DBMS > Apache Hive vs. EJDB vs. Transwarp KunDB

System Properties Comparison Apache Hive vs. EJDB vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	53.09
Rank	#18	Overall
	#12	Relational DBMS

Score	0.14
Rank	#331	Overall
	#47	Document stores

Score	0.02
Rank	#375	Overall
	#156	Relational DBMS

Website

hive.apache.org

github.com/Softmotions/ejdb

www.transwarp.cn/en/product/kundb

Technical documentation

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

github.com/Softmotions/ejdb/blob/master/README.md

Developer

Apache Software Foundation

initially developed by Facebook

Softmotions

Transwarp

Initial release

2012

Current release

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPLv2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM

server-less

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift

in-process shared library

Supported programming languages

C++
Java
PHP
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

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

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

User concepts

Access control

Access rights for users, groups and roles

yes

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