DBMS > Google BigQuery vs. Microsoft Azure AI Search vs. Sphinx vs. TDengine vs. Warp 10

System Properties Comparison Google BigQuery vs. Microsoft Azure AI Search vs. Sphinx vs. TDengine vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Search-as-a-service for web and mobile app development

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

Time Series DBMS and big data platform

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Search engine

Time Series DBMS

Secondary database models

Vector DBMS

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/search

sphinxsearch.com

github.com/taosdata/TDengine
tdengine.com

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/en-us/azure/search

sphinxsearch.com/docs

docs.tdengine.com

www.warp10.io/content/02_Getting_started

Developer

Google

Microsoft

Sphinx Technologies Inc.

TDEngine, previously Taos Data

SenX

Initial release

2010

2015

2001

2019

2015

Current release

3.5.1, February 2023

3.0, August 2022

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

AGPL V3, also commercial editions available

Open Source

Apache License 2.0

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

C++

Java

Server operating systems

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

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

yes

SQL-like query language (SphinxQL)

Standard SQL with extensions for time-series applications

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

Proprietary protocol

JDBC
RESTful HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
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

user defined functions

in JavaScript

yes

WarpScript

Triggers

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

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

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

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

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

using Azure authentication

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Market metrics

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Licensing and pricing models

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