DBMS > Blueflood vs. Datomic vs. Google BigQuery vs. Greenplum vs. InfinityDB

System Properties Comparison Blueflood vs. Datomic vs. Google BigQuery vs. Greenplum vs. InfinityDB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Website

blueflood.io

www.datomic.com

cloud.google.com/bigquery

greenplum.org

boilerbay.com

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.datomic.com

cloud.google.com/bigquery/docs

docs.greenplum.org

boilerbay.com/infinitydb/manual

Developer

Rackspace

Cognitect

Google

Pivotal Software Inc.

Boiler Bay Inc.

Initial release

2013

2012

2010

2005

2002

Current release

1.0.6735, June 2023

7.0.0, September 2023

4.0

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

commercial

Open Source

Apache 2.0

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

Java

Java, Clojure

Java

Server operating systems

Linux
OS X

All OS with a Java VM

hosted

Linux

All OS with a Java VM

Data scheme

predefined scheme

yes

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

Typing

predefined data types such as float or date

yes

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

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

APIs and other access methods

HTTP REST

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

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

Supported programming languages

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

Java

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

But extensive use of caching in the application peers

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

But extensive use of caching in the application peers

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

READ-COMMITTED or SERIALIZED

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

ACID

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

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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