DBMS > CrateDB vs. Kyligence Enterprise vs. Oracle Rdb vs. Transwarp Hippo

System Properties Comparison CrateDB vs. Kyligence Enterprise vs. Oracle Rdb vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

A distributed analytics engine for big data, built on top of Apache Kylin

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Vector DBMS

Secondary database models

Relational 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	0.44
Rank	#259	Overall
	#118	Relational DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

cratedb.com

kyligence.io/kyligence-enterprise

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

www.transwarp.cn/en/subproduct/hippo

Technical documentation

cratedb.com/docs

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

Developer

Crate

Kyligence, Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2013

2016

1984

2023

Current release

5.8.1, August 2024

7.4.1.1, 2021

1.0, May 2023

License

Commercial or Open Source

Open Source

commercial

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

Java

C++

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

HP Open VMS

Linux
macOS

Data scheme

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

yes

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

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

RESTful HTTP 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++
Java
Python

Server-side scripts

Stored procedures

user defined functions (Javascript)

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

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

yes, on a single node

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

Role based access control and fine grained access rights

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More resources

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