DBMS > Datomic vs. Transwarp Hippo vs. Valentina Server vs. Yaacomo

System Properties Comparison Datomic vs. Transwarp Hippo vs. Valentina Server vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Object-relational database and reports server

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Vector DBMS

Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

www.datomic.com

www.transwarp.cn/en/subproduct/hippo

www.valentina-db.net

yaacomo.com

Technical documentation

docs.datomic.com

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Cognitect

Paradigma Software

Q2WEB GmbH

Initial release

2012

2023

1999

2009

Current release

1.0.7075, December 2023

1.0, May 2023

5.7.5

License

Commercial or Open Source

commercial

limited edition free

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, Clojure

C++

Server operating systems

All OS with a Java VM

Linux
macOS

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

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

yes

APIs and other access methods

RESTful HTTP API

ODBC

JDBC
ODBC

Supported programming languages

Clojure
Java

C++
Java
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

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