SDS-2.x, Scalable Data Engineering Science

This is part of Project MEP: Meme Evolution Programme and supported by databricks academic partners program.

Copyright 2016-2019 Dillon George, Dan Lilja and Raazesh Sainudiin

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Scalable Spatio-Temporal Constraint Satisfaction Problem (ST-CSP)

Suppose you have base spatial sets in 2D space (points, lines, polygons, etc) given by:

$\{S_i \}_{i=1}^m$

and base temporal sets in 1D given by intervals in time: $\{T_j \}_{j=1}^n .$

Then one can obtain space-time set in 3D as unions of Cartesian products of such base spatial and temporal sets.

A simple constraint satisfaction problem can be posed as a Boolean-valued statement involving such space-time sets.

Often, the first seps that a geospatial scientist or analyst needs to take in order to build intuition about the phenomenon of interest invovles such CSP problems.

Some simple examples of such CSP problems are:

Given a set of GPS trajectories for multiple entities find the set of entities that intersect with a given space-time set or a collection of space-time sets.
Find the set of entitites that were close to one another based on their trajectories during a given period of time.
Find entities that are spatio-temporally inconsistent - such as being in two places at the same time.
...

Microsoft Research's Beijing Taxicab Trajectories using Magellan

T-Drive trajectory data sample

Yu Zheng

12 August 2011

Abstract

This is a sample of T-Drive trajectory dataset that contains a one-week trajectories of 10,357 taxis. The total number of points in this dataset is about 15 million and the total distance of the trajectories reaches 9 million kilometers.

Please cite the following papers when using the dataset: [1] Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun. Driving with knowledge from the physical world. In The 17th ACM SIGKDD international conference on Knowledge Discovery and Data mining, KDD'11, New York, NY, USA, 2011. ACM. [2] Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. T-drive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '10, pages 99-108, New York, NY, USA,2010. ACM.

More details on the dataset and related papers are available here.

import org.apache.hadoop.io.BytesWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapreduce.Job

import org.apache.hadoop.io.BytesWritable import org.apache.hadoop.io.Text import org.apache.hadoop.mapreduce.Job

%sh
# latest available MSR Beijing Taxi trajectory downloaded...
# if you are in the databricks community edition you should NOT execute this cell and download the premade all.txt.gz file as explained in the next line
# wget http://lamastex.org/datasets/public/geospatial/msr/all.txt.gz
wget http://lamastex.org/datasets/public/geospatial/msr/T-driveTaxiTrajectories.zip

