DBMS > EXASOL vs. Machbase Neo vs. Quasardb vs. Snowflake vs. Tarantool

System Properties Comparison EXASOL vs. Machbase Neo vs. Quasardb vs. Snowflake vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

TimeSeries DBMS for AIoT and BigData

Distributed, high-performance timeseries database

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

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

Website

Technical documentation

www.exasol.com/resources

machbase.com/dbms

doc.quasar.ai/master

docs.snowflake.net/manuals/index.html

www.tarantool.io/en/doc

Developer

Exasol

Machbase

quasardb

Snowflake Computing Inc.

Initial release

2000

2013

2009

2014

2008

Current release

V8.0, August 2023

3.14.1, January 2024

2.10.0, May 2022

License

Commercial or Open Source

commercial

free test version available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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++

C and C++

Server operating systems

Linux
macOS
Windows

BSD
Linux
OS X
Windows

hosted

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

integer and binary

yes

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.

yes

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

Full-featured ANSI SQL support

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

HTTP API

CLI Client
JDBC
ODBC

Open binary protocol

Supported programming languages

Java
Lua
Python
R

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

Lua, C and SQL stored procedures

Triggers

yes

similar concept for controling cloud resources

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

yes

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

selectable replication factor

Source-replica replication with selectable replication factor

yes

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

Hadoop integration

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

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

by using LevelDB

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

volatile and lookup table

yes

Transient mode

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

simple password-based access control

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept, user roles and pluggable authentication

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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