DBMS > Apache Pinot vs. BigObject vs. eXtremeDB vs. Vertica vs. VoltDB

System Properties Comparison Apache Pinot vs. BigObject vs. eXtremeDB vs. Vertica vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Analytic DBMS for real-time computations and queries

Natively in-memory DBMS with options for persistency, high-availability and clustering

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.

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS
Time Series DBMS

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

Technical documentation

docs.pinot.apache.org

docs.bigobject.io

www.mcobject.com/docs/extremedb.htm

vertica.com/documentation

docs.voltdb.com

Developer

Apache Software Foundation and contributors

BigObject, Inc.

McObject

OpenText

previously Micro Focus and Hewlett Packard

VoltDB Inc.

Initial release

2015

2001

2005

2010

Current release

1.0.0, September 2023

8.2, 2021

12.0.3, January 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

Limited community edition free

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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++

Java, C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux

Linux
OS X

for development

Data scheme

yes

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.

support of XML interfaces available

Secondary indexes

yes

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

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

with the option: eXtremeSQL

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

yes

only a subset of SQL 99

APIs and other access methods

JDBC

fluentd
ODBC
RESTful HTTP API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

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

character-based, interactive, front-end utility

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Go
Java
Python

.Net
C
C#
C++
Java
Lua
Python
Scala

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Java

Triggers

yes

by defining events

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning / sharding

horizontal partitioning, hierarchical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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; supports Kerberos, LDAP, Ident and hash

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more

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

Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more

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

Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more

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

Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

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

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
» more

Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
» more

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

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