DBMS > Kdb vs. MarkLogic vs. Spark SQL vs. SQLite vs. Teradata Aster

System Properties Comparison Kdb vs. MarkLogic vs. Spark SQL vs. SQLite vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

High performance Time Series DBMS

Operational and transactional Enterprise NoSQL database

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

Widely used embeddable, in-process RDBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Time Series DBMS
Vector DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Relational DBMS

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

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

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

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

code.kx.com

docs.marklogic.com

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

www.sqlite.org/docs.html

Developer

Kx Systems, a division of First Derivatives plc

MarkLogic Corp.

Apache Software Foundation

Dwayne Richard Hipp

Teradata

Initial release

2000

kdb was released 2000, kdb+ in 2003

2001

2014

2000

2005

Current release

3.6, May 2018

11.0, December 2022

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

free 32-bit version

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

Public Domain

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

server-less

Linux

Data scheme

yes

schema-free

Schema can be enforced

yes

dynamic column types

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

in Aster File Store

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like query language (q)

yes

SQL92

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

Java
Python
R
Scala

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

yes

via XQuery or JavaScript

R packages

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes, utilizing Spark Core

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

yes

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

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

via file-system locks

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

yes, with Range Indexes

yes

User concepts

Access control

rights management via user accounts

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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