DBMS > Amazon DocumentDB vs. InfinityDB vs. MarkLogic vs. SAP Adaptive Server vs. SQLite

System Properties Comparison Amazon DocumentDB vs. InfinityDB vs. MarkLogic vs. SAP Adaptive Server vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A Java embedded Key-Value Store which extends the Java Map interface

Operational and transactional Enterprise NoSQL database

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Widely used embeddable, in-process RDBMS

Primary database model

Document store

Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/documentdb

boilerbay.com

www.marklogic.com

www.sap.com/products/technology-platform/sybase-ase.html

www.sqlite.org

Technical documentation

aws.amazon.com/documentdb/resources

boilerbay.com/infinitydb/manual

docs.marklogic.com

help.sap.com/docs/SAP_ASE

www.sqlite.org/docs.html

Developer

Boiler Bay Inc.

MarkLogic Corp.

SAP, Sybase

Dwayne Richard Hipp

Initial release

2019

2002

2001

1987

2000

Current release

4.0

11.0, December 2022

16.0

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

Public Domain

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++

C and C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

server-less

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Schema can be enforced

yes

dynamic column types

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

not rigid because of 'dynamic typing' concept.

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

SQL92

yes

SQL-92 is not fully supported

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

Atomic single-document operations

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes, with Range Indexes

yes

User concepts

Access control

Access rights for users and roles

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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