DBMS > Apache Druid vs. BaseX vs. DuckDB vs. GeoSpock vs. Snowflake

System Properties Comparison Apache Druid vs. BaseX vs. DuckDB vs. GeoSpock vs. Snowflake

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Spatial and temporal data processing engine for extreme data scale

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

Primary database model

Relational DBMS
Time Series DBMS

Native XML DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

geospock.com

Technical documentation

druid.apache.org/docs/latest/design

docs.basex.org

duckdb.org/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

BaseX GmbH

GeoSpock

Snowflake Computing Inc.

Initial release

2012

2007

2018

2014

Current release

29.0.1, April 2024

10.7, August 2023

0.10, February 2024

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

BSD license

Open Source

MIT License

commercial

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

C++

Java, Javascript

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

server-less

hosted

Data scheme

yes

schema-less columns are supported

schema-free

yes

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

Typing

predefined data types such as float or date

yes

XQuery supports 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

Secondary indexes

yes

temporal, categorical

SQL

Support of SQL

SQL for querying

yes

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

JDBC

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

via events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Automatic sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

yes

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

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Users with fine-grained authorization concept on 4 levels

Access rights for users can be defined per table

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

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