DBMS > Apache Impala vs. Kinetica vs. NSDb vs. TimesTen vs. Yanza

System Properties Comparison Apache Impala vs. Kinetica vs. NSDb vs. TimesTen vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

Fully vectorized database across both GPUs and CPUs

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

In-Memory RDBMS compatible to Oracle

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

impala.apache.org

www.kinetica.com

nsdb.io

www.oracle.com/database/technologies/related/timesten.html

yanza.com

Technical documentation

impala.apache.org/impala-docs.html

docs.kinetica.com

nsdb.io/Architecture

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Kinetica

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Yanza

Initial release

2013

2012

2017

1998

2015

Current release

4.1.0, June 2022

7.1, August 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C, C++

Java, Scala

Server operating systems

Linux

Linux
macOS

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Java
Scala

C
C++
Java
PL/SQL

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

PL/SQL

Triggers

yes

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

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

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

yes

Using Apache Lucene

yes

by means of logfiles and checkpoints

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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

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

provided by Google News

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica Launches Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise
18 March 2024, GlobeNewswire

Kinetica Delivers Real-Time Vector Similarity Search
22 March 2024, Geospatial World

provided by Google News

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