DBMS > 4D vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB vs. RisingWave vs. Solr

System Properties Comparison 4D vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB vs. RisingWave vs. Solr

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

Globally distributed, horizontally scalable, multi-model database service

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.4d.com

kyligence.io/kyligence-enterprise

azure.microsoft.com/services/cosmos-db

www.risingwave.com/database

solr.apache.org

Technical documentation

developer.4d.com

learn.microsoft.com/azure/cosmos-db

docs.risingwave.com/docs/current/intro

solr.apache.org/resources.html

Developer

4D, Inc

Kyligence, Inc.

Microsoft

RisingWave Labs

Apache Software Foundation

Initial release

1984

2016

2014

2022

2006

Current release

v20, April 2023

1.2, September 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Rust

Java

Server operating systems

OS X
Windows

Linux

hosted

Docker
Linux
macOS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

JSON types

Standard SQL-types and JSON

yes

supports customizable data types and automatic typing

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

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

close to SQL 92

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

yes

Solr Parallel SQL Interface

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
PostgreSQL wire protocol

Java API
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Go
Java
JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

JavaScript

UDFs in Python or Java

Java plugins

Triggers

yes

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

Users and groups

Access rights can be defined down to the item level

Users and Roles

yes

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