DBMS > BoltDB vs. Kdb vs. RocksDB vs. SwayDB vs. Weaviate

System Properties Comparison BoltDB vs. Kdb vs. RocksDB vs. SwayDB vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

High performance Time Series DBMS

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

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

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

Primary database model

Key-value store

Time Series DBMS
Vector DBMS

Key-value store

Vector DBMS

Secondary database models

Relational DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

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

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	1.52
Rank	#153	Overall
	#5	Vector DBMS

Website

github.com/boltdb/bolt

kx.com

rocksdb.org

swaydb.simer.au

github.com/weaviate/weaviate
weaviate.io

Technical documentation

code.kx.com

github.com/facebook/rocksdb/wiki

weaviate.io/developers/weaviate

Developer

Kx Systems, a division of First Derivatives plc

Facebook, Inc.

Simer Plaha

Weaviate B.V.

Initial release

2013

2000

kdb was released 2000, kdb+ in 2003

2013

2018

2019

Current release

3.6, May 2018

9.2.1, May 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

free 32-bit version

Open Source

BSD

Open Source

GNU Affero GPL V3.0

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

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

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

table attribute 'grouped'

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 (q)

GraphQL is used as query language

APIs and other access methods

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

C++ API
Java API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

Java
Kotlin
Scala

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

rights management via user accounts

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

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

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

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

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

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

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

All companies that have data.
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

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

Licensing and pricing models

upon request
» more

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.