Spark Structured APIs
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The document presents an overview of Spark and its structured APIs using Databricks, focusing on key concepts such as RDDs, DataFrames, and Datasets. It discusses the unique characteristics of RDDs, the structural advantages of DataFrames, and the type-safe nature of Datasets. The session emphasizes etiquette for participants, including punctuality and feedback submission, while providing insights into using Databricks for enterprise data solutions.
![Whatʼs RDD?
[Resilient Distributed Datasets]
2013 2017 2018
● An RDD represents an immutable, partitioned collection of records that can be operated on in
parallel.
● RDDs gives you complete control because every record in RDD is just a Java or Python object.
RDD
Dependencies Partitions
Compute Function
Partition => Iterator[T]
Characteristics of an RDD](https://image.slidesharecdn.com/sparkstructuredapis-230117141240-e9157d3e/85/Spark-Structured-APIs-6-320.jpg)
![RDD Characteristics
2013 2017 2018
1. Dependencies
➢ The List of dependencies that instructs spark how an RDD is constructed.
➢ Spark can recreate an RDD from these dependencies and replicate operations on them.
(This characteristic gives RDDs resiliency)
2. Partitions
➢ This provide spark the ability to distribute the work to parallelize computation across executors.
➢ Spark also uses locality information to send work to executors close to the data.
(This characteristic gives RDDs distribution)
3. Compute Function
➢ An abstract method that computes the input split partition in the TaskContext to produce a
collection of values (of type T)
compute(split: Partition, context: TaskContext): Iterator[T]](https://image.slidesharecdn.com/sparkstructuredapis-230117141240-e9157d3e/85/Spark-Structured-APIs-7-320.jpg)
![Dataframe
Solution is in structuring
What we mean by Structuring?
● Ordering and Structuring for allowing to arrange your data in
tabular format.
● Expressing computation using patterns like filtering, selecting,
counting etc.
The DataFrame API
Distributed in-memory tables with named columns and schemas, (where each_column ==
specific_datatype[String, Int, Timestamp etc.] )
To Human Eye DataFrame is like a table.](https://image.slidesharecdn.com/sparkstructuredapis-230117141240-e9157d3e/85/Spark-Structured-APIs-10-320.jpg)
![The Datasets API
A Type-Safe one
According to the Dataset Documentation:
➢ A strongly typed collection of domain-specific objects that can be
transformed in parallel using functional or relational operations. Each
Dataset [in Scala] also has an untyped view called a DataFrame, which
is a Dataset of Row.
DataFrame
DataSets
Structured
APIs
Untyped APIs
Typed APIs
● Dataframe = Dataset[Row]
● Alias in Scala
● Dataset[T]
● In Scala & Java](https://image.slidesharecdn.com/sparkstructuredapis-230117141240-e9157d3e/85/Spark-Structured-APIs-15-320.jpg)
Spark Structured APIs
- 1. Spark Structured APIs Using Databricks Presented By: Raviyanshu Singh Software Consultant Knoldus Inc
- 2. Lack of etiquette and manners is a huge turn off. KnolX Etiquettes Punctuality Join the session 5 minutes prior to the session start time. We start on time and conclude on time! Feedback Make sure to submit a constructive feedback for all sessions as it is very helpful for the presenter. Silent Mode Keep your mobile devices in silent mode, feel free to move out of session in case you need to attend an urgent call. Avoid Disturbance Avoid unwanted chit chat during the session.
