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Etsy Search Architecture
How we Query & Index 26 Million One-of-a-Kind Items
Aaron Gardner
Engineering Director, Etsy
agardner@etsy.com / @aargard
QCon SF 2014
November 3rd, 2014
InfoQ.com: News & Community Site
• 750,000 unique visitors/month
• Published in 4 languages (English, Chinese, Japanese and Brazilian
Portuguese)
• Post content from our QCon conferences
• News 15-20 / week
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Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/etsy-search-ecosystem
Purpose of QCon
- to empower software development by facilitating the spread of
knowledge and innovation
Strategy
- practitioner-driven conference designed for YOU: influencers of
change and innovation in your teams
- speakers and topics driving the evolution and innovation
- connecting and catalyzing the influencers and innovators
Highlights
- attended by more than 12,000 delegates since 2007
- held in 9 cities worldwide
Presented at QCon San Francisco
www.qconsf.com
Etsy by the numbers
• 40 million members
• 1+ million sellers
• 26 million listed items
• 25%+ of orders cross a border
• $1.35 billion in sales in 2013
The Diagram
Etsy Search Architecture circa Nov 2014
–Kellan Elliott-McCrea - CTO, Etsy
“The pursuit of learning is supported by
simple architecture that easily adapts
to new information.”
Etsy Search Architecture circa Nov 2014
Clusters & Bundles & Tiers, oh my
Life of a query
https://www.etsy.com/search?q=guitar%20strap
Life of a query
• A/B eligibility checks
• stuff
• Choose & query a search host
Pick search host randomly
// COOL
array_rand($servers);
// LESS COOL (we used to do this)
exdec(substr(
md5($numServers.’+’.$query),
0,4)) % (count($servers));
Life of a query
What’s this?
1414677775480 listings 29012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1414677775480 search_args(query:guitar
straps, limit:42, offset:0,
sortBy:relevance, sortOrder:desc,
filter:ListingSearchFilter(price_min:
0.0, price_max:0.0, ship_to:US,
marketplace:null, color:null, user_id:0,
tags:null, materials:null,
currency_code:USD, fields:null,
favorite_shop_user_ids:[],
property_constraints:[], query:null,
language:en-US, maturity:null,
delivery_days:0, category2:null, ...
Thrift: Cross-Lang service & code-
gen
• Define API service & data types
• Generates PHP & Java client/server
code
• Pros: — Fast, Easy to extend at low
level
• Cons — Not HTTP
Life of a query
Find relevant items with
standard Information
Retrieval (IR) techniques
Life of a query
Ranking uses scores published
from Big Data ML stack
Life of a query
[12345,31415926…]
Return only PKs from Search
• Keep search index small, better for…
• Replication
• Caches
• JVM GC happiness
• Less data to send back to Webs
• Web stack great at loading content by
PKs
Query-Results Cache
• key=query, val=PKs
• serve-stale-while-revalidate
• move cache out of JVM heap
Etsy Banner Protocol
0xC0DECF
The Banner Protocol Dance
• Client picks search host
• Send Thrift search query
• Wait for 4-byte “OK” up to 10ms
• If timeout: try another host
• else “OK”: wait for real results
Sharding: Not When, but How
Box(es) O’ Shards
Patches upstream
qtime 95th: 850 —> 175ms
50th: 39 —> 20ms
Denorm & Indexing
Now let’s follow the data
Denorm DB = Speed & Sanity
• We reindex a lot — For features, index cleanup
• Tune denorm DB for speed
• Don’t hurt user-serving DBs with indexers
• 1 denorm store =1 dumb indexing lib
• Easier to maintain, debug, & add new content
Denorm & Indexing
• Sellers & buyers & crons do stuff
• Web stack changes data in site DBs
Denorm & Indexing
• Denorm Cron (PHP) finds IDs touched since a cursor
• Spawns async PHP jobs to denorm & store blobs
Denorm & Indexing
• Incremental indexers have own cron &
cursor
Index Age for 8 Shards
Denorm Reuse
X
Events & Solr EFFs
Solr External File Fields
curlajax
Continuously Deploying
Search
Clusters revisited
2x Everything
Magic Config File
<?php
/**
* Which search cluster is live.
* ------------------------------------------
* THIS IS A GENERATED FILE. DO NOT EDIT.
* ------------------------------------------
* Generated on Sat, 01 Nov 14 06:35:03 +0000
*/
$CONFIG[‘search_mtime'] = '1414823703';
$CONFIG['search_live'] = 'flop';
$CONFIG['search_dark'] = 'flip';
#1: push new code to Dark Side
(Flop)
#2: push config to TEST that points to
Dark Side with new code. You Test.
#3: push config to PROD that points
to new code, making it The Live Side
Watch Graphs, Logs, IRC, etc.
One Last Thing
On-Call Architecture
…
Thanks
Aaron Gardner
agardner@etsy.com
@aargard
Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/etsy-
search-ecosystem

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