DBMS > Apache Impala vs. atoti vs. KairosDB vs. SQream DB

System Properties Comparison Apache Impala vs. atoti vs. KairosDB vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Distributed Time Series DBMS based on Cassandra or H2

a GPU-based, columnar RDBMS for big data analytics workloads

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	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

impala.apache.org

atoti.io

github.com/kairosdb/kairosdb

sqream.com

Technical documentation

impala.apache.org/impala-docs.html

docs.atoti.io

kairosdb.github.io

docs.sqream.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

ActiveViam

SQream Technologies

Initial release

2013

2017

Current release

4.1.0, June 2022

1.2.2, November 2018

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free versions available

Open Source

Apache 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++

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL 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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Multidimensional Expressions (MDX)

yes

APIs and other access methods

JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

.Net
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript

Node.js
PHP
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Python

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

based on Cassandra

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

simple password-based access control

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More resources

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