DBMS > Quasardb vs. RocksDB vs. Vertica vs. Weaviate

System Properties Comparison Quasardb vs. RocksDB vs. Vertica vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Distributed, high-performance timeseries database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

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

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Score	1.52
Rank	#153	Overall
	#6	Vector DBMS

Website

quasar.ai

rocksdb.org

www.vertica.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

doc.quasar.ai/master

github.com/facebook/rocksdb/wiki

vertica.com/documentation

weaviate.io/developers/weaviate

Developer

quasardb

Facebook, Inc.

OpenText

previously Micro Focus and Hewlett Packard

Weaviate B.V.

Initial release

2009

2013

2005

2019

Current release

3.14.1, January 2024

9.2.1, May 2024

12.0.3, January 2023

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

Open Source

BSD

commercial

Limited community edition free

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

BSD
Linux
OS X
Windows

Linux

Data scheme

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

integer and binary

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.

Secondary indexes

yes

with tags

No Indexes Required. Different internal optimization strategy, but same functionality included.

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

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

GraphQL is used as query language

APIs and other access methods

HTTP API

C++ API
Java API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

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

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Bi-directional Spark integration

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

yes

ACID

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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As a database supporting the development of generative AI and semantic search applications...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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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

Cost-based models and subscription-based models are both available. One license is...
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Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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