DBMS > Apache Drill vs. GeoSpock vs. Google BigQuery vs. RethinkDB vs. Titan

System Properties Comparison Apache Drill vs. GeoSpock vs. Google BigQuery vs. RethinkDB vs. Titan

Editorial information provided by DB-Engines

Name

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

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store

Graph DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

drill.apache.org

geospock.com

cloud.google.com/bigquery

rethinkdb.com

github.com/thinkaurelius/titan

Technical documentation

drill.apache.org/docs

cloud.google.com/bigquery/docs

rethinkdb.com/docs

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation

GeoSpock

Google

The Linux Foundation

since July 2017

Aurelius, owned by DataStax

Initial release

2012

2010

2009

2012

Current release

1.20.3, January 2023

2.0, September 2019

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2

Open Source

Apache license, version 2.0

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, Javascript

C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

temporal, categorical

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

Sharding

range based

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

Transaction concepts

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

Since BigQuery is designed for querying data

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

Depending on the underlying data source

User concepts

Access control

Depending on the underlying data source

Access rights for users can be defined per table

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

Google Cloud Identity & Access Management (IAM)

yes

users and table-level permissions

User authentification and security via Rexster Graph Server

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.