DBMS > Kdb vs. Microsoft Azure Cosmos DB vs. Prometheus vs. Realm vs. Sqrrl

System Properties Comparison Kdb vs. Microsoft Azure Cosmos DB vs. Prometheus vs. Realm vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

High performance Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

Open-source Time Series DBMS and monitoring system

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Relational DBMS

Spatial 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	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Website

kx.com

azure.microsoft.com/services/cosmos-db

prometheus.io

realm.io

sqrrl.com

Technical documentation

code.kx.com

learn.microsoft.com/azure/cosmos-db

prometheus.io/docs

realm.io/docs

Developer

Kx Systems, a division of First Derivatives plc

Microsoft

Realm, acquired by MongoDB in May 2019

Amazon

originally Sqrrl Data, Inc.

Initial release

2000

kdb was released 2000, kdb+ in 2003

2014

2015

2014

2012

Current release

3.6, May 2018

License

Commercial or Open Source

commercial

free 32-bit version

commercial

Open Source

Apache 2.0

Open Source

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

Java

Server operating systems

Linux
OS X
Solaris
Windows

hosted

Linux
Windows

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

table attribute 'grouped'

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (q)

SQL-like query language

APIs and other access methods

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

user defined functions

JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes

with views

JavaScript

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

none

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

by Federation

none

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Atomic updates per row, document, or graph entity

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

In-Memory realm

User concepts

Access control

rights management via user accounts

Access rights can be defined down to the item level

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

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...
» more

Licensing and pricing models

upon request
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.