DBMS > Elasticsearch vs. Kingbase vs. Riak KV vs. Snowflake

System Properties Comparison Elasticsearch vs. Kingbase vs. Riak KV vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

Distributed, fault tolerant key-value store

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Search engine

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial DBMS

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	0.50
Rank	#257	Overall
	#119	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.elastic.co/elasticsearch

www.kingbase.com.cn

www.snowflake.com

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

Developer

Elastic

BeiJing KINGBASE Information technologies inc.

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Initial release

2010

1999

2009

2014

Current release

8.6, January 2023

V8.0, August 2021

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

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

C and Java

Erlang

Server operating systems

All OS with a Java VM

Linux
Windows

Linux
OS X

hosted

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

restricted

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

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

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Erlang

user defined functions

Triggers

yes

by using the 'percolation' feature

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

no "single point of failure"

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

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.

Memcached and Redis integration

User concepts

Access control

fine grained access rights according to SQL-standard

yes, using Riak Security

Users with fine-grained authorization concept, user roles and pluggable authentication

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