DBMS > Heroic vs. Microsoft Azure Synapse Analytics vs. OpenMLDB vs. Postgres-XL

System Properties Comparison Heroic vs. Microsoft Azure Synapse Analytics vs. OpenMLDB vs. Postgres-XL

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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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

An open-source machine learning database that provides a feature platform for training and inference

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

Primary database model

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

openmldb.ai

www.postgres-xl.org

Technical documentation

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

openmldb.ai/docs/zh/main

www.postgres-xl.org/documentation

Developer

Spotify

Microsoft

4 Paradigm Inc.

Initial release

2014

2016

2020

2014

since 2012, originally named StormDB

Current release

2024-2 February 2024

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

C++, Java, Scala

Server operating systems

hosted

Linux

Linux
macOS

Data scheme

schema-free

yes

Fixed schema

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

via Elasticsearch

yes

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

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

ADO.NET
JDBC
ODBC

JDBC
SQLAlchemy

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

Supported programming languages

C#
Java
PHP

C++
Go
Java
Python
Scala

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

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

MVCC

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

yes

fine grained access rights according to SQL-standard

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