DBMS > EsgynDB vs. Hawkular Metrics vs. Microsoft Access vs. Tarantool

System Properties Comparison EsgynDB vs. Hawkular Metrics vs. Microsoft Access vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	1.67
Rank	#150	Overall
	#25	Document stores
	#25	Key-value stores
	#69	Relational DBMS

Website

www.esgyn.cn

www.hawkular.org

www.microsoft.com/en-us/microsoft-365/access

www.tarantool.io

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

developer.microsoft.com/en-us/access

www.tarantool.io/en/doc

Developer

Esgyn

Community supported by Red Hat

Microsoft

Initial release

2015

2014

1992

2008

Current release

1902 (16.0.11328.20222), March 2019

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Java

C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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.

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

Full-featured ANSI SQL support

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP REST

ADO.NET
DAO
ODBC
OLE DB

Open binary protocol

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

yes

since Access 2010 using the ACE-engine

Lua, C and SQL stored procedures

Triggers

yes

via Hawkular Alerting

yes

since Access 2010 using the ACE-engine

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

but no files for transaction logging

yes, write ahead logging

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

fine grained access rights according to SQL-standard

a simple user-level security was built in till version Access 2003

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

MS Access drops in DB-Engines Ranking
2 May 2013, Paul Andlinger

Microsoft SQL Server regained rank 2 in the DB-Engines popularity ranking
3 December 2012, Matthias Gelbmann

New DB-Engines Ranking shows the popularity of database management systems
3 October 2012, Matthias Gelbmann, Paul Andlinger

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Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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