DBMS > atoti vs. Datomic vs. NSDb vs. RethinkDB vs. TimescaleDB

System Properties Comparison atoti vs. Datomic vs. NSDb vs. RethinkDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

Technical documentation

Developer

ActiveViam

Cognitect

The Linux Foundation

since July 2017

Timescale

Initial release

2012

2017

2009

2017

Current release

1.0.6735, June 2023

2.4.1, August 2020

2.15.0, May 2024

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

Apache 2.0

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

Java

Java, Clojure

Java, Scala

C++

Server operating systems

All OS with a Java VM

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

string, binary, float, bool, date, geometry

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

gRPC
HTTP REST
WebSocket

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

Supported programming languages

Clojure
Java

Java
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Python

yes

Transaction Functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

By using transaction functions

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

Sharding

range based

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

MVCC based

yes

Durability

Support for making data persistent

yes

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

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

yes

users and table-level permissions

fine grained access rights according to SQL-standard

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