DBMS > Ingres vs. KeyDB vs. MarkLogic vs. Microsoft Azure Cosmos DB

System Properties Comparison Ingres vs. KeyDB vs. MarkLogic vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

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	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

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

Website

www.actian.com/databases/ingres

github.com/Snapchat/KeyDB
keydb.dev

www.progress.com/marklogic

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.actian.com/ingres

docs.keydb.dev

www.progress.com/marklogic/documentation

learn.microsoft.com/azure/cosmos-db

Developer

Actian Corporation

EQ Alpha Technology Ltd.

MarkLogic Corp.

Microsoft

Initial release

1974

originally developed at University Berkely in early 1970s

2019

2001

2014

Current release

11.2, May 2022

11.0, December 2022

License

Commercial or Open Source

commercial

Open Source

BSD-3

commercial

restricted free version is available

commercial

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++

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

by using the Redis Search module

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL92

SQL-like query language

APIs and other access methods

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Proprietary protocol

RESP - REdis Serialization Protoco

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

Supported programming languages

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

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

Server-side scripts

Stored procedures

yes

Lua

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes

yes, with Range Indexes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control and ACL

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

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