A Gentle Introduction to Locality Sensitive Hashing with Apache Spark
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An Implementation war story of locality sensitive hashing with Apache Spark, with performance lessons.
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def findCandidates(record: Iterable[String], hashers: Array[Int => Int],
mBands: BandType) = {
val hash = getHash(record, hashers)
val subArrays = partitionArray(hash).zipWithIndex
subArrays.flatMap { case (band, bandIndex) =>
val hashedBucket = mBands.lookup(bandIndex).
headOption.
flatMap{_.get(band)}
hashedBucket
}.flatten.toSet
}
19](https://image.slidesharecdn.com/hashing-150821160417-lva1-app6892/85/A-Gentle-Introduction-to-Locality-Sensitive-Hashing-with-Apache-Spark-19-320.jpg)
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HACC: Fitting the Universe Inside a Supercomputerinside-BigData.com In this deck from the DOE CSGF Program Review meeting, Nicholas Frontiere from the University of Chicago presents: HACC - Fitting the Universe Inside a Supercomputer.
"In response to the plethora of data from current and future large-scale structure surveys of the universe, sophisticated simulations are required to obtain commensurate theoretical predictions. We have developed the Hardware/Hybrid Accelerated Cosmology Code (HACC), capable of sustained performance on powerful and architecturally diverse supercomputers to address this numerical challenge. We will investigate the numerical methods utilized to solve a problem that evolves trillions of particles, with a dynamic range of a million to one."
Watch the video: https://wp.me/p3RLHQ-i4l
Learn more: https://www.krellinst.org/csgf/conf/2017/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Strava Labs: Exploring a Billion Activity Dataset from Athletes with Apache S...
Strava Labs: Exploring a Billion Activity Dataset from Athletes with Apache S...Databricks At Strava we have extensively leveraged Apache Spark to explore our data of over a billion activities, from tens of millions of athletes. This talk will be a survey of the more unique and exciting applications: A Global Heatmap gives a ~2 meter resolution density map of one billion runs, rides, and other activities consisting of three trillion GPS points from 17 billion miles of exercise data. The heatmap was rewritten from a non-scalable system into a highly scalable Spark job enabling great gains in speed, cost, and quality. Locally sensitive hashing for GPS traces was used to efficiently cluster 1 billion activities. Additional processes categorize and extract data from each cluster, such as names and statistics. Clustering gives an automated process to extract worldwide geographical patterns of athletes.
Applications include route discovery, recommendation systems, and detection of events and races. A coarse spatiotemporal index of all activity data is stored in Apache Cassandra. Spark streaming jobs maintain this index and compute all space-time intersections (“flybys”) of activities in this index. Intersecting activity pairs are then checked for spatiotemporal correlation, indicated by connected components in the graph of highly correlated pairs form “Group Activities”, creating a social graph of shared activities and workout partners. Data from several hundred thousand runners was used to build an improved model of the relationship between running difficulty and elevation gradient (Grade Adjusted Pace).
Cascading Map-Side Joins over HBase for Scalable Join Processing
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In this paper, we introduce the Map-Side Index Nested Loop Join (MAPSIN join) which combines scalable indexing capabilities of NoSQL data stores like HBase, that suffer from an insufficient distributed processing layer, with MapReduce, which in turn does not provide appropriate storage structures for efficient large-scale join processing.
While retaining the flexibility of commonly used reduce-side joins, we leverage the effectiveness of map-side joins without any changes to the underlying framework. We demonstrate the significant benefits of MAPSIN joins for the processing of SPARQL basic graph patterns on large RDF datasets by an evaluation with the LUBM and SP2Bench benchmarks.
For selective queries, MAPSIN join based query execution outperforms reduce-side join based execution by an order of magnitude.
snarks <3 hash functions
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Basics of Distributed Systems - Distributed Storage
Basics of Distributed Systems - Distributed StorageNilesh Salpe The document discusses distributed systems. It defines a distributed system as a collection of computers that appear as one computer to users. Key characteristics are that the computers operate concurrently but fail independently and do not share a global clock. Examples given are Amazon.com and Cassandra database. The document then discusses various aspects of distributed systems including distributed storage, computation, synchronization, consensus, messaging, load balancing and serialization.