--2019-06-19 09:16:04-- http://lamastex.org/datasets/public/geospatial/msr/T-driveTaxiTrajectories.zip Resolving lamastex.org (lamastex.org)... 166.62.28.100 Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 153217794 (146M) [application/zip] Saving to: ‘T-driveTaxiTrajectories.zip’ 0K .......... .......... .......... .......... .......... 0% 149K 16m45s 50K .......... .......... .......... .......... .......... 0% 296K 12m35s 100K .......... .......... .......... .......... .......... 0% 298K 11m10s 150K .......... .......... .......... .......... .......... 0% 41.9M 8m23s 200K .......... .......... .......... .......... .......... 0% 300K 8m22s 250K .......... .......... .......... .......... .......... 0% 46.2M 6m59s 300K .......... .......... .......... .......... .......... 0% 50.3M 5m59s 350K .......... .......... .......... .......... .......... 0% 41.6M 5m15s 400K .......... .......... .......... .......... .......... 0% 43.4M 4m40s 450K .......... .......... .......... .......... .......... 0% 304K 5m1s 500K .......... .......... .......... .......... .......... 0% 44.7M 4m34s 550K .......... .......... .......... .......... 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.......... .......... .......... 8% 39.3M 28s 13350K .......... .......... .......... .......... .......... 8% 48.8M 27s 13400K .......... .......... .......... .......... .......... 8% 51.7M 27s 13450K .......... .......... .......... .......... .......... 9% 36.9M 27s 13500K .......... .......... .......... .......... .......... 9% 51.8M 27s 13550K .......... .......... .......... .......... .......... 9% 41.1M 27s 13600K .......... .......... .......... .......... .......... 9% 48.1M 27s 13650K .......... .......... .......... .......... .......... 9% 40.2M 27s 13700K .......... .......... .......... .......... .......... 9% 17.8M 27s 13750K .......... .......... .......... .......... .......... 9% 44.6M 27s 13800K .......... .......... .......... .......... .......... 9% 39.9M 27s 13850K .......... .......... .......... .......... .......... 9% 45.6M 27s 13900K .......... .......... .......... .......... .......... 9% 34.5M 26s 13950K .......... .......... .......... .......... .......... 9% 31.8M 26s 14000K .......... .......... .......... .......... .......... 9% 34.7M 26s 14050K .......... .......... .......... .......... .......... 9% 25.8M 26s 14100K .......... .......... .......... .......... .......... 9% 21.5M 26s 14150K .......... .......... .......... .......... .......... 9% 76.3M 26s 14200K .......... .......... .......... .......... .......... 9% 204M 26s 14250K .......... .......... .......... .......... .......... 9% 39.4M 26s 14300K .......... .......... .......... .......... .......... 9% 38.3M 26s 14350K .......... .......... .......... .......... .......... 9% 47.1M 26s 14400K .......... .......... .......... .......... .......... 9% 43.0M 26s 14450K .......... .......... .......... .......... .......... 9% 38.9M 25s 14500K .......... .......... .......... .......... .......... 9% 43.5M 25s 14550K .......... .......... .......... .......... .......... 9% 42.7M 25s 14600K .......... .......... .......... .......... .......... 9% 38.6M 25s 14650K .......... .......... .......... .......... .......... 9% 45.7M 25s 14700K .......... .......... .......... .......... .......... 9% 380K 26s 14750K .......... .......... .......... .......... .......... 9% 37.5M 26s 14800K .......... .......... .......... .......... .......... 9% 43.5M 26s 14850K .......... .......... .......... .......... .......... 9% 32.3M 26s 14900K .......... .......... .......... .......... .......... 9% 49.2M 26s 14950K .......... .......... .......... .......... .......... 10% 52.9M 26s 15000K .......... .......... .......... .......... .......... 10% 37.7M 26s 15050K .......... .......... .......... .......... .......... 10% 44.3M 26s 15100K .......... .......... .......... .......... .......... 10% 63.6M 26s 15150K .......... .......... .......... .......... .......... 10% 54.8M 25s 15200K .......... .......... .......... .......... .......... 10% 17.9M 25s 15250K .......... .......... .......... .......... .......... 10% 19.3M 25s 15300K .......... .......... .......... .......... .......... 10% 40.5M 25s 15350K .......... .......... .......... .......... .......... 10% 48.9M 25s 15400K .......... .......... .......... .......... .......... 10% 34.0M 25s 15450K .......... .......... .......... .......... .......... 10% 56.3M 25s 15500K .......... .......... .......... .......... .......... 10% 42.5M 25s 15550K .......... .......... .......... .......... .......... 10% 49.0M 25s 15600K .......... .......... .......... .......... .......... 10% 50.9M 25s 15650K .......... .......... .......... .......... .......... 10% 44.9M 25s 15700K .......... .......... .......... .......... .......... 10% 46.4M 25s 15750K .......... .......... .......... .......... .......... 10% 41.2M 24s 15800K .......... .......... .......... .......... .......... 10% 26.2M 24s 15850K .......... .......... .......... .......... .......... 10% 37.4M 24s 15900K .......... .......... .......... .......... .......... 10% 50.3M 24s *** WARNING: skipped 179535 bytes of output *** 133300K .......... .......... .......... .......... .......... 89% 19.2M 2s 133350K .......... .......... .......... .......... .......... 89% 22.7M 2s 133400K .......... .......... .......... .......... .......... 89% 20.0M 2s 133450K .......... .......... .......... .......... .......... 89% 259M 2s 133500K .......... .......... .......... .......... .......... 89% 48.2M 2s 133550K .......... .......... .......... .......... .......... 89% 42.1M 2s 133600K .......... .......... .......... .......... .......... 89% 34.4M 2s 133650K .......... .......... .......... .......... .......... 89% 51.4M 2s 133700K .......... .......... .......... .......... .......... 89% 18.4M 2s 133750K .......... .......... .......... .......... .......... 89% 35.6M 2s 133800K .......... .......... .......... .......... .......... 89% 15.2M 2s 133850K .......... .......... .......... .......... .......... 89% 8.88M 2s 133900K .......... .......... .......... .......... .......... 89% 24.0M 2s 133950K .......... .......... .......... .......... .......... 89% 11.9M 2s 134000K .......... .......... .......... .......... .......... 89% 7.92M 2s 134050K .......... .......... .......... .......... .......... 89% 25.1M 2s 134100K .......... .......... .......... .......... .......... 89% 16.6M 2s 134150K .......... .......... .......... .......... .......... 89% 21.4M 2s 134200K .......... .......... .......... .......... .......... 89% 911K 2s 134250K .......... .......... .......... .......... .......... 