DBMS > Datomic vs. Dragonfly vs. Ingres vs. RocksDB vs. Teradata Aster

System Properties Comparison Datomic vs. Dragonfly vs. Ingres vs. RocksDB 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

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

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Well established RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Platform for big data analytics on multistructured data sources and types

Primary database model

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.datomic.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.actian.com/databases/ingres

rocksdb.org

Technical documentation

docs.datomic.com

www.dragonflydb.io/docs

docs.actian.com/ingres

github.com/facebook/rocksdb/wiki

Developer

Cognitect

DragonflyDB team and community contributors

Actian Corporation

Facebook, Inc.

Teradata

Initial release

2012

2023

1974

originally developed at University Berkely in early 1970s

2013

2005

Current release

1.0.7075, December 2023

1.0, March 2023

11.2, May 2022

9.2.1, May 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

BSL 1.1

commercial

Open Source

BSD

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

Java, Clojure

C++

Server operating systems

All OS with a Java VM

Linux

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

scheme-free

yes

schema-free

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

strings, hashes, lists, sets, sorted sets, bit arrays

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.

but tools for importing/exporting data from/to XML-files available

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

C++ API
Java API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Clojure
Java

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

Lua

yes

R packages

Triggers

By using transaction functions

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

Ingres Replicator

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

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Atomic execution of command blocks and scripts

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

MVCC

yes

Durability

Support for making data persistent

yes

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

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

yes

User concepts

Access control

Password-based authentication

fine grained access rights according to SQL-standard

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