If you've used a modern, interactive map such as Google or Bing Maps, you've consumed "map tiles". Map tiles are small images rendering a piece of the mosaic that is the whole map. Using conventional means, rendering tiles for the whole globe at multiple resolutions is a huge data processing effort. Even highly optimized, it spans a couple TBs and a few days of computation. Enter Hadoop. In this talk, I'll show you how to generate your own custom tiles using Hadoop. There will be pretty pictures.

1. © Hortonworks Inc. 2011
Bring Cartography to the Cloud
with Apache Hadoop
Nick Dimiduk
Member of Technical Staff, HBase
FOSS4G-NA, 2013-05-23
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2. © Hortonworks Inc. 2011
Beginnings…
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mapbox.com/blog/
rendering-the-world/
bmander.com/dotmap/index.html

3. © Hortonworks Inc. 2011
Definitions
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car•tog•ra•phy
|kärˈtägrəәfē|
noun
the science or practice of drawing maps.
rendering map tiles from some kind of
geographic data.
cloud
|kloud|
noun
a visible mass of condensed water vapor
floating in the atmosphere, typically high
above the ground.
on demand consumption of
computation and storage resources.

4. © Hortonworks Inc. 2011
Background
Architecting the Future of Big Data
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Apache Hadoop in Review
• Apache Hadoop Distributed Filesystem (HDFS)
– Distributed, fault-tolerant, throughput-optimized data storage
– Uses a filesystem analogy, not structured tables
– The Google File System, 2003, Ghemawat et al.
– http://research.google.com/archive/gfs.html
• Apache Hadoop MapReduce (MR)
– Distributed, fault-tolerant, batch-oriented data processing
– Line- or record-oriented processing of the entire dataset *
– “[Application] schema on read”
– MapReduce: Simplified Data Processing on Large Clusters, 2004, Dean and
Ghemawat
– http://research.google.com/archive/mapreduce.html
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* For more on writing MapReduce applications, see “MapReduce
Patterns, Algorithms, and Use Cases”
http://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/

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MapReduce in Detail
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highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/

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MapReduce in Detail
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highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/

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What we care about
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$ map < input | sort | reduce > output

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How Seamlessly?
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$ git show e65731e:bin/10_simulated_hadoop.sh
gzcat "$INPUT_FILES"
| python "${PYTHON_DIR}/sample_shapes.py"
| sort
| python "${PYTHON_DIR}/draw_tiles.py"
$ git show e65731e:bin/11_hadoop_local.sh
hadoop jar target/tile-brute-0.1.0-SNAPSHOT.jar
-input /tmp/input.csv
-output "$OUTPUT_DIR"
-mapper "python ${PYTHON_DIR}/sample_shapes.py"
-reducer "python ${PYTHON_DIR}/draw_tiles.py"

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To the Code!
github.com/ndimiduk/tilebrute
Architecting the Future of Big Data
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Our Tools
• Python + GIS
– GDAL
– Shapely
– Mapnik
• Java
• Apache Hadoop
• Bash
• MrJob
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Prepare the Input
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TIGER/Line Shapefiles
www.census.gov/geo/maps-data/data/tiger-line.html
$ tail -n6 bin/00_prepare_input.sh
ogr2ogr `: invoke gdal tool ogr2ogr`
-t_srs epsg:4326 `: reproject the data`
-f CSV `: in CSV format`
$OUTPUT `: producing output file`
$INPUT `: from input file`
-lco GEOMETRY=AS_WKT `: including geometries as WKT`
$ head -n2 /tmp/input.csv
WKT,STATEFP10,COUNTYFP10,TRACTCE10,BLOCKCE,BLOCKID10,PARTFLG,HOUSING10,POP10
"POLYGON ((-118.81473 47.233499,...))",53,001,950100,1042,530019501001042,N,1,5

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Prepare the Input
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TIGER/Line Shapefiles
www.census.gov/geo/maps-data/data/tiger-line.html
$ tail -n6 bin/00_prepare_input.sh
ogr2ogr `: invoke gdal tool ogr2ogr`
-t_srs epsg:4326 `: reproject the data`
-f CSV `: in CSV format`
$OUTPUT `: producing output file`
$INPUT `: from input file`
-lco GEOMETRY=AS_WKT `: including geometries as WKT`
$ head -n2 /tmp/input.csv
WKT,STATEFP10,COUNTYFP10,TRACTCE10,BLOCKCE,BLOCKID10,PARTFLG,HOUSING10,POP10
"POLYGON ((-118.81473 47.233499,...))",53,001,950100,1042,530019501001042,N,1,5

14. © Hortonworks Inc. 2011
Map: Sample Geometries
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[,[WKT, population]] => mapper => ['tx,ty,z', 'px,py']
def main():
for geom, population in read_feature(stdin):
for lng, lat in sample_geometry(geom, population):
for key, val in make_kv(lat, lng):
emit(key, val)
$ map < input | sort | reduce > output

15. © Hortonworks Inc. 2011
Map: Sample Geometries
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$ head -n1 input.csv | python -m tilebrute.sample_shapes
2,5,4 -13224181.65427 5981084.37214
5,11,5 -13224181.65427 5981084.37214
10,22,6 -13224181.65427 5981084.37214
21,44,7 -13224181.65427 5981084.37214
43,89,8 -13224181.65427 5981084.37214
87,179,9 -13224181.65427 5981084.37214
174,359,10 -13224181.65427 5981084.37214
348,718,11 -13224181.65427 5981084.37214
696,1436,12 -13224181.65427 5981084.37214
1392,2873,13 -13224181.65427 5981084.37214
2785,5746,14 -13224181.65427 5981084.37214
5571,11493,15 -13224181.65427 5981084.37214
11142,22986,16 -13224181.65427 5981084.37214
22284,45973,17 -13224181.65427 5981084.37214
$ map < input | sort | reduce > output

