute.atan</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.atan2.html">pyarrow.compute.atan2</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.cos.html">pyarrow.compute.cos</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.cos_checked.html">pyarrow.compute.cos_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sin.html">pyarrow.compute.sin</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sin_checked.html">pyarrow.compute.sin_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.tan.html">pyarrow.compute.tan</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.tan_checked.html">pyarrow.compute.tan_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.equal.html">pyarrow.compute.equal</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.ipc.MessageReader.html">pyarrow.ipc.MessageReader</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.flight.FlightUnavailableError.html">pyarrow.flight.FlightUnavailableError</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.flight.FlightWriteSizeExceededError.html">pyarrow.flight.FlightWriteSizeExceededError</a></li> |
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| <section id="streaming-serialization-and-ipc"> |
| <span id="ipc"></span><h1>Streaming, Serialization, and IPC<a class="headerlink" href="#streaming-serialization-and-ipc" title="Permalink to this heading">#</a></h1> |
| <section id="writing-and-reading-streams"> |
| <h2>Writing and Reading Streams<a class="headerlink" href="#writing-and-reading-streams" title="Permalink to this heading">#</a></h2> |
| <p>Arrow defines two types of binary formats for serializing record batches:</p> |
| <ul class="simple"> |
| <li><p><strong>Streaming format</strong>: for sending an arbitrary length sequence of record |
| batches. The format must be processed from start to end, and does not support |
| random access</p></li> |
| <li><p><strong>File or Random Access format</strong>: for serializing a fixed number of record |
| batches. Supports random access, and thus is very useful when used with |
| memory maps</p></li> |
| </ul> |
| <p>To follow this section, make sure to first read the section on <a class="reference internal" href="memory.html#io"><span class="std std-ref">Memory and |
| IO</span></a>.</p> |
| <section id="using-streams"> |
| <h3>Using streams<a class="headerlink" href="#using-streams" title="Permalink to this heading">#</a></h3> |
| <p>First, let’s create a small record batch:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| |
| <span class="gp">In [2]: </span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="gp"> ...: </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]),</span> |
| <span class="gp"> ...: </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'bar'</span><span class="p">,</span> <span class="s1">'baz'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]),</span> |
| <span class="gp"> ...: </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span> |
| <span class="gp"> ...: </span><span class="p">]</span> |
| <span class="gp"> ...: </span> |
| |
| <span class="gp">In [3]: </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'f0'</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">,</span> <span class="s1">'f2'</span><span class="p">])</span> |
| |
| <span class="gp">In [4]: </span><span class="n">batch</span><span class="o">.</span><span class="n">num_rows</span> |
| <span class="gh">Out[4]: </span><span class="go">4</span> |
| |
| <span class="gp">In [5]: </span><span class="n">batch</span><span class="o">.</span><span class="n">num_columns</span> |
| <span class="gh">Out[5]: </span><span class="go">3</span> |
| </pre></div> |
| </div> |
| <p>Now, we can begin writing a stream containing some number of these batches. For |
| this we use <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchStreamWriter</span></code>, which can write to a |
| writeable <code class="docutils literal notranslate"><span class="pre">NativeFile</span></code> object or a writeable Python object. For convenience, |
| this one can be created with <a class="reference internal" href="generated/pyarrow.ipc.new_stream.html#pyarrow.ipc.new_stream" title="pyarrow.ipc.new_stream"><code class="xref py py-func docutils literal notranslate"><span class="pre">new_stream()</span></code></a>:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">sink</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">BufferOutputStream</span><span class="p">()</span> |
| |
| <span class="gp">In [7]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">new_stream</span><span class="p">(</span><span class="n">sink</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span> |
| <span class="gp"> ...: </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="mi">5</span><span class="p">):</span> |
| <span class="gp"> ...: </span> <span class="n">writer</span><span class="o">.</span><span class="n">write_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gp"> ...: </span> |
| </pre></div> |
| </div> |
| <p>Here we used an in-memory Arrow buffer stream (<code class="docutils literal notranslate"><span class="pre">sink</span></code>), |
| but this could have been a socket or some other IO sink.</p> |
| <p>When creating the <code class="docutils literal notranslate"><span class="pre">StreamWriter</span></code>, we pass the schema, since the schema |
| (column names and types) must be the same for all of the batches sent in this |
| particular stream. Now we can do:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">buf</span> <span class="o">=</span> <span class="n">sink</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span> |
| |
| <span class="gp">In [9]: </span><span class="n">buf</span><span class="o">.</span><span class="n">size</span> |
| <span class="gh">Out[9]: </span><span class="go">1984</span> |
| </pre></div> |
| </div> |
| <p>Now <code class="docutils literal notranslate"><span class="pre">buf</span></code> contains the complete stream as an in-memory byte buffer. We can |
| read such a stream with <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchStreamReader</span></code> or the |
| convenience function <code class="docutils literal notranslate"><span class="pre">pyarrow.ipc.open_stream</span></code>:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_stream</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> <span class="k">as</span> <span class="n">reader</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="n">schema</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">schema</span> |
