DBMS > Apache IoTDB vs. HarperDB vs. Percona Server for MongoDB vs. RocksDB vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. HarperDB vs. Percona Server for MongoDB vs. RocksDB vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Platform for big data analytics on multistructured data sources and types

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

iotdb.apache.org

www.harperdb.io

www.percona.com/mongodb/software/percona-server-for-mongodb

rocksdb.org

Technical documentation

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

docs.harperdb.io/docs

docs.percona.com/percona-distribution-for-mongodb

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

HarperDB

Percona

Facebook, Inc.

Teradata

Initial release

2018

2017

2015

2013

2005

Current release

1.1.0, April 2023

3.1, August 2021

3.4.10-2.10, November 2017

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

GPL Version 2

Open Source

BSD

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

Java

Node.js

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X

Linux

Data scheme

yes

dynamic schema

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

JSON data types

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like data manipulation statements

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

proprietary protocol using JSON

C++ API
Java API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

JavaScript

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

A table resides as a whole on one (or more) nodes in a cluster

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

the nodes on which a table resides can be defined

Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

via In-Memory Engine

yes

User concepts

Access control

yes

Access rights for users and roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.