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Life is inseparable from water, and it is also inseparable from data. We are surrounded by data and live in data. When there is more and more data, it becomes big data.
In the technological roadmap of “Made in China 2025”, industrial big data is planned as an important breakthrough point, and in the next ten years, with Intelligent systems built with data as the core will become the core driving force supporting intelligent manufacturing and the industrial Internet. If you want to understand big data, you need to understand big data-related query, processing, machine learning, graph calculation and statistics.Analysis etc. As a new generation of lightweight big data rapid processing platform, Apache Spark integrates various capabilities related to big data and is the first choice for understanding big data.
Simply speaking, Spark is a fast and versatile large-scale data processing engine that can be used for various applications, including real-time stream processing, machine learning, and interactive querying. etc., can be built on different storage and operating systems through Spark. Today’s Gewuhui will take you to get to know Spark, a rising and rapidly growing star of big data processing.
1SpBurundins Escortark Growth Process
In 2009, Spark was born in the AMPLab of Berkeley University. It was initially a research project of Berkeley University. At the beginning, Spark was just an experimental project. The amount of code is very small, only about 3900 lines of code, and it is a lightweight framework.
In 2010, Berkeley University officially open sourced the Spark project.
In June 2013, Spark became a project under the Apache Foundation and entered a period of rapid development. Third-party developers contributed a large amount of code and it was very active.
In February 2014, Spark became Apache’s top project at a rapid rate.
Spark 1.0.0 was released at the end of May 2014.
Spark 2.0.0 was released in June 2016
Spark 2.4.0 was released in November 2018
Spark as Hadoop An important member of the ecosystem, its growth rate is terrifying. It took less than five years from birth to becoming ApacBurundi Sugar Daddyhe’s top project. However, in the current surrounding environment and background where the amount of data is rapidly increasing, there is a reason why Spark, as an efficient computing framework, has received such great tracking attention.
2Spark’s features
Fast speed
Spark can perform batch and streaming processing with high performance by using the advanced DAG scheduler, query optimizer and physical execution engine. Using logical regression algorithms for iterative calculations, Spark is more than 100 times faster than Hadoop.
Easy to use
Spark currently supports a variety of programming languages, such as Java, Scala, Python, and R. Anyone who is familiar with any of these languages can directly start writing Spark programs, which is very convenientBurundins Escort. It also supports more than 80 advanced algorithms, allowing users to quickly build different applications. Moreover, Spark also supports interactive Python and Scala shells, which means that it is very convenient to use Spark in these shells: BI Escorts Clusters are used to verify the way to solve problems, instead of requiring packaging, uploading clusters, verification, etc. as before. This is very important for prototype development.
High versatility
Spark currently consists of four major components. As follows:
Spark SQL: SQL on Hadoop, which can provide interactive queries and report queries, called through interfaces such as JDBC;
Spark Streaming:: Streaming computing engine;
Spark MLlib: Machine learning library;
Spark GraphX: Graph computing engine.
With these four major components, it has successfully solved the big data field, Burundins Sugardaddy offline batch processing, The most important tasks and problems such as interactive query, real-time stream computing, machine learning and graph computing, these different types of processing can be seamlessly used in the same application. Spark’s unified solution is very attractive. After all, any company Burundins Sugardaddy wants to use a unified platform to solve problems and reduce development and maintenance. The human cost and the physical cost of the deployment platform. Of course, as a unified solution, Spark does not sacrifice performance at the expense. On the contrary, Spark has huge advantages in terms of performance.
Can be Burundins EscortRongSession
Spark can run on standalone, YARN, Mesos, Kubernetes and EC2 scheduling platforms. The Standalone mode does not rely on third-party resource managers and schedulers, which lowers the threshold for using Spark and makes it very easy for everyone to deploy and use Spark.
Spark can process all data supported by Hadoop, including HDFS, Apach HBase, Apach Kudu, Apach Cassanda, etc. This is particularly important for users who have deployed Hadoop clusters, because they can take advantage of Spark’s powerful processing capabilities without doing any data migration.
3Spark’s advantages over MapReduce
Spark and MapReduce are both computing frameworks, but as a rising star, Spark has learned from MapReduce and made improvements on its basis, making the algorithm performance significantly better than MapReduce. The following is a rough summary. The difference between the two:
1) Spark stores the core data of the calculation in the memory, and the iterative calculation Burundins Escort is more efficient High; the core functions of MapReduce need to be implemented on disk, and there are many disk IO operations, which affects performance.
2) Spark has high fault tolerance. It achieves efficient fault tolerance of RDD operators through the Lineage mechanism. If a certain part is lost or makes an error, it can be resolved through Burundi Sugar DaddyThe kinship of the calculation process of the entire data set is used to complete the reconstruction; if MapReBI Escortsduce Fault tolerance may have to be recalculated, which is costly.
3) Spark is more versatile. Spark provides multiple performance operators in the two categories of transformation and action, making the operation more convenient; MapReduce only provides two operations: map and reduce.
4) Spark framework and ecology Burundi Sugar Daddy is more complex, first of all, there are RDD, lineage, execution When the directed acyclic graph DAG, stage division, etc. Many times, spark operations need to be tuned according to the needs of different business scenarios to achieve performance requirements; the MapReduce framework and its ecology are relatively simple, and the performance requirements are relatively weak, but the operation is relatively stable and suitable for Persistent background operation.
4Spark and Industrial Internet Platform
Industrial internet brings industrial data With the rapid development of the world, traditional stand-alone computers cannot handle the processing, analysis and in-depth exploration of massive data due to their own software and hardware limitations. However, Spark, as a distributed computing framework, can easily cope with these scenarios. On the industrial Internet platform, Spark can quickly complete the processing and transformation of massive flow data at industrial sites and easily handle it. The rapid batch processing and analysis of massive data in the industrial big data platform, and its integrated machine learning framework can conduct in-depth mining and analysis of massive industrial data, thereby assisting managers in decision-making analysis.
Based on the excellent design concept of Spark framework itself Burundi Sugar and Given the vigorous development of the community, it is believed that Spark will play an increasingly important role in the industrial internet platform in the future.
The author of this article: Huang Huan, Big Data Engineer of Gechuang Dongzhi (please indicate the origin and author when transcribing and publishing)
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