89% 1.15M 2s 134300K .......... .......... .......... .......... .......... 89% 47.7M 2s 134350K .......... .......... .......... .......... .......... 89% 30.5M 2s 134400K .......... .......... .......... .......... .......... 89% 8.01M 2s 134450K .......... .......... .......... .......... .......... 89% 17.8M 2s 134500K .......... .......... .......... .......... .......... 89% 23.2M 2s 134550K .......... .......... .......... .......... .......... 89% 16.8M 2s 134600K .......... .......... .......... .......... .......... 89% 20.1M 2s 134650K .......... .......... .......... .......... .......... 90% 30.1M 2s 134700K .......... .......... .......... .......... .......... 90% 27.8M 2s 134750K .......... .......... .......... .......... .......... 90% 29.1M 2s 134800K .......... .......... .......... .......... .......... 90% 15.5M 2s 134850K .......... .......... .......... .......... .......... 90% 19.0M 2s 134900K .......... .......... .......... .......... .......... 90% 19.1M 2s 134950K .......... .......... .......... .......... .......... 90% 20.0M 2s 135000K .......... .......... .......... .......... .......... 90% 42.3M 2s 135050K .......... .......... .......... .......... .......... 90% 46.6M 2s 135100K .......... .......... .......... .......... .......... 90% 35.2M 2s 135150K .......... .......... .......... .......... .......... 90% 38.4M 2s 135200K .......... .......... .......... .......... .......... 90% 46.4M 2s 135250K .......... .......... .......... .......... .......... 90% 41.3M 2s 135300K .......... .......... .......... .......... .......... 90% 26.9M 2s 135350K .......... .......... .......... .......... .......... 90% 17.4M 2s 135400K .......... .......... .......... .......... .......... 90% 9.05M 2s 135450K .......... .......... .......... .......... .......... 90% 15.2M 2s 135500K .......... .......... .......... .......... .......... 90% 17.1M 2s 135550K .......... .......... .......... .......... .......... 90% 6.90M 2s 135600K .......... .......... .......... .......... .......... 90% 17.0M 2s 135650K .......... .......... .......... .......... .......... 90% 23.3M 2s 135700K .......... .......... .......... .......... .......... 90% 1.25M 2s 135750K .......... .......... .......... .......... .......... 90% 2.61M 2s 135800K .......... .......... .......... .......... .......... 90% 1.20M 2s 135850K .......... .......... .......... .......... .......... 90% 38.4M 2s 135900K .......... .......... .......... .......... .......... 90% 29.2M 2s 135950K .......... .......... .......... .......... .......... 90% 6.84M 2s 136000K .......... .......... .......... .......... .......... 90% 20.5M 2s 136050K .......... .......... .......... .......... .......... 90% 36.9M 2s 136100K .......... .......... .......... .......... .......... 90% 12.8M 2s 136150K .......... .......... .......... .......... .......... 91% 29.8M 2s 136200K .......... .......... .......... .......... .......... 91% 35.6M 2s 136250K .......... .......... .......... .......... .......... 91% 27.8M 2s 136300K .......... .......... .......... .......... .......... 91% 20.9M 2s 136350K .......... .......... .......... .......... .......... 91% 15.7M 2s 136400K .......... .......... .......... .......... .......... 91% 21.6M 2s 136450K .......... .......... .......... .......... .......... 91% 12.2M 2s 136500K .......... .......... .......... .......... .......... 91% 50.7M 2s 136550K .......... .......... .......... .......... .......... 91% 41.9M 2s 136600K .......... .......... .......... .......... .......... 91% 35.3M 2s 136650K .......... .......... .......... .......... .......... 91% 39.1M 2s 136700K .......... .......... .......... .......... .......... 91% 44.9M 2s 136750K .......... .......... .......... .......... .......... 91% 19.3M 2s 136800K .......... .......... .......... .......... .......... 91% 44.3M 2s 136850K .......... .......... .......... .......... .......... 91% 44.6M 2s 136900K .......... .......... .......... .......... .......... 91% 27.0M 2s 136950K .......... .......... .......... .......... .......... 91% 8.67M 1s 137000K .......... .......... .......... .......... .......... 91% 10.7M 1s 137050K .......... .......... .......... .......... .......... 91% 7.96M 1s 137100K .......... .......... .......... .......... .......... 91% 23.2M 1s 137150K .......... .......... .......... .......... .......... 91% 17.1M 1s 137200K .......... .......... .......... .......... .......... 91% 19.1M 1s 137250K .......... .......... .......... .......... .......... 91% 887K 1s 137300K .......... .......... .......... .......... .......... 91% 1.33M 1s 137350K .......... .......... .......... .......... .......... 91% 8.66M 1s 137400K .......... .......... .......... .......... .......... 91% 51.7M 1s 137450K .......... .......... .......... .......... .......... 91% 31.4M 1s 137500K .......... .......... .......... .......... .......... 91% 6.64M 1s 137550K .......... .......... .......... .......... .......... 91% 23.9M 1s 137600K .......... .......... .......... .......... .......... 91% 30.2M 1s 137650K .......... .......... .......... .......... .......... 92% 13.5M 1s 137700K .......... .......... .......... .......... .......... 92% 29.8M 1s 137750K .......... .......... .......... .......... .......... 92% 22.8M 1s 137800K .......... .......... .......... .......... .......... 92% 39.8M 1s 137850K .......... .......... .......... .......... .......... 92% 14.2M 1s 137900K .......... .......... .......... .......... .......... 92% 19.2M 1s 137950K .......... .......... .......... .......... .......... 92% 21.9M 1s 138000K .......... .......... .......... .......... .......... 92% 15.3M 1s 138050K .......... .......... .......... .......... .......... 92% 53.8M 1s 138100K .......... .......... .......... .......... .......... 92% 32.0M 1s 138150K .......... .......... .......... .......... .......... 92% 31.4M 1s 138200K .......... .......... .......... .......... .......... 92% 30.2M 1s 138250K .......... .......... .......... .......... .......... 92% 45.4M 1s 138300K .......... .......... .......... .......... .......... 92% 29.3M 1s 138350K .......... .......... .......... .......... .......... 92% 37.0M 1s 138400K .......... .......... .......... .......... .......... 92% 37.5M 1s 138450K .......... .......... .......... .......... .......... 92% 8.59M 1s 138500K .......... .......... .......... .......... .......... 92% 24.7M 1s 138550K .......... .......... .......... .......... .......... 92% 11.6M 1s 138600K .......... .......... .......... .......... .......... 92% 7.10M 1s 138650K .......... .......... .......... .......... .......... 92% 41.1M 1s 138700K .......... .......... .......... .......... .......... 