DBMS > etcd vs. Google Cloud Firestore vs. InfinityDB vs. Microsoft SQL Server vs. TinkerGraph

System Properties Comparison etcd vs. Google Cloud Firestore vs. InfinityDB vs. Microsoft SQL Server vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

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.

A Java embedded Key-Value Store which extends the Java Map interface

Microsofts flagship relational DBMS

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

etcd.io
github.com/etcd-io/etcd

firebase.google.com/products/firestore

boilerbay.com

www.microsoft.com/en-us/sql-server

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

firebase.google.com/docs/firestore

boilerbay.com/infinitydb/manual

learn.microsoft.com/en-US/sql/sql-server

Developer

Google

Boiler Bay Inc.

Microsoft

Initial release

2017

2002

1989

2009

Current release

3.4, August 2019

4.0

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

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++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

All OS with a Java VM

Linux
Windows

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

APIs and other access methods

gRPC
JSON over HTTP

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

TinkerPop 3

Supported programming languages

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

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

Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Groovy
Java

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes, watching key changes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

none

yes, but depending on the SQL-Server Edition

none

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

READ-COMMITTED or SERIALIZED

Immediate Consistency

none

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

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.

fine grained access rights according to SQL-standard

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