| <span class="gp"> ....: </span> <span class="n">batches</span> <span class="o">=</span> <span class="p">[</span><span class="n">b</span> <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">reader</span><span class="p">]</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [11]: </span><span class="n">schema</span> |
| <span class="gh">Out[11]: </span> |
| <span class="go">f0: int64</span> |
| <span class="go">f1: string</span> |
| <span class="go">f2: bool</span> |
| |
| <span class="gp">In [12]: </span><span class="nb">len</span><span class="p">(</span><span class="n">batches</span><span class="p">)</span> |
| <span class="gh">Out[12]: </span><span class="go">5</span> |
| </pre></div> |
| </div> |
| <p>We can check the returned batches are the same as the original input:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">batches</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gh">Out[13]: </span><span class="go">True</span> |
| </pre></div> |
| </div> |
| <p>An important point is that if the input source supports zero-copy reads |
| (e.g. like a memory map, or <code class="docutils literal notranslate"><span class="pre">pyarrow.BufferReader</span></code>), then the returned |
| batches are also zero-copy and do not allocate any new memory on read.</p> |
| </section> |
| <section id="writing-and-reading-random-access-files"> |
| <h3>Writing and Reading Random Access Files<a class="headerlink" href="#writing-and-reading-random-access-files" title="Permalink to this heading">#</a></h3> |
| <p>The <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchFileWriter</span></code> has the same API as |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchStreamWriter</span></code>. You can create one with |
| <a class="reference internal" href="generated/pyarrow.ipc.new_file.html#pyarrow.ipc.new_file" title="pyarrow.ipc.new_file"><code class="xref py py-func docutils literal notranslate"><span class="pre">new_file()</span></code></a>:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">sink</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">BufferOutputStream</span><span class="p">()</span> |
| |
| <span class="gp">In [15]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">new_file</span><span class="p">(</span><span class="n">sink</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span> |
| <span class="gp"> ....: </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="mi">10</span><span class="p">):</span> |
| <span class="gp"> ....: </span> <span class="n">writer</span><span class="o">.</span><span class="n">write_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [16]: </span><span class="n">buf</span> <span class="o">=</span> <span class="n">sink</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span> |
| |
| <span class="gp">In [17]: </span><span class="n">buf</span><span class="o">.</span><span class="n">size</span> |
| <span class="gh">Out[17]: </span><span class="go">4226</span> |
| </pre></div> |
| </div> |
| <p>The difference between <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchFileReader</span></code> and |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchStreamReader</span></code> is that the input source must have a |
| <code class="docutils literal notranslate"><span class="pre">seek</span></code> method for random access. The stream reader only requires read |
| operations. We can also use the <a class="reference internal" href="generated/pyarrow.ipc.open_file.html#pyarrow.ipc.open_file" title="pyarrow.ipc.open_file"><code class="xref py py-func docutils literal notranslate"><span class="pre">open_file()</span></code></a> method to open a file:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [18]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_file</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> <span class="k">as</span> <span class="n">reader</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="n">num_record_batches</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">num_record_batches</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [19]: </span><span class="n">b</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Because we have access to the entire payload, we know the number of record |
| batches in the file, and can read any at random.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">num_record_batches</span> |
| <span class="gh">Out[20]: </span><span class="go">10</span> |
| |
| <span class="gp">In [21]: </span><span class="n">b</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gh">Out[21]: </span><span class="go">True</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="reading-from-stream-and-file-format-for-pandas"> |
| <h3>Reading from Stream and File Format for pandas<a class="headerlink" href="#reading-from-stream-and-file-format-for-pandas" title="Permalink to this heading">#</a></h3> |
| <p>The stream and file reader classes have a special <code class="docutils literal notranslate"><span class="pre">read_pandas</span></code> method to |
| simplify reading multiple record batches and converting them to a single |
| DataFrame output:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [22]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_file</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> <span class="k">as</span> <span class="n">reader</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="n">df</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_pandas</span><span class="p">()</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [23]: </span><span class="n">df</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span> |
| <span class="gh">Out[23]: </span> |
| <span class="go"> f0 f1 f2</span> |
| <span class="go">0 1 foo True</span> |
| <span class="go">1 2 bar None</span> |
| <span class="go">2 3 baz False</span> |
| <span class="go">3 4 None True</span> |
| <span class="go">4 1 foo True</span> |
| </pre></div> |
| </div> |
| </section> |
| </section> |
| <section id="efficiently-writing-and-reading-arrow-data"> |
| <h2>Efficiently Writing and Reading Arrow Data<a class="headerlink" href="#efficiently-writing-and-reading-arrow-data" title="Permalink to this heading">#</a></h2> |
| <p>Being optimized for zero copy and memory mapped data, Arrow allows to easily |
| read and write arrays consuming the minimum amount of resident memory.</p> |
| <p>When writing and reading raw Arrow data, we can use the Arrow File Format |
| or the Arrow Streaming Format.</p> |