92% 17.4M 1s 138750K .......... .......... .......... .......... .......... 92% 19.8M 1s 138800K .......... .......... .......... .......... .......... 92% 882K 1s 138850K .......... .......... .......... .......... .......... 92% 1.21M 1s 138900K .......... .......... .......... .......... .......... 92% 26.9M 1s 138950K .......... .......... .......... .......... .......... 92% 38.6M 1s 139000K .......... .......... .......... .......... .......... 92% 8.72M 1s 139050K .......... .......... .......... .......... .......... 92% 16.9M 1s 139100K .......... .......... .......... .......... .......... 92% 25.6M 1s 139150K .......... .......... .......... .......... .......... 93% 12.4M 1s 139200K .......... .......... .......... .......... .......... 93% 30.3M 1s 139250K .......... .......... .......... .......... .......... 93% 24.6M 1s 139300K .......... .......... .......... .......... .......... 93% 25.4M 1s 139350K .......... .......... .......... .......... .......... 93% 40.5M 1s 139400K .......... .......... .......... .......... .......... 93% 13.5M 1s 139450K .......... .......... .......... .......... .......... 93% 22.3M 1s 139500K .......... .......... .......... .......... .......... 93% 11.6M 1s 139550K .......... .......... .......... .......... .......... 93% 22.6M 1s 139600K .......... .......... .......... .......... .......... 93% 33.0M 1s 139650K .......... .......... .......... .......... .......... 93% 46.1M 1s 139700K .......... .......... .......... .......... .......... 93% 37.2M 1s 139750K .......... .......... .......... .......... .......... 93% 49.1M 1s 139800K .......... .......... .......... .......... .......... 93% 45.9M 1s 139850K .......... .......... .......... .......... .......... 93% 30.2M 1s 139900K .......... .......... .......... .......... .......... 93% 43.0M 1s 139950K .......... .......... .......... .......... .......... 93% 31.6M 1s 140000K .......... .......... .......... .......... .......... 93% 8.79M 1s 140050K .......... .......... .......... .......... .......... 93% 10.5M 1s 140100K .......... .......... .......... .......... .......... 93% 34.0M 1s 140150K .......... .......... .......... .......... .......... 93% 7.31M 1s 140200K .......... .......... .......... .......... .......... 93% 17.1M 1s 140250K .......... .......... .......... .......... .......... 93% 23.1M 1s 140300K .......... .......... .......... .......... .......... 93% 877K 1s 140350K .......... .......... .......... .......... .......... 93% 1.32M 1s 140400K .......... .......... .......... .......... .......... 93% 8.81M 1s 140450K .......... .......... .......... .......... .......... 93% 45.9M 1s 140500K .......... .......... .......... .......... .......... 93% 36.0M 1s 140550K .......... .......... .......... .......... .......... 93% 6.89M 1s 140600K .......... .......... .......... .......... .......... 94% 21.2M 1s 140650K .......... .......... .......... .......... .......... 94% 46.8M 1s 140700K .......... .......... .......... .......... .......... 94% 13.0M 1s 140750K .......... .......... .......... .......... .......... 94% 22.3M 1s 140800K .......... .......... .......... .......... .......... 94% 33.6M 1s 140850K .......... .......... .......... .......... .......... 94% 23.2M 1s 140900K .......... .......... .......... .......... .......... 94% 15.1M 1s 140950K .......... .......... .......... .......... .......... 94% 21.2M 1s 141000K .......... .......... .......... .......... .......... 94% 22.1M 1s 141050K .......... .......... .......... .......... .......... 94% 14.1M 1s 141100K .......... .......... .......... .......... .......... 94% 25.1M 1s 141150K .......... .......... .......... .......... .......... 94% 27.6M 1s 141200K .......... .......... .......... .......... .......... 94% 45.7M 1s 141250K .......... .......... .......... .......... .......... 94% 43.3M 1s 141300K .......... .......... .......... .......... .......... 94% 47.0M 1s 141350K .......... .......... .......... .......... .......... 94% 46.6M 1s 141400K .......... .......... .......... .......... .......... 94% 31.5M 1s 141450K .......... .......... .......... .......... .......... 94% 47.4M 1s 141500K .......... .......... .......... .......... .......... 94% 26.5M 1s 141550K .......... .......... .......... .......... .......... 94% 7.89M 1s 141600K .......... .......... .......... .......... .......... 94% 11.8M 1s 141650K .......... .......... .......... .......... .......... 94% 7.64M 1s 141700K .......... .......... .......... .......... .......... 94% 34.8M 1s 141750K .......... .......... .......... .......... .......... 94% 16.4M 1s 141800K .......... .......... .......... .......... .......... 94% 25.1M 1s 141850K .......... .......... .......... .......... .......... 94% 876K 1s 141900K .......... .......... .......... .......... .......... 94% 1.25M 1s 141950K .......... .......... .......... .......... .......... 94% 14.4M 1s 142000K .......... .......... .......... .......... .......... 94% 40.3M 1s 142050K .......... .......... .......... .......... .......... 94% 34.7M 1s 142100K .......... .......... .......... .......... .......... 95% 7.20M 1s 142150K .......... .......... .......... .......... .......... 95% 22.6M 1s 142200K .......... .......... .......... .......... .......... 95% 13.2M 1s 142250K .......... .......... .......... .......... .......... 95% 18.8M 1s 142300K .......... .......... .......... .......... .......... 95% 51.0M 1s 142350K .......... .......... .......... .......... .......... 95% 30.2M 1s 142400K .......... .......... .......... .......... .......... 95% 27.8M 1s 142450K .......... .......... .......... .......... .......... 95% 15.3M 1s 142500K .......... .......... .......... .......... .......... 95% 18.6M 1s 142550K .......... .......... .......... .......... .......... 95% 11.4M 1s 142600K .......... .......... .......... .......... .......... 95% 24.0M 1s 142650K .......... .......... .......... .......... .......... 95% 34.9M 1s 142700K .......... .......... .......... .......... .......... 95% 36.6M 1s 142750K .......... .......... .......... .......... .......... 95% 29.2M 1s 142800K .......... .......... .......... .......... .......... 95% 32.0M 1s 142850K .......... .......... .......... .......... .......... 95% 54.8M 1s 142900K .......... .......... .......... .......... .......... 95% 45.9M 1s 142950K .......... .......... .......... .......... .......... 95% 57.9M 1s 143000K .......... .......... .......... .......... .......... 95% 30.0M 1s 143050K .......... .......... .......... .......... .......... 95% 8.95M 1s 143100K .......... .......... .......... .......... .......... 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%sh
# Check that the file is there
ls -al

