DBMS > Apache Impala vs. Datomic vs. GeoMesa vs. Riak KV

System Properties Comparison Apache Impala vs. Datomic vs. GeoMesa vs. Riak KV

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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.

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Spatial DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

impala.apache.org

www.datomic.com

www.geomesa.org

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

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

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

CCRi and others

OpenSource, formerly Basho Technologies

Initial release

2013

2012

2014

2009

Current release

4.1.0, June 2022

1.0.7180, July 2024

5.0.1, July 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

Apache License 2.0

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux

All OS with a Java VM

Linux
OS X

Data scheme

yes

schema-free

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

Erlang

Triggers

By using transaction functions

yes

pre-commit hooks and post-commit hooks

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

Sharding

no "single point of failure"

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

selectable replication factor

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

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

yes, using Riak Security

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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