DBMS > Heroic vs. OpenEdge vs. searchxml vs. SingleStore

System Properties Comparison Heroic vs. OpenEdge vs. searchxml vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Application development environment with integrated database management system

DBMS for structured and unstructured content wrapped with an application server

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Time Series DBMS

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	3.75
Rank	#88	Overall
	#47	Relational DBMS

Score	0.01
Rank	#386	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	5.91
Rank	#61	Overall
	#34	Relational DBMS

Website

github.com/spotify/heroic

www.progress.com/openedge

www.searchxml.net/category/products

www.singlestore.com

Technical documentation

spotify.github.io/heroic

documentation.progress.com/output/ua/OpenEdge_latest

www.searchxml.net/support/handouts

docs.singlestore.com

Developer

Spotify

Progress Software Corporation

informationpartners gmbh

SingleStore Inc.

Initial release

2014

1984

2015

2013

Current release

OpenEdge 12.2, March 2020

1.0

8.5, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java

C++

C++, Go

Server operating systems

AIX
HP-UX
Linux
Solaris
Windows

Windows

Linux

64 bit version required

Data scheme

schema-free

yes

schema-free

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

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

close to SQL 92

yes

but no triggers and foreign keys

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

Progress proprietary ABL (Advanced Business Language)

C++

most other programming languages supported via APIs

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

on the application server

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

since Version 11.4

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

sychronisation to multiple collections

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users and groups

Domain, group and role-based access control at the document level and for application services

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
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Competitive advantages

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Typical application scenarios

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Key customers

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
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Market metrics

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

F ree Tier and Enterprise Edition
» 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

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More resources

DB-Engines blog posts

Turbocharge Your Application Development Using WebAssembly With SingleStoreDB
17 October 2022, Akmal Chaudhri, SingleStore (sponsor)

Cloud-Based Analytics With SingleStoreDB
9 June 2022, Akmal Chaudhri, SingleStore (sponsor)

SingleStore: The Increasing Momentum of Multi-Model Database Systems
14 February 2022, Akmal Chaudhri, SingleStore (sponsor)

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Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

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OpenEdge Application Development | Progress OpenEdge
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11 March 2024, GBHackers

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24 January 2024, VentureBeat

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29 January 2024, InfoWorld

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provided by Google News

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