DBMS > BoltDB vs. Linter vs. Postgres-XL vs. RocksDB vs. Weaviate

System Properties Comparison BoltDB vs. Linter vs. Postgres-XL vs. RocksDB vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

RDBMS for high security requirements

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

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

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

Primary database model

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.76
Rank	#224	Overall
	#32	Key-value stores

Score	0.10
Rank	#350	Overall
	#153	Relational DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	1.89
Rank	#138	Overall
	#4	Vector DBMS

Website

github.com/boltdb/bolt

linter.ru

www.postgres-xl.org

rocksdb.org

github.com/weaviate/weaviate
weaviate.io

Technical documentation

www.postgres-xl.org/documentation

github.com/facebook/rocksdb/wiki

weaviate.io/developers/weaviate

Developer

relex.ru

Facebook, Inc.

Weaviate B.V.

Initial release

2013

1990

2014

since 2012, originally named StormDB

2013

2019

Current release

10 R1, October 2018

8.11.4, April 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Mozilla public license

Open Source

BSD

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 and C++

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS

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

XML type, but no XML query functionality

Secondary indexes

yes

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

SQL

Support of SQL

yes

distributed, parallel query execution

GraphQL is used as query language

APIs and other access methods

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

C++ API
Java API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

C
C++
Go
Java
Perl
Python
Ruby

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

ACID

MVCC

yes

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

fine grained access rights according to SQL-standard

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,...
» 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.