DBMS > CrateDB vs. Hazelcast vs. Transbase vs. Vertica

System Properties Comparison CrateDB vs. Hazelcast vs. Transbase vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

A widely adopted in-memory data grid

A resource-optimized, high-performance, universally applicable RDBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Relational DBMS

Column oriented

Secondary database models

Relational DBMS

Document store

JSON support with IMDG 3.12

Spatial DBMS
Time Series 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	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.05
Rank	#355	Overall
	#150	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

cratedb.com

hazelcast.com

www.transaction.de/en/products/transbase.html

www.vertica.com

Technical documentation

cratedb.com/docs

hazelcast.org/imdg/docs

www.transaction.de/en/products/transbase/features.html

vertica.com/documentation

Developer

Crate

Hazelcast

Transaction Software GmbH

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

2008

1987

2005

Current release

5.8.1, August 2024

5.3.6, November 2023

Transbase 8.3, 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free development license

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

C++

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

FreeBSD
Linux
macOS
Solaris
Windows

Linux

Data scheme

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

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

the object must implement a serialization strategy

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

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

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

Event Listeners, Executor Services

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Events

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

Replicated Map

Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate 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

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

yes

ACID

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

User concepts

Access control

rights management via user accounts

Role-based access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

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