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spark-sql-Developing-Custom-Data-Source.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>Apache Spark™ Workshop | Spark SQL | Developing Custom Data Source</title>
<meta name="description" content="Apache Spark™ Workshop | Spark SQL | Developing Custom Data Source">
<meta name="author" content="Jacek Laskowski">
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© <a href="https://medium.com/@jaceklaskowski">Jacek Laskowski</a> 2019 / <a href="https://twitter.com/jaceklaskowski">@JacekLaskowski</a>
</footer>
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<div class="slides">
<section class="intro" data-transition="zoom" id="home">
<p>
<img width="12%" style="background:none; border:none; box-shadow:none;" data-src="images/spark-logo.png">
<img width="6%" src="images/jacek_laskowski_20141201_512px.png" style="border: 0">
</p>
<h1 style="font-size: 3.37em;">Developing Custom Data Source</h1>
<h3>Apache Spark 2.4.4 / Spark SQL</h3>
<hr />
<h4 style="font-size: smaller;">
<a href="https://twitter.com/jaceklaskowski">@jaceklaskowski</a> / <a href="https://stackoverflow.com/users/1305344/jacek-laskowski">StackOverflow</a> / <a href="https://github.com/jaceklaskowski">GitHub</a>
<br>
The "Internals" Books: <a href="https://bit.ly/apache-spark-internals">Apache Spark</a> / <a href="https://bit.ly/spark-sql-internals">Spark SQL</a> / <a href="https://bit.ly/spark-structured-streaming">Spark Structured Streaming</a>
</h4>
</section>
<section id="agenda" data-markdown>
<textarea data-template>
## Agenda
1. [Introduction](#/introduction)
1. ["Final Product"](#/final-product)
1. [RelationProvider](#/RelationProvider)
1. [DataSourceRegister](#/DataSourceRegister)
1. [BaseRelation](#/BaseRelation)
1. [PrunedFilteredScan](#/PrunedFilteredScan)
1. [RDD, Partitions and compute](#/rdd-partitions-compute)
1. [Demo](#/demo)
</textarea>
</section>
<section id="introduction" data-markdown style="font-size: 90%">
<textarea data-template>
## Introduction
1. **Data Source** - a Spark SQL component that is used to load or save datasets from external data storages or systems
1. **Data Source API** - a set of interfaces (contracts) in Spark SQL to implement a custom data source
1. **DataSource** - a class in Spark SQL that is responsible for **data source resolution**
1. Data Source is referenced in a Spark SQL application using **DataFrameReader.format** or **DataFrameWriter.format**
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [DataSource — Pluggable Data Provider Framework](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-DataSource.html)
</textarea>
</section>
<section>
<section id="final-product" data-markdown style="font-size: 90%">
<textarea data-template>
## "Final Product" / Load Side (1 of 2)
<pre style="margin-left: 0px;"><code style="width: 900px;" class="lang-scala hljs">spark
.read
.format("XXX") // <-- your custom data source / format
.option("header", true) // <-- the data source supports options
.load("data.xxx")
.select("id", "name") // column pruning
.where($"id" > 5) // filter pushdown
.show
</code></pre>
</textarea>
</section>
<section id="final-product-save-side" data-markdown style="font-size: 90%">
<textarea data-template>
## "Final Product" / Save Side (2 of 2)
<pre style="margin-left: 0px;"><code style="width: 900px;" class="lang-scala hljs">dataframe
.write
.format("XXX") // <-- your custom data source / format
.option("header", true) // <-- the data source supports options
.mode("overwrite") // modes
.save("data.xxx")
</code></pre>
</textarea>
</section>
</section>
<section>
<section id="BaseRelation" data-markdown style="font-size: 85%">
<textarea data-template>
## BaseRelation
1. **BaseRelation** - the contract of relations with a known schema
* Schema is the metadata of a data
* Schema describes the shape of a data
1. Does not define data itself
* Use the data-specific contracts <small><i>(in the following slides)</i></small>
<pre><code class="lang-scala hljs">
abstract class BaseRelation {
def sqlContext: SQLContext
def schema: StructType
def sizeInBytes: Long = sqlContext.conf.defaultSizeInBytes
def needConversion: Boolean = true