total 157912 drwxr-xr-x 1 root root 4096 Jun 19 09:16 . drwxr-xr-x 1 root root 4096 Jun 19 06:36 .. drwxr-xr-x 2 root root 4096 Jan 1 1970 conf -rw-r--r-- 1 root root 731 Jun 19 06:37 derby.log drwxr-xr-x 3 root root 4096 Jun 19 06:37 eventlogs drwxr-xr-x 2 root root 4096 Jun 19 09:15 ganglia drwxr-xr-x 2 root root 4096 Jun 19 06:37 library-install-logs drwxr-xr-x 2 root root 4096 Jun 19 09:02 logs -rw-r--r-- 1 root root 153217794 May 4 2018 T-driveTaxiTrajectories.zip -rw-r--r-- 1 root root 816717 Jun 19 08:18 TinyUppsalaCentrumWgot.osm -rw-r--r-- 1 root root 7628216 Jun 19 08:15 UppsalaCentrumWgot.osm

%sh
du -sh T-driveTaxiTrajectories.zip

147M T-driveTaxiTrajectories.zip

%sh

# Ensure directory exists use '-p' flag to avoid directory already exists warning
mkdir /dbfs/files/trajectories

# This command will unzip the files, concatenate all the data to a single file, gzip it and then put this single file in dbfs.
# It takes about 3 minutes to run.

