DBMS > Heroic vs. Ingres vs. Microsoft Azure Synapse Analytics vs. XTDB

System Properties Comparison Heroic vs. Ingres vs. Microsoft Azure Synapse Analytics vs. XTDB

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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

Well established RDBMS

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Time Series DBMS

Relational DBMS

Document store

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/spotify/heroic

www.actian.com/databases/ingres

azure.microsoft.com/services/synapse-analytics

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

spotify.github.io/heroic

docs.actian.com/ingres

docs.microsoft.com/azure/synapse-analytics

www.xtdb.com/docs

Developer

Spotify

Actian Corporation

Microsoft

Juxt Ltd.

Initial release

2014

1974

originally developed at University Berkely in early 1970s

2016

2019

Current release

11.2, May 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT 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++

Clojure

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

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

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

C#
Java
PHP

Clojure
Java

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Ingres Replicator

yes

yes, each node contains all data

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

yes

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

yes

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