DBMS > EsgynDB vs. GigaSpaces vs. Kinetica vs. Snowflake vs. XTDB

System Properties Comparison EsgynDB vs. GigaSpaces vs. Kinetica vs. Snowflake vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Fully vectorized database across both GPUs and CPUs

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store

Secondary database models

Graph DBMS
Search engine

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	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

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

Technical documentation

docs.gigaspaces.com/latest/landing.html

docs.kinetica.com

docs.snowflake.net/manuals/index.html

www.xtdb.com/docs

Developer

Esgyn

Gigaspaces Technologies

Kinetica

Snowflake Computing Inc.

Juxt Ltd.

Initial release

2015

2000

2012

2014

2019

Current release

15.5, September 2020

7.1, August 2021

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

MIT License

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

Java, C++, .Net

C, C++

Clojure

Server operating systems

Linux

Linux
macOS
Solaris
Windows

Linux

hosted

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

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

SQL-like DML and DDL statements

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java
Python
Scala

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions

Triggers

yes, event driven architecture

yes

triggers when inserted values for one or more columns fall within a specified range

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency or Eventual Consistency depending on configuration

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.

yes

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Access rights for users and roles on table level

Users with fine-grained authorization concept, user roles and pluggable authentication

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