DBMS > Apache Drill vs. Kinetica vs. Riak TS vs. SAP HANA vs. TimescaleDB

System Properties Comparison Apache Drill vs. Kinetica vs. Riak TS vs. SAP HANA vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

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

In-memory, column based data store. Available as appliance or cloud service

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

drill.apache.org

www.kinetica.com

www.sap.com/products/hana.html

www.timescale.com

Technical documentation

drill.apache.org/docs

docs.kinetica.com

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

help.sap.com/hana

docs.timescale.com

Developer

Apache Software Foundation

Kinetica

Open Source, formerly Basho Technologies

SAP

Timescale

Initial release

2012

2015

2010

2017

Current release

1.20.3, January 2023

7.1, August 2021

3.0.0, September 2022

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Erlang

Server operating systems

Linux
OS X
Windows

Linux

Linux
OS X

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like DML and DDL statements

yes, limited

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C++

C++
Java
JavaScript (Node.js)
Python

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

Erlang

SQLScript, R

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

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

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

GPU vRAM or System RAM

yes

User concepts

Access control

Depending on the underlying data source

Access rights for users and roles on table level

yes

fine grained access rights according to SQL-standard

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