DBMS > DuckDB vs. Google BigQuery vs. Hypertable vs. Sphinx

System Properties Comparison DuckDB vs. Google BigQuery vs. Hypertable vs. Sphinx

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Primary database model

Relational DBMS

Wide column store

Search engine

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

duckdb.org

cloud.google.com/bigquery

sphinxsearch.com

Technical documentation

duckdb.org/docs

cloud.google.com/bigquery/docs

sphinxsearch.com/docs

Developer

Google

Hypertable Inc.

Sphinx Technologies Inc.

Initial release

2018

2010

2009

2001

Current release

1.0.0, June 2024

0.9.8.11, March 2016

3.5.1, February 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2, commercial licence 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

C++

Server operating systems

server-less

hosted

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

restricted

only exact value or prefix value scans

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

RESTful HTTP/JSON API

C++ API
Thrift

Proprietary protocol

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor on file system level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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.

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)

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