DBMS > Datomic vs. Firebolt vs. GigaSpaces vs. RocksDB

System Properties Comparison Datomic vs. Firebolt vs. GigaSpaces vs. RocksDB

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Name

Description

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

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Secondary database models

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Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

Technical documentation

docs.datomic.com

docs.firebolt.io

docs.gigaspaces.com/latest/landing.html

github.com/facebook/rocksdb/wiki

Developer

Cognitect

Firebolt Analytics Inc.

Gigaspaces Technologies

Facebook, Inc.

Initial release

2012

2020

2000

2013

Current release

1.0.7075, December 2023

15.5, September 2020

9.2.1, May 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

BSD

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, Clojure

Java, C++, .Net

C++

Server operating systems

All OS with a Java VM

hosted

Linux
macOS
Solaris
Windows

Linux

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

APIs and other access methods

RESTful HTTP API

.Net
ODBC
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

C++ API
Java API

Supported programming languages

Clojure
Java

Go
JavaScript (Node.js)
Python

.Net
C++
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

depending on storage layer

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

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

yes

User concepts

Access control

Role-based access control

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