DBMS > Google BigQuery vs. InterSystems Caché vs. Postgres-XL vs. Sphinx vs. VelocityDB

System Properties Comparison Google BigQuery vs. InterSystems Caché vs. Postgres-XL vs. Sphinx vs. VelocityDB

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Search engine

Graph DBMS
Object oriented DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

cloud.google.com/bigquery

www.intersystems.com/products/cache

www.postgres-xl.org

sphinxsearch.com

velocitydb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.intersystems.com

www.postgres-xl.org/documentation

sphinxsearch.com/docs

velocitydb.com/UserGuide

Developer

Google

InterSystems

Sphinx Technologies Inc.

VelocityDB Inc

Initial release

2010

1997

2014

since 2012, originally named StormDB

2001

2011

Current release

2018.1.4, May 2020

10 R1, October 2018

3.5.1, February 2023

7.x

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

GPL version 2, commercial licence available

commercial

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

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Any that supports .NET

Data scheme

yes

depending on used data model

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol

.Net

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

ACID

MVCC

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Based on Windows Authentication

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