DBMS > Heroic vs. Kingbase vs. Solr vs. SWC-DB

System Properties Comparison Heroic vs. Kingbase vs. Solr vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

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

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

Time Series DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.50
Rank	#257	Overall
	#119	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

github.com/spotify/heroic

www.kingbase.com.cn

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

spotify.github.io/heroic

solr.apache.org/resources.html

Developer

Spotify

BeiJing KINGBASE Information technologies inc.

Apache Software Foundation

Alex Kashirin

Initial release

2014

1999

2006

2020

Current release

V8.0, August 2021

9.6.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

Open Source

GPL V3

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 and Java

Java

C++

Server operating systems

Linux
Windows

All OS with a Java VM

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

Linux

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

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

via Elasticsearch

yes

All search fields are automatically indexed

SQL

Support of SQL

Standard with numerous extensions

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

C++

Server-side scripts

Stored procedures

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

yes

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More resources

DB-Engines blog posts

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12 January 2016, Paul Andlinger

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4 February 2013, Paul Andlinger

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