DBMS > Quasardb vs. SAP HANA vs. Solr vs. Weaviate

System Properties Comparison Quasardb vs. SAP HANA vs. Solr vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Distributed, high-performance timeseries database

In-memory, column based data store. Available as appliance or cloud service

A widely used distributed, scalable search engine based on Apache Lucene

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	1.89
Rank	#138	Overall
	#4	Vector DBMS

Website

quasar.ai

www.sap.com/products/hana.html

solr.apache.org

github.com/weaviate/weaviate
weaviate.io

Technical documentation

doc.quasar.ai/master

help.sap.com/hana

solr.apache.org/resources.html

weaviate.io/developers/weaviate

Developer

quasardb

SAP

Apache Software Foundation

Weaviate B.V.

Initial release

2009

2010

2006

2019

Current release

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

9.5.0, February 2024

1.19, May 2023

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Apache Version 2

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

BSD
Linux
OS X
Windows

Appliance or cloud-service

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

integer and binary

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

with tags

yes

All search 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

Solr Parallel SQL Interface

GraphQL is used as query language

APIs and other access methods

HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

yes

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.
» more

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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