DBMS > Hive vs. Spark SQL

System Properties Comparison Hive vs. Spark SQL

Please select another system to include it in the comparison.

Our visitors often compare Hive and Spark SQL with Impala, Snowflake and MongoDB.

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	84.74
Rank	#14	Overall
	#9	Relational DBMS

Score	16.10
Rank	#35	Overall
	#21	Relational DBMS

Website

hive.apache.org

spark.apache.org/sql

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation info

initially developed by Facebook

Apache Software Foundation

Initial release

2012

2014

Current release

2.3.6, August 2019

v2.4.4, September 2019

License

Commercial or Open Source

Open Source info

Apache Version 2

Open Source info

Apache 2.0

Cloud-based only info

Only available as a cloud service

DBaaS offerings (sponsored links) info

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support info

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts info

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods info

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods info

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts info

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys info

Referential integrity

Transaction concepts info

Support to ensure data integrity after non-atomic manipulations of data

Concurrency info

Support for concurrent manipulation of data

yes

Durability info

Support for making data persistent

yes

In-memory capabilities info

Is there an option to define some or all structures to be held in-memory only.

User concepts info

Access control

Access rights for users, groups and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.