DBMS > Apache Impala vs. Apache IoTDB vs. Hypertable vs. Oracle vs. Prometheus

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Hypertable vs. Oracle vs. Prometheus

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used RDBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Secondary database models

Document store

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

impala.apache.org

iotdb.apache.org

www.oracle.com/database

prometheus.io

Technical documentation

impala.apache.org/impala-docs.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.oracle.com/en/database

prometheus.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Hypertable Inc.

Oracle

Initial release

2013

2018

2009

1980

2015

Current release

4.1.0, June 2022

1.1.0, April 2023

0.9.8.11, March 2016

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

C and C++

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

an inofficial Windows port is available

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
Windows

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC
Native API

C++ API
Thrift

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor on file system level

Multi-source replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Integration with Hadoop and Spark

yes

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

fine grained access rights according to SQL-standard

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