DBMS > Drizzle vs. KairosDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Sqrrl

System Properties Comparison Drizzle vs. KairosDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Sqrrl

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Distributed Time Series DBMS based on Cassandra or H2

Globally distributed, horizontally scalable, multi-model database service

Fully managed big data interactive analytics platform

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column oriented

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

github.com/kairosdb/kairosdb

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/data-explorer

sqrrl.com

Technical documentation

kairosdb.github.io

learn.microsoft.com/azure/cosmos-db

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

Developer

Drizzle project, originally started by Brian Aker

Microsoft

Amazon

originally Sqrrl Data, Inc.

Initial release

2008

2013

2014

2019

2012

Current release

7.2.4, September 2012

1.2.2, November 2018

cloud service with continuous releases

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

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++

Java

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

all fields are automatically indexed

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC

Graphite protocol
HTTP REST
Telnet API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C++
Java
PHP

Java
JavaScript

Node.js
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

JavaScript

Yes, possible languages: KQL, Python, R

Triggers

hooks for callbacks inside the server can be used.

JavaScript

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

yes

Implicit feature of the cloud service

yes

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

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

simple password-based access control

Access rights can be defined down to the item level

Azure Active Directory Authentication

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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