unzip -o T-driveTaxiTrajectories.zip
rm all.txt
cat release/taxi_log_2008_by_id/*.txt >> all.txt
gzip -k all.txt
cp all.txt.gz /dbfs/files/trajectories/  # or you can just wget http://lamastex.org/datasets/public/geospatial/msr/all.txt.gz

mkdir: cannot create directory ‘/dbfs/files/trajectories’: File exists Archive: T-driveTaxiTrajectories.zip inflating: release/taxi_log_2008_by_id/1.txt inflating: release/taxi_log_2008_by_id/10.txt inflating: release/taxi_log_2008_by_id/100.txt inflating: release/taxi_log_2008_by_id/1000.txt inflating: release/taxi_log_2008_by_id/10000.txt inflating: release/taxi_log_2008_by_id/10001.txt inflating: release/taxi_log_2008_by_id/10002.txt inflating: release/taxi_log_2008_by_id/10003.txt inflating: release/taxi_log_2008_by_id/10004.txt inflating: release/taxi_log_2008_by_id/10005.txt inflating: release/taxi_log_2008_by_id/10006.txt inflating: release/taxi_log_2008_by_id/10007.txt inflating: release/taxi_log_2008_by_id/10008.txt inflating: release/taxi_log_2008_by_id/10009.txt inflating: release/taxi_log_2008_by_id/1001.txt inflating: release/taxi_log_2008_by_id/10010.txt inflating: release/taxi_log_2008_by_id/10011.txt inflating: release/taxi_log_2008_by_id/10012.txt inflating: 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inflating: release/taxi_log_2008_by_id/9999.txt inflating: release/Thumbs.db inflating: release/user_guide.pdf gzip: all.txt.gz already exists; not overwritten

%sh

# This command will copy over each file to dbfs without any form of processing. It will take a long time and
# also surpass the limits of the free community edition.

unzip -o T-driveTaxiTrajectories.zip -d /dbfs/files/trajectories

Archive: T-driveTaxiTrajectories.zip inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/1.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/100.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/1000.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10000.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10001.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10002.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10003.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10004.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10005.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10006.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10007.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10008.txt inflating: 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/dbfs/files/trajectories/release/taxi_log_2008_by_id/1008.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10080.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10081.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10082.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10083.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10084.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10085.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10086.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10087.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10088.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10089.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/1009.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10090.txt inflating: 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/dbfs/files/trajectories/release/taxi_log_2008_by_id/10114.txt extracting: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10115.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10116.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10117.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10118.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10119.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/1012.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10120.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10121.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10122.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10123.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10124.txt inflating: /dbfs/files/trajectories/release/taxi_log_2008_by_id/10125.txt inflating: 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%fs ls "dbfs:/files/trajectories/release/taxi_log_2008_by_id"

Command skipped

// Load files into an RDD
val fileContentsRDD = sc.textFile("dbfs:/files/trajectories/all.txt.gz")
val linesRDD = fileContentsRDD.flatMap(l => l.split("\n"))

fileContentsRDD: org.apache.spark.rdd.RDD[String] = dbfs:/files/trajectories/all.txt.gz MapPartitionsRDD[373] at textFile at command-1112260032678445:2 linesRDD: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[374] at flatMap at command-1112260032678445:3

display(linesRDD.toDF)


