DBMS > Apache Impala vs. Datomic vs. GeoMesa vs. STSdb

System Properties Comparison Apache Impala vs. Datomic vs. GeoMesa vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Spatial DBMS

Key-value store

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

impala.apache.org

www.datomic.com

www.geomesa.org

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

www.geomesa.org/documentation/stable/user/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

CCRi and others

STS Soft SC

Initial release

2013

2012

2014

2011

Current release

4.1.0, June 2022

1.0.7075, December 2023

5.0.0, May 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

Apache License 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Clojure

Scala

Server operating systems

Linux

All OS with a Java VM

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

depending on storage layer

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

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.

yes

recommended only for testing and development

depending on storage layer

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Recent citations in the news

Apache Impala becomes Top-Level Project
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