DBMS > Apache IoTDB vs. GBase vs. HarperDB vs. Hazelcast vs. IBM Db2 Event Store

System Properties Comparison Apache IoTDB vs. GBase vs. HarperDB vs. Hazelcast vs. IBM Db2 Event Store

Editorial information provided by DB-Engines

Name

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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 widely adopted in-memory data grid

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Event Store
Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

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

Website

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

Technical documentation

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

docs.harperdb.io/docs

hazelcast.org/imdg/docs

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

Developer

Apache Software Foundation

General Data Technology Co., Ltd.

HarperDB

Hazelcast

IBM

Initial release

2018

2004

2017

2008

2017

Current release

1.1.0, April 2023

GBase 8a, GBase 8s, GBase 8c

3.1, August 2021

5.3.6, November 2023

2.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition available

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

C, Java, Python

Node.js

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

Data scheme

yes

dynamic schema

schema-free

yes

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

SQL-like data manipulation statements

SQL-like query language

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
Native API

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

Custom Functions

since release 3.1

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning (by range, list and hash) and vertical partitioning

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

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

yes

Replicated Map

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

yes, using LMDB

yes

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

In-memory capabilities

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

yes

User concepts

Access control

yes

Access rights for users and roles

Role-based access control

fine grained access rights according to SQL-standard

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