DBMS > Bangdb vs. CockroachDB vs. MarkLogic vs. Microsoft SQL Server

System Properties Comparison Bangdb vs. CockroachDB vs. MarkLogic vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Operational and transactional Enterprise NoSQL database

Microsofts flagship relational DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

bangdb.com

www.cockroachlabs.com

www.marklogic.com

www.microsoft.com/en-us/sql-server

Technical documentation

docs.bangdb.com

www.cockroachlabs.com/docs

docs.marklogic.com

learn.microsoft.com/en-US/sql/sql-server

Developer

Sachin Sinha, BangDB

Cockroach Labs

MarkLogic Corp.

Microsoft

Initial release

2012

2015

2001

1989

Current release

BangDB 2.0, October 2021

24.1.0, May 2024

11.0, December 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0, commercial license available

commercial

restricted free version is available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Server operating systems

Linux

Linux
macOS
Windows

Linux
OS X
Windows

Linux
Windows

Data scheme

schema-free

dynamic schema

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes, wire compatible with PostgreSQL

yes

SQL92

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

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)

Supported programming languages

C
C#
C++
Java
Python

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication using RAFT

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes, with Range Indexes

yes

User concepts

Access control

yes (enterprise version only)

Role-based access control

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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