DBMS > GridGain vs. LevelDB vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. Qdrant

System Properties Comparison GridGain vs. LevelDB vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. Qdrant

Editorial information provided by DB-Engines

Name

Description

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

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

Extremely fast embedded database for small devices, IoT and Mobile

A high-performance vector database with neural network or semantic-based matching

Primary database model

Key-value store
Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Object oriented DBMS

Vector DBMS

Secondary database models

Spatial DBMS

Time Series 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	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	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

www.gridgain.com

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

objectbox.io

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

www.gridgain.com/docs/index.html

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

learn.microsoft.com/azure/cosmos-db

docs.objectbox.io

qdrant.tech/documentation

Developer

GridGain Systems, Inc.

Google

Microsoft

ObjectBox Limited

Qdrant

Initial release

2007

2011

2014

2017

2021

Current release

GridGain 8.5.1

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

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

Java, C++, .Net

C++

C and C++

Rust

Server operating systems

Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

hosted

Android
iOS
Linux
macOS
Windows

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

Numbers, Strings, Geo, Boolean

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

All properties auto-indexed by default

yes

Keywords, numberic ranges, geo, full-text

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

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

Proprietary native API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

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

JavaScript

Triggers

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

yes

Implicit feature of the cloud service

online/offline synchronization between client and server

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

Immediate Consistency

Eventual Consistency, tunable 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 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

Access rights can be defined down to the item level

yes

Key-based authentication

More information provided by the system vendor

News

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

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