DBMS > Apache Impala vs. Riak TS vs. Sphinx vs. TypeDB

System Properties Comparison Apache Impala vs. Riak TS vs. Sphinx vs. TypeDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Open source search engine for searching in data from different sources, e.g. relational databases

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Relational DBMS

Time Series DBMS

Search engine

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.65
Rank	#234	Overall
	#20	Graph DBMS
	#107	Relational DBMS

Website

impala.apache.org

sphinxsearch.com

typedb.com

Technical documentation

impala.apache.org/impala-docs.html

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

sphinxsearch.com/docs

typedb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Open Source, formerly Basho Technologies

Sphinx Technologies Inc.

Vaticle

Initial release

2013

2015

2001

2016

Current release

4.1.0, June 2022

3.0.0, September 2022

3.5.1, February 2023

2.26.3, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL version 2, commercial licence available

Open Source

GPL Version 3, commercial licenses 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++

Erlang

C++

Java

Server operating systems

Linux

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

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

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

HTTP API
Native Erlang Interface

Proprietary protocol

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

All languages supporting JDBC/ODBC

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++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

substituted by the relationship feature

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

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

TypeDB is a polymorphic database with a conceptual data model, a strong subtyping...
» more

Competitive advantages

TypeDB provides a new level of expressivity, extensibility, interoperability, and...
» more

Typical application scenarios

Life sciences : TypeDB makes working with biological data much easier and accelerates...
» more

Licensing and pricing models

Apache f or language drivers, and AGPL and Commercial for the database server. The...
» more

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More resources

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