DBMS > Apache Drill vs. Google BigQuery vs. Hyprcubd vs. IRONdb vs. Titan

System Properties Comparison Apache Drill vs. Google BigQuery vs. Hyprcubd vs. IRONdb vs. Titan

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

IRONdb seems to be discontinued. Therefore it is 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

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Graph DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

drill.apache.org

cloud.google.com/bigquery

hyprcubd.com (offline)

www.circonus.com/solutions/time-series-database/

github.com/thinkaurelius/titan

Technical documentation

drill.apache.org/docs

cloud.google.com/bigquery/docs

docs.circonus.com/irondb/category/getting-started

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation

Google

Hyprcubd, Inc.

Circonus LLC.

Aurelius, owned by DataStax

Initial release

2012

2010

2017

2012

Current release

1.20.3, January 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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

C and C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

text, numeric, histograms

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 SELECT statement is SQL:2003 compliant

yes

SQL-like query language

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

gRPC (https)

HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Automatic, metric affinity per node

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

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 per node, eventual consistency across nodes

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

ACID

Concurrency

Support for concurrent manipulation of data

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

Google Cloud Identity & Access Management (IAM)

token access

User authentification and security via Rexster Graph Server

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