DBMS > Postgres-XL vs. Spark SQL vs. Sphinx vs. VoltDB

System Properties Comparison Postgres-XL vs. Spark SQL vs. Sphinx vs. VoltDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Open source search engine for searching in data from different sources, e.g. relational databases

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

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

Technical documentation

www.postgres-xl.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

sphinxsearch.com/docs

docs.voltdb.com

Developer

Apache Software Foundation

Sphinx Technologies Inc.

VoltDB Inc.

Initial release

2014

since 2012, originally named StormDB

2014

2001

2010

Current release

10 R1, October 2018

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Mozilla public license

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

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

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

C++

Java, C++

Server operating systems

Linux
macOS

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X

for development

Data scheme

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

yes

only a subset of SQL 99

APIs and other access methods

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

JDBC
ODBC

Proprietary protocol

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

MVCC

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

Snapshots and command logging

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

Users and roles with access to stored procedures

More information provided by the system vendor

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

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Switching From Sphinx to MkDocs Documentation — What Did I Gain and Lose
2 February 2024, Towards Data Science

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

Perplexity AI: From Its Use To Operation, Everything You Need To Know About Googles Newest Challenger
11 January 2024, Free Press Journal

The Pirate Bay was recently down for over a week due to a DDoS attack
29 October 2019, The Hacker News

How to Build 600+ Links in One Month
4 September 2020, Search Engine Journal

provided by Google News

Unveiling Volt Active Data's game-changing approach to limitless app performance
16 October 2023, YourStory

VoltDB Launches Active(N) Lossless Cross Data Center Replication
31 August 2021, PR Newswire

VoltDB Turns to Real-Time Analytics with NewSQL Database
30 January 2014, Datanami

VoltDB Upgrades Power, Security of Its In-Memory Database
1 February 2017, eWeek

VoltDB Aims for Fast Big Data Development -- ADTmag
29 January 2015, ADT Magazine

provided by Google News

Share this page