DBMS > Ehcache vs. FatDB vs. Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer

System Properties Comparison Ehcache vs. FatDB vs. Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A widely adopted Java cache with tiered storage options

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Fully managed big data interactive analytics platform

Primary database model

Key-value store

Document store
Key-value store

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

column oriented

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	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.ehcache.org

github.com/spotify/heroic

www.leanxcale.com

azure.microsoft.com/services/data-explorer

Technical documentation

www.ehcache.org/documentation

spotify.github.io/heroic

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

Developer

Terracotta Inc, owned by Software AG

FatCloud

Spotify

LeanXcale

Microsoft

Initial release

2009

2012

2014

2015

2019

Current release

3.10.0, March 2022

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

Server operating systems

All OS with a Java VM

Windows

hosted

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

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

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

via Elasticsearch

all fields are automatically indexed

SQL

Support of SQL

Via inetgration in SQL Server

yes

through Apache Derby

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JCache

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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

Supported programming languages

Java

C
Java
Scala

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

Server-side scripts

Stored procedures

yes

via applications

Yes, possible languages: KQL, Python, R

Triggers

yes

Cache Event Listeners

yes

via applications

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

selectable replication factor

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Can implement custom security layer via applications

Azure Active Directory Authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Atlassian asks customers to patch critical Jira vulnerability
22 July 2021, BleepingComputer

Critical Jira Flaw in Atlassian Could Lead to RCE
22 July 2021, Threatpost

DZone Coding Java JBoss 5 to 7 in 11 steps
9 January 2014, dzone.com

provided by Google News

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, Microsoft

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, microsoft.com

Controlling costs in Azure Data Explorer using down-sampling and aggregation
11 February 2019, Microsoft

Individually great, collectively unmatched: Announcing updates to 3 great Azure Data Services
7 February 2019, Microsoft

Log and Telemetry Analytics Performance Benchmark
16 August 2022, Gigaom

provided by Google News

Share this page