DBMS > Dragonfly vs. Google BigQuery vs. Microsoft Azure Data Explorer vs. Quasardb

System Properties Comparison Dragonfly vs. Google BigQuery vs. Microsoft Azure Data Explorer vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Large scale data warehouse service with append-only tables

Fully managed big data interactive analytics platform

Distributed, high-performance timeseries database

Primary database model

Key-value store

Relational DBMS

column oriented

Time Series 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	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

cloud.google.com/bigquery

azure.microsoft.com/services/data-explorer

quasar.ai

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/bigquery/docs

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

doc.quasar.ai/master

Developer

DragonflyDB team and community contributors

Google

Microsoft

quasardb

Initial release

2023

2010

2019

2009

Current release

1.0, March 2023

cloud service with continuous releases

3.14.1, January 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

Linux

hosted

BSD
Linux
OS X
Windows

Data scheme

scheme-free

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes

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

yes

integer and binary

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

all fields are automatically indexed

yes

with tags

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP/JSON API

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

HTTP API

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

Yes, possible languages: KQL, Python, R

Triggers

publish/subscribe channels provide some trigger functionality

yes

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

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

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

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Password-based authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Azure Active Directory Authentication

Cryptographically strong user authentication and audit trail

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