DBMS > GridGain vs. HugeGraph vs. LevelDB vs. Microsoft Azure Cosmos DB

System Properties Comparison GridGain vs. HugeGraph vs. LevelDB vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

A fast-speed and highly-scalable Graph DBMS

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

Primary database model

Key-value store
Relational DBMS

Graph DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph 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

Website

www.gridgain.com

github.com/hugegraph
hugegraph.apache.org

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

Technical documentation

www.gridgain.com/docs/index.html

hugegraph.apache.org/docs

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

learn.microsoft.com/azure/cosmos-db

Developer

GridGain Systems, Inc.

Baidu

Google

Microsoft

Initial release

2007

2018

2011

2014

Current release

GridGain 8.5.1

0.9

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Unix

Illumos
Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

All properties auto-indexed by default

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Groovy
Java
Python

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

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

asynchronous Gremlin script jobs

JavaScript

Triggers

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

depending on used storage backend, e.g. Cassandra and HBase

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Security Hooks for custom implementations

Users, roles and permissions

Access rights can be defined down to the item level

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