DBMS > GBase vs. Hive vs. XTDB

System Properties Comparison GBase vs. Hive vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

An analytical database for business intelligence with large customers in China.

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

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

Score	1.04
Rank	#190	Overall
	#88	Relational DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

www.gbase.cn

hive.apache.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

www.xtdb.com/docs

Developer

Apache Software Foundation

initially developed by Facebook

Juxt Ltd.

Initial release

2012

2019

Current release

GBase 8a

3.1.3, April 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Clojure

Server operating systems

All OS with a Java VM

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Thrift

HTTP REST
JDBC

Supported programming languages

C++
Java
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, each node contains all data

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

Eventual Consistency

Foreign keys

Referential integrity

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, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

User concepts

Access control

Access rights for users, groups and roles

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More resources

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