DBMS > Apache Druid vs. Elasticsearch vs. Firebase Realtime Database vs. RocksDB vs. Spark SQL

System Properties Comparison Apache Druid vs. Elasticsearch vs. Firebase Realtime Database vs. RocksDB vs. Spark SQL

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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

druid.apache.org

www.elastic.co/elasticsearch

firebase.google.com/products/realtime-database

rocksdb.org

spark.apache.org/sql

Technical documentation

druid.apache.org/docs/latest/design

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

firebase.google.com/docs/database

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation and contributors

Elastic

Google

acquired by Google 2014

Facebook, Inc.

Apache Software Foundation

Initial release

2012

2010

2012

2013

2014

Current release

29.0.1, April 2024

8.6, January 2023

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Elastic License

commercial

Open Source

BSD

Open Source

Apache 2.0

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++

Scala

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

Flexible type definitions. Once a type is defined, it is persistent

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

C++ API
Java API

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Java
JavaScript
Objective-C

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Triggers

yes

by using the 'percolation' feature

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Foreign keys

Referential integrity

Transaction concepts

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

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.

Memcached and Redis integration

yes

User concepts

Access control

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

yes, based on authentication and database rules

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