DBMS > Apache Druid vs. Google Cloud Firestore vs. RocksDB vs. Rockset vs. Tibero

System Properties Comparison Apache Druid vs. Google Cloud Firestore vs. RocksDB vs. Rockset vs. Tibero

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

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

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Relational DBMS
Search engine

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	7.85
Rank	#51	Overall
	#8	Document stores

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

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

druid.apache.org

firebase.google.com/products/firestore

rocksdb.org

rockset.com

us.tmaxsoft.com/products/tibero

Technical documentation

druid.apache.org/docs/latest/design

firebase.google.com/docs/firestore

github.com/facebook/rocksdb/wiki

docs.rockset.com

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation and contributors

Google

Facebook, Inc.

Rockset

TmaxSoft

Initial release

2012

2017

2013

2019

2003

Current release

29.0.1, April 2024

8.11.4, April 2024

6, April 2015

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C and Assembler

Server operating systems

Linux
OS X
Unix

hosted

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL for querying

Read-only SQL queries, including JOINs

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

C++ API
Java API

HTTP REST

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C++
Go
Java
Perl
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Persistent Stored Procedure (PSM)

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

horizontal partitioning

Automatic sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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

planned for next version

User concepts

Access control

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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