DBMS > BoltDB vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. Spark SQL

System Properties Comparison BoltDB vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Postgres-XL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/boltdb/bolt

www.marklogic.com

azure.microsoft.com/services/cosmos-db

www.postgres-xl.org

spark.apache.org/sql

Technical documentation

docs.marklogic.com

learn.microsoft.com/azure/cosmos-db

www.postgres-xl.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

MarkLogic Corp.

Microsoft

Apache Software Foundation

Initial release

2013

2001

2014

since 2012, originally named StormDB

2014

Current release

11.0, December 2022

10 R1, October 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

restricted free version is available

commercial

Open Source

Mozilla public license

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

Schema can be enforced

schema-free

yes

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL92

SQL-like query language

yes

distributed, parallel query execution

SQL-like DML and DDL statements

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

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

user defined functions

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

none

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

none

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

yes

ACID

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

MVCC

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

User concepts

Access control

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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