def unhandledFilters(filters: Array[Filter]): Array[Filter] = filters
}
</code></pre>
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [BaseRelation — Collection of Tuples with Schema](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-BaseRelation.html)
</textarea>
</section>
<section id="PrunedScan" data-markdown style="font-size: 85%">
<textarea data-template>
## PrunedScan
1. **PrunedScan** - the contract of relations that support:
* **Column Pruning** - eliminating unneeded columns
<pre><code class="lang-scala hljs">
trait PrunedScan {
def buildScan(requiredColumns: Array[String]): RDD[Row]
}
</code></pre>
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [PrunedScan Contract — Relations with Column Pruning](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-PrunedScan.html)
</textarea>
</section>
<section id="PrunedFilteredScan" data-markdown style="font-size: 85%">
<textarea data-template>
## PrunedFilteredScan
1. **PrunedFilteredScan** - the contract of relations that support:
* **Column Pruning** - eliminating unneeded columns
* **Filter Pushdown** - filtering using selected predicates
<pre><code class="lang-scala hljs">
trait PrunedFilteredScan {
def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row]
}
</code></pre>
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [PrunedFilteredScan Contract — Relations with Column Pruning and Filter Pushdown](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-PrunedFilteredScan.html)
</textarea>
</section>
<section id="TableScan" data-markdown style="font-size: 85%">
<textarea data-template>
## TableScan
1. **TableScan** - the contract of relations that support:
* **Column Pruning** - eliminating unneeded columns
<pre><code class="lang-scala hljs">
trait TableScan {
def buildScan(): RDD[Row]
}
</code></pre>
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [TableScan Contract — Relations with Column Pruning](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-TableScan.html)
</textarea>
</section>
</section>
<section id="RelationProvider" data-markdown>
<textarea data-template>
## RelationProvider
1. **RelationProvider** - the contract of `BaseRelation` providers with schema inference (discovery)
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [RelationProvider Contract — Relation Providers With Schema Inference](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-RelationProvider.html)
</textarea>
</section>
<section id="DataSourceRegister" data-markdown>
<textarea data-template>
## DataSourceRegister
1. **DataSourceRegister** is...FIXME
1. **META-INF/services/org.apache.spark.sql.sources.DataSourceRegister**
* DataSource uses Java's ServiceLoader to load DataSourceRegisters
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* FIXME
</textarea>
</section>
<section id="rdd-partitions-compute" data-markdown>
<textarea data-template>
## RDD, Partitions and compute
1. **RDD** is...FIXME
1. **Partition** is...FIXME
1. **compute** is...FIXME
1. Switch to [The Internals of Apache Spark](https://bit.ly/apache-spark-internals)
* FIXME
</textarea>
</section>
<section id="demo" data-markdown>
<textarea data-template>
## Demo
</textarea>
</section>
<section id="recap" data-markdown>
<textarea data-template>
## Recap
1. [DataSource](#/DataSource)
1. ["Final Product"](#/final-product)
1. [RelationProvider](#/RelationProvider)
1. [DataSourceRegister](#/DataSourceRegister)
1. [BaseRelation](#/BaseRelation)
1. [PrunedFilteredScan](#/PrunedFilteredScan)
1. [RDD, Partitions and compute](#/rdd-partitions-compute)
1. [Demo](#/demo)
</textarea>
</section>
<section style="text-align: left" data-markdown id="questions">
<textarea data-template>
# Questions?
* Read [The Internals of Apache Spark](https://bit.ly/apache-spark-internals)
* Read [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* Read [The Internals of Spark Structured Streaming](https://bit.ly/spark-structured-streaming)
* Follow [@jaceklaskowski](https://twitter.com/jaceklaskowski) on twitter
* Upvote [my questions and answers on StackOverflow](http://stackoverflow.com/users/1305344/jacek-laskowski)
</textarea>
</section>
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