10000,2008-02-02 13:39:48,116.35924,39.93669
10000,2008-02-02 13:44:50,116.35929,39.93688
10000,2008-02-02 13:49:52,116.35926,39.93681
10000,2008-02-02 13:54:54,116.35928,39.93646
10000,2008-02-02 13:54:54,116.35928,39.93646
10000,2008-02-02 13:59:56,116.35926,39.93664
10000,2008-02-02 14:04:58,116.35933,39.93683
10000,2008-02-02 14:10:00,116.35925,39.93661
10000,2008-02-02 14:15:02,116.35927,39.93672
10000,2008-02-02 14:20:04,116.35923,39.93663
10000,2008-02-02 14:25:06,116.35918,39.93654
10000,2008-02-02 14:30:08,116.35923,39.93657
10000,2008-02-02 14:35:10,116.35913,39.93646
10000,2008-02-02 14:40:12,116.35914,39.93646
10000,2008-02-02 14:45:14,116.35924,39.93657
10000,2008-02-02 14:50:16,116.35883,39.93676
10000,2008-02-02 14:55:18,116.39469,39.94764
10000,2008-02-02 15:10:24,116.57093,40.0385
10000,2008-02-02 15:15:26,116.58611,40.07727
10000,2008-02-02 15:50:39,116.57919,40.04747
10000,2008-02-02 16:00:43,116.466,39.97265
10000,2008-02-02 16:05:45,116.45546,39.9426
10000,2008-02-02 16:10:47,116.45523,39.93272
10000,2008-02-02 16:15:49,116.4543,39.93239
10000,2008-02-02 16:20:51,116.44984,39.94003
10000,2008-02-02 16:25:53,116.43311,39.93987
10000,2008-02-02 16:30:55,116.4278,39.94268
10000,2008-02-02 16:35:57,116.42347,39.94846
10000,2008-02-02 16:40:59,116.3929,39.94768
10000,2008-02-02 16:46:01,116.37333,39.94754
10000,2008-02-02 16:51:03,116.35789,39.94485
10000,2008-02-02 16:56:05,116.34083,39.93792
10000,2008-02-02 17:01:07,116.29488,39.94436
10000,2008-02-02 17:06:08,116.2836,39.93108
10000,2008-02-02 17:11:11,116.27844,39.93098
10000,2008-02-02 17:16:12,116.28677,39.92274
10000,2008-02-02 17:21:14,116.30417,39.93008
10000,2008-02-02 17:26:16,116.3038,39.92401
10000,2008-02-02 17:31:18,116.291,39.91679
10000,2008-02-02 17:36:20,116.29601,39.90801
10000,2008-02-02 17:41:22,116.28916,39.9065
10000,2008-02-02 17:51:26,116.29788,39.9431
10000,2008-02-02 17:56:28,116.31001,39.95028
10000,2008-02-02 18:01:30,116.30761,39.95005
10000,2008-02-02 18:01:30,116.30761,39.95005
10000,2008-02-02 18:06:32,116.30766,39.94994
10000,2008-02-02 18:11:34,116.30768,39.94995
10000,2008-02-02 18:16:36,116.30767,39.94995
10000,2008-02-02 18:21:38,116.30765,39.94997
10000,2008-02-02 18:26:40,116.30765,39.94994
10000,2008-02-02 18:31:42,116.31787,39.94911
10000,2008-02-02 18:36:44,116.30842,39.93938
10000,2008-02-02 18:46:48,116.27669,39.94533
10000,2008-02-02 18:51:50,116.27232,39.94546
10000,2008-02-02 18:56:52,116.26794,39.94184
10000,2008-02-02 19:01:54,116.26896,39.91997
10000,2008-02-02 19:06:56,116.26797,39.8856
10000,2008-02-02 19:11:58,116.27653,39.85633
10000,2008-02-02 19:17:00,116.27687,39.84575
10000,2008-02-02 19:22:02,116.27729,39.85817
10000,2008-02-02 19:32:06,116.2682,39.88983
10000,2008-02-02 19:37:08,116.26832,39.90234
10000,2008-02-02 19:42:10,116.27676,39.90615
10000,2008-02-02 19:52:14,116.30611,39.90656
10000,2008-02-02 19:57:16,116.30056,39.92293
10000,2008-02-02 20:02:18,116.282,39.92306
10000,2008-02-02 20:02:18,116.282,39.92306
10000,2008-02-02 20:02:18,116.282,39.92306
10000,2008-02-02 20:02:18,116.282,39.92306
10000,2008-02-02 20:07:20,116.26068,39.92326
10000,2008-02-02 20:12:22,116.24605,39.92509
10000,2008-02-02 20:17:24,116.2327,39.92837
10000,2008-02-02 20:22:26,116.25229,39.92811
10000,2008-02-02 20:27:27,116.25859,39.92338
10000,2008-02-02 20:32:29,116.2879,39.92277
10000,2008-02-02 20:37:31,116.31716,39.92227
10000,2008-02-02 20:42:33,116.32555,39.92183
10000,2008-02-02 20:47:35,116.34593,39.92212
10000,2008-02-02 20:52:37,116.35401,39.91266
10000,2008-02-02 20:57:39,116.35709,39.90907
10000,2008-02-02 21:02:41,116.35698,39.90082
10000,2008-02-02 21:07:43,116.35713,39.89946
10000,2008-02-02 21:12:45,116.35713,39.89784
10000,2008-02-02 21:17:47,116.35723,39.89009
10000,2008-02-02 21:22:49,116.35731,39.88515
10000,2008-02-02 21:27:51,116.36459,39.87708
10000,2008-02-02 21:32:53,116.36119,39.88414
10000,2008-02-02 21:37:55,116.35192,39.88817
10000,2008-02-02 21:42:57,116.32201,39.89594
10000,2008-02-02 21:47:59,116.31515,39.90317
10000,2008-02-02 21:53:01,116.32928,39.90874
10000,2008-02-02 21:58:03,116.34247,39.91261
10000,2008-02-02 22:03:05,116.35,39.91254
10000,2008-02-02 22:03:05,116.35,39.91254
10000,2008-02-02 22:08:07,116.34767,39.92238
10000,2008-02-02 22:13:09,116.34645,39.92682
10000,2008-02-02 22:23:56,116.35943,39.93673
10000,2008-02-02 22:34:00,116.35943,39.93675
10000,2008-02-02 22:44:04,116.35943,39.93675
10000,2008-02-02 23:14:16,116.35944,39.93673

Showing the first 1000 rows.

import magellan.{Point, Polygon, PolyLine}

import org.apache.spark.sql.magellan.dsl.expressions._
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._

import java.sql.Timestamp

import magellan.{Point, Polygon, PolyLine} import org.apache.spark.sql.magellan.dsl.expressions._ import org.apache.spark.sql.Row import org.apache.spark.sql.types._ import org.apache.spark.sql.functions._ import java.sql.Timestamp

case class taxiRecord(
  taxiId: Int,
  time: String,
  point: Point
  )

defined class taxiRecord

import java.text.SimpleDateFormat 
val dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")

import java.text.SimpleDateFormat dateFormat: java.text.SimpleDateFormat = java.text.SimpleDateFormat@4f76f1a0

val taxiData = linesRDD.map{line =>
  try {
    val parts = line.split(",")
    val id = parts(0).toInt
    val time = parts(1)
    
    val point = Point(parts(2).toDouble, 
                      parts(3).toDouble)
    
    taxiRecord(id, time, point)
    
