DBMS > EJDB vs. Google Cloud Datastore vs. Hypertable vs. Machbase Neo vs. OrigoDB

System Properties Comparison EJDB vs. Google Cloud Datastore vs. Hypertable vs. Machbase Neo vs. OrigoDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An open source BigTable implementation based on distributed file systems such as Hadoop

TimeSeries DBMS for AIoT and BigData

A fully ACID in-memory object graph database

Primary database model

Document store

Wide column store

Time Series DBMS

Document store
Object oriented DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

github.com/Softmotions/ejdb

cloud.google.com/datastore

machbase.com

origodb.com

Technical documentation

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

cloud.google.com/datastore/docs

machbase.com/dbms

origodb.com/docs

Developer

Softmotions

Google

Hypertable Inc.

Machbase

Robert Friberg et al

Initial release

2012

2008

2009

2013

2009

under the name LiveDB

Current release

0.9.8.11, March 2016

V8.0, August 2023

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

GNU version 3. Commercial license available

commercial

free test version available

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

server-less

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS
Windows

Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes, details here

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

in-process shared library

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

C++ API
Thrift

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

selectable replication factor on file system level

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

via ReferenceProperties or Ancestor paths

depending on model

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

volatile and lookup table

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

Role based authorization

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