DBMS > GigaSpaces vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. YDB

System Properties Comparison GigaSpaces vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. YDB

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Fully vectorized database across both GPUs and CPUs

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Relational DBMS

Secondary database models

Graph DBMS
Search engine

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

www.gigaspaces.com

www.kinetica.com

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.gigaspaces.com/latest/landing.html

docs.kinetica.com

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

learn.microsoft.com/azure/cosmos-db

ydb.tech/en/docs

Developer

Gigaspaces Technologies

Kinetica

Google

Microsoft

Yandex

Initial release

2000

2012

2011

2014

2019

Current release

15.5, September 2020

7.1, August 2021

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

BSD

commercial

Open Source

Apache 2.0; commercial license available

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++

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux

Illumos
Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

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.

XML can be used for describing objects metadata

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (YQL)

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JDBC
ODBC
RESTful HTTP API

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

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
C++
Java
Python
Scala

C++
Java
JavaScript (Node.js)
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

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

Triggers

yes, event driven architecture

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

none

yes

Implicit feature of the cloud service

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate 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

GPU vRAM or System RAM

User concepts

Access control

Role-based access control

Access rights for users and roles on table level

Access rights can be defined down to the item level

Access rights defined for Yandex Cloud users

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