Overview
- Understand the difference between APIs 3 sparks: RDD, data frames and data sets
- We will see how to create RDD, data frames and data sets.
Introduction
Have passed 11 years since Apache Spark began to exist and, impressively and continuously, became the first choice of big data developers. Developers have always loved providing simple and powerful APIs that can perform any type of big data analysis..
Initially, in 2011 came up with the concept of RDD, after in 2013 with Dataframes and later in 2015 with the concept of Datasets. None of them have depreciated, we can still use them all. In this post, we will understand and see the difference between the three.
Table of Contents
- What are RDD?
- When to use RDD?
- What are data frames?
- What are data sets?
- RDD vs Dataframes vs Datasets?
What are RDD?
RDDs or resilient distributed data sets are the fundamental data structure of Spark. Es la colección de objetos que es capaz de almacenar los datos particionados en los múltiples nodos del clusterA cluster is a set of interconnected companies and organizations that operate in the same sector or geographical area, and that collaborate to improve their competitiveness. These groupings allow for the sharing of resources, Knowledge and technologies, fostering innovation and economic growth. Clusters can span a variety of industries, from technology to agriculture, and are fundamental for regional development and job creation.... y además les posibilita hacer el procesamiento en paraleloParallel processing is a technique that allows multiple operations to be executed simultaneously, Breaking down complex tasks into smaller subtasks. This methodology optimizes the use of computational resources and reduces processing time, being especially useful in applications such as the analysis of large volumes of data, Simulations and graphic rendering. Su implementación se ha vuelto esencial en sistemas de alto rendimiento y en la computación moderna.....
It is fault tolerant if you perform multiple transformations on the RDD and then, for any circustance, falla algún nodeNodo is a digital platform that facilitates the connection between professionals and companies in search of talent. Through an intuitive system, allows users to create profiles, share experiences and access job opportunities. Its focus on collaboration and networking makes Nodo a valuable tool for those who want to expand their professional network and find projects that align with their skills and goals..... El RDD, then, is able to recover automatically.
There is 3 alternativas para crear un ASD:
- Parallel an existing data collection
- Reference to the stored external data file
- Creating RDD from an existing RDD
When to use RDD?
We can use RDD in the following situations:
- When we want to do low-level transformations on the dataset. Read more about RDD Transformations: PySpark to perform Transformations
- Does not automatically infer schema from ingested data, we need to specify the schema of each and every dataset when we create an RDD. Learn how to infer the RDD scheme here: Building Machine Learning Pipelines with PySpark
What are data frames?
It was first introduced in Spark version 1.3 to overcome the limitations of Spark RDD. Spark Dataframes are the distributed collection of data points, but here, data is organized in named columns. Enable developers to debug code throughout runtime, what was not allowed with RDDs.
Data frames can read and write the data in various formats like CSV, JSONJSON, o JavaScript Object Notation, It is a lightweight data exchange format that is easy for humans to read and write, and easy for machines to analyze and generate. It is commonly used in web applications to send and receive information between a server and a client. Its structure is based on key-value pairs, making it versatile and widely adopted in software development.., EURO, HDFSHDFS, o Hadoop Distributed File System, It is a key infrastructure for storing large volumes of data. Designed to run on common hardware, HDFS enables data distribution across multiple nodes, ensuring high availability and fault tolerance. Its architecture is based on a master-slave model, where a master node manages the system and slave nodes store the data, facilitating the efficient processing of information.. y tablas HIVEHive is a decentralized social media platform that allows its users to share content and connect with others without the intervention of a central authority. Uses blockchain technology to ensure data security and ownership. Unlike other social networks, Hive allows users to monetize their content through crypto rewards, which encourages the creation and active exchange of information..... It is already optimized to process large data sets for most preprocessing tasks, so we don't need to write complex functions on our own.
Uses a catalyst optimizer for optimization purposes. If you want to read more about the catalyst optimizer, I highly recommend that you read this post: Practical tutorial for analyzing data using Spark SQL
Let's see how to create a data frame using PySpark.
What are data sets?
Spark Datasets is an extension of the Data Frames API with the benefits of RDDs and Datasets. It's fast and provides a type-safe interface. Type safety means that the compiler will validate the data types of all columns in the dataset during compilation only and will throw an error if there is any discrepancy in the data types.
RDD users will find it somewhat similar to the code, but it is faster than RDD. Can efficiently process structured and unstructured data.
We still can't create Spark datasets in Python. Dataset API is only enabled in Scala and Java.
RDD vs. Data Frames vs. Data Sets
| RDD | Data frames | Data sets | |
| Data representation | RDD is a distributed collection of data items without any schema. | It is also the distributed collection organized in the named columns. | It is an extension of Dataframes with more features such as type safety and object-oriented interface. |
| Improvement | No built-in optimization engine for RDD. Developers must write the optimized code themselves. | Uses a catalyst optimizer for optimization. | Also uses a catalyst optimizer for optimization purposes. |
| Scheme projection | Here, we need to set the schema manually. | It will automatically discover the dataset schema. | In addition, it will automatically find the schema of the dataset through the SQL engine. |
| Aggregation operation | RDD is slower than data frames and data sets to perform simple operations like grouping data. | Provides a simple API to perform aggregation operations. Performs aggregation faster than RDDs and datasets. | Dataset is faster than RDDs but slightly slower than Dataframes. |
Final notes
In this post, hemos visto la diferencia entre las tres API principales de Apache SparkApache Spark is an open-source data processing engine that enables the analysis of large volumes of information quickly and efficiently. Its design is based on memory, which optimizes performance compared to other batch processing tools. Spark is widely used in big data applications, Machine Learning and Real-Time Analytics, thanks to its ease of use and.... Then, to complete, if you want rich semantics, high-level abstractions, type safety, elija Dataframes o Datasets. If you need more control over the preprocessing part, you can always use the RDDs.
I recommend that you check out these additional resources on Apache Spark to boost your knowledge.:
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