DBMS > Heroic vs. Manticore Search vs. Oracle vs. TigerGraph vs. Transbase

System Properties Comparison Heroic vs. Manticore Search vs. Oracle vs. TigerGraph vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Multi-storage database for search, including full-text search.

Widely used RDBMS

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

github.com/spotify/heroic

manticoresearch.com

www.oracle.com/database

www.tigergraph.com

www.transaction.de/en/products/transbase.html

Technical documentation

spotify.github.io/heroic

manual.manticoresearch.com

docs.oracle.com/en/database

docs.tigergraph.com

www.transaction.de/en/products/transbase/features.html

Developer

Spotify

Manticore Software

Oracle

Transaction Software GmbH

Initial release

2014

2017

1980

2017

1987

Current release

6.0, February 2023

23c, September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

commercial

restricted free version is available

commercial

free development license

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

Java

C++

C and C++

C++

C and C++

Server operating systems

FreeBSD
Linux
macOS
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

Fixed schema

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

via Elasticsearch

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language (GSQL)

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C++
Java

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

PL/SQL

also stored procedures in Java possible

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

isolation level can be parameterized

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

fine grained access rights according to SQL-standard

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