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<title>Fun with macros and parquet-avro - Das Keyboard Shredder</title>
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<meta name="description" content="I recently had some fun building parquet-avro-extra, an add-on module for parquet-avro using Scala macros. I did it mainly to learn Scala macros but also to make it easier to use Parquet with Avro in a data pipeline. Parquet and Avro Parquet is a columnar storage system designed for HDFS. It offers some nice improvements over row-major systems including better compression and less I/O with column projection and predicate pushdown. Avro is a data serialization system that enables type-safe access to structured data with complex schema. The parquet-avro module makes it possible to store data in Parquet format on disk and process them as Avro objects inside a JVM data pipeline like Scalding or Spark. Projection Parquet allows reading only a subset of columns via projection. Here’s an Scalding example from Tapad. Projection[Signal]("field1", "field2.field2a") Note that fields specifications are strings even though the API has access to Avro type Signal which has strongly typed getter methods. This is slightly counter-intuitive since most Scala developers are used to transformations like pipe.map(_.getField). It’s however can be easily solved with macro since the syntax tree of is accessible. A modified version has signature of …" />
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<meta property="og:description" content="I recently had some fun building parquet-avro-extra, an add-on module for parquet-avro using Scala macros. I did it mainly to learn Scala macros but also to make it easier to use Parquet with Avro in a data pipeline. Parquet and Avro Parquet is a columnar storage system designed for HDFS. It offers some nice improvements over row-major systems including better compression and less I/O with column projection and predicate pushdown. Avro is a data serialization system that enables type-safe access to structured data with complex schema. The parquet-avro module makes it possible to store data in Parquet format on disk and process them as Avro objects inside a JVM data pipeline like Scalding or Spark. Projection Parquet allows reading only a subset of columns via projection. Here’s an Scalding example from Tapad. Projection[Signal]("field1", "field2.field2a") Note that fields specifications are strings even though the API has access to Avro type Signal which has strongly typed getter methods. This is slightly counter-intuitive since most Scala developers are used to transformations like pipe.map(_.getField). It’s however can be easily solved with macro since the syntax tree of is accessible. A modified version has signature of …"/>
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<p>I recently had some fun building <a href="https://github.com/nevillelyh/parquet-avro-extra">parquet-avro-extra</a>, an add-on module for <a href="https://github.com/Parquet/parquet-mr/tree/master/parquet-avro">parquet-avro</a> using <a href="http://scalamacros.org/">Scala macros</a>. I did it mainly to learn Scala macros but also to make it easier to use <a href="http://parquet.incubator.apache.org/">Parquet</a> with <a href="http://avro.apache.org/">Avro</a> in a data pipeline.</p>
<h2>Parquet and Avro</h2>
<p>Parquet is a columnar storage system designed for <span class="caps">HDFS</span>. It offers some nice improvements over row-major systems including better compression and less I/O with column projection and predicate pushdown. Avro is a data serialization system that enables type-safe access to structured data with complex schema. The <code>parquet-avro</code> module makes it possible to store data in Parquet format on disk and process them as Avro objects inside a <span class="caps">JVM</span> data pipeline like <a href="https://github.com/twitter/scalding">Scalding</a> or <a href="http://spark.apache.org/">Spark</a>.</p>
<h2>Projection</h2>
<p>Parquet allows reading only a subset of columns via projection. Here’s an Scalding <a href="https://github.com/epishkin/scalding/tree/parquet_avro/scalding-parquet">example</a> from <a href="http://www.tapad.com/">Tapad</a>.</p>
<div class="highlight"><pre><span></span><span class="nc">Projection</span><span class="o">[</span><span class="kt">Signal</span><span class="o">](</span><span class="s">"field1"</span><span class="o">,</span> <span class="s">"field2.field2a"</span><span class="o">)</span>
</pre></div>
<p>Note that fields specifications are strings even though the <span class="caps">API</span> has access to Avro type <code>Signal</code> which has strongly typed getter methods.</p>
<p>This is slightly counter-intuitive since most Scala developers are used to transformations like <code>pipe.map(_.getField)</code>. It’s however can be easily solved with macro since the syntax tree of is accessible. A modified version has signature of <code>def apply[T](getters: (T => Any)*): Schema</code> and can be used like this:</p>
<div class="highlight"><pre><span></span><span class="nc">Projection</span><span class="o">[</span><span class="kt">Signal</span><span class="o">](</span><span class="k">_</span><span class="o">.</span><span class="n">getField1</span><span class="o">,</span> <span class="k">_</span><span class="o">.</span><span class="n">getField2</span><span class="o">.</span><span class="n">getField2a</span><span class="o">)</span>
</pre></div>
<p>The macro version looks more natural plus you get auto-complete support from the <span class="caps">IDE</span> and avoid typos.</p>
<h2>Predicate</h2>
<p>Predicate is an even better use case for macros. The Parquet predicate <span class="caps">API</span> supports a fixed set of column types and operators, and the user must use the correct factory methods to construct an expression tree. A simple lambda like <code>i: Item => i.price < 100 && i.getReviews >= 10</code> becomes this:</p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">parquet.filter2.predicate.FilterApi</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">parquet.filter2.predicate.FilterPredicate</span><span class="o">;</span>
<span class="n">FilterPredicate</span> <span class="n">p</span> <span class="o">=</span> <span class="n">FilterApi</span><span class="o">(</span>
<span class="n">FilterApi</span><span class="o">.</span><span class="na">lt</span><span class="o">(</span><span class="n">FilterApi</span><span class="o">.</span><span class="na">floatColumn</span><span class="o">(</span><span class="s">"price"</span><span class="o">),</span> <span class="mi">100</span><span class="n">f</span><span class="o">),</span>
<span class="n">FilterApi</span><span class="o">.</span><span class="na">gteq</span><span class="o">(</span><span class="n">FilterApi</span><span class="o">.</span><span class="na">intColumn</span><span class="o">(</span><span class="s">"reviews"</span><span class="o">),</span> <span class="mi">10</span><span class="o">));</span>
</pre></div>
<p>Obviously this very cumbersome and even worse than the projection case. But with macros it feels almost like writing a regular Scala predicate lambda:</p>
<div class="highlight"><pre><span></span><span class="nc">Predicate</span><span class="o">[</span><span class="kt">Item</span><span class="o">](</span><span class="n">i</span> <span class="k">=></span> <span class="n">i</span><span class="o">.</span><span class="n">getPrice</span> <span class="o"><</span> <span class="mi">100</span> <span class="o">&&</span> <span class="n">i</span><span class="o">.</span><span class="n">getReviews</span> <span class="o">>=</span> <span class="mi">10</span><span class="o">)</span>
</pre></div>
<h2>Lessons learned</h2>
<p>I have some experience with C++ templates and Clojure macros, but Scala macros is still pretty challenging to get started. A couple of notes:</p>
<ul>
<li>Since one can write pretty complex code inside a macro, I had to remind myself that they don’t compute data at runtime, but merely transform the syntax tree.</li>
<li>Pattern matching and deconstruction are handy for extracting tree elements.</li>
<li>Quasiquotes can be chained and returned in recursion for complex transformations, just be aware of the different types first.</li>
<li>It’s great exercise for your recursion skills.</li>
</ul>
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