DBMS > Cachelot.io vs. CrateDB vs. EventStoreDB vs. Google Cloud Firestore vs. Lovefield

System Properties Comparison Cachelot.io vs. CrateDB vs. EventStoreDB vs. Google Cloud Firestore vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Distributed Database based on Lucene

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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 relational database for web apps written in pure JavaScript

Primary database model

Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Event Store

Document store

Relational DBMS

Secondary database models

Relational DBMS

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

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

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

cachelot.io

cratedb.com

www.eventstore.com

firebase.google.com/products/firestore

google.github.io/lovefield

Technical documentation

cratedb.com/docs

developers.eventstore.com

firebase.google.com/docs/firestore

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Crate

Event Store Limited

Google

Initial release

2015

2013

2012

2017

2014

Current release

21.2, February 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

commercial

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

C++

Java

JavaScript

Server operating systems

FreeBSD
Linux
OS X

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

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

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.

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

Memcached protocol

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

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

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

JavaScript

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes, Firebase Rules & Cloud Functions

Triggers

yes, with Cloud Functions

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

rights management via user accounts

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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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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