DBMS > Apache Druid vs. Datomic vs. Microsoft Azure Cosmos DB vs. NSDb vs. SQream DB

System Properties Comparison Apache Druid vs. Datomic vs. Microsoft Azure Cosmos DB vs. NSDb vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

druid.apache.org

www.datomic.com

azure.microsoft.com/services/cosmos-db

nsdb.io

sqream.com

Technical documentation

druid.apache.org/docs/latest/design

docs.datomic.com

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

docs.sqream.com

Developer

Apache Software Foundation and contributors

Cognitect

Microsoft

SQream Technologies

Initial release

2012

2014

2017

Current release

29.0.1, April 2024

1.0.6735, June 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Clojure

Java, Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

hosted

Linux
macOS

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

yes, ANSI Standard SQL Types

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.

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

SQL for querying

SQL-like query language

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP API

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

gRPC
HTTP REST
WebSocket

.Net
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Clojure
Java

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

Java
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

JavaScript

user defined functions in Python

Triggers

By using transaction functions

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

But extensive use of caching in the application peers

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights can be defined down to the item level

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