DBMS > Google Cloud Spanner vs. HBase vs. Redis vs. Riak KV vs. STSdb

System Properties Comparison Google Cloud Spanner vs. HBase vs. Redis vs. Riak KV vs. STSdb

Editorial information provided by DB-Engines

Name

Description

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Distributed, fault tolerant key-value store

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Wide column store

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Key-value store

Secondary database models

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	157.80
Rank	#6	Overall
	#1	Key-value stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/spanner

hbase.apache.org

redis.com
redis.io

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/spanner/docs

hbase.apache.org/book.html

docs.redis.com/latest/index.html
redis.io/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

OpenSource, formerly Basho Technologies

STS Soft SC

Initial release

2017

2008

2009

2011

Current release

2.3.4, January 2021

7.2.4, January 2024

3.2.0, December 2022

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPLv2, commercial license available

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.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

Java

Erlang

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
OS X

Windows

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

partial

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

yes

primitive types and user defined types (classes)

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

with RediSearch module

restricted

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

with RediSQL module

APIs and other access methods

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

proprietary protocol

RESP - REdis Serialization Protocol

HTTP API
Native Erlang Interface

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C#
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Lua; Redis Functions coming in Redis 7 (slides and Github)

Erlang

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

through RedisGears

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Eventual Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

links between data sets can be stored

Transaction concepts

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

ACID

Strict serializable isolation

Single row ACID (across millions of columns)

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

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

User concepts

Access control

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

yes, using Riak Security

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