DBMS > Interbase vs. Machbase Neo vs. Spark SQL vs. Tarantool vs. Trafodion

System Properties Comparison Interbase vs. Machbase Neo vs. Spark SQL vs. Tarantool vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Light-weight proven RDBMS

originally from Borland

TimeSeries DBMS for AIoT and BigData

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

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

Website

www.embarcadero.com/products/interbase

Technical documentation

docs.embarcadero.com/products/interbase

machbase.com/dbms

spark.apache.org/docs/latest/sql-programming-guide.html

www.tarantool.io/en/doc

trafodion.apache.org/documentation.html

Developer

Embarcadero

Machbase

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

1984

2013

2014

2008

2014

Current release

InterBase 2020, December 2019

V8.0, August 2023

3.5.0 ( 2.13), September 2023

2.10.0, May 2022

2.3.0, February 2019

License

Commercial or Open Source

commercial

free test version available

Open Source

Apache 2.0

Open Source

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

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

C and C++

C++, Java

Server operating systems

Android
iOS
Linux
OS X
Windows

Linux
macOS
Windows

Linux
OS X
Windows

BSD
Linux
macOS

Linux

Data scheme

yes

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

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

Full-featured ANSI SQL support

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

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

JDBC
ODBC

Open binary protocol

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
Python
R
Scala

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

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Interbase procedure and trigger language

Lua, C and SQL stored procedures

Java Stored Procedures

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

selectable replication factor

none

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

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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

Immediate Consistency

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

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, full featured in-memory storage engine with persistence

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based 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

fine grained access rights according to SQL-standard

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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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