DBMS > CrateDB vs. GBase vs. Hypertable vs. IBM Db2 Event Store

System Properties Comparison CrateDB vs. GBase vs. Hypertable vs. IBM Db2 Event Store

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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

Distributed Database based on Lucene

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

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

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Wide column store

Event Store
Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

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

Website

cratedb.com

www.gbase.cn

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

Technical documentation

cratedb.com/docs

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

Developer

Crate

General Data Technology Co., Ltd.

Hypertable Inc.

IBM

Initial release

2013

2004

2009

2017

Current release

5.8.1, August 2024

GBase 8a, GBase 8s, GBase 8c

0.9.8.11, March 2016

2.0

License

Commercial or Open Source

Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, Python

C++

C and C++

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Linux, macOS, Windows for the developer addition

Data scheme

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

yes

schema-free

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

Standard with numerous extensions

yes

through the embedded Spark runtime

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

C++ API
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

selectable replication factor on file system level

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

In-memory capabilities

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

yes

User concepts

Access control

rights management via user accounts

yes

fine grained access rights according to SQL-standard

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