DBMS > Apache Druid vs. Databricks vs. Geode vs. RocksDB

System Properties Comparison Apache Druid vs. Databricks vs. Geode vs. RocksDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Primary database model

Relational DBMS
Time Series DBMS

Document store
Relational DBMS

Key-value store

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.databricks.com

geode.apache.org/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation and contributors

Databricks

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Facebook, Inc.

Initial release

2012

2013

2002

2013

Current release

29.0.1, April 2024

1.1, February 2017

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

C++

Server operating systems

Linux
OS X
Unix

hosted

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Data scheme

yes

schema-less columns are supported

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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

with Databricks SQL

SQL-like query language (OQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

C++ API
Java API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Python
R
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes, on a single node

yes

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.

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights per client and object definable

More information provided by the system vendor

Specific characteristics

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.