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.

Zilliz Cloud – Cloud-native service for Milvus

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C++

C++, Go

C and C++

C++, Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

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

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

Vector, Numeric and String

yes

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

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL92, SQL-like TQL (Toshiba Query Language)

yes

with proprietary extensions

yes

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

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
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C++
Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

proprietary syntax

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Source-replica replication

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate consistency within container, eventual consistency across containers

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

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

ACID at container level

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights for users can be defined per database

Role based access control and fine grained access rights

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
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GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
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RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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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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Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
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Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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GitHub trending repository
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As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

See CrateDB pricing >
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Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.