DBMS > Hive vs. LeanXcale vs. Trafodion vs. VelocityDB

System Properties Comparison Hive vs. LeanXcale vs. Trafodion vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Transactional SQL-on-Hadoop DBMS

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

Technical documentation

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

trafodion.apache.org/documentation.html

velocitydb.com/UserGuide

Developer

Apache Software Foundation

initially developed by Facebook

LeanXcale

Apache Software Foundation, originally developed by HP

VelocityDB Inc

Initial release

2012

2015

2014

2011

Current release

3.1.3, April 2022

2.3.0, February 2019

7.x

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++, Java

Server operating systems

All OS with a Java VM

Linux

Any that supports .NET

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through Apache Derby

yes

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
ODBC

.Net

Supported programming languages

C++
Java
PHP
Python

C
Java
Scala

All languages supporting JDBC/ODBC/ADO.Net

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate 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

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Based on Windows Authentication

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More resources

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