DBMS > Google BigQuery vs. Greenplum vs. InfinityDB vs. IRONdb vs. SWC-DB

System Properties Comparison Google BigQuery vs. Greenplum vs. InfinityDB vs. IRONdb vs. SWC-DB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

A Java embedded Key-Value Store which extends the Java Map interface

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

cloud.google.com/bigquery

greenplum.org

boilerbay.com

www.circonus.com/solutions/time-series-database/

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

cloud.google.com/bigquery/docs

docs.greenplum.org

boilerbay.com/infinitydb/manual

docs.circonus.com/irondb/category/getting-started

Developer

Google

Pivotal Software Inc.

Boiler Bay Inc.

Circonus LLC.

Alex Kashirin

Initial release

2010

2005

2002

2017

2020

Current release

7.0.0, September 2023

4.0

V0.10.20, January 2018

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL V3

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

C and C++

C++

Server operating systems

hosted

Linux

All OS with a Java VM

Linux

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

text, numeric, histograms

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

since Version 4.2

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP API

Proprietary protocol
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Automatic, metric affinity per node

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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