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<!DOCTYPE html>
<html lang="en" prefix="og: http://ogp.me/ns# fb: https://www.facebook.com/2008/fbml">
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<title>Stackdiver as a Service</title>
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Stackdiver as a Service </a>
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<article>
<h2><a href="https://www.lyh.me/for-comprehensions.html">For comprehensions</a></h2>
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<footer class="post-info">
<span class="label label-default">Date</span>
<span class="published">
<i class="fa fa-calendar"></i><time datetime="2017-03-28T12:23:00-04:00"> Tue 28 March 2017</time>
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<div class="summary"><p>I made these <a href="/slides/for-yield.html">slides</a> on for comprehension a while ago but forgot to post them but here you go.</p>
<iframe src="/slides/for-yield.html" width="800" height="450"></iframe>
<a class="btn btn-default btn-xs" href="https://www.lyh.me/for-comprehensions.html">more ...</a>
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<article>
<h2><a href="https://www.lyh.me/scio-at-scala-by-the-bay.html">Scio at Scala by the Bay</a></h2>
<div class="well well-sm">
<footer class="post-info">
<span class="label label-default">Date</span>
<span class="published">
<i class="fa fa-calendar"></i><time datetime="2016-11-14T13:04:00-05:00"> Mon 14 November 2016</time>
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<div class="summary"><p>It’s been 7 months since we first announced <a href="https://github.com/spotify/scio">Scio</a> at <a href="https://cloudplatformonline.com/NEXT2016.html"><span class="caps">GCPNEXT16</span></a>. There’re now dozens of internal teams and a couple of other companies using Scio to run hundreds of pipelines on a daily basis. Within <a href="https://www.spotify.com/">Spotify</a>, Scio is now the prefered framework for building new data pipelines on <a href="http://cloud.google.com/">Google Cloud Platform</a>. We’ve also made 19 released and added tons of features and improvements. Below is a list of some notable ones.</p>
<ul>
<li>Interactive <span class="caps">REPL</span></li>
<li>Type safe BigQuery macro improvements and <a href="https://github.com/spotify/scio-idea-plugin">Scio-<span class="caps">IDEA</span>-plugin</a></li>
<li>BigQuery <a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/">standard <span class="caps">SQL</span> 2011 syntax</a> support</li>
<li><span class="caps">HDFS</span> source and sink</li>
<li>Avro file compression support</li>
<li>Bigtable multi-table sink and utility for cluster scaling</li>
<li>Protobuf file support and usability improvements</li>
<li>Accumulator usability improvements</li>
<li>End-to-end testing utilities and matchers improvements</li>
<li>Join performance improvements and skewed join</li>
<li>Metrics interface and enhancements</li>
</ul>
<p>I talked about Scio at <a href="http://scala.bythebay.io/">Scala by the Bay</a> last week and here are the slides.</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/13tC6anQaZpNE8" width="800" height="490" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
<p><div style="margin-bottom:5px"> <strong> <a href="//www.slideshare.net/sinisalyh/scio-a-scala-api-for-google-cloud-dataflow-apache-beam" title="Scio - A Scala API for Google Cloud Dataflow & Apache Beam" target="_blank">Scio - A Scala <span class="caps">API</span> for Google Cloud Dataflow <span class="amp">&</span> Apache Beam</a> </strong> from <strong><a target="_blank" href="//www.slideshare.net/sinisalyh">Neville Li</a></strong> </div></p>
<a class="btn btn-default btn-xs" href="https://www.lyh.me/scio-at-scala-by-the-bay.html">more ...</a>
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<article>
<h2><a href="https://www.lyh.me/semigroups.html">Semigroups</a></h2>
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<span class="published">
<i class="fa fa-calendar"></i><time datetime="2016-09-21T19:22:00-04:00"> Wed 21 September 2016</time>
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<div class="summary"><p>It’s been a while since my last post. Both <a href="https://github.com/spotify/scio">Scio</a> and Scala have been picking up a lot of momentum within <a href="https://www.spotify.com">Spotify</a> and a lot of people are starting to leveraging the power of <a href="https://github.com/twitter/algebird">Algebird</a>. So here’s another mini-talk on <a href="/slides/semigroups.html">Semigroups</a>.</p>
<iframe src="/slides/semigroups.html" width="800" height="450"></iframe>
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<article>
<h2><a href="https://www.lyh.me/scio-a-scala-api-for-google-cloud-dataflow.html">Scio, a Scala <span class="caps">API</span> for Google Cloud Dataflow</a></h2>
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<span class="published">
<i class="fa fa-calendar"></i><time datetime="2016-04-21T12:47:00-04:00"> Thu 21 April 2016</time>
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<div class="summary"><p>We recently open sourced <a href="https://github.com/spotify/scio">Scio</a>, a Scala <span class="caps">API</span> for <a href="https://github.com/GoogleCloudPlatform/DataflowJavaSDK">Google Cloud Dataflow</a>. Here are the slides of our talk at <a href="https://cloudplatformonline.com/NEXT2016.html"><span class="caps">GCPNEXT16</span></a> a few weeks ago.</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/rRPUB4cSAvgF8M" width="800" height="490" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
<p><div style="margin-bottom:5px"> <strong> <a href="//www.slideshare.net/sinisalyh/from-stream-to-recommendation-using-apache-beam-with-cloud-pubsub-and-cloud-dataflow" title="From stream to recommendation using apache beam with cloud pubsub and cloud dataflow" target="_blank">From stream to recommendation using apache beam with cloud pubsub and cloud dataflow</a> </strong> from <strong><a href="//www.slideshare.net/sinisalyh" target="_blank">Neville Li</a></strong> </div></p>
<p>The first half of the talk covers our experiments with Dataflow and Pub/Sub for streaming application while the second half covers Scio and BigQuery for batch analysis and machine learning.</p>
