DBMS > Heroic vs. Manticore Search vs. NSDb vs. SQLite vs. Weaviate

System Properties Comparison Heroic vs. Manticore Search vs. NSDb vs. SQLite vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

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

Multi-storage database for search, including full-text search.

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

Widely used embeddable, in-process RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	1.48
Rank	#149	Overall
	#8	Vector DBMS

Website

github.com/spotify/heroic

manticoresearch.com

nsdb.io

www.sqlite.org

github.com/weaviate/weaviate
weaviate.io

Technical documentation

spotify.github.io/heroic

manual.manticoresearch.com

nsdb.io/Architecture

www.sqlite.org/docs.html

weaviate.io/developers/weaviate

Developer

Spotify

Manticore Software

Dwayne Richard Hipp

Weaviate B.V.

Initial release

2014

2017

2000

2019

Current release

6.0, February 2023

3.46.1 (13 August 2024), August 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

Open Source

Apache Version 2.0

Open Source

Public Domain

Open Source

commercial license available with Weaviate Enterprise

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

C++

Java, Scala

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
macOS

server-less

Data scheme

schema-free

Fixed schema

yes

dynamic column types

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes: int, bigint, decimal, string

yes

not rigid because of 'dynamic typing' concept.

yes

string, int, float, geo point, date, cross reference, fuzzy references

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.

Can index from XML

Secondary indexes

yes

via Elasticsearch

yes

full-text index on all search fields

all fields are automatically indexed

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

GraphQL is used as query language

APIs and other access methods

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

gRPC
HTTP REST
WebSocket

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication based on Galera library

none

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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