DBMS > Apache Doris vs. Axibase vs. Vertica

System Properties Comparison Apache Doris vs. Axibase vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

Relational DBMS

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.69
Rank	#237	Overall
	#108	Relational DBMS

Score	0.32
Rank	#288	Overall
	#25	Time Series DBMS

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

axibase.com/docs/atsd/finance

www.vertica.com

Technical documentation

github.com/apache/doris/wiki

vertica.com/documentation

Developer

Apache Software Foundation, originally contributed from Baidu

Axibase Corporation

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2017

2013

2005

Current release

1.2.2, February 2023

15585

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

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++

Server operating systems

Linux

Data scheme

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

short, integer, long, float, double, decimal, string

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.

Secondary indexes

yes

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

SQL

Support of SQL

yes

SQL-like query language

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

APIs and other access methods

JDBC
MySQL client

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

character-based, interactive, front-end utility

Supported programming languages

Java

Go
Java
PHP
Python
R
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

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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

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...
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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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More resources

Recent citations in the news

Migrating from ClickHouse to Apache Doris: Boosting OLAP Performance
9 October 2023, hackernoon.com

How to Digest 15 Billion Logs Per Day and Keep Big Queries Within 1 Second
1 September 2023, KDnuggets

LLM-Powered OLAP: the Tencent Experience with Apache Doris
28 August 2023, hackernoon.com

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

How Big Data Is Saving Lives in Real Time: IoV Data Analytics Helps Prevent Accidents
28 November 2023, KDnuggets

provided by Google News

OCI Object Storage Completes Technical Validation of Vertica in Eon Mode
16 October 2023, blogs.oracle.com

Vertica by OpenText and Anritsu Sign New Deal for Next-Gen Architecture and 5G Network Capabilities
17 May 2023, PR Newswire

Stonebraker Seeks to Invert the Computing Paradigm with DBOS
12 March 2024, Datanami

OpenText expands enterprise portfolio with AI and Micro Focus integrations
25 July 2023, VentureBeat

OpenText integrates Micro Focus tech through Cloud Editions 23.3
26 July 2023, Techzine Europe

provided by Google News

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