DBMS > Databricks vs. InfinityDB vs. Tarantool vs. TerminusDB

System Properties Comparison Databricks vs. InfinityDB vs. Tarantool vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

Document store
Relational DBMS

Key-value store

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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

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

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

Technical documentation

docs.databricks.com

boilerbay.com/infinitydb/manual

www.tarantool.io/en/doc

terminusdb.github.io/terminusdb/#

Developer

Databricks

Boiler Bay Inc.

DataChemist Ltd.

Initial release

2013

2002

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

Java

C and C++

Prolog, Rust

Server operating systems

hosted

All OS with a Java VM

BSD
Linux
macOS

Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

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

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

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

with Databricks SQL

Full-featured ANSI SQL support

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

Open binary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Python
R
Scala

Java

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

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua, C and SQL stored procedures

yes

Triggers

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

yes

none

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

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

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, 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

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

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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