Using Big Data techniques to query and store OpenStreetMap data. Stephen Knox, digital.Arup

Using Big Data techniques
with Open Street Map
Stephen Knox
Arup
Partly based on research for an MSc in Geographical Information Systems and Science
Kingston University 2015

Disclaimer
• I am in no way an expert on Hadoop!
• I am a Geographic Information Systems specialist who can program
(and is interested in big data)
• Hopefully I can tell you something you didn’t know about
OpenStreetMap and geographic big data processing

Outline
• Background to OpenStreetMap (OSM) and growth
• Background to Geographic Big Data
• Dissertation Research
• Aims & Objectives
• Methodology
• Results
• Conclusions
• My general experiences of using Hadoop/SpatialHadoop and related
tools

INPUT
STORAGE
GRAPHICAL
OUTPUT
(MAPS)
DATA
OUTPUT

OSM Size and Growth
• Current Data – c. 0.5 – 1 TB
• Current and Historical Data – 5.15TB
• Growing at 1TB per annum
0
5
10
15
20
25
30
35
40
45
50
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
DB dump Size (XML BZ2)
2 processor cores
8GB RAM
6TB disk
4 processor cores
64GB RAM
6TB disk
64GB SSD
8 processor cores
256GB RAM
24TB disk
400GB SSD
OSM DB server
?
Source: Planet OSM http://planet.openstreetmap.org
Source: OSM http://wiki.openstreetmap.org/wiki/Servers
Source: OSM http://munin.openstreetmap.org/openstreetmap/katla.openstreetmap/postgres_size_openstreetmap_9_1_main.html

OSM Potential Growth (1)
Population
Africa Antarctica Asia
Australia Central America Europe
N. America S. America
Land Area
Data in OSM
+38%
+29%
+22%
+27%
+16%
+10%
+21%
Source: Geofabrik individual region download pages for OSM size and growth (http://download.geofabrik.de/index.html) , ArcGIS Continents
(http://www.arcgis.com/home/item.html?id=3c4741e22e2e4af2bd4050511b9fc6ad) and UN Department of Economic & Social Affairs Total
Population – Both Sexes (http://esa.un.org/unpd/wpp/Excel-
Data/EXCEL_FILES/1_Population/WPP2012_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.XLS)

Scaling systems
Scale-up Scale-out(parallel) Scale-out(NoSql)
• More
memory
• More cores
• More SSD
• More hard
disk
Controlling
Server
$$$
$$$
$$
$
$
$$
$$
$$$
Hardware costs
Software
acquisition &
development costs
Maintenance costs
Training costs
$$
$$$
$$$
$
Sources: Scale-up vs Scale-out for Hadoop: Time to rethink?
http://www.msr-waypoint.com/pubs/204499/a20-appuswamy.pdf
Scaling Up vs. scaling Out: Hidden Costs:
http://blog.codinghorror.com/scaling-up-vs-scaling-out-hidden-costs/

It’s getting complicated …. !
Source: The 451 Group https://blogs.the451group.com/information_management/2011/04/15/nosql-newsql-and-beyond/

What is the right tool for the job?
1MB 1GB 1TB 1PB 1EB
?
Transaction
Logs
Tool
Application /
Data

NoSQL Spatial
• Key research topic is indexing across multiple nodes
Source: Geowave Docs http://ngageoint.github.io/geowave/documentation.html#theory
• Implementations that add spatial capabilities to NoSQL databases
• SpatialHadoop, Hadoop GIS, ESRI tools for Hadoop
• SpatialSpark, GeoTrellis
• Geomesa, Geowave
• MongoDB (extension)
• Geocouch

Dissertation - Aims
• Investigate whether a parallel non-relational solution could be used
to:
• Analyse data from OSM (read-only)?
• Become the main storage platform (reads & writes)?
In terms of performance, and practicality (whole life cost)
• Does the size and growth rate of OSM make it likely that a non-
relational parallel storage solution will become technically or
economically desirable in the future?

Dissertation - Methodology
• Compare common current OSM tasks to an equivalent task using Big
Data tools
• Chose technologies in the Hadoop ecosystem rather than parallel
databases. Used SpatialHadoop and Hbase as principal platforms
• Started using a test Hadoop cluster @ work, but ran into issues, so
used cloud platforms
• Keep processing power and cost constant, so performance could be
directly compared
1 16 core server
64GB RAM
8 2-core servers
8GB RAM each
Master node
Broadly equivalent in cost and equivalent in nominal performance

SpatialHadoop
• University of Minnesota Open Source project
• Uses pig as an execution engine
• Creates spatial indexes and operators for big geographic datasets

Methodology (continued)
• 3 stages:
• Data loading & preparation for data analysis
• Test whether a data reader to read the OSM binary format was quicker than using the
XML format
• Data querying (read / analyse data)
• Spatial – give me the total features in this area [using spatial index]
• Non-spatial (e.g. count the total number of shops in the osm database)
• Simulation of master database (reads and writes)
• downloading existing data to work on (by bounding box)
• uploading new data changes

