DBMS > BigObject vs. Google Cloud Firestore vs. Riak TS vs. RocksDB vs. Tkrzw

System Properties Comparison BigObject vs. Google Cloud Firestore vs. Riak TS vs. RocksDB vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Time Series DBMS

Key-value store

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

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

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

bigobject.io

firebase.google.com/products/firestore

rocksdb.org

dbmx.net/tkrzw

Technical documentation

docs.bigobject.io

firebase.google.com/docs/firestore

www.tiot.jp/riak-docs/riak/ts/latest

github.com/facebook/rocksdb/wiki

Developer

BigObject, Inc.

Google

Open Source, formerly Basho Technologies

Facebook, Inc.

Mikio Hirabayashi

Initial release

2015

2017

2015

2013

2020

Current release

3.0.0, September 2022

9.2.1, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

BSD

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
OS X

Linux

Linux
macOS

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

HTTP API
Native Erlang Interface

C++ API
Java API

Supported programming languages

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes, Firebase Rules & Cloud Functions

Erlang

Triggers

yes, with Cloud Functions

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

links between datasets can be stored

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

using specific database classes

User concepts

Access control

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

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