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Lucene @ Yelp Sudarshan Gaikaiwari

Bio 1. Over a decade of experience in information retrieval 2. Used IR techniques at Symantec's DLP group 3. Search Engineer at Yelp

Outline 1. Overview of search services at Yelp 2. Federation Motivation 3. Lucy Indexing 4. Lucy Searching 5. Efficiently Retrieving top k hits

The services we provide

Lucy: business search

Lucy also powers phone search

Cathy: she 'talks' a lot

Listsearch: it searches lists....

Reviewsearch: it searches reviews....

DYM: did you really mean that?

Suggest: auto completion

Federation Motivation


Search is too slow

Hard Disk Seek Latency Disk seek 10,000,000 ns

Source Software Engineering Advice from Building Large-Scale Distributed Systems Jeffery Dean

RAM read latency Main memory reference 100 ns

Pinning Index in RAM ● vmtouch ● mlock ●

Problem Index is too large fit in memory on a single machine

Geographical sharding

Geographical Sharding drawbacks 1. Cumbersome manual process to determine shard boundary 2. No guarantee that a boundary can be found.

Federation 1. Split index across multiple machines 2. Shard on business id 3. TF-IDF scores from different machines should be comparable

Mapping businesses to shards 1. Assigning businesses to shards shard = shardlist[hash(business_id) % len(shardlist)] Problems 1. Involves re-indexing all the businesses if we want to add a new shard

Virtual Nodes

Advantages 1. Flexibility (move vbuckets from one shard to another) 2. Split hot spot shards

Lucy Master Slave Architecture Separate indexing (masters) A master for each shard of a service Searching (slaves) A slave for every replica of a service

Lucy Indexing

Lucy Searching

Federator: Combining results across shards 1. Once we distribute an index across shards we need a component which will search all these shards and combine their results. 2. Written in Python (runs inside a python web process). 3. Uses Tornado IO loop to send requests to all shards. 4. The transfer protocol for the requests in JSON RPC

Lucy Server

Tokens to Business Attributes

Executing queries 1. Gather the top results for a query 2. Collect attribute statitics for attributes like places, categories

Lucene 1. Efficiently executes queries over the index 2. Provides how relevant the business is to the words in the query (word score) 3. Upgrading lucene to 2.9/3.1 is WIP

Successive geobounds relaxation

Successive geobounds relaxation


Efficiently Retrieving top k hits 1. When user moves through multiple pages the number of hits to be returned increases num hits = start + count 2. So if we need to retrieve 500 hits the naive way would be to retrieve 500 hits from each shard and then sort them

Distribution of hits in shards

Probability a hit is in a shard

Binomial Distribution Probability (r of top k hits) are in a particular shard



Formula Std Deviation




Hits selected from each shard k = 100 p = 0.2

Results Missed (%)







Simulation Graph

Results 1. ~ 50% savings over 100 hits (44 hits requested from each shard) 2. 77% savings over 1000 hits (228 hits requested from each shard)

Future work 1. In memory index 2. Move towards real time search

Come Join Us!

Thank You

Lucene @ Yelp  

This talk describes how the Yelp uses Lucene to provide search services. It includes Statistics of Yelp search usage Overview of Yelp search...

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