DBMS > Apache Druid vs. Google BigQuery vs. Hive vs. Memcached vs. SwayDB

System Properties Comparison Apache Druid vs. Google BigQuery vs. Hive vs. Memcached vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

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

In-memory key-value store, originally intended for caching

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

druid.apache.org

cloud.google.com/bigquery

hive.apache.org

www.memcached.org

swaydb.simer.au

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

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

github.com/memcached/memcached/wiki

Developer

Apache Software Foundation and contributors

Google

Apache Software Foundation

initially developed by Facebook

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Simer Plaha

Initial release

2012

2010

2012

2003

2018

Current release

29.0.1, April 2024

3.1.3, April 2022

1.6.25, March 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2

Open Source

BSD license

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux
OS X
Unix

hosted

All OS with a Java VM

FreeBSD
Linux
OS X
Unix
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

Proprietary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

none

Repcached, a Memcached patch, provides this functionallity

none

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

yes

using SASL (Simple Authentication and Security Layer) protocol

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