DBMS > Google Cloud Bigtable vs. Hive vs. Manticore Search vs. Oracle

System Properties Comparison Google Cloud Bigtable vs. Hive vs. Manticore Search vs. Oracle

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Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

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

Widely used RDBMS

Primary database model

Key-value store
Wide column store

Relational DBMS

Search engine

Relational DBMS

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	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Website

cloud.google.com/bigtable

hive.apache.org

manticoresearch.com

www.oracle.com/database

Technical documentation

cloud.google.com/bigtable/docs

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

manual.manticoresearch.com

docs.oracle.com/en/database

Developer

Google

Apache Software Foundation

initially developed by Facebook

Manticore Software

Oracle

Initial release

2015

2012

2017

1980

Current release

3.1.3, April 2022

6.0, February 2023

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL version 2

commercial

restricted free version is available

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++

C and C++

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
macOS
Windows

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

Data scheme

schema-free

yes

Fixed schema

yes

Schemaless in JSON and XML columns

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Thrift

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

PL/SQL

also stored procedures in Java possible

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

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

isolation level can be parameterized

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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