SlideShare a Scribd company logo
Python, SQLAlchemy
and Scrapy at Kpler
Jean Maynier
CTO
Business : commodities markets
Domain: trading / maritime transport
Customers: physical traders / analysts
Needs: follow cargoes flows and prices impact
Commercial Database
“Meta” AIS (10 networks)
Ports Authorities
Gas Inventories
Methodology :4 data layers
Thefirst “Cargo-Tracking” system,
bridging thegapbetween LNGShipping
andTrading.
Why python ?
Simple, fast to prototype
Data libraries (sqlAlchemy, Pandas, Scikit-learn)
Data oriented community
Scrapy: best scraping framework
Step 1
Scrapy
Simplify crawling
Reusable extractors (xpath, css selectors)
PaaS: Scrapinghub
Blacklist: Tor, crawlera for IP rotation
Step 2
Process raw data
Clean and normalize data
Positions
Port calls
Contracts
Prices
Python PubSub system
Persist data : sqlAlchemy & Postgresql
Step 3
Enrich data
Done
Vessel dock/undock
events
Predict next destination
Find buyer/seller
Basic routing
Todo / WIP
Estimate price
Routing with weather
Portfolio optimisation
Fleet optimisation
Step 4
Architecture
Websites sources
API sources
Scraping
Python/scrapy
Cache
Elasticseach
Data
ETL + models
python
Web
REST API
scala
client
mobile client
Excel
Metrics trends
Present
DBs < 100Gb
50 sources
500 vessels
Positions every 3min
Future
1-10 Tb
100 sources
10k to 100k vessels
Position every 30s
Performance problem !
Batch to data streaming
Parallelization
Granularity : item vs source
Handle failure, no data loss, monitoring
Akka streaming, Spark streaming, Celery, Storm
Step 5
Storm at Kpler: POC
Websites sources
API sources
Scraping
Python/scrapy
ETL + models
Position Dock/undock
Custom prices Match prices
Next
destination
Trades
Cargo volumes
We are recruiting, join us !
jobs@kpler.com

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