DBMS > Apache Impala vs. Hyprcubd vs. IBM Db2 Event Store vs. SWC-DB

System Properties Comparison Apache Impala vs. Hyprcubd vs. IBM Db2 Event Store vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Serverless Time Series DBMS

Distributed Event Store optimized for Internet of Things use cases

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Time Series DBMS

Event Store
Time Series DBMS

Wide column store

Secondary database models

Document store

Time Series DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

impala.apache.org

hyprcubd.com (offline)

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

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

impala.apache.org/impala-docs.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hyprcubd, Inc.

IBM

Alex Kashirin

Initial release

2013

2017

2020

Current release

4.1.0, June 2022

2.0

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free developer edition available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

C++

Server operating systems

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, 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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

JDBC
ODBC

gRPC (https)

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Proprietary protocol
Thrift

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

C++

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard 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

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

token access

fine grained access rights according to SQL-standard

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