DBMS > H2GIS vs. Snowflake vs. Spark SQL vs. XTDB

System Properties Comparison H2GIS vs. Snowflake vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Spatial DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

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

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.h2gis.org

www.snowflake.com

spark.apache.org/sql

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

Technical documentation

www.h2gis.org/docs/home

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.xtdb.com/docs

Developer

CNRS

Snowflake Computing Inc.

Apache Software Foundation

Juxt Ltd.

Initial release

2013

2014

2019

Current release

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

LGPL 3.0

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Clojure

Server operating systems

hosted

Linux
OS X
Windows

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

Data scheme

yes

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

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

CLI Client
JDBC
ODBC

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

Java

JavaScript (Node.js)
Python

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

based on H2

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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, multi-version concurrency control (MVCC)

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

User concepts

Access control

yes

based on H2

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

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