DBMS > Apache Drill vs. Transwarp Hippo vs. VelocityDB

System Properties Comparison Apache Drill vs. Transwarp Hippo vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

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

Primary database model

Document store
Relational DBMS

Vector DBMS

Graph DBMS
Object oriented DBMS

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Trend Chart

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	0.05
Rank	#386	Overall
	#16	Vector DBMS

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

Website

drill.apache.org

www.transwarp.cn/en/subproduct/hippo

velocitydb.com

Technical documentation

drill.apache.org/docs

velocitydb.com/UserGuide

Developer

Apache Software Foundation

VelocityDB Inc

Initial release

2012

2023

2011

Current release

1.20.3, January 2023

1.0, May 2023

7.x

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux
OS X
Windows

Linux
macOS

Any that supports .NET

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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 SELECT statement is SQL:2003 compliant

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

.Net

Supported programming languages

C++

C++
Java
Python

.Net

Server-side scripts

Stored procedures

user defined functions

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate 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

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Role based access control and fine grained access rights

Based on Windows Authentication

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