DBMS > FeatureBase vs. Sphinx vs. Splice Machine vs. WakandaDB

System Properties Comparison FeatureBase vs. Sphinx vs. Splice Machine vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Open source search engine for searching in data from different sources, e.g. relational databases

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	0.23
Rank	#296	Overall
	#134	Relational DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

docs.featurebase.com

sphinxsearch.com/docs

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Molecula and Pilosa Open Source Contributors

Sphinx Technologies Inc.

Splice Machine

Wakanda SAS

Initial release

2017

2001

2014

2012

Current release

2022, May 2022

3.5.1, February 2023

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

Open Source

AGPLv3, extended commercial license available

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

C++

Java

C++, JavaScript

Server operating systems

Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL queries

SQL-like query language (SphinxQL)

yes

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

Java
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

JavaScript

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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, using Linux fsync

yes

The original contents of fields are not stored in the Sphinx index.

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

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

yes

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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