DBMS > Graph Engine vs. LevelDB vs. Microsoft Azure Table Storage vs. openGemini

System Properties Comparison Graph Engine vs. LevelDB vs. Microsoft Azure Table Storage vs. openGemini

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Editorial information provided by DB-Engines

Name

Description

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

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

A Wide Column Store for rapid development using massive semi-structured datasets

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Primary database model

Graph DBMS
Key-value store

Key-value store

Wide column store

Time Series DBMS

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Website

www.graphengine.io

github.com/google/leveldb

azure.microsoft.com/en-us/services/storage/tables

www.opengemini.org
github.com/openGemini

Technical documentation

www.graphengine.io/docs/manual

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

docs.opengemini.org/guide

Developer

Microsoft

Google

Microsoft

Huawei and openGemini community

Initial release

2010

2011

2012

2022

Current release

1.23, February 2021

1.1, July 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

BSD

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

.NET and C

C++

Server operating systems

.NET

Illumos
Linux
OS X
Windows

hosted

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, 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

SQL-like query language

APIs and other access methods

RESTful HTTP API

HTTP REST

Supported programming languages

C#
C++
F#
Visual Basic

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

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

Transaction concepts

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

User concepts

Access control

Access rights based on private key authentication or shared access signatures

Administrators and common users accounts

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More resources

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