Spark vs Hive - What's the Difference

Spark vs Hive - Comparison of the two popular big data tools to understand their features and capabilities for complex data processing.

Spark vs Hive - What's the Difference
 |  BY ProjectPro

Apache Hive and Apache Spark are the two popular Big Data tools available for complex data processing. To effectively utilize the Big Data tools, it is essential to understand the features and capabilities of the tools. Spark vs. Hive comparison elaborates on the two tools’ architecture, features, limitations, and key differences.


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Spark vs Hive - Architecture

spark vs hive

Apache Hive is a data Warehouse platform with capabilities for managing massive data volumes. The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Hive is built on top of Hadoop and provides the measures to read, write, and manage the data. HQL or HiveQL is the query language in use with Apache Hive to perform querying and analytics activities. The following is the architecture of Hive. 

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architecture of Hive

Apache Hive Architecture

Apache Hive has a simple architecture with a Hive interface, and it uses HDFS for data storage. Data in Apache Hive can come from multiple servers and sources for effective and efficient processing in a distributed manner. 

Apache Spark, on the other hand, is an analytics framework to process high-volume datasets. The tool offers a rich interface with easy usage by offering APIs in numerous languages, such as Python, R, etc. Apache Spark also offers hassle-free integration with other high-level tools. Spark SQL, for instance, enables structured data processing with SQL. Similarly, GraphX is a valuable tool for processing graphs. Spark also comes with faster operational and computational speed. Intermediate operations occur in Spark within the memory, thereby bringing down the number of reading/write operations. 

Spark Architecture

Spark Architecture

Spark architecture can vary as per the specifications and requirements. The above figure shows the common elements present in the architecture. 

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Hive vs Spark - Key Features and Capabilities

Apache Hive - Key Features

Some of the key features of Apache Hive are: 

  • The tool uses Hadoop as the storage engine with access to the multiple servers and storage options integrated with Hadoop

  • Hive Query Language, HQL for querying and analytics activities 

  • Easy to use and scalable 

  • Compatible with numerous storage types, such as ORC, HBase, and likewise 

  • Stable batch-processing framework

  • Supports Extract, Transform, Load (ETL)

  • Effective fault tolerance as present in Hadoop 

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Apache Spark - Key Features

Some of the key features of Apache Spark are:

  • Supports MapReduce, SQL queries, Machine Learning (MLlib), and graph processing 

  • Multiple language support like Java, R, Python, etc. 

  • Highly flexible and scalable 

  • Real-time stream processing 

  • Spark Stream – Extension of Spark enables live-stream from massive data volumes from different web sources.

Apache Spark vs Apache Hive - Key Differences 

Hive and Spark are the two products of Apache with several differences in their architecture, features, processing, etc. Hive uses HQL, while Spark uses SQL as the language for querying the data. Access rights is another difference between the two tools with Hive offering access rights and grouping the users as per their roles. However, no such option is present in Spark SQL. Selective replication factor is another difference between the two tools. Hive offers the feature for redundant storage while no such feature is present in Spark SQL. 

Hive supports JDBC, ODBC, and thrift drivers. Result generation, therefore, uses these drivers and connections. In Apache Spark SQL, result generation is in the form of datasets and DataFrame APIs. Spark SQL supports real-time online transaction processing along with row-level updates. These features are not present in Apache Hive. 

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Spark vs Hive 

Parameter

Apache Hive

Apache Spark

Framework/System

It is a distributed data warehouse platform to store and manage massive data volumes

It is an analytical framework to perform large-scale analytics 

Release Year

2012

2014

License

Open-source 

Open-source

File management system

The default file management system is HDFS

The tool has no default file management system. It instead relies on other systems, such as Amazon S3, etc. 

Querying and data extraction language

HQL

SQL

Speed

Slower in comparison with Spark as Hive runs on top of Hadoop

Faster operational and computational speeds

Implementation language

It is possible to implement the tool on Java

Implementation is possible on multiple languages, such as Python, R, Scala, and Java

Server Operating Systems

All OSs with Java Virtual Machine

Multiple operating systems, such as Windows, Linux, etc. 

Read/Write Operations

Number is higher than Spark

Number is lower as it performs intermediate operations within the memory

APIs and Access Methods

JDBC, ODBC

JDBC, ODBC, and Thrift

Partitioning Methods

Data sharding methods

Spark core

Replication Factor

Selectable Replication Factor

No replication factor

Access Rights

Access right for users and roles

No access rights

Database Model

RDBMS is the primary database model in Apache Hive

The primary database model in Spark is also RDBMS; however, it also supports NoSQL databases

 

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Spark vs Hive - Limitations

Both Apache Hive and Apache Spark have a few limitations and specific areas of improvement. Hive, for instance, does not support sub-queries and unstructured data. It is also not a suitable choice for real-time online transaction processing applications. Data update and deletion operations are also not possible with Hive. The tool also has acceptable latency for interactive data browsing, and it causes adverse implications on the overall performance.  Apache Spark does not have its file management system. The tool also does not have an automatic code optimization process. Spark does not support transactional tables, and also offers no support to Char type. 

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Apache Hive and Apache Spark are two popular big data tools for data management and Big Data analytics. Hive is primarily designed to perform extraction and analytics using SQL-like queries, while Spark is an analytical platform offering high-speed performance. Both these tools have respective benefits and cons with specific capabilities and features. Spark, for instance, is highly memory expensive, thereby increasing the total hardware costs. Hive, on the other hand, does not support real-time transaction processing. 

Both these tools are open-source and the products of Apache. However, it is incorrect to consider either of the tools as the replacement of the other. The selection of the tool must be as per the specifications and requirements considering the operating systems, database models, languages, and likewise. 

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