DBMS > Apache Impala vs. IBM Db2 Event Store vs. KairosDB vs. MySQL vs. SiriDB

System Properties Comparison Apache Impala vs. IBM Db2 Event Store vs. KairosDB vs. MySQL vs. SiriDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Distributed Event Store optimized for Internet of Things use cases

Distributed Time Series DBMS based on Cassandra or H2

Widely used open source RDBMS

Open Source Time Series DBMS

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

impala.apache.org

www.ibm.com/products/db2-event-store

github.com/kairosdb/kairosdb

www.mysql.com

siridb.com

Technical documentation

impala.apache.org/impala-docs.html

www.ibm.com/docs/en/db2-event-store

kairosdb.github.io

dev.mysql.com/doc

docs.siridb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Cesbit

Initial release

2013

2017

2013

1995

2017

Current release

4.1.0, June 2022

2.0

1.2.2, November 2018

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and C++

Java

C and C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Java
JavaScript

Node.js
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard replication

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

simple rights management via user accounts

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