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Sort
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$ head -n1 input.csv | python -m tilebrute.sample_shapes | sort
10,22,6 -13224414.42332 5983539.01581
10,22,6 -13225723.87449 5981201.60336
10,22,6 -13225793.67181 5983127.53706
10,22,6 -13226046.70101 5983375.66839
10,22,6 -13226331.90155 5984272.31303
11138,22981,16 -13226331.90155 5984272.31303
11139,22983,16 -13225793.67181 5983127.53706
11139,22983,16 -13226046.70101 5983375.66839
11139,22986,16 -13225723.87449 5981201.60336
11141,22982,16 -13224414.42332 5983539.01581
$ map < input | sort | reduce > output

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Reduce: Draw Tiles
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def main():
for tile,points in groupby(read_points(stdin), lambda x: x[0]):
zoom = get_zoom(tile)
map = init_map(zoom, points)
map.zoom_all()
im = mapnik.Image(256,256)
mapnik.render(map,im)
emit(tile, encode_image(im))
$ map < input | sort | reduce > output
$ head -n1 input.csv | python -m tilebrute.sample_shapes | sort | head -n5 |
python -m tilebrute.draw_tiles
10,22,6 iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAYAAABccqhmAAADJ...+aBAAAAAElFTkSuQmCC

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Write Output
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public void write(Text tileId, Text tile) throws IOException {
String[] tileIdSplits = tileId.toString().split(",");
assert tileIdSplits.length == 3;
String tx = tileIdSplits[0];
String ty = tileIdSplits[1];
String zoom = tileIdSplits[2];
Path tilePath = new Path(outputPath, zoom + "/" + tx + "/" + ty + ".png");
fs.mkdirs(tilePath.getParent());
byte[] buf = Base64.decodeBase64(tile.toString());
final FSDataOutputStream fout = fs.create(tilePath, progress);
fout.write(buf);
fout.close();
}

19. © Hortonworks Inc. 2011
To the Cloud!
Architecting the Future of Big Data
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Basic Services: EC2, S3
• EC2: Elastic Compute Cloud
– Virtual machines on demand
– Different “instance types” with different hardware profiles
– m1.large (2 cores, 7.5G), c1.xlarge (8 cores, 7G)
• S3: Simple Storage Service
– Distributed, replicated storage
– Native Hadoop integration
– Also exposed over http(s), easy tile hosting
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21. © Hortonworks Inc. 2011
Add-on Service: EMR
• EMR: Elastic MapReduce
– “Hadoop as a Service”
– On-demand, pre-installed and configured Hadoop clusters
– +1: standardize of provisioning, deployment, monitoring
– -1: “stable” (old) software
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22. © Hortonworks Inc. 2011
MrJob: Python for EMR
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class TileBrute(MRJob):
HADOOP_OUTPUT_FORMAT = 'tilebrute.hadoop.mapred.MapTileOutputFormat'
def mapper_cmd(self):
return bash_wrap('$PYTHON -m tilebrute.sample_shapes')
def reducer_cmd(self):
return bash_wrap('$PYTHON -m tilebrute.draw_tiles')
github.com/Yelp/mrjob

23. © Hortonworks Inc. 2011
Results
Architecting the Future of Big Data
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14z, 2624x, 5722y

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14z, 2624x, 5722y

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How much code?
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$ find -f src -f bin | egrep '.(java|sh|py)$' | grep -v test | xargs cloc --quiet
http://cloc.sourceforge.net v 1.56 T=0.5 s (28.0 files/s, 1868.0 lines/s)
-------------------------------------------------------------------------------
Language files blank comment code
-------------------------------------------------------------------------------
Python 4 69 105 299
Bourne Shell 8 51 85 210
Java 2 25 16 74
-------------------------------------------------------------------------------
SUM: 14 145 206 583
-------------------------------------------------------------------------------

28. © Hortonworks Inc. 2011
Performance
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• 1 x m1.large (2 cores)
– 195575 input features (WA state)
– 3 zoom levels (6, 7, 8)
– 1 hour
• 19 x c1.xlarge (152 cores)
– 308745538 input features (all data)
– 3 zoom levels (6, 7, 8)
– 3 hours 15 minutes

29. © Hortonworks Inc. 2011
TODOs
• Macro-level performance optimizations (configuration)
– Balancing mappers and reducers, memory allocation, &c.
– On-demand Hadoop means tuning the cluster to the application
• Micro-level performance optimizations (code)
– Smarter sampling logic
– Mapnik API considerations
– Multi-threaded S3 PUTs
– https://forums.aws.amazon.com/thread.jspa?threadID=125135
• Write tiles in MBTiles format
• Write tiles to HBase
• Compression!
• Ogrbrute?
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30. © Hortonworks Inc. 2011
Thanks!
Architecting the Future of Big Data
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M A N N I N G
Nick Dimiduk
Amandeep Khurana
FOREWORD BY
Michael Stack
hbaseinaction.com
Nick Dimiduk
github.com/ndimiduk
@xefyr
n10k.com