DBMS > NSDb vs. Postgres-XL vs. Weaviate vs. Yaacomo

System Properties Comparison NSDb vs. Postgres-XL vs. Weaviate vs. Yaacomo

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	1.52
Rank	#153	Overall
	#6	Vector DBMS

Website

nsdb.io

www.postgres-xl.org

github.com/weaviate/weaviate
weaviate.io

yaacomo.com

Technical documentation

nsdb.io/Architecture

www.postgres-xl.org/documentation

weaviate.io/developers/weaviate

Developer

Weaviate B.V.

Q2WEB GmbH

Initial release

2017

2014

since 2012, originally named StormDB

2019

2009

Current release

10 R1, October 2018

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Mozilla public license

Open Source

commercial license available with Weaviate Enterprise

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, Scala

Server operating systems

Linux
macOS

Android
Linux
Windows

Data scheme

yes

yes, maps to GraphQL interface

yes

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

yes

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

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

XML type, but no XML query functionality

Secondary indexes

all fields are automatically indexed

yes

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

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

GraphQL is used as query language

yes

APIs and other access methods

gRPC
HTTP REST
WebSocket

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

GraphQL query language
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Java
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
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

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

API Keys
OpenID Connect Discovery

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

show all

Recent citations in the news

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24 January 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
8 February 2024, Datanami

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17 December 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18 July 2023, Towards Data Science

Weaviate Raises $50 Million Series B Funding to Meet Soaring Demand for AI Native Vector Database Technology ...
21 April 2023, PR Newswire

provided by Google News

Share this page