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This article is about how to achieve Apache Hive and Spark comparative analysis, the editor feels very practical, so share with you to learn, I hope you can learn something after reading this article, say no more, follow the editor to have a look.

Hive and Spark have been very successful with their advantages in dealing with large-scale data, in other words, they do big data analysis. The following focuses on the development history and various characteristics of the two products, and illustrates all kinds of complex data processing problems that the two products can solve by comparing their capabilities.

What is Hive?

Hive is an open source distributed data warehouse database running on the Hadoop distributed file system for querying and analyzing big data. Data is stored in tabular form (just like a relational database management system). Data operations can be performed using a SQL interface called HiveQL. Hive introduces SQL functionality on top of Hadoop, making it a horizontally scalable database, which is an excellent choice for DWH environments.

A glimpse of the Development History of Hive

Hive (later Apache) was originally developed by Facebook, and developers found their data growing exponentially from GBs to TBs in a matter of days. At that time, Facebook used Python to load data into the RDBMS database. Because RDBMS databases can only scale vertically, they quickly face performance and scalability problems. They need a database that can scale horizontally and handle large amounts of data. Hadoop was already very popular at the time; soon after, Hive, built on top of Hadoop, appeared. Hive is similar to a RDBMS database, but not a complete RDBMS.

Why choose Hive?

The core reason for choosing Hive is that it is the SQL interface running on Hadoop. Furthermore, it reduces the complexity of the MapReduce framework. Hive helps enterprises perform large-scale data analysis on HDFS, making it a horizontally scalable database. Its SQL interface HiveQL enables developers with a RDBMS background to build and develop performance and extend the data warehouse type framework.

Hive features and functions

Hive has enterprise-level features and functions, which can help enterprises build efficient high-end data warehouse solutions.

Some of these features include:

Hive uses Hadoop as the storage engine and runs only on HDF.

Built specifically for data warehouse operations, not for OLTP or OLAP.

HiveQL, as a SQL engine, can help build complex SQL queries for data warehouse type operations. Hive can be integrated with other distributed databases such as HBase and NoSQL databases such as Cassandra.

Hive structure

The Hive architecture is very simple. It has a Hive interface and uses HDFS to store data across multiple servers for distributed data processing.

Hive for data Warehouse system

Hive is a database built for data warehouse operations, especially those that deal with terabytes or gigabytes of data. Similar to RDBMS's database, but not exactly the same. As mentioned earlier, it is a horizontally scaled database and leverages the capabilities of Hadoop to make it a high-scale database for rapid execution. It can run on thousands of nodes and can take advantage of commercial hardware. This makes Hive a cost-effective product with high performance and scalability.

Hive integration function

Because it supports the ANSI SQL standard, Hive can work with HBase and Cassandra. And other database integration. These tools have limited support for SQL and can help applications perform analysis and reporting on larger datasets. Hive can also be integrated with data flow tools such as Spark, Kafka, and Flume.

Limitations of Hive

Hive is a pure data warehouse database that stores data in the form of tables. Therefore, it can only handle structured data that is read and written using SQL queries, not unstructured data. In addition, Hive is not suitable for OLTP or OLAP operations.

Apache Hive VS Spark: different purposes, the same success

What is Spark?

Spark is a distributed big data framework that helps extract and process large amounts of data in RDD format for analysis. In short, it is not a database, but a framework that uses the RDD (resilient distributed data) approach to access external distributed datasets from datastores such as Hive, Hadoop, and HBase. Because Spark performs complex analysis in memory, it runs very quickly.

What is Spark Streaming?

Spark Streaming is an extension of Spark that allows real-time streaming of real-time data from Web sources to create various analyses. Although there are other tools such as Kafka and Flume that can do this, Spark is a good choice and it is necessary to perform really complex data analysis. Spark has its own SQL engine, which works well when integrated with Kafka and Flume.

A glimpse of the Development History of Spark

Spark is proposed as an alternative to MapReduce, and MapReduce is a slow and resource-intensive programming model. Because Spark analyzes data in memory, you don't have to rely on disk space or network bandwidth.

Why choose Spark?

The core advantage of Spark is its ability to perform complex memory analysis and data stream sizes up to gigabytes, making it more efficient and faster than MapReduce. Spark can extract data from any data store running on Hadoop and perform complex analysis in parallel in memory. This feature reduces disk input / output and network contention, increasing its speed tenfold or even a hundredfold. In addition, the data analysis framework in Spark can also be built using Java, Scala, Python, R, or even SQL.

Spark architecture

The Spark architecture can vary according to requirements. Typically, the Spark architecture includes Spark streams, Spark SQL, machine learning libraries, graphics processing, Spark core engines, and data stores such as HDFS, MongoDB, and Cassandra.

Spark features and functions

Lightning-fast analysis

Spark extracts data from Hadoop and performs analysis in memory. The data is pulled into memory in parallel in blocks. The final dataset is then delivered to the destination. The dataset can also reside in memory until it is used.

Spark Streaming

Spark Streaming is an extension of Spark that can transfer large amounts of data in real time from widely used web sources. Because Spark has the ability to perform advanced analysis, it is particularly prominent compared with other data flow tools such as Kafka and Flume.

Support for a variety of API

Spark supports different programming languages, such as Java, Python and Scala, which are very popular in big data and the field of data analysis. This allows the data analysis framework to be written in any language.

Massive data processing capacity

As mentioned earlier, advanced data analysis usually needs to be performed on massive datasets. Before the advent of Spark, these analyses were done using the MapReduce method. Spark supports not only MapReduce, but also SQL-based data extraction. Spark can perform faster analysis for applications that need to perform data extraction on large datasets.

Data storage and tool integration

Spark can be integrated with various data stores running on Hadoop, such as Hive and HBase. You can also extract data from NoSQL databases like MongoDB. Unlike other applications that perform analysis in the database, Spark extracts data from the data store once and then performs the analysis on the extracted dataset in memory.

An extension of Spark-Spark Streaming can be integrated with Kafka and Flume to build efficient and high-performance data pipelines.

The difference between Hive and Spark

Hive and Spark are different products built by big data Space for different purposes. Hive is a distributed database and Spark is a framework for data analysis.

Differences in features and functions

Hive and Spark are both very popular tools in big data's world. Hive is the best choice to use SQL to perform data analysis on large amounts of data. On the other hand, Spark is the best choice for running big data's analysis, which provides a faster and more modern alternative than MapReduce.

The above is how to achieve the comparative analysis of Apache Hive and Spark, the editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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