DBMS > Apache Impala vs. NSDb vs. Sadas Engine vs. SiriDB

System Properties Comparison Apache Impala vs. NSDb vs. Sadas Engine vs. SiriDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

nsdb.io/Architecture

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

SADAS s.r.l.

Cesbit

Initial release

2013

2017

2006

2017

Current release

4.1.0, June 2022

8.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

free trial version available

Open Source

MIT License

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, Scala

C++

Server operating systems

Linux

Linux
macOS

AIX
Linux
Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

Numeric data

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

gRPC
HTTP REST
WebSocket

JDBC
ODBC
Proprietary protocol

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

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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

provided by Google News

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