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Growing Your Types Without Growing Your WorkloadFrançois Garillot Growing Your Types Without Growing Your Workload: a talk about the expression problem in Rust, given to the Montreal Rust meetup in January 2020
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Mobility insights at Swisscom - Understanding collective mobility in SwitzerlandFrançois Garillot Swisscom is the leading mobile-service provider in Switzerland, with a market share high enough to enable us to model and understand the collective mobility in every area of the country. To accomplish that, we built an urban planning tool that helps cities better manage their infrastructure based on data-based insights, produced with Apache Spark, YARN, Kafka and a good dose of machine learning. In this talk, we will explain how building such a tool involves mining a massive amount of raw data (1.5E9 records/day) to extract fine-grained mobility features from raw network traces. These features are obtained using different machine learning algorithms. For example, we built an algorithm that segments a trajectory into mobile and static periods and trained classifiers that enable us to distinguish between different means of transport. As we sketch the different algorithmic components, we will present our approach to continuously run and test them, which involves complex pipelines managed with Oozie and fuelled with ground truth data. Finally, we will delve into the streaming part of our analytics and see how network events allow Swisscom to understand the characteristics of the flow of people on roads and paths of interest. This requires making a link between network coverage information and geographical positioning in the space of milliseconds and using Spark streaming with libraries that were originally designed for batch processing. We will conclude on the advantages and pitfalls of Spark involved in running this kind of pipeline on a multi-tenant cluster. Audiences should come back from this talk with an overall picture of the use of Apache Spark and related components of its ecosystem in the field of trajectory mining.
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Spark Streaming : Dealing with State
Spark Streaming : Dealing with StateFrançois Garillot These are the slides for the http://www.meetup.com/Big-Data-Romandie/events/230345605/
Most of the interesting bits are in the attached notebooks though :
https://gist.github.com/huitseeker/a868af0dd8064cfe9806f4974a955386
https://gist.github.com/huitseeker/3c6d1246178eea56d958a4757a8cadbd
To be used with :
https://github.com/andypetrella/spark-notebook
Ramping up your Devops Fu for Big Data developers
Ramping up your Devops Fu for Big Data developersFrançois Garillot This document discusses ramping up DevOps for Big Data developers using Apache Mesos and Spark. It provides an overview of Mesos and its principles as a cluster manager and resource manager. It then discusses Spark and its advantages over Hadoop, including its speed, flexibility, and fault tolerance. The document also covers automating deployment of Mesos, HDFS and Spark using tools like Ansible, Packer and Tinc. It discusses ongoing work to further integrate Mesos and Spark.
Diving In The Deep End Of The Big Data Pool
Diving In The Deep End Of The Big Data PoolFrançois Garillot A team of 4 data science PhDs worked with an organization's data for 3 weeks to answer a question. They used unsupervised clustering on web browsing history to identify new customer segments. Due to computational limitations, they implemented locality-sensitive hashing to cluster over 22 million nodes and 123 million edges representing customers' web browsing histories. On the last day, they obtained final results identifying singles as a customer segment, which aligned with existing marketing articles about the rise of singles as an economic segment. The key lessons were to lay a bare minimum pipeline first before refining, that distributed systems are difficult so focus on the problem, and that fuel and social support from colleagues are essential when tackling big challenges.
