Using Big Data techniques with Open

Street MapStephen Knox

ArupPartly 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

2006 2016

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

2006 2007 2008 2009 2010 2011 2012 2013 2014 20150

5

10

15

20

25

30

35

40

45

50

DB dump Size (XML BZ2)

2 processor cores8GB RAM6TB disk

4 processor cores64GB RAM6TB disk64GB SSD

8 processor cores256GB RAM24TB disk400GB 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 AsiaAustralia Central America EuropeN. America S. America

Land Area

Africa Antarctica AsiaAustralia Central America EuropeN. America S. America

Data in OSM

Africa Antarctica AsiaAustralia Central America EuropeN. America S. America

+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 systemsScale-up Scale-out(parallel) Scale-out(NoSql)

• More memory

• More cores• More SSD• More hard

disk

Controlling Server

$$$$$$$$$

$$$$$$$$

Hardware costsSoftware acquisition & development costsMaintenance costsTraining 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 compared1 16 core server64GB RAM

8 2-core servers8GB 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 DataFile & 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 DataIndex type Time Taken

Grid 75m

R-tree 81m

Quad-tree 56mOperation Cluster config Cluster Time Standalone config Standalone time

Europe data small bounding box

8 x 2-core high memory nodes (13GB RAM)

Grid: 50sR-tree: 25sQ-tree: 6s

1 x 16 core machine(104GB RAM)

<1s

Europe data medium bounding box

8 x 2-core high memory nodes (13GB RAM)

Grid: 85sR-tree: 141sQ-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: 91mR-tree: 83sQ-tree: 56s

1 x 16 core machine(104GB RAM)

39s

Europe data huge bounding box (3°2)

8 x 2 core high memory 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

8 x 2 core high memory 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

indexesOperation Cluster configuration Cluster Time Standalone

configurationStandalone time

Data loading England PBF (610MB)

8 x 2-core high memory nodes (13GB ram each)

30m 1 x 16 core machine (104GB RAM)

527m

Data retrieval (small town) 8 x 2-core high memory nodes (13GB ram each)

1 x 16 core machine (104GB RAM)

3s

Data retrieval (large town) 8 x 2-core high memory nodes (13GB ram each)

1 x 16 core machine (104GB RAM)

113s

Data retrieval (city) 8 x 2-core high memory nodes (13GB ram each)

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)