DBMS > EXASOL vs. Hive vs. LokiJS vs. Snowflake vs. Tarantool

System Properties Comparison EXASOL vs. Hive vs. LokiJS vs. Snowflake vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

In-memory JavaScript DBMS

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

Document store

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.43
Rank	#264	Overall
	#42	Document stores

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

www.exasol.com

hive.apache.org

github.com/techfort/LokiJS

www.snowflake.com

www.tarantool.io

Technical documentation

www.exasol.com/resources

cwiki.apache.org/confluence/display/Hive/Home

techfort.github.io/LokiJS

docs.snowflake.net/manuals/index.html

www.tarantool.io/en/doc

Developer

Exasol

Apache Software Foundation

initially developed by Facebook

Snowflake Computing Inc.

Initial release

2000

2012

2014

2008

Current release

3.1.3, April 2022

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

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

Java

JavaScript

C and C++

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

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

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

via views

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

Full-featured ANSI SQL support

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Thrift

JavaScript API

CLI Client
JDBC
ODBC

Open binary protocol

Supported programming languages

Java
Lua
Python
R

C++
Java
PHP
Python

JavaScript

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

View functions in JavaScript

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

none

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

none

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

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

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

atomic operations within a single collection possible

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

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

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

Access rights for users, groups and roles

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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