DBMS > Apache Drill vs. GeoMesa vs. NSDb vs. Titan

System Properties Comparison Apache Drill vs. GeoMesa vs. NSDb vs. Titan

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Editorial information provided by DB-Engines

Name

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Relational DBMS

Spatial DBMS

Time Series DBMS

Graph DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

drill.apache.org

www.geomesa.org

nsdb.io

github.com/thinkaurelius/titan

Technical documentation

drill.apache.org/docs

www.geomesa.org/documentation/stable/user/index.html

nsdb.io/Architecture

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation

CCRi and others

Aurelius, owned by DataStax

Initial release

2012

2014

2017

2012

Current release

1.20.3, January 2023

5.0.1, July 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java, Scala

Java

Server operating systems

Linux
OS X
Windows

Linux
macOS

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C++

Java
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

Sharding

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

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

depending on storage layer

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

Using Apache Lucene

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

depending on storage layer

User concepts

Access control

Depending on the underlying data source

yes

depending on the DBMS used for storage

User authentification and security via Rexster Graph Server

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Graph DBMS increased their popularity by 500% within the last 2 years
3 March 2015, Paul Andlinger

Graph DBMSs are gaining in popularity faster than any other database category
21 January 2014, Matthias Gelbmann

show all

Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Analyse Kafka messages with SQL queries using Apache Drill
27 September 2019, Towards Data Science

MapR Advances Support for Flexible and High Performance Analytics on JSON and S3 Data with Apache Drill
30 January 2019, Business Wire

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

Drill Mines Diverse Data Sets, Google Style
20 May 2015, The Next Platform

provided by Google News

Amazon DynamoDB Storage Backend for Titan: Distributed Graph Database
24 August 2015, AWS Blog

DSE Graph review: Graph database does double duty
14 November 2019, InfoWorld

Beyond Titan: The Evolution of DataStax’s New Graph Database
21 June 2016, Datanami

Database Deep Dives: JanusGraph
8 August 2019, IBM

TigerGraph, a graph database born to roar
19 September 2017, ZDNet

provided by Google News

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