DBMS > EsgynDB vs. Newts vs. Vertica vs. XTDB

System Properties Comparison EsgynDB vs. Newts vs. Vertica vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS based on Cassandra

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.esgyn.cn

opennms.github.io/newts

www.vertica.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

github.com/OpenNMS/newts/wiki

vertica.com/documentation

www.xtdb.com/docs

Developer

Esgyn

OpenNMS Group

OpenText

previously Micro Focus and Hewlett Packard

Juxt Ltd.

Initial release

2015

2014

2005

2019

Current release

12.0.3, January 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

MIT License

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

Java

C++

Clojure

Server operating systems

Linux

Linux
OS X
Windows

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP REST
Java API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java

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

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning, hierarchical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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