| <p>To dump an array to file, you can use the <a class="reference internal" href="generated/pyarrow.ipc.new_file.html#pyarrow.ipc.new_file" title="pyarrow.ipc.new_file"><code class="xref py py-meth docutils literal notranslate"><span class="pre">new_file()</span></code></a> |
| which will provide a new <a class="reference internal" href="generated/pyarrow.ipc.RecordBatchFileWriter.html#pyarrow.ipc.RecordBatchFileWriter" title="pyarrow.ipc.RecordBatchFileWriter"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchFileWriter</span></code></a> instance |
| that can be used to write batches of data to that file.</p> |
| <p>For example to write an array of 10M integers, we could write it in 1000 chunks |
| of 10000 entries:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [24]: </span><span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">10000</span> |
| |
| <span class="gp">In [25]: </span><span class="n">NUM_BATCHES</span> <span class="o">=</span> <span class="mi">1000</span> |
| |
| <span class="gp">In [26]: </span><span class="n">schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span><span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'nums'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int32</span><span class="p">())])</span> |
| |
| <span class="gp">In [27]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">OSFile</span><span class="p">(</span><span class="s1">'bigfile.arrow'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">sink</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">new_file</span><span class="p">(</span><span class="n">sink</span><span class="p">,</span> <span class="n">schema</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_BATCHES</span><span class="p">):</span> |
| <span class="gp"> ....: </span> <span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([</span><span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">BATCH_SIZE</span><span class="p">),</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int32</span><span class="p">())],</span> <span class="n">schema</span><span class="p">)</span> |
| <span class="gp"> ....: </span> <span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gp"> ....: </span> |
| </pre></div> |
| </div> |
| <p>record batches support multiple columns, so in practice we always write the |
| equivalent of a <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-class docutils literal notranslate"><span class="pre">Table</span></code></a>.</p> |
| <p>Writing in batches is effective because we in theory need to keep in memory only |
| the current batch we are writing. But when reading back, we can be even more effective |
| by directly mapping the data from disk and avoid allocating any new memory on read.</p> |
| <p>Under normal conditions, reading back our file will consume a few hundred megabytes |
| of memory:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [28]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">OSFile</span><span class="p">(</span><span class="s1">'bigfile.arrow'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">source</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="n">loaded_array</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_file</span><span class="p">(</span><span class="n">source</span><span class="p">)</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [29]: </span><span class="nb">print</span><span class="p">(</span><span class="s2">"LEN:"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">loaded_array</span><span class="p">))</span> |
| <span class="go">LEN: 10000000</span> |
| |
| <span class="gp">In [30]: </span><span class="nb">print</span><span class="p">(</span><span class="s2">"RSS: </span><span class="si">{}</span><span class="s2">MB"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">total_allocated_bytes</span><span class="p">()</span> <span class="o">>></span> <span class="mi">20</span><span class="p">))</span> |
| <span class="go">RSS: 38MB</span> |
| </pre></div> |
| </div> |
| <p>To more efficiently read big data from disk, we can memory map the file, so that |
| Arrow can directly reference the data mapped from disk and avoid having to |
| allocate its own memory. |
| In such case the operating system will be able to page in the mapped memory |
| lazily and page it out without any write back cost when under pressure, |
| allowing to more easily read arrays bigger than the total memory.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [31]: </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">memory_map</span><span class="p">(</span><span class="s1">'bigfile.arrow'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">source</span><span class="p">:</span> |
| <span class="gp"> ....: </span> <span class="n">loaded_array</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_file</span><span class="p">(</span><span class="n">source</span><span class="p">)</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span> |
| <span class="gp"> ....: </span> |
| |
| <span class="gp">In [32]: </span><span class="nb">print</span><span class="p">(</span><span class="s2">"LEN:"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">loaded_array</span><span class="p">))</span> |
| <span class="go">LEN: 10000000</span> |
| |
| <span class="gp">In [33]: </span><span class="nb">print</span><span class="p">(</span><span class="s2">"RSS: </span><span class="si">{}</span><span class="s2">MB"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">total_allocated_bytes</span><span class="p">()</span> <span class="o">>></span> <span class="mi">20</span><span class="p">))</span> |
| <span class="go">RSS: 0MB</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Other high level APIs like <a class="reference internal" href="generated/pyarrow.parquet.read_table.html#pyarrow.parquet.read_table" title="pyarrow.parquet.read_table"><code class="xref py py-meth docutils literal notranslate"><span class="pre">read_table()</span></code></a> also provide a |
| <code class="docutils literal notranslate"><span class="pre">memory_map</span></code> option. But in those cases, the memory mapping can’t help with |
| reducing resident memory consumption. See <a class="reference internal" href="parquet.html#parquet-mmap"><span class="std std-ref">Reading Parquet and Memory Mapping</span></a> for details.</p> |
| </div> |
| </section> |
| </section> |
| |
| |
| </article> |
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| <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#writing-and-reading-streams">Writing and Reading Streams</a><ul class="visible nav section-nav flex-column"> |
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