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

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

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Distributed Database based on Lucene

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

Document store
Spatial DBMS
Search engine
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	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	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

cratedb.com

rocksdb.org

swaydb.simer.au

github.com/weaviate/weaviate
weaviate.io

Technical documentation

cratedb.com/docs

github.com/facebook/rocksdb/wiki

weaviate.io/developers/weaviate

Developer

Crate

Facebook, Inc.

Simer Plaha

Weaviate B.V.

Initial release

2013

2018

2019

Current release

9.2.1, May 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

MIT License

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

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.

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, but no triggers and constraints, and PostgreSQL compatibility

GraphQL is used as query language

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

C++ API
Java API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C
C++
Go
Java
Perl
Python
Ruby

Java
Kotlin
Scala

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions (Javascript)

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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As a database supporting the development of generative AI and semantic search applications...
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Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
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All companies that have data.
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Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

See CrateDB pricing >
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Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.