DBMS > Google Cloud Bigtable vs. KeyDB vs. Oracle NoSQL vs. SpaceTime

System Properties Comparison Google Cloud Bigtable vs. KeyDB vs. Oracle NoSQL vs. SpaceTime

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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.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Key-value store
Wide column store

Key-value store

Document store
Key-value store
Relational DBMS

Spatial DBMS

Secondary database models

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	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

cloud.google.com/bigtable

github.com/Snapchat/KeyDB
keydb.dev

www.oracle.com/database/nosql/technologies/nosql

www.mireo.com/spacetime

Technical documentation

cloud.google.com/bigtable/docs

docs.keydb.dev

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Google

EQ Alpha Technology Ltd.

Oracle

Mireo

Initial release

2015

2019

2011

2020

Current release

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

C++

Java

C++

Server operating systems

hosted

Linux

Linux
Solaris SPARC/x86

Linux

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

optional

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

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

APIs and other access methods

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

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API

Supported programming languages

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

C#
C++
Python

Server-side scripts

Stored procedures

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Optimistic locking, atomic execution of commands blocks and scripts

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

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

simple password-based access control and ACL

Access rights for users and roles

yes

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