  } catch {
    // Label invalid datapoints
      case e: Throwable => {
        val p = Point(-1.0, -1.0)
        val id = -1
        val time = "0000-00-00 00:00:00"
        taxiRecord( id, time, p)
      }
  }
}
.toDF
.select($"taxiId", to_utc_timestamp($"time", "yyyy-MM-dd HH:mm:ss").as("timeStamp"), $"point") // warning unixTime
.repartition(100)
.where($"taxiId" > -1)
.cache()

taxiData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [taxiId: int, timeStamp: timestamp ... 1 more field]

taxiData.show

+------+-------------------+--------------------+ |taxiId| timeStamp| point| +------+-------------------+--------------------+ | 2454|2008-02-04 22:01:01|Point(116.58287, ...| | 2925|2008-02-08 16:28:30|Point(116.60126, ...| | 6974|2008-02-04 07:42:03|Point(116.43697, ...| | 8662|2008-02-05 12:38:33|Point(116.37491, ...| | 5488|2008-02-04 13:59:00|Point(116.33306, ...| | 3583|2008-02-06 20:13:26|Point(116.34007, ...| | 3242|2008-02-04 02:45:23|Point(116.45911, ...| | 2205|2008-02-02 17:53:47|Point(116.40252, ...| | 6556|2008-02-03 12:27:20|Point(116.58243, ...| | 3878|2008-02-04 04:56:57|Point(116.49717, ...| | 6656|2008-02-03 07:08:18|Point(116.64105, ...| | 7215|2008-02-02 20:52:50|Point(116.49212, ...| | 8345|2008-02-04 23:07:37|Point(116.33331, ...| | 3579|2008-02-03 23:41:04|Point(116.31527, ...| | 1436|2008-02-05 22:37:21|Point(116.45273, ...| | 7284|2008-02-06 07:10:18|Point(117.11203, ...| | 3546|2008-02-07 15:38:17|Point(116.02403, ...| | 950|2008-02-04 07:48:11|Point(116.25267, ...| | 5555|2008-02-05 21:34:01|Point(116.30294, ...| | 4798|2008-02-07 01:06:30|Point(116.3439, 4...| +------+-------------------+--------------------+ only showing top 20 rows

taxiData.count

res3: Long = 70651936

taxiData

// Helper function to extract latitude and longitued
val pointToTuple = udf( (point: Point) => Array(point.getX(), point.getY()))

val taxiDataNoMagellan = taxiData.select($"taxiId", $"timeStamp", pointToTuple($"point").as("latlon")).drop("point")

taxiDataNoMagellan.write.mode("overwrite").parquet("dbfs:/datasets/t-drive-trips")

pointToTuple: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,ArrayType(DoubleType,false),Some(List(org.apache.spark.sql.types.PointUDT@45b39dda))) taxiDataNoMagellan: org.apache.spark.sql.DataFrame = [taxiId: int, timeStamp: timestamp ... 1 more field]

taxiData.write.mode("overwrite").parquet("dbfs:/datasets/t-drive-trips-magellan") // magellan

spark.read.parquet("dbfs:/datasets/t-drive-trips-magellan").count()

res23: Long = 70651936

%run scalable-data-science/sds-2-x-tuning/00_dbcTuningUtilities2run

printRecordsPerPartition: (df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row])Unit computeFileStats: (path: String)(Long, Long) cacheAs: (df: org.apache.spark.sql.DataFrame, name: String, level: org.apache.spark.storage.StorageLevel)org.apache.spark.sql.DataFrame benchmarkCount: (func: () => org.apache.spark.sql.DataFrame)(org.apache.spark.sql.DataFrame, Long, Long) defined class JobResults defined class Node defined class Tracker tracker: Tracker = Tracker@33a7b056

printRecordsPerPartition(taxiData)

Per-Partition Counts: * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706519 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706520 * 706519 * 706519 * 706519

taxiData.count()

res10: Long = 70651936

taxiData.rdd.getNumPartitions

res9: Int = 100

SDS-2.x, Scalable Data Engineering Science

Scalable Spatio-Temporal Constraint Satisfaction Problem (ST-CSP)

Microsoft Research's Beijing Taxicab Trajectories using Magellan

T-Drive trajectory data sample

Steps in this notebook

1. Download the Taxi Trips

Below we illustrate the small files problem

Now that the data is in DBFS, lets turn it into a dataframe.

Let's do some diagnostic tuning