DBMS > Dragonfly vs. Heroic vs. Microsoft Azure AI Search vs. Teradata Aster vs. Tkrzw

System Properties Comparison Dragonfly vs. Heroic vs. Microsoft Azure AI Search vs. Teradata Aster vs. Tkrzw

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

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Search-as-a-service for web and mobile app development

Platform for big data analytics on multistructured data sources and types

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Vector DBMS

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

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

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

github.com/spotify/heroic

azure.microsoft.com/en-us/services/search

dbmx.net/tkrzw

Technical documentation

www.dragonflydb.io/docs

spotify.github.io/heroic

learn.microsoft.com/en-us/azure/search

Developer

DragonflyDB team and community contributors

Spotify

Microsoft

Teradata

Mikio Hirabayashi

Initial release

2023

2014

2015

2005

2020

Current release

1.0, March 2023

0.9.3, August 2020

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

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

C++

Java

C++

Server operating systems

Linux

hosted

Linux

Linux
macOS

Data scheme

scheme-free

schema-free

yes

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

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

schema-free

Typing

predefined data types such as float or date

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.

yes

in Aster File Store

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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#
Java
JavaScript
Python

C
C#
C++
Java
Python
R

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Lua

R packages

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

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

none

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

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

using specific database classes

User concepts

Access control

Password-based authentication

yes

using Azure authentication

fine grained access rights according to SQL-standard

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