DBMS > EsgynDB vs. GeoSpock vs. LevelDB vs. Microsoft Azure Cosmos DB vs. TerminusDB

System Properties Comparison EsgynDB vs. GeoSpock vs. LevelDB vs. Microsoft Azure Cosmos DB vs. TerminusDB

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Spatial and temporal data processing engine for extreme data scale

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Globally distributed, horizontally scalable, multi-model database service

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

Document store
RDF store

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

www.esgyn.cn

geospock.com

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

terminusdb.com

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

learn.microsoft.com/azure/cosmos-db

terminusdb.github.io/terminusdb/#

Developer

Esgyn

GeoSpock

Google

Microsoft

DataChemist Ltd.

Initial release

2015

2011

2014

2018

Current release

2.0, September 2019

1.23, February 2021

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

Open Source

GPL V3

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++, Java

Java, Javascript

C++

Prolog, Rust

Server operating systems

Linux

hosted

Illumos
Linux
OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

temporal, categorical

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

ANSI SQL for query only (using Presto)

SQL-like query language

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript
Python

Server-side scripts

Stored procedures

Java Stored Procedures

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

Sharding

Implicit feature of the cloud service

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

yes

Implicit feature of the cloud service

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

in-memory journaling

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per table

Access rights can be defined down to the item level

Role-based access control

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