DBMS > EsgynDB vs. InfinityDB vs. Snowflake vs. Tarantool vs. TerminusDB

System Properties Comparison EsgynDB vs. InfinityDB vs. Snowflake vs. Tarantool vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A Java embedded Key-Value Store which extends the Java Map interface

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Document store
RDF store

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

Technical documentation

boilerbay.com/infinitydb/manual

docs.snowflake.net/manuals/index.html

www.tarantool.io/en/doc

terminusdb.github.io/terminusdb/#

Developer

Esgyn

Boiler Bay Inc.

Snowflake Computing Inc.

DataChemist Ltd.

Initial release

2015

2002

2014

2008

2018

Current release

4.0

2.10.0, May 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

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

Open Source

GPL V3

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++, Java

Java

C and C++

Prolog, Rust

Server operating systems

Linux

All OS with a Java VM

hosted

BSD
Linux
macOS

Linux

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

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

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

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

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.

yes

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

CLI Client
JDBC
ODBC

Open binary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java

JavaScript (Node.js)
Python

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

JavaScript
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Lua, C and SQL stored procedures

yes

Triggers

similar concept for controling cloud resources

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

yes

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.

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

yes

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

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

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

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

in-memory journaling

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

fine grained access rights according to SQL-standard

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

Role-based access control

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