DBMS > Databricks vs. Datomic vs. IRONdb vs. RocksDB vs. Rockset

System Properties Comparison Databricks vs. Datomic vs. IRONdb vs. RocksDB vs. Rockset

Editorial information provided by DB-Engines

Name

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

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

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

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Document store
Relational DBMS

Secondary database models

Relational DBMS
Search engine

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Website

www.databricks.com

www.datomic.com

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

rocksdb.org

rockset.com

Technical documentation

docs.databricks.com

docs.datomic.com

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

github.com/facebook/rocksdb/wiki

docs.rockset.com

Developer

Databricks

Cognitect

Circonus LLC.

Facebook, Inc.

Rockset

Initial release

2013

2012

2017

2013

2019

Current release

1.0.7075, December 2023

V0.10.20, January 2018

9.2.1, May 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

BSD

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, Clojure

C and C++

C++

Server operating systems

hosted

All OS with a Java VM

Linux

hosted

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

with Databricks SQL

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

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

HTTP API

C++ API
Java API

HTTP REST

Supported programming languages

Python
R
Scala

Clojure
Java

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

C
C++
Go
Java
Perl
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Transaction Functions

yes, in Lua

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Automatic, metric affinity per node

horizontal partitioning

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

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

Access rights for users and organizations can be defined via Rockset console

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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