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

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

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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

Distributed, fault tolerant key-value store

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS

Key-value store

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

Spatial DBMS

Secondary database models

Document store

Relational DBMS

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	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

impala.apache.org

www.datomic.com

www.mireo.com/spacetime

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

OpenSource, formerly Basho Technologies

Mireo

Initial release

2013

2012

2009

2020

Current release

4.1.0, June 2022

1.0.7075, December 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, Clojure

Erlang

C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X

Linux

Data scheme

yes

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP API
Native Erlang Interface

RESTful HTTP API

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

C#
C++
Python

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

Sharding

no "single point of failure"

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

selectable replication factor

Real-time block device replication (DRBD)

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

Eventual Consistency

Immediate 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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes, using Riak Security

yes

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

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Nubank buys firm behind Clojure programming language
28 July 2020, Finextra

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1 December 2020, The New Stack

Brazil’s Nubank acquires US software firm Cognitect, creator of Clojure and Datomic
24 July 2020, LatamList

James Dixon Imagines A Data Lake That Matters
26 January 2015, Forbes

provided by Google News

Basho Revamps Riak Open-Source Database
22 September 2023, InformationWeek

A Critique of Resizable Hash Tables: Riak Core & Random Slicing
26 August 2018, InfoQ.com

Riak NoSQL snapped up by Bet365
12 September 2017, ComputerWeekly.com

Basho to Bolster Riak with DB Plug-Ins
5 May 2014, Datanami

Basho Advances NoSQL Riak Enterprise 2.0 With Search, Advanced Data Types
15 September 2014, Data Center Knowledge

provided by Google News

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