DBMS > Coveo vs. Riak TS vs. Snowflake vs. Splice Machine vs. WakandaDB

System Properties Comparison Coveo vs. Riak TS vs. Snowflake vs. Splice Machine vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

docs.coveo.com

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

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Coveo

Open Source, formerly Basho Technologies

Snowflake Computing Inc.

Splice Machine

Wakanda SAS

Initial release

2012

2015

2014

2012

Current release

3.0.0, September 2022

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

AGPLv3, extended commercial license available

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

Erlang

Java

C++, JavaScript

Server operating systems

hosted

Linux
OS X

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

schema-free

yes

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

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes, limited

yes

APIs and other access methods

RESTful HTTP API

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

C#
Java
JavaScript
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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

Erlang

user defined functions

yes

Java

yes

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

granular access controls, API key management, content filters

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

Access rights for users, groups and roles according to SQL-standard

yes

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