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Search@airbnb
- 1. Building Search@Airbnb Mousom Dhar Gupta
- 3. Search
- 4. That Awesome Slide Title of Yours
- 5. Technical Stack ____________________________ DropWizard as a service framework (incl. Jetty, Jersey, Jackson) ZooKeeper (via Smartstack) for service discovery. Lucene for index storage and simple retrieval. In-house built forward index, real-time indexing, ranking, advanced filtering.
- 6. Web App Search1 150 Search Threads Lucene Index ~30 replicas of same index dataJVM …Search2 SearchN Search Overview
- 7. search Lucene Lucene Lucene Lucene Lucene Lucene Lucene Lucene Combiner Filtering and Ranking Shards ____________________________ Each box has 8 shards of Lucene Index Latency is 50% less than a single shard index
- 8. Challenges ____________________________ Bootstrap (creating the index from scratch) Ensuring consistency of the index with ground truth data in real time Indexing
- 9. What’s in the Lucene index? ____________________________ Positions of listings indexed using Lucene’s spatial module (RecursivePrefixTreeStrategy) Categorical and numerical properties like room type and maximum occupancy Full text (descriptions, reviews, etc.) ~40 fields per listing from a variety of data sources, all updated in real time
- 10. fraud SpinalTap … calendar master DataStore Medusa Search 1 Search N Search 2 Realtime Update
- 11. Tails binary update logs from Mysql Servers (5.6+) Converts changes in any of the tables into actionable objects called “Mutations” (Inserts, deletes, Updates) Broadcasts them to Medusa using Kafka Spinaltap
- 12. fraud SpinalTap … calenda r master DataStore Medusa Search 1 Search N Search 2 Realtime Update
- 13. Source of truth for search index data. Listens to updates from Spinaltap and builds new IndexData by querying ~15 mysql tables from three different databases. Persists everything in a DataStore and broadcasts latest version to all search nodes. Uses ZooKeeper for leader election. Medusa
- 14. fraud SpinalTap … calenda r master DataStore Medusa Search 1 Search N Search 2 Realtime Update
- 15. What’s in the forward index? ____________________________ Holds all the metadata about a listing required by scoring and filtering. We also have complicated business rules to calculate Price, Availability, InstantBook etc which needs a ton of metadata. ~50 fields built from multiple data source and updated in realtime. public final class ForwardIndexData { private final CalendarData calendarData; private final PricingData pricingData; private final HostInfo hostInfo; . . . . . . . . } ! public final class CalendarData { private final DateRanges reservationDates; private final SeasonalValues startDayOfWeeks; . . . . } ! private final class SeasonalValues<T> { private final DateRange startDate; private final T value; . . . . } Forward Index
- 16. Availability ____________________________ ! Depends on the profile of guest. The checkin date must be one of the valid start days of the week. Must satisfy seasonal minimum nights. There must be enough preparation time for the host. Import busy dates from external calendars to avoid booking conflict.
- 17. Pricing ____________________________ ! Depends on number of guests , number of nights. How close or further away the checkin date is. How long is the trip, does the host have Weekly and Monthly pricing. Is there special price override for these nights.
- 18. Instant Book ____________________________ ! Depends on number of guests , number of nights. Profile of the guest like positive reviews, does have profile photo? How much preparation time the host has etc.
- 19. Needs to store objects with 50-100 fields as values keyed by listing id. Should avoid the cost of serialization/deserialization during every fetch. Data must be available in-memory for fast lookup, but also persisted on disk. Highly Concurrent, writer shouldn’t block the readers (One writer but >100 reader threads) Requirements Why did we need our custom Forward Index?
- 20. // Forward Index public interface ForwardIndex<V> { ! Map<Long, V> asMap(); void put(long id, V value); ! void putAll(Map<Long, V> values); ! void remove(long id); ! void commit(); ! } Forward Index Interface // Writer forwardIndex.put(listingId, listingData); . . . // write to disk and also make it visible to readers. forwardIndex.commit(); // Reader // Fetch forward index data from in-memory map Map<Long, ListingData> fwdIndex = forwardIndex.asMap(); ListingData data = fwdIndex.get(listingId); ! // Use it to evaluate business rules checkAvailability(data, searchRequest); calculatePrice(data, searchRequest)
- 21. NonBlocking In-Memory HashMap DiskStore // Forward Index public class ForwardIndexStore<V> implements ForwardIndex<V> { private final DB<V> diskStore; private final Cache<V> cache; ! . . . . ! @Override Map<Long, V> asMap() { return Collections.unmodifiableMap(cache); } void put(long id, V value) { diskStore.put(id, value); cache.put(id, value); } ! . . . . ! void commit() { diskStore.commit(); cache.commit(); } } Forward Index Implementation
- 22. Ranking Problem ____________________________ Not a text search problem Users are almost never searching for a specific item, rather they’re looking to “Discover” The most common component of a query is location Highly personalized – the user is a part of the query Optimizing for conversion (Search -> Inquiry -> Booking) Evolution through continuous experimentation Ranking
- 23. Ranking Components ____________________________ Relevance Quality Bookability Personalization Desirability of location etc. Ranking
- 24. Several hundred signals used to build machine learning models: ! Properties of the listing (reviews, location, etc.) Behavioral signals (mined from request logs) Image quality and click ability (computer vision) Host behavior (response time/rate, cancellations, etc.) Host preferences model DB snapshots Logs
- 25. Life of a Query Query Understanding Retrieval Populator First Pass Scorer Geocoding Configuring retrieval options Choosing ranking models Quality Bookability Relevance Second Pass Ranking Result Generation AirEvents Filtering by Price and Availability 25 results 2000 results 25 results
- 26. Second Pass Ranking ____________________________ Traditional ranking works like this: ! then sort by In contrast, second pass operates on the entire list at once: ! Makes it possible to implement features like result diversity, etc.
- 27. Life of a Query Query Understanding Retrieval Populator First Pass Scorer Geocoding Configuring retrieval options Choosing ranking models Quality Bookability Relevance Second Pass Ranking Result Generation AirEvents Filtering by Price and Availability 25 results 2000 results 25 results