- 3. Our Agenda 01 What is Spark 02 What’s an RDD 03 Dataframes 04 Datasets Databricks 05 05 06 Demo
- 4. What is Spark? Unified Analytics Engine Apache Spark is a unified engine designed for large-scale distributed data processing, on premises in data centers or in the cloud. Spark’s design philosophy is based on these principles: ● Speed ● Ease of Use ● Modularity ● Extensibility
- 5. 00 Spark APIs Trio RDD, Dataframe & Datasets Distributed collections of JVM objects Functional Operators (Map, filter etc) 2011 Distributed collections of Row objects. Expression based operations and UDFs Fast/Efficient and internal representations 2013 Internally rows, externally JVM objects. “Best of both the worlds”: type safe + fast 2015 RDD Dataframe Datasets The Timeline of Three
- 6. Whatʼs RDD? [Resilient Distributed Datasets] 2013 2017 2018 ● An RDD represents an immutable, partitioned collection of records that can be operated on in parallel. ● RDDs gives you complete control because every record in RDD is just a Java or Python object. RDD Dependencies Partitions Compute Function Partition => Iterator[T] Characteristics of an RDD
- 7. RDD Characteristics 2013 2017 2018 1. Dependencies ➢ The List of dependencies that instructs spark how an RDD is constructed. ➢ Spark can recreate an RDD from these dependencies and replicate operations on them. (This characteristic gives RDDs resiliency) 2. Partitions ➢ This provide spark the ability to distribute the work to parallelize computation across executors. ➢ Spark also uses locality information to send work to executors close to the data. (This characteristic gives RDDs distribution) 3. Compute Function ➢ An abstract method that computes the input split partition in the TaskContext to produce a collection of values (of type T) compute(split: Partition, context: TaskContext): Iterator[T]
- 9. Whatʼs the Problem? RDDs Expresses How-to Not What-to Compute Function (or computation) is opaque to Spark Slow for non JVM languages like Python No optimization by Spark No data compression techniques Leading to inadvertent inefficiencies
- 10. Dataframe Solution is in structuring What we mean by Structuring? ● Ordering and Structuring for allowing to arrange your data in tabular format. ● Expressing computation using patterns like filtering, selecting, counting etc. The DataFrame API Distributed in-memory tables with named columns and schemas, (where each_column == specific_datatype[String, Int, Timestamp etc.] ) To Human Eye DataFrame is like a table.
- 11. Visualizing Dataframes With Custom Data
- 12. Spark Operations on Data Manoeuvring Data Transformation Spark Operation Head of IT Actions Finance Manager Marketing Manager ● Transforming a Spark DF into a new DF without altering the original data. ● Giving Immutability property. ● Actions are operations that returns the raw value. ● It triggers the Lazy Evaluation of all the recorded transformation Transformations Actions show() take() count() collect() orderBy() groupBy() filter() select()
- 13. Common Dataframe Ops Projections & Filter ➢ A way to return only the rows matching a certain relational condition by using filters. ➢ Projections are done with the select() method, while filters can be expressed using the filter() or where() method. val topHits = df.select("Id", "First", "Url") .where($"Hits" > 10000) Renaming, Adding, and Dropping Columns ➢ Using withColumnRenamed() we can rename the column, just withColumn() will add new column and drop() will drop the column specified inside it. val newDf = df.withColumnRenamed("First","First_Name").withColumnRenamed("Last", "Last_Name") val dfWithTS = newDf.withColumn("Issued_Date", to_timestamp(col("Published"), "dd/MM/yyyy")) .drop("Published")
- 14. Common Dataframe Ops Aggregation ➢ Transformations and actions on DataFrames, such as groupBy(), orderBy(), and count(), offer the ability to aggregate by column names and then aggregate counts across them. val mostShare = dfWithTS.select("Campaigns","First_Name").where(col("Campaigns").isNotNull) .groupBy("Campaigns") .count() .orderBy(desc("count"))
- 15. The Datasets API A Type-Safe one According to the Dataset Documentation: ➢ A strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset [in Scala] also has an untyped view called a DataFrame, which is a Dataset of Row. DataFrame DataSets Structured APIs Untyped APIs Typed APIs ● Dataframe = Dataset[Row] ● Alias in Scala ● Dataset[T] ● In Scala & Java
- 16. Visualizing Datasets Case Class (Type-Safe Hero)
- 17. Datasets Ops
- 18. Databricks? A LakeHouse Company ● The Databricks Lakehouse Platform provides a unified set of tools for building, deploying, sharing, and maintaining enterprise-grade data solutions at scale. ● Databricks integrates with cloud storage and security in your cloud account, and manages and deploys cloud infrastructure on your behalf.
- 19. Common Tools In Databricks Core Data Tasks REST API Interactive Notebooks ML Model Serving Workflows Scheduler Source Controlling (GIt) SQL Editor & Dashboard Compute Management Data Ingestion
- 20. DEMO
- 21. Thank You !