The science of Big Data (Big Data) develops rapidly and strongly, requiring the support of specialized computing tools. Apache Spark was born as a necessity, providing an effective solution for data analysis and processing. So What is Apache Spark?? How is Apache Spark structured, what are the advantages and disadvantages? Join Mat Bao to find the answer in the following article.
What is Apache Spark?
Table of Contents
1. What is Apache? What is Apache Spark?
Apache, officially known as Apache HTTP Server, is a free and open source web server software. Apache has been developed by the Apache Software Foundation since 1995, accounting for about 46% of the worldwide website market share. Apache helps webmasters put content on the website, supports the establishment of a safe, effective and economical website.
Apache Spark effectively supports data analysis and processing
Apache Spark is an open source data processing framework at scale. Spark allows users to quickly build predictive models and perform computations on multiple computers or datasets at the same time, without extracting experimental computation samples. In 2009, Apache Spark was initially developed by AMPLab, then donated to the Apache Software Foundation in 2013.
2. Apache Spark components
“What is Apache Spark??” and its main components are the most frequently asked questions. An Apache Spark will have 5 components:
Spark Core is considered the foundation and operating conditions for all components in Apache Spark. This component assumes the task of computing, processing in memory (In – memory computing) and referencing data stored in external storage systems.
This is a component that provides a new type of data abstraction (SchemaRDD) to support structured and semi-structured data. Spark SQL performs operations on DataFrames in Java, Python or Scala, through the help of DSL (Domain – specific language) and SQL.
Main components of Apache Spark
Spark Streaming analyzes streams by treating streams as mini batches to perform RDD transformation. Thereby, the code written for batch processing can be reused into stream processing, making lambda architecture development easier. However, in data processing, this creates a certain delay.
Spark MLlib is a machine learning platform with a distributed memory-based architecture. Compared to the version running on Hadoop, Spark Mllib is 9 times faster.
GraphX is a graphics processing platform that provides APIs that represent computations in graphs using the Pregel Api.
3. What is the architecture of Apache Spark like?
After learn “What is Apache Spark??” then the next thing you need to know is its architecture. Apache Spark is composed of two parts, the executors and the drivers. In particular, the driver helps to convert user code into many tasks (tasks), distributed on processing nodes (worker nodes).
Architecture of Apache Spark
The executor will perform tasks and run on the processing nodes that the driver assigns to it. Besides, Spark can also run in standalone cluster mode which only requires JVM and Apache Spark framework on each machine in the cluster. However, using a cluster management tool as an intermediary between the two components will help to allocate resources on demand and be better utilized.
Apache Spark builds instructions for data processing in a directed acyclic graph, or DAG. DAG is Spark’s scheduling layer, which determines which tasks are executed on which node and in what sequence.
4. Advantages of What is Apache Spark?
Not only possessing components with useful features, Apache Spark also has many outstanding advantages.
- simple and easy to use
Apache Spark was developed to make it easier for users to access parallel computing technology. Users only need to equip the Basic knowledge of databases, Python or Scala programming is already usable. This is also the biggest difference between Apache Spark and Hadoop.
- Impressive real-time analytics capabilities
Apache Spark can process batches of real-time data – data that comes from real-time event streams. The processing speed is extremely impressive, up to millions of events per second. Receiving data from the source and processing the data take place almost simultaneously. Besides, Apache Spark is also useful for fraud detection when performing banking transactions.
Apache Spark is capable of real-time batch processing
- Supported by high-level libraries
Apache Spark gets the support of high-level libraries such as streaming data, SQL queries, machine learning, and graph processing. Not only do these standard libraries increase developer productivity, they also ensure seamless connectivity for complex workflows.
- High compatibility and support for many programming languages
Apache Spark is compatible with all the file formats and data sources supported by the Hadoop cluster. The programming languages used are Scala, Java, Python and R.
Apache Spark brings the ultimate solution for big data analysis and processing industry. With its strengths, this Apache will continue to thrive in the future, especially in the field of IT and core technology industries. Hope the above article of Mat Bao has helped you answer the question “What is Apache Spark??” and learn more about the outstanding features of this tool.
The image and content of the article are compiled by Mat Bao.
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