DBMS > BigchainDB vs. Datomic vs. Ehcache vs. Google BigQuery vs. TimesTen

System Properties Comparison BigchainDB vs. Datomic vs. Ehcache vs. Google BigQuery vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

www.bigchaindb.com

www.datomic.com

www.ehcache.org

cloud.google.com/bigquery

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

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.datomic.com

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

docs.oracle.com/database/timesten-18.1

Developer

Cognitect

Terracotta Inc, owned by Software AG

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2012

2009

2010

1998

Current release

1.0.7075, December 2023

3.10.0, March 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

AGPL v3

commercial

limited edition free

Open Source

Apache Version 2; commercial licenses 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

Python

Java, Clojure

Java

Server operating systems

Linux

All OS with a Java VM

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
RESTful HTTP API

RESTful HTTP API

JCache

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

Clojure
Java

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

PL/SQL

Triggers

By using transaction functions

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

by using Terracotta Server

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

yes

by using Terracotta Server

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

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

yes

using a tiered cache-storage approach

yes

by means of logfiles and checkpoints

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

yes

User concepts

Access control

yes

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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