DBMS > Apache Drill vs. Datomic vs. GeoSpock

System Properties Comparison Apache Drill vs. Datomic vs. GeoSpock

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Editorial information provided by DB-Engines

Name

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

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Spatial and temporal data processing engine for extreme data scale

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Website

drill.apache.org

www.datomic.com

geospock.com

Technical documentation

drill.apache.org/docs

docs.datomic.com

Developer

Apache Software Foundation

Cognitect

GeoSpock

Initial release

2012

Current release

1.20.3, January 2023

1.0.6735, June 2023

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

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, Clojure

Java, Javascript

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

temporal, categorical

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

ANSI SQL for query only (using Presto)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC

Supported programming languages

C++

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

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

Depending on the underlying data source

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

Depending on the underlying data source

yes

recommended only for testing and development

User concepts

Access control

Depending on the underlying data source

Access rights for users can be defined per table

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More resources

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