DBMS > Apache Druid vs. Microsoft Azure Data Explorer vs. SwayDB vs. VoltDB

System Properties Comparison Apache Druid vs. Microsoft Azure Data Explorer vs. SwayDB vs. VoltDB

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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

Fully managed big data interactive analytics platform

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

column oriented

Key-value store

Relational DBMS

Secondary database models

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

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Score	1.46
Rank	#159	Overall
	#74	Relational DBMS

Website

druid.apache.org

azure.microsoft.com/services/data-explorer

swaydb.simer.au

www.voltdb.com

Technical documentation

druid.apache.org/docs/latest/design

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

docs.voltdb.com

Developer

Apache Software Foundation and contributors

Microsoft

Simer Plaha

VoltDB Inc.

Initial release

2012

2019

2018

2010

Current release

29.0.1, April 2024

cloud service with continuous releases

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GNU Affero GPL V3.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Scala

Java, C++

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X

for development

Data scheme

yes

schema-less columns are supported

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

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

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.

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL for querying

Kusto Query Language (KQL), SQL subset

yes

only a subset of SQL 99

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Java
Kotlin
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Java

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

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

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

Atomic execution of operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Azure Active Directory Authentication

Users and roles with access to stored procedures

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