DBMS > MarkLogic vs. Microsoft Azure Table Storage vs. RocksDB vs. Solr vs. Tibero

System Properties Comparison MarkLogic vs. Microsoft Azure Table Storage vs. RocksDB vs. Solr vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Operational and transactional Enterprise NoSQL database

A Wide Column Store for rapid development using massive semi-structured datasets

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A widely used distributed, scalable search engine based on Apache Lucene

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Secondary database models

Spatial DBMS

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

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

www.marklogic.com

azure.microsoft.com/en-us/services/storage/tables

rocksdb.org

solr.apache.org

us.tmaxsoft.com/products/tibero

Technical documentation

docs.marklogic.com

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

MarkLogic Corp.

Microsoft

Facebook, Inc.

Apache Software Foundation

TmaxSoft

Initial release

2001

2012

2013

2006

2003

Current release

11.0, December 2022

8.11.4, April 2024

9.6.0, April 2024

6, April 2015

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

BSD

Open Source

Apache Version 2

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++

Java

C and Assembler

Server operating systems

Linux
OS X
Windows

hosted

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

Schema can be enforced

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL92

Solr Parallel SQL Interface

yes

APIs and other access methods

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

RESTful HTTP API

C++ API
Java API

Java API
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

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

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Java plugins

Persistent Stored Procedure (PSM)

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

optimistic locking

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

planned for next version

User concepts

Access control

Role-based access control at the document and subdocument levels

Access rights based on private key authentication or shared access signatures

yes

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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