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spark-sql-dataset-caching-and-persistence.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 | Caching and Persistence</title>
<meta name="description" content="Apache Spark™ Workshop | Spark SQL | Caching and Persistence">
<meta name="author" content="Jacek Laskowski">
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© <a href="https://medium.com/@jaceklaskowski">Jacek Laskowski</a> 2022 / <a href="https://twitter.com/jaceklaskowski">@JacekLaskowski</a>
</footer>
</div>
<div class="slides">
<section class="intro" data-transition="zoom" id="home">
<p>
<img width="17%" style="background:none; border:none; box-shadow:none;" data-src="images/spark-logo.png">
<img width="10%" src="images/jacek_laskowski_20201229_200x200.png" style="border: 0">
</p>
<h1 style="font-size: 3.17em;">Caching and Persistence</h1>
<h3>Apache Spark 3.2 / Spark SQL</h3>
<br />
<br />
<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> / <a
href="https://www.linkedin.com/in/jaceklaskowski/">LinkedIn</a>
<br>
The "Internals" Books: <a href="https://books.japila.pl/apache-spark-internals">Apache Spark</a> / <a
href="https://books.japila.pl/spark-sql-internals/">Spark SQL</a> / <a
href="https://books.japila.pl/delta-lake-internals">Delta Lake</a>
</h4>
</section>
<section id="agenda" data-markdown>
<textarea data-template>
## Agenda
1. [Dataset Caching and Persistence](#/intro)
1. [StorageLevel](#/storagelevel)
1. [web UI's Storage Tab](#/web-ui)
1. [CacheManager](#/cache-manager)
</textarea>
</section>
<section>
<section id="intro" data-markdown style="font-size: 90%;">
<textarea data-template>
## Dataset Caching and Persistence <small>(1 of 2)</small>
1. **Persisting (or caching) a dataset** in memory or disk for faster (local) access
1. **Dataset.cache** persists the Dataset with the default MEMORY_AND_DISK storage level
1. **Dataset.persist** persist the Dataset with a given storage level
1. **Dataset.unpersist** un-persist (removes) any cached block data from memory and disk
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [Dataset Caching and Persistence](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-caching-and-persistence.html)
</textarea>
</section>
<section id="intro-2" data-markdown style="font-size: 90%;">
<textarea data-template>
## Dataset Caching and Persistence <small>(2 of 2)</small>
1. Dataset turns into an RDD at execution time
1. Persisting a Dataset boils down to persisting the RDD
1. RDD blocks are stored on executors only
1. Every executor has exactly one BlockManager
1. BlockManager is a key-value store of blocks of data
1. Switch to [The Internals of Apache Spark](https://bit.ly/apache-spark-internals)
* [BlockManager — Key-Value Store of Blocks of Data](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/spark-BlockManager.html)
</textarea>
</section>
</section>
<section>
<section id="storagelevel" data-markdown>
<textarea data-template>
## StorageLevel <small>(1 of 2)</small>
1. [StorageLevel](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.storage.StorageLevel) describes how block data of an RDD should be stored:
1. **useDisk**
1. **useMemory**
1. **useOffHeap**
1. **deserialized**
1. **replication** (default: 1)
1. Predefined 12 StorageLevels with functions to create new ones <small>(on the following slide)</small>
</textarea>
</section>
<section data-markdown id="predefined-storagelevels">
<textarea data-template>
## Predefined StorageLevels <small>(2 of 2)</small>
<p>
<img style="background:none; border:none; box-shadow:none;" data-src="images/spark-sql-storage-levels.png">
<small>Source: [RDD Persistence](http://spark.apache.org/docs/latest/rdd-programming-guide.html#rdd-persistence)</small>
</p>
</textarea>
</section>
</section>
<section id="web-ui" data-markdown>
<textarea data-template>
## web UI's Storage Tab
<p>
<img style="background:none; border:none; box-shadow:none;" data-src="images/spark-sql-webui-storage-tab.png">
<small>http://localhost:4040/storage/</small>
</p>
1. Switch to [The Internals of Apache Spark](https://bit.ly/apache-spark-internals)
* [Storage Tab](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/spark-webui-storage.html)
</textarea>
</section>
<section id="cache-manager" data-markdown>
<textarea data-template>
## CacheManager
1. **CacheManager** is an in-memory cache (registry) of structured queries (by their logical plans)
1. Shared across SparkSessions through SharedState
* Available as **spark.sharedState.cacheManager**
1. Spark developers interact with CacheManager using Dataset operators (cache, persist, and unpersist)
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [CacheManager — In-Memory Cache for Tables and Views](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-CacheManager.html)
</textarea>
</section>
<section id="recap" data-markdown>
<textarea data-template>
## Recap
1. [Dataset Caching and Persistence](#/intro)
1. [StorageLevel](#/storagelevel)
1. [web UI's Storage Tab](#/web-ui)
1. [CacheManager](#/cache-manager)
</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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