DBMS > Heroic vs. NSDb vs. Qdrant vs. SwayDB vs. Transbase

System Properties Comparison Heroic vs. NSDb vs. Qdrant vs. SwayDB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

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

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

A high-performance vector database with neural network or semantic-based matching

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Score	0.05
Rank	#355	Overall
	#150	Relational DBMS

Website

github.com/spotify/heroic

nsdb.io

github.com/qdrant/qdrant
qdrant.tech

swaydb.simer.au

www.transaction.de/en/products/transbase.html

Technical documentation

spotify.github.io/heroic

nsdb.io/Architecture

qdrant.tech/documentation

www.transaction.de/en/products/transbase/features.html

Developer

Spotify

Qdrant

Simer Plaha

Transaction Software GmbH

Initial release

2014

2017

2021

2018

1987

Current release

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

GNU Affero GPL V3.0

commercial

free development license

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, Scala

Rust

Scala

C and C++

Server operating systems

Linux
macOS

Docker
Linux
macOS
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

Numbers, Strings, Geo, Boolean

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.

Secondary indexes

yes

via Elasticsearch

all fields are automatically indexed

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

gRPC
HTTP REST
WebSocket

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
Scala

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Java
Kotlin
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Collection-level replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of operations

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

User concepts

Access control

Key-based authentication

fine grained access rights according to SQL-standard

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