DBMS > Apache Druid vs. Cassandra vs. GeoSpock vs. Google Cloud Spanner

System Properties Comparison Apache Druid vs. Cassandra vs. GeoSpock vs. Google Cloud Spanner

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Spatial and temporal data processing engine for extreme data scale

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Primary database model

Relational DBMS
Time Series DBMS

Wide column store

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Time Series DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	103.86
Rank	#12	Overall
	#1	Wide column stores

Score	2.89
Rank	#105	Overall
	#53	Relational DBMS

Website

druid.apache.org

cassandra.apache.org

geospock.com

cloud.google.com/spanner

Technical documentation

druid.apache.org/docs/latest/design

cassandra.apache.org/doc/latest

cloud.google.com/spanner/docs

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top level project, originally developped by Facebook

GeoSpock

Google

Initial release

2012

2008

2017

Current release

29.0.1, April 2024

4.1.3, July 2023

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Java, Javascript

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Windows

hosted

Data scheme

yes

schema-less columns are supported

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.

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

temporal, categorical

yes

SQL

Support of SQL

SQL for querying

SQL-like SELECT, DML and DDL statements (CQL)

ANSI SQL for query only (using Presto)

yes

Query statements complying to ANSI 2011

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

JDBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

no "single point of failure"

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication with 3 replicas for regional instances.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Strict serializable isolation

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.

User concepts

Access control

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

Access rights for users can be defined per object

Access rights for users can be defined per table

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

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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