<a class="btn btn-default btn-xs" href="https://www.lyh.me/scio-a-scala-api-for-google-cloud-dataflow.html">more ...</a>
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<article>
<h2><a href="https://www.lyh.me/scala-data-pipelines-spotify.html">Scala Data Pipelines @ Spotify</a></h2>
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<span class="label label-default">Date</span>
<span class="published">
<i class="fa fa-calendar"></i><time datetime="2015-11-10T11:26:00-05:00"> Tue 10 November 2015</time>
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<span class="label label-default">Category</span>
<a href="https://www.lyh.me/category/code.html">code</a>
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<div class="summary"><p>It’s been a while since I last updated this blog. I did a talk at <a href="http://bigdatascala.bythebay.io/">Big Data Scala</a> this summer and here are the <a href="http://www.slideshare.net/sinisalyh/scala-data-pipelines-spotify">slides</a></p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/10GyruQgE9UUpv" width="800" height="490" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
<p><div style="margin-bottom:5px"> <strong> <a href="//www.slideshare.net/sinisalyh/scala-data-pipelines-spotify" title="Scala Data Pipelines @ Spotify" target="_blank">Scala Data Pipelines @ Spotify</a> </strong> from <strong><a href="//www.slideshare.net/sinisalyh" target="_blank">Neville Li</a></strong> </div></p>
<a class="btn btn-default btn-xs" href="https://www.lyh.me/scala-data-pipelines-spotify.html">more ...</a>
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<article>
<h2><a href="https://www.lyh.me/primitives.html">Primitives</a></h2>
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<i class="fa fa-calendar"></i><time datetime="2015-05-14T08:50:00-04:00"> Thu 14 May 2015</time>
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<div class="summary"><p>Another day, another talk. This one is on primitives and here are the <a href="/slides/primitives.html">slides</a></p>
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<h2><a href="https://www.lyh.me/type-classes.html">Type Classes</a></h2>
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<i class="fa fa-calendar"></i><time datetime="2015-04-20T15:39:00-04:00"> Mon 20 April 2015</time>
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<div class="summary"><p>I started giving more Scala talks internally at <a href="http://www.spotify.com/">Spotify</a>. The first one is on type classes and here are the <a href="/slides/type-classes.html">slides</a></p>
<iframe src="/slides/type-classes.html" width="800" height="450"></iframe>
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<h2><a href="https://www.lyh.me/nescala-2015-talk.html">NEScala 2015 talk</a></h2>
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<i class="fa fa-calendar"></i><time datetime="2015-02-04T11:02:00-05:00"> Wed 04 February 2015</time>
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<div class="summary"><p>I gave a lightning talk at <a href="http://www.nescala.org/">Northeast Scala Symposium</a> last week on Macros in Datapipelines and here are the <a href="/slides/macros.html">slides</a>.</p>
<iframe src="/slides/macros.html" width="800" height="450"></iframe>
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<article>
<h2><a href="https://www.lyh.me/fun-with-macros-and-parquet-avro.html">Fun with macros and parquet-avro</a></h2>
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<span class="published">
<i class="fa fa-calendar"></i><time datetime="2015-01-08T14:01:00-05:00"> Thu 08 January 2015</time>
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<div class="summary"><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><code><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>
</code></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 …</p>
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<h2><a href="https://www.lyh.me/three-reasons-a-data-engineer-should-learn-scala.html">Three Reasons a Data Engineer Should Learn Scala</a></h2>
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<i class="fa fa-calendar"></i><time datetime="2014-11-17T23:15:00-05:00"> Mon 17 November 2014</time>
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<div class="summary"><p><em>This article was written in collaboration with <a href="https://www.hakkalabs.co">Hakka Labs</a> (<a href="https://www.hakkalabs.co/articles/three-reasons-data-eng-learn-scala">original link</a>)</em></p>
<p>There has been a lot of debate over Scala lately, including criticisms like <a href="http://java.dzone.com/articles/i-dont-scala">this</a>, <a href="http://overwatering.org/blog/2013/12/scala-1-star-would-not-program-again/">this</a>, <a href="http://www.infoq.com/news/2011/11/yammer-scala">this</a>, and defenses like <a href="http://blog.gridgainsystems.com/in-defense-of-scala-response-to-i-dont-like-scala/">this</a> and <a href="http://blog.gridgainsystems.com/in-defense-of-scala-part-2/">this</a>. Most of the criticisms seem to focus on the language’s complexity, performance, and integration with existing tools and libraries, while some praise its elegant syntax, powerful type system, and good fit for domain-specific languages.</p>
<p>However most of the discussions seem based on experiences building production backend or web systems where there are a lot of other options already. There are mature, battle tested options like Java, Erlang or even <span class="caps">PHP</span>, and there are Go, node.js, or Python for those who are more adventurous or prefer agility over performance.</p>
<p>Here I want to argue that there’s a best tool for every job, and Scala shines for data processing and machine learning, for the following reasons:</p>
<ul>
<li>good balance between productivity and performance</li>
<li>integration with big data ecosystem</li>
<li>functional paradigm</li>
</ul>
<h2>Productivity without sacrificing performance</h2>
<p>In the big data <span class="amp">&</span> machine learning world where most developers are from Python/R/Matlab background, Scala’s syntax, or the subset needed for the domain, is a lot less intimidating than that …</p>
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