DBMS > EsgynDB vs. etcd vs. FileMaker vs. Microsoft Azure Cosmos DB vs. TimesTen

System Properties Comparison EsgynDB vs. etcd vs. FileMaker vs. Microsoft Azure Cosmos DB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A distributed reliable key-value store

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Globally distributed, horizontally scalable, multi-model database service

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.esgyn.cn

etcd.io
github.com/etcd-io/etcd

www.claris.com/filemaker

azure.microsoft.com/services/cosmos-db

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.claris.com/resources/documentation

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/database/timesten-18.1

Developer

Esgyn

Claris

a subsidiary of Apple

Microsoft

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

1983

2014

1998

Current release

3.4, August 2019

19.4.1, November 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 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

Linux

FreeBSD
Linux
Windows

experimental

iOS

client part only
Linux
OS X
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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

A http query request can return the data in XML format

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

via plugins

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

Filemaker WebDirect
JDBC
ODBC

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

PHP

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Java Stored Procedures

yes

JavaScript

PL/SQL

Triggers

yes, watching key changes

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Source-replica replication, warm standby

since Version 14

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple rights management via user accounts and connection to external directory services

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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