Uncompressed XML Compressed XML PBF
UK OSM data 17GB 1.2GB 765MB

Results – Loading Data
File & size Cluster Time
UK PBF* (765MB) 4 high memory
nodes
37m
UK XML (17GB) 4 high memory
nodes
75.5m
UK XML BZ2+
(1.2GB)
4 high memory
nodes
66m
Europe PBF (15.7GB) 8 high memory
nodes
246m
Europe XML (345GB) Not undertaken –
too big to process
Europe XML BZ2
(24GB)
8 high memory
nodes
Did not
complete
Europe PBF (15.7GB) 16 high memory
nodes
143m
Europe XML Not undertaken –
too big to process
Europe XML BZ2
(24GB)
16 high memory
nodes
Did not
complete
* Protocol Buffer Format – binary format
+ without taking into account decompression time – c. 7 minutes
File & size Cluster Time
UK XML BZ2 (1.2GB) 1 x 8 core machine
(52GB RAM)
17m
Europe XML BZ2
(24GB)
1 x 16 core machine
(104GB RAM)
578m
OverpassHadoop

Results – Querying Data
Index type Time Taken
Grid 75m
R-tree 81m
Quad-tree 56m
Operation Cluster config Cluster Time Standalone config Standalone time
Europe data small
bounding box
8 x 2-core high memory
nodes (13GB RAM)
Grid: 50s
R-tree: 25s
Q-tree: 6s
1 x 16 core machine
(104GB RAM)
<1s
Europe data medium
bounding box
nodes (13GB RAM)
Grid: 85s
R-tree: 141s
Q-tree:12s
1 x 16 core machine
(104GB RAM)
4s
Europe data large
bounding box (1°2)
8 x 2 core high memory
nodes (13GB RAM)
Grid: 91m
R-tree: 83s
Q-tree: 56s
1 x 16 core machine
(104GB RAM)
39s
Europe data huge
bounding box (3°2)
nodes (13GB RAM)
Only attempted with
Q-tree: 88s
1 x 16 core machine
(104GB RAM)
Out of memory
Shops query 8 x 2 core high memory
nodes (13GB RAM)
729s 1 x 16 core machine
(104GB RAM)
349s (but also got
out of mem
errors)
Shops query after
indexing
nodes (13GB RAM)
40s BUT… indexing took 714 seconds!

Results – Reading & Writing Data
• Used Hbase and Jython, but did not have time to implement spatial
indexes
Operation Cluster configuration Cluster Time Standalone
configuration
Standalone time
Data loading England PBF
(610MB)
nodes (13GB ram each)
30m 1 x 16 core machine
(104GB RAM)
527m
Data retrieval (small town) 8 x 2-core high memory
1 x 16 core machine
(104GB RAM)
3s
Data retrieval (large town) 8 x 2-core high memory
1 x 16 core machine
(104GB RAM)
113s
Data retrieval (city) 8 x 2-core high memory
1 x 16 core machine
(104GB RAM)
Did not complete (> 300s
and 50,000 nodes)

Conclusions
• It’s possible to replicate much of what OSM requires in Hadoop
• Open Street Map is growing quickly, but it is a long way from requiring horizontal
sharing of databases
• In general, it is not quicker to run geographic queries in a cluster at the TB order
of magnitude (at least with current OSM tools)
• Indexes do significantly speed up geographic queries (Quad-tree seems to be the
best)
• There is a high barrier of entry (technical & cost) for Hadoop and ecosystem that
will make it difficult for OSM to adopt the technology
• OSM should also consider parallel databases if they do have a requirement to
scale-out as there is less mismatch between their current system
• Spatial extensions to big data platforms are relatively immature, but there is a
huge potential there to do data analytics on massive datasets and gain new
insights
• I’ve learnt a lot personally!

Experiences with Azure
+Easy to use – click to deploy
+Good free trial program
+Good integration with storage
- Less customisable
- It was impossible to deploy >= 8
node clusters (rate limits?) so I
gave up
- Technical support was
responsive but not especially
helpful

Experiences with Google Cloud
+Already had Hortonworks
Hadoop distribution automated
setup
+Easy to customise – everything
on GitHub.
+Uses a standard setup (Ambari)
- Not always reliable
- Free trial was quite limited
- More difficult to connect with
Google Storage buckets
- Bit more work to deploy solution
as code-based and have to
download 3rd party tool (gcloud)

General Hadoop experiences
• Choosing the correct tool can be a significant part of the problem
• Setting up Hadoop clusters is hard!
• Spatial Big Data is still a little niche (although I did get lots of help)
• Running Hadoop jobs (even with Pig) is hard!
• Trial and error to experiment with memory requirements
• Size of files is a real barrier (especially when you are paying!)
• Often jobs failed half way through
• Debugging is not easy
• Have to recompile Java whenever there is a change (and sometimes deploy to
nodes)

Using Big Data techniques to query and store OpenStreetMap data. Stephen Knox, digital.Arup

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