DBMS > GeoSpock vs. GigaSpaces vs. GridDB vs. Redis vs. Sphinx

System Properties Comparison GeoSpock vs. GigaSpaces vs. GridDB vs. Redis vs. Sphinx

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Scalable in-memory time series database optimized for IoT and Big Data

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.

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Time Series DBMS

Key-value store

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

Search engine

Secondary database models

Time Series DBMS

Graph DBMS
Search engine

Key-value store
Relational DBMS

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	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

geospock.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

docs.griddb.net

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

sphinxsearch.com/docs

Developer

GeoSpock

Gigaspaces Technologies

Toshiba Corporation

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Sphinx Technologies Inc.

Initial release

2000

2013

2009

2001

Current release

2.0, September 2019

15.5, September 2020

5.1, August 2022

7.2.4, January 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

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

Open Source

GPL version 2, commercial licence 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, Javascript

Java, C++, .Net

C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

partial

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

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.

XML can be used for describing objects metadata

Secondary indexes

temporal, categorical

yes

with RediSearch module

yes

full-text index on all search fields

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-99 for query and DML statements

SQL92, SQL-like TQL (Toshiba Query Language)

with RediSQL module

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

proprietary protocol

RESP - REdis Serialization Protocol

Proprietary protocol

Supported programming languages

.Net
C++
Java
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

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

Triggers

yes, event driven architecture

yes

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate consistency within container, eventual consistency across containers

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

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID at container level

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

The original contents of fields are not stored in the Sphinx index.

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 can be defined per table

Role-based access control

Access rights for users can be defined per database

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

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.