DBMS > Datomic vs. EJDB vs. Graph Engine vs. Transwarp Hippo

System Properties Comparison Datomic vs. EJDB vs. Graph Engine vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

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

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Primary database model

Relational DBMS

Document store

Graph DBMS
Key-value store

Vector DBMS

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

www.datomic.com

github.com/Softmotions/ejdb

www.graphengine.io

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.datomic.com

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

www.graphengine.io/docs/manual

Developer

Cognitect

Softmotions

Microsoft

Initial release

2012

2010

2023

Current release

1.0.7180, July 2024

1.0, May 2023

License

Commercial or Open Source

commercial

limited edition free

Open Source

GPLv2

Open Source

MIT License

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

.NET and C

C++

Server operating systems

All OS with a Java VM

server-less

.NET

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

Vector, Numeric and String

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

APIs and other access methods

RESTful HTTP API

in-process shared library

RESTful HTTP API

Supported programming languages

Clojure
Java

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

C#
C++
F#
Visual Basic

C++
Java
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

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

typically not needed, however similar functionality with collection joins possible

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

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

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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