DBMS > Heroic vs. Microsoft Azure Synapse Analytics vs. Postgres-XL vs. Riak TS vs. SpatiaLite

System Properties Comparison Heroic vs. Microsoft Azure Synapse Analytics vs. Postgres-XL vs. Riak TS vs. SpatiaLite

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

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Spatial extension of SQLite

Primary database model

Secondary database models

Document store
Spatial DBMS

Relational DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

www.postgres-xl.org

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

www.postgres-xl.org/documentation

www.tiot.jp/riak-docs/riak/ts/latest

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Spotify

Microsoft

Open Source, formerly Basho Technologies

Alessandro Furieri

Initial release

2014

2016

2014

since 2012, originally named StormDB

2015

2008

Current release

10 R1, October 2018

3.0.0, September 2022

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Mozilla public license

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

Erlang

C++

Server operating systems

hosted

Linux
macOS

Linux
OS X

server-less

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

XML type, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

yes

restricted

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes, limited

yes

APIs and other access methods

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

ADO.NET
JDBC
ODBC

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

HTTP API
Native Erlang Interface

Supported programming languages

C#
Java
PHP

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

yes

links between datasets can be stored

yes

Transaction concepts

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

ACID

MVCC

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.

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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