DBMS > Apache Impala vs. Sadas Engine vs. Sequoiadb vs. Tkrzw vs. Yaacomo

System Properties Comparison Apache Impala vs. Sadas Engine vs. Sequoiadb vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Analytic DBMS for Hadoop

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

NewSQL database with distributed OLTP and SQL

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store
Relational DBMS

Key-value store

Relational DBMS

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	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

yaacomo.com

Technical documentation

impala.apache.org/impala-docs.html

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

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

SADAS s.r.l.

Sequoiadb Ltd.

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2013

2006

2013

2020

2009

Current release

4.1.0, June 2022

8.0

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free trial version available

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2.0

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++

Server operating systems

Linux

AIX
Linux
Windows

Linux

Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C++
Java
PHP
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication

none

Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

using specific database classes

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 password-based access control

fine grained access rights according to SQL-standard

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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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