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Data Distribution.

This repository contains different data analysis of data distribution in the OSM dataset.

Spark locally

If you don't have access to a Spark cluster, it is possible to execute it locally. A laptop with 16Gb memory and 8 cores should be enough. In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.

To start Spark in local mode, after download and uncompress:

sbin/start-all.sh

To access to the UI: http://localhost:8080/

To stop Spark in local mode:

sbin/stop-all.sh

Extract blocks

To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:

spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs

Node IDs distribution

Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core. It will take around 30 minutes.

/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100

Tile distribution

Following, example of how to distribution report for tiles of 10000x10000, locally, using 5 cores and 4Gb per core. It will take around 30 minutes.

/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000

Zeppelin

To start the notebook, from a temporal folder:

mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0

About

Analisys of data distribution of OSM dataset.

Topics

osm4scalascalaspark

Resources

Readme
Apache-2.0 license

Code of conduct

Code of conduct
Activity
Custom properties

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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function addCopyButtons() {
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var style = document.createElement('style');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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highlight(document.body);
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mutations.forEach(function(m) {
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if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
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Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
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GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - simplexspatial/simplexspatial-data-distribution-analysis: Analisys of data distribution of OSM dataset. · GitHub
Skip to content
BranchesTags
Repository files navigation
Contributor Covenant
Data Distribution.
This repository contains different data analysis of data distribution in the OSM dataset.
Spark locally
If you don't have access to a Spark cluster, it is possible to execute it locally.
A laptop with 16Gb memory and 8 cores should be enough.
In my case, I'm using a Desktop with 16cores and 32Gb RAM. Full specs at the very bottom.
To start Spark in local mode, after download and uncompress:
sbin/start-all.sh
To access to the UI: http://localhost:8080/
To stop Spark in local mode:
sbin/stop-all.sh
Extract blocks
To be able to parallelize, lets extract all blocks. Full universe will take 4 minutes:
spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "local[*]" \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
extract \
-i file:///home/angelcc/Downloads/osm/planet/planet-200309.osm.pbf \
-o file:///home/angelcc/Downloads/osm/planet/blobs
Node IDs distribution
Following, example of how to report for 100 "partitions", locally, using 5 cores and 4Gb per core.
It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
mod \
-p 100 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/nodeId/100
Tile distribution
Following, example of how to distribution report for tiles of 10000x10000, locally,
using 5 cores and 4Gb per core. It will take around 30 minutes.
/home/angelcc/apps/spark-2.4.5-bin-hadoop2.7/bin/spark-submit \
--class com.simplexportal.simplexspatial.analysis.Driver \
--master "spark://angelcc-B450-AORUS-ELITE:7077" \
--deploy-mode cluster \
--executor-memory 4G \
--total-executor-cores 5 \
--num-executors 1 \
target/scala-2.11/simplexspatial-data-distribution-analysis-assembly-0.1.jar \
tile \
--latPartitions 10000 \
--lonPartitions 10000 \
-i file:///home/angelcc/Downloads/osm/planet/blobs \
-o file:///home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000
Zeppelin
To start the notebook, from a temporal folder:
mkdir logs notebook
docker run -p 8081:8080 --rm \
-v $PWD/logs:/logs \
-v $PWD/notebook:/notebook \
-v /home/angelcc/Downloads/osm/planet/distribution/nodeId/100:/zeppelin/data/nodeId \
-v /home/angelcc/Downloads/osm/planet/distribution/tile/10000x10000:/zeppelin/data/tile \
-e ZEPPELIN_LOG_DIR='/logs' \
-e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
--name zeppelin \
apache/zeppelin:0.9.0
About
Analisys of data distribution of OSM dataset.
Topics
osm4scalascalaspark
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages