DBMS > Google Cloud Firestore vs. LevelDB vs. Oracle Berkeley DB vs. SpatiaLite vs. Splice Machine

System Properties Comparison Google Cloud Firestore vs. LevelDB vs. Oracle Berkeley DB vs. SpatiaLite vs. Splice Machine

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.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used in-process key-value store

Spatial extension of SQLite

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	6.63
Rank	#54	Overall
	#9	Document stores

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	1.56
Rank	#142	Overall
	#3	Spatial DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

firebase.google.com/products/firestore

github.com/google/leveldb

www.oracle.com/database/technologies/related/berkeleydb.html

www.gaia-gis.it/fossil/libspatialite/index

splicemachine.com

Technical documentation

firebase.google.com/docs/firestore

github.com/google/leveldb/blob/main/doc/index.md

docs.oracle.com/cd/E17076_05/html/index.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

splicemachine.com/how-it-works

Developer

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Alessandro Furieri

Splice Machine

Initial release

2017

2011

1994

2008

2014

Current release

1.23, February 2021

18.1.40, May 2020

5.0.0, August 2020

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

commercial license available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Open Source

AGPL 3.0, commercial license available

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

C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Java

Server operating systems

hosted

Illumos
Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less

Linux
OS X
Solaris
Windows

Data scheme

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

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

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Java

Triggers

yes, with Cloud Functions

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

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.

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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Spatial database management systems
6 April 2021, Matthias Gelbmann

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