DBMS > Microsoft Azure Synapse Analytics vs. Riak KV vs. STSdb vs. Tarantool

System Properties Comparison Microsoft Azure Synapse Analytics vs. Riak KV vs. STSdb vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, fault tolerant key-value store

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Key-value store

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

azure.microsoft.com/services/synapse-analytics

github.com/STSSoft/STSdb4

www.tarantool.io

Technical documentation

docs.microsoft.com/azure/synapse-analytics

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

www.tarantool.io/en/doc

Developer

Microsoft

OpenSource, formerly Basho Technologies

STS Soft SC

Initial release

2016

2009

2011

2008

Current release

3.2.0, December 2022

4.0.8, September 2015

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPLv2, commercial license available

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

C++

Erlang

C and C++

Server operating systems

hosted

Linux
OS X

Windows

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

string, double, decimal, uuid, integer, blob, boolean, datetime

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

restricted

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

.NET Client API

Open binary protocol

Supported programming languages

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

C#
Java

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Transact SQL

Erlang

Lua, C and SQL stored procedures

Triggers

yes

pre-commit hooks and post-commit hooks

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

no "single point of failure"

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

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

links between data sets can be stored

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

yes, using Riak Security

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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