DBMS > Apache IoTDB vs. Apache Phoenix vs. Couchbase vs. Sphinx vs. Yanza

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. Couchbase vs. Sphinx vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A scale-out RDBMS with evolutionary schema built on Apache HBase

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

yanza.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

phoenix.apache.org

docs.couchbase.com

sphinxsearch.com/docs

Developer

Apache Software Foundation

Couchbase, Inc.

Sphinx Technologies Inc.

Yanza

Initial release

2018

2014

2011

2001

2015

Current release

1.1.0, April 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

GPL version 2, commercial licence available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C, C++, Go and Erlang

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Unix
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

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

SQL

Support of SQL

SQL-like query language

yes

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
Native API

JDBC

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Proprietary protocol

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Triggers

yes

via the TAP protocol

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Automatic Sharding

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

Multi-source replication

including cross data center replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

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

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

Ephemeral buckets

User concepts

Access control

yes

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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