DBMS > Google BigQuery vs. Oracle Rdb vs. Solr vs. Vitess

System Properties Comparison Google BigQuery vs. Oracle Rdb vs. Solr vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely used distributed, scalable search engine based on Apache Lucene

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/rdb.html

solr.apache.org

vitess.io

Technical documentation

cloud.google.com/bigquery/docs

www.oracle.com/database/technologies/related/rdb-doc.html

solr.apache.org/resources.html

vitess.io/docs

Developer

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

The Linux Foundation, PlanetScale

Initial release

2010

1984

2006

2013

Current release

7.4.1.1, 2021

9.6.0, April 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0, commercial licenses 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

HP Open VMS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Docker
Linux
macOS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java plugins

yes

proprietary syntax

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

yes, on a single node

optimistic locking

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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)

yes

Users with fine-grained authorization concept

no user groups or roles

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