DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. LMDB vs. Sphinx vs. Tkrzw

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. LMDB vs. Sphinx vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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 high performant, light-weight, embedded key-value database library

Open source search engine for searching in data from different sources, e.g. relational databases

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

Document store
Key-value store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

www.symas.com/symas-embedded-database-lmdb

sphinxsearch.com

dbmx.net/tkrzw

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

www.lmdb.tech/doc

sphinxsearch.com/docs

Developer

Amazon

Google

Symas

Sphinx Technologies Inc.

Mikio Hirabayashi

Initial release

2012

2017

2011

2001

2020

Current release

0.9.32, January 2024

3.5.1, February 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 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

C++

Server operating systems

hosted

Linux
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

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

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Triggers

yes

by integration with AWS Lambda

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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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