DBMS > Ehcache vs. Google Cloud Firestore vs. HarperDB vs. Machbase Neo vs. Snowflake

System Properties Comparison Ehcache vs. Google Cloud Firestore vs. HarperDB vs. Machbase Neo vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

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.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

TimeSeries DBMS for AIoT and BigData

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

Primary database model

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.ehcache.org

firebase.google.com/products/firestore

www.harperdb.io

machbase.com

www.snowflake.com

Technical documentation

www.ehcache.org/documentation

firebase.google.com/docs/firestore

docs.harperdb.io/docs

machbase.com/dbms

docs.snowflake.net/manuals/index.html

Developer

Terracotta Inc, owned by Software AG

Google

HarperDB

Machbase

Snowflake Computing Inc.

Initial release

2009

2017

2013

2014

Current release

3.10.0, March 2022

3.1, August 2021

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free community edition available

commercial

free test version available

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

Java

Node.js

Server operating systems

All OS with a Java VM

hosted

Linux
OS X

Linux
macOS
Windows

hosted

Data scheme

schema-free

dynamic schema

yes

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

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

yes

APIs and other access methods

JCache

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Java

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Custom Functions

since release 3.1

user defined functions

Triggers

yes

Cache Event Listeners

yes, with Cloud Functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication

yes

the nodes on which a table resides can be defined

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

volatile and lookup table

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 and roles

simple password-based access control

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

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