DBMS > Google BigQuery vs. Google Cloud Spanner vs. H2 vs. Interbase vs. RavenDB

System Properties Comparison Google BigQuery vs. Google Cloud Spanner vs. H2 vs. Interbase vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Light-weight proven RDBMS

originally from Borland

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

cloud.google.com/bigquery

cloud.google.com/spanner

www.h2database.com

www.embarcadero.com/products/interbase

ravendb.net

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/spanner/docs

www.h2database.com/html/main.html

docs.embarcadero.com/products/interbase

ravendb.net/docs

Developer

Google

Thomas Mueller

Embarcadero

Hibernating Rhinos

Initial release

2010

2017

2005

1984

2010

Current release

2.2.220, July 2023

InterBase 2020, December 2019

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

AGPL version 3, commercial license 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

Server operating systems

hosted

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

yes

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

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

JDBC
ODBC

ADO.NET
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Java

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

yes

Interbase procedure and trigger language

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

With clustering: 2 database servers on different computers operate on identical copies of a database

Interbase Change Views

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

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

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Strict serializable isolation

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

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 based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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