DBMS > Google Cloud Bigtable vs. InfinityDB vs. Lovefield

System Properties Comparison Google Cloud Bigtable vs. InfinityDB vs. Lovefield

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A Java embedded Key-Value Store which extends the Java Map interface

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Key-value store
Wide column store

Key-value store

Relational DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

cloud.google.com/bigtable

boilerbay.com

google.github.io/lovefield

Technical documentation

cloud.google.com/bigtable/docs

boilerbay.com/infinitydb/manual

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

Developer

Google

Boiler Bay Inc.

Google

Initial release

2015

2002

2014

Current release

4.0

2.1.12, February 2017

License

Commercial or 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.

Implementation language

Java

JavaScript

Server operating systems

hosted

All OS with a Java VM

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Supported programming languages

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

Java

JavaScript

Server-side scripts

Stored procedures

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

Atomic single-row operations

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

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

More information provided by the system vendor

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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Google expands BigQuery with Gemini, brings vector support to cloud databases
29 February 2024, VentureBeat

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Share this page