Scala Collections : Java 8 on Steroids
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psug-44.eventbrite.fr
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A Gentle Introduction to Locality Sensitive Hashing with Apache Spark
- 1. A GENTLE INTRODUCTION TO APACHE SPARK AND LOCALITY-SENSITIVE HASHING 1
- 3. LOCALITY-SENSITIVE HASHING ▸ A story : Why LSH ▸ How it works & hash families ▸ LSH distribution ▸ Beware : WIP 3
- 4. SPARK TENETS ▸ broadcast variables ▸ per-partition commands ▸ shuffle sparsely 4
- 5. 5
- 6. 6
- 7. 7
- 8. SEGMENTATION ▸ small sample: 289421 users ▸ larger sample : 5684403 users 46K websites, ultimately users 4 personal laptops, 4 provided laptops 8
- 9. K-MEANS COMPLEXITY Find with the 'elbow method' on within-cluster sum of squares. Then 9
- 10. EM - GAUSSIAN MIXTURE With dimensions, mixtures, 10
- 11. LOCALITY-SENSITIVE HASHING FUNCTIONS A family H of hashing functions is -sensitive if: ▸ if then ▸ if then 11
- 12. DISTANCES ! (THOSE AND MANY OTHER) ▸ Hamming distance : where is a randomly chosen index ▸ Jaccard : ▸ Cosine distance: 12
- 14. EARTH MOVER'S DISTANCE Find optimal F minimizing: Then: 14
- 15. A WORD ON MODULARITY LSH for EMD introduced by Charikar in the Simhash paper (2002). Yet no place to plug your LSH family in implementation (e.g. scikit, mrsqueeze) ! 15
- 16. LSH AMPLIFICATION : CONCATENATIONS AND PARALLEL ▸ basic LSH: ▸ AND (series) construction: ▸ OR (parallel) construction : 16
- 17. 17
- 18. BASIC LSH val hashCollection = records.map(s => (getId(s), s)). mapValues(s => getHash(s, hashers)) val subArray = hashCollection.flatMap { case (recordId, hash) => hash.grouped(hashLength / numberBands).zipWithIndex.map{ case (band, bandIndex) => (bandIndex, (band, sentenceId)) } } 18
- 19. LOOKUP def findCandidates(record: Iterable[String], hashers: Array[Int => Int], mBands: BandType) = { val hash = getHash(record, hashers) val subArrays = partitionArray(hash).zipWithIndex subArrays.flatMap { case (band, bandIndex) => val hashedBucket = mBands.lookup(bandIndex). headOption. flatMap{_.get(band)} hashedBucket }.flatten.toSet } 19
- 20. getHash(record,hashers) DISTRIBUTE RANDOM SEEDS, NOT PERMUTATION FUNCTIONS records.mapPartitions { iter => val rng = new Scala.util.random() iter.map(x => hashers.flatMap{h => getHashFunction(rng, h)(x)}) } 20
- 21. AND YET, OOM 21
- 22. BASIC LSH WITH A 2-STABLE GAUSSIAN DISTRIBUTION With data points, choose and , to solve the problem 22
- 23. WEB LOGS ARE SPARSE Input : hits per user, over 6 months, 2x50-ish integers/user (4GB) Output of length 1000 integers per user : 10 (parallel) bands, 100 (concatenated) hashes 64-bit integers : 40 GB Yet ! 23
- 24. ENTROPY LSH (PANIGRAPHI 2006) REPLACE TABLES BY OFFSETS , , chosen randomly from the surface of , the sphere of radius centered at 24
- 25. ENTROPY LSH WITH A 2-STABLE GAUSSIAN DISTRIBUTION With data points, choose and , to solve the problem with as few as hash tables 25
- 26. BUT ... NETWORK COSTS ▸ Basic LSH : look up buckets, ▸ Entropy LSH : search for offsets 26
- 27. LAYERED LSH (BAHMANI ET AL. 2012) Output of your LSH family is in , with e.g. a cosine norm. For closer points, the chance of hashes hashing to the same bucket is high! 27
- 28. LAYERED LSH Have an LSH family for your norm on Likely that for all offsets 28
- 29. LAYERED LSH Output of hash generation is (GH(p), (H(p), p)) for all p. In Spark, group, or custom partitioner for (H(p), p) RDD. Network cost : 29
- 30. PERFORMANCE 30
- 31. FUTURE WORK HAVE A (BIG) WEBLOG ? ▸ Weve ▸ Yandex 31
- 32. FUTURE WORK LOCALITY-SENSITIVE HASHING FORESTS ! 32