DBMS > CrateDB vs. Oracle Rdb vs. Vertica vs. Vitess

System Properties Comparison CrateDB vs. Oracle Rdb vs. Vertica vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

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.

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

cratedb.com

www.oracle.com/database/technologies/related/rdb.html

www.vertica.com

vitess.io

Technical documentation

cratedb.com/docs

www.oracle.com/database/technologies/related/rdb-doc.html

vertica.com/documentation

vitess.io/docs

Developer

Crate

Oracle, originally developed by Digital Equipment Corporation (DEC)

OpenText

previously Micro Focus and Hewlett Packard

The Linux Foundation, PlanetScale

Initial release

2013

1984

2005

2013

Current release

5.8.1, August 2024

7.4.1.1, 2021

12.0.3, January 2023

15.0.2, December 2022

License

Commercial or Open Source

Open Source

commercial

Limited community edition free

Open Source

Apache Version 2.0, commercial licenses available

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

Java

C++

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

HP Open VMS

Linux

Docker
Linux
macOS

Data scheme

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

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

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

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

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

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

yes

with proprietary extensions

APIs and other access methods

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

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

character-based, interactive, front-end utility

ADO.NET
JDBC
MySQL protocol
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
R

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

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes, PostgreSQL PL/pgSQL, with minor differences

yes

proprietary syntax

Triggers

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, hierarchical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Multi-source replication

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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

yes, on a single node

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

Users with fine-grained authorization concept

no user groups or roles

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More resources

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