DBMS > Postgres-XL vs. RavenDB vs. Vertica vs. Yaacomo

System Properties Comparison Postgres-XL vs. RavenDB vs. Vertica vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open Source Operational and Transactional Enterprise NoSQL Document Database

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.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Column oriented

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.postgres-xl.org

ravendb.net

www.vertica.com

yaacomo.com

Technical documentation

www.postgres-xl.org/documentation

ravendb.net/docs

vertica.com/documentation

Developer

Hibernating Rhinos

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2014

since 2012, originally named StormDB

2010

2005

2009

Current release

10 R1, October 2018

5.4, July 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Mozilla public license

Open Source

AGPL version 3, commercial license available

commercial

Limited community edition free

commercial

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

C++

Server operating systems

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Linux

Android
Linux
Windows

Data scheme

yes

schema-free

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

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

XML type, but no XML query functionality

Secondary indexes

yes

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

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like query language (RQL)

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

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

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

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning, hierarchical partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

Source-replica replication

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

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID, Cluster-wide transaction available

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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