DBMS > Google Cloud Bigtable vs. Hive vs. LevelDB vs. MySQL

System Properties Comparison Google Cloud Bigtable vs. Hive vs. LevelDB vs. MySQL

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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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used open source RDBMS

Primary database model

Key-value store
Wide column store

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

cloud.google.com/bigtable

hive.apache.org

github.com/google/leveldb

www.mysql.com

Technical documentation

cloud.google.com/bigtable/docs

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

github.com/google/leveldb/blob/main/doc/index.md

dev.mysql.com/doc

Developer

Google

Apache Software Foundation

initially developed by Facebook

Google

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2012

2011

1995

Current release

3.1.3, April 2022

1.23, February 2021

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C and C++

Server operating systems

hosted

All OS with a Java VM

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

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

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

C++
Java
PHP
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

not for MyISAM storage engine

Transaction concepts

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

Atomic single-row operations

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

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

Users with fine-grained authorization concept

no user groups or roles

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