DBMS > HBase vs. IBM Db2 Event Store vs. TempoIQ vs. ToroDB vs. XTDB

System Properties Comparison HBase vs. IBM Db2 Event Store vs. TempoIQ vs. ToroDB vs. XTDB

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Distributed Event Store optimized for Internet of Things use cases

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

A MongoDB-compatible JSON document store, built on top of PostgreSQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Wide column store

Event Store
Time Series DBMS

Time Series DBMS

Document store

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

hbase.apache.org

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

tempoiq.com (offline)

github.com/torodb/server

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

hbase.apache.org/book.html

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

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

TempoIQ

8Kdata

Juxt Ltd.

Initial release

2008

2017

2012

2016

2019

Current release

2.3.4, January 2021

2.0

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free developer edition available

commercial

Open Source

AGPL-V3

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Clojure

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Linux, macOS, Windows for the developer addition

All OS with a Java 7 VM

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

string, integer, double, boolean, date, object_id

yes, extensible-data-notation format

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

yes

through the embedded Spark runtime

limited SQL, making use of Apache Calcite

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

C#
Java
JavaScript

Node.js
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Active-active shard replication

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

simple authentication-based access control

Access rights for users and roles

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