DBMS > atoti vs. Cubrid vs. Google Cloud Spanner vs. Sphinx vs. TDengine

System Properties Comparison atoti vs. Cubrid vs. Google Cloud Spanner vs. Sphinx vs. TDengine

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

Time Series DBMS and big data platform

Primary database model

Secondary database models

Relational DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

atoti.io

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/spanner

sphinxsearch.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.atoti.io

cubrid.org/manuals

cloud.google.com/spanner/docs

sphinxsearch.com/docs

docs.tdengine.com

Developer

ActiveViam

CUBRID Corporation, CUBRID Foundation

Google

Sphinx Technologies Inc.

TDEngine, previously Taos Data

Initial release

2008

2017

2001

2019

Current release

11.0, January 2021

3.5.1, February 2023

3.0, August 2022

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

AGPL V3, also commercial editions 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

Java

C, C++, Java

C++

Server operating systems

Linux
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
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

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

Query statements complying to ANSI 2011

SQL-like query language (SphinxQL)

Standard SQL with extensions for time-series applications

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Proprietary protocol

JDBC
RESTful HTTP API

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

Python

Java Stored Procedures

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication with 3 replicas for regional instances.

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

ACID

Strict serializable isolation

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

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8 May 2024

TDengine 3.3.0.0 Release Notes
7 May 2024

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6 May 2024

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19 April 2024

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15 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
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