DBMS > GBase vs. Hive vs. Lovefield vs. Spark SQL

System Properties Comparison GBase vs. Hive vs. Lovefield vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

Embeddable relational database for web apps written in pure JavaScript

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	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.gbase.cn

hive.apache.org

google.github.io/lovefield

spark.apache.org/sql

Technical documentation

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

github.com/google/lovefield/blob/master/docs/spec_index.md

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

Developer

General Data Technology Co., Ltd.

Apache Software Foundation

initially developed by Facebook

Google

Apache Software Foundation

Initial release

2004

2012

2014

Current release

GBase 8a, GBase 8s, GBase 8c

3.1.3, April 2022

2.1.12, February 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

Java

JavaScript

Scala

Server operating systems

Linux

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

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

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

C++
Java
PHP
Python

JavaScript

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

yes

Access rights for users, groups and roles

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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