DBMS > BoltDB vs. Faircom EDGE vs. Google Cloud Spanner vs. Quasardb vs. Snowflake

System Properties Comparison BoltDB vs. Faircom EDGE vs. Google Cloud Spanner vs. Quasardb vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Distributed, high-performance timeseries database

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/boltdb/bolt

www.faircom.com/products/faircom-edge

cloud.google.com/spanner

quasar.ai

www.snowflake.com

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/spanner/docs

doc.quasar.ai/master

docs.snowflake.net/manuals/index.html

Developer

FairCom Corporation

Google

quasardb

Snowflake Computing Inc.

Initial release

2013

1979

2017

2009

2014

Current release

V3, October 2020

3.14.1, January 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Restricted, free version available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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

ANSI C, C++

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Android
Linux

ARM, x86
Raspbian
Windows

hosted

BSD
Linux
OS X
Windows

hosted

Data scheme

schema-free

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

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

ANSI SQL queries

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Go
Java
JavaScript (Node.js)
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

Sharding

consistent hashing

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication with 3 replicas for regional instances.

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

yes

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept, user roles and pluggable authentication

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