This document discusses search architecture and optimization for e-commerce platforms. It describes how search is a critical feature that powers recommendations and sales. Key challenges include large catalogs that change frequently, diverse user needs like geo-specific ranking, and balancing multiple objectives. The document outlines the technical infrastructure supporting search, including serving architecture, indexing workflows, and approaches to improve quality like query understanding and personalization.

3. ● Search is one of the most
important discovery tools in
E-commerce.
● Powers other features like
merchandising (promotions),
recommendations etc.
● Accounts for big fraction of the
units sold and GMV.

4. ● Important signals that
affect search: Price,
offers, popularity,
availability, serviceability
etc.
● Used in ranking of
products.
● Exposed as filters and
sorts to end users.
● These signals are very
dynamic, particularly
during sales.

5. ● E-commerce search != websearch.
● Documents have a structure to them
● Queries have an implicit structure
● Challenges:
○ Large document collection with a long heavy tail
○ Extremely high rate of changes/updates (Thousands per sec)
○ Geo specific ranking
○ Multi-objective optimization (GMV, Units, Ads revenue, Long
Term Value)
● Opportunities:
○ Broad queries: personalization can play a huge role

6. ● Queries per day: XXX Millions / week
● Latencies:
○ Average: ~ 100 ms
○ Median: ~ 50 ms
○ 90th percentile: ~ 500 ms
● Documents retrieved and scored from index:
○ Median: 1K to 10K
○ 95th percentile: 200K to 500K
○ 99th percentile: 500K to 3M+
● Search CTR: Around 50%

7. ● Architectural overview of the search platform
○ Serving and Ingestion
○ Serving functional view
○ Serving architectural view
○ Ingestion architectural view
○ Example ingestion topology
● Search quality
○ Challenges
○ Life of a query: Typical flow for query understanding
○ Illustrative problems

8. ● 1,000,000 Compute Cores
● 2.56 Petabytes RAM
● 120 Petabytes Disk
Storage
● 1 Petabytes NVMe SSD
● 128 Tbps bisection
bandwidth Clos network

10. Query Rewriter
(Spell Check, Concept, NLP, Intent,
Augmentation,Retrieval/Scoring query
formulation)
Reverse Proxy
(Geo Coding, User Context, Caching,
Isolation, Rate Limit, Tee-off test framework)
Search Broker
(Distributed Search across shards, Blending
Of Results from shards)
Searcher
(Matching, Scoring, Faceting, Top-K Retrieval
(pass-1 ranking))
Text index NRT index
Metadata
Re-ranking
(Pass-2 Ranking) - ML Model
Pluggable
Ranking Models
Pluggable
Rewriter Modules

11. Serving:
Arch View

14. ● Architectural overview of the search platform
○ Serving and Ingestion
○ Serving functional view
○ Serving architectural view
○ Ingestion architectural view
○ Example ingestion topology
● Search quality
○ Challenges
○ Life of a query: Typical flow for query understanding
○ Illustrative problems

15. ● Marketplace
○ Catalog entries vary in quality from seller to seller. Spam is
rampant.
● Diversity of users
● Mobile heavy users: Real estate on UI
● Poor internet connectivity

16. ● Literacy/Internet awareness
● Language
● Economic power
● Regional preferences
Abstraction: City-tier
Query/Intent Solicitation
Result Presentation
Product Ranking

17. 40% increase in proportion of tier-3 customers vis-a-vis metro

18. Query: samsang
Relative ratio of query Tier-3 Vs Metro: 1.8
Query: jins
Relative ratio of query Tier-3 Vs Metro: 2.2

20. Query
Scoring
Normalisation(Index time as well)
- String clean-up
- lower
Spell Correction
- Resource-based
- term->term
- Query->query
- Online
Init
Context
Phrasing (Index time as well)
- Frequent bi/tri grams
Stemming (Index time as well)
- Core e-commerce
stemmer
- plurals
Common MetaData Store (Query Level)
- Raw Data: metrics (CTR, Impression, NDCG…)
- Derived Data: Store, LM score, Features
Synonyms
- Resource-based
Intent
- Deductions
- Tagging (CRF)
Query Rewrite
- Best query selection
- Partial match
SOLR interface
Query Understanding
Output Generator
Retrieval
ranking
logic
Store Classifier
Query LM
Feature Store
Classification

21. • Special patterns:
– Segmented words: lgnexus5
Counting: “samsang” & no-click followed
by “samsung”& click a million times
– Context aware counting
• Language modeling and edit distance
• Term to vector models in deep learning.
Specific
General

22. ● Intent: From query tokens to (implicit) attributes that are
represented by those tokens
● Examples:
○ “red tape shoes” -> (brand) “red tape” (store) “shoes”
○ “kids party dress 4-5 years pack of 2” -> (ideal_for) “kids”
(occasion) “party” (store) “dress” (size) “4-5 years”
(pack_of) “pack of 2”
○ “samsung e6 cases” -> (“compatible_with”) “samsung e6”
(store) “cases”
● Memorization, Language modeling, CRF

23. Past orders Product Views
Users’ activity on the platform
Customised Search Ranking
for User-segment

24. economical expensive
shoes
watches
Past orders Product Views
5 price ranges defined for each
vertical.
1 2 3 4 5
User-Segments based on price affinities
Users’ past activity on the platform.
Customised Search Ranking
for each User-segment
Price
Personalization
#ofusers