DBMS > Apache Druid vs. GigaSpaces vs. Microsoft Azure Data Explorer vs. Redis vs. Riak KV

System Properties Comparison Apache Druid vs. GigaSpaces vs. Microsoft Azure Data Explorer vs. Redis vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Fully managed big data interactive analytics platform

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

Primary database model

Relational DBMS
Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

column oriented

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

Secondary database models

Graph DBMS
Search engine

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	155.94
Rank	#6	Overall
	#1	Key-value stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

druid.apache.org

www.gigaspaces.com

azure.microsoft.com/services/data-explorer

redis.com
redis.io

Technical documentation

druid.apache.org/docs/latest/design

docs.gigaspaces.com/latest/landing.html

docs.microsoft.com/en-us/azure/data-explorer

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

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

Developer

Apache Software Foundation and contributors

Gigaspaces Technologies

Microsoft

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

OpenSource, formerly Basho Technologies

Initial release

2012

2000

2019

2009

Current release

29.0.1, April 2024

15.5, September 2020

cloud service with continuous releases

7.2.5, May 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

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

Open Source

Apache version 2, commercial enterprise edition

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

Java, C++, .Net

Erlang

Server operating systems

Linux
OS X
Unix

Linux
macOS
Solaris
Windows

hosted

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
OS X

Data scheme

yes

schema-less columns are supported

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

yes

Secondary indexes

yes

all fields are automatically indexed

yes

with RediSearch module

restricted

SQL

Support of SQL

SQL for querying

SQL-99 for query and DML statements

Kusto Query Language (KQL), SQL subset

with RediSQL module

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

proprietary protocol

RESP - REdis Serialization Protocol

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C++
Java
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

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

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

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

Erlang

Triggers

yes, event driven architecture

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

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

manual/auto, time-based

Sharding

Implicit feature of the cloud service

Sharding

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Spark connector (open source): github.com/Azure/azure-kusto-spark

through RedisGears

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency
Immediate 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

links between data sets can be stored

Transaction concepts

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

ACID

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role-based access control

Azure Active Directory Authentication

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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