DBMS > Heroic vs. LevelDB vs. Microsoft Azure Synapse Analytics vs. Riak TS

System Properties Comparison Heroic vs. LevelDB vs. Microsoft Azure Synapse Analytics vs. Riak TS

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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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

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

Primary database model

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

github.com/spotify/heroic

github.com/google/leveldb

azure.microsoft.com/services/synapse-analytics

Technical documentation

spotify.github.io/heroic

github.com/google/leveldb/blob/main/doc/index.md

docs.microsoft.com/azure/synapse-analytics

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

Developer

Spotify

Google

Microsoft

Open Source, formerly Basho Technologies

Initial release

2014

2011

2016

2015

Current release

1.23, February 2021

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD

commercial

Open Source

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

Server operating systems

Illumos
Linux
OS X
Windows

hosted

Linux
OS X

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

restricted

SQL

Support of SQL

yes

yes, limited

APIs and other access methods

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

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
Java
PHP

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

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

selectable replication factor

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

links between datasets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

yes

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