Machine Learning for Ranking Answers End-to-End at Quora

Ideas you will meet

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

Search

Inverted indexes, relevance, autocomplete and search engines.

Machine Learning Systems

Recommendations, ranking, feature stores and serving models at scale.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Search, Analytics and Geo · 11 minMeasuring search: precision, recall, MRR and NDCGHow to know whether a ranking change helped: relevance judgments, the standard metrics, a worked NDCG example, and why offline numbers need online confirmation.Read hereClose

  Why measure at all

  Search quality feels subjective, and tuning by trying a few queries is how regressions ship. A small evaluation set with agreed metrics turns "this feels better" into a number you can track.

  Relevance judgments

  You need, for a sample of queries, a label for each returned document: relevant or not, or a grade such as 0 (bad) to 3 (perfect). Sources:

  • Human raters following written guidelines (best quality, costly).
  • Click and engagement logs (cheap and plentiful, but biased towards what was shown first).
  • Synthetic labels from a strong model, checked against a human sample.

  The core metrics

  • Precision@k: of the top k results, the fraction that are relevant. Good when users only look at the first page.
  • Recall@k: of all relevant documents, the fraction found in the top k. Matters when missing something is costly, such as legal or support search.
  • MRR (mean reciprocal rank): average of 1 / rank of the first relevant result. Good for "find the one right answer" queries.
  • DCG and NDCG: use graded relevance and discount lower positions. DCG = sum of (2^grade - 1) / log2(position + 1). NDCG divides by the DCG of the ideal order, giving a value from 0 to 1.

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  Figure SDA-SAG-04-1 · Computing NDCG · © forcsguy.com

  A worked example

  Results graded 3, 0, 2 (in the order returned):

  Position	Grade	Gain 2^g - 1	Discount log2(p+1)	Contribution
  1	3	7	1.00	7.00
  2	0	0	1.58	0.00
  3	2	3	2.00	1.50

  DCG = 8.5. The ideal order (3, 2, 0) gives 7 + 3 / 1.58 = 8.89. NDCG = 0.96. Swapping the second and third results would have scored a perfect 1.0.

  Offline versus online

  Offline metrics are fast and repeatable, so use them to reject bad ideas and compare candidates. They are still a proxy. Confirm winners with an online experiment: split live traffic, compare click-through, successful sessions, reformulation rate and latency, and watch for segments that got worse (new users, rare queries).

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  Figure SDA-SAG-04-2 · Offline first, then online · © forcsguy.com

  Pitfalls

  • A tiny or unrepresentative query set overfits quickly.
  • Averages hide failures: also look at the worst queries and at queries with no results.
  • Click data rewards what was already ranked high; use it alongside, not instead of, judged sets.

  Real-world reading

  Related reading

  • Introduction to Information Retrieval: evaluationManning, Raghavan and Schutze · Stanford UniversityBooks
  • Introduction to Information Retrieval: assessing relevanceManning, Raghavan and Schutze · Stanford UniversityBooks
  • The Probabilistic Relevance Framework: BM25 and BeyondStephen Robertson and Hugo Zaragoza · City University of LondonPapers
  • Machine Learning for Ranking Answers End-to-End at QuoraQuora engineers · QuoraCase Studies

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  • Elasticsearch GuideElasticDocumentation

  Open this lesson with its quiz and exercise

• Search, Analytics and Geo · 11 minSharding a search index: scatter, gather and mergeHow a search index is split across machines, why queries fan out to every shard, how top-k results are merged, and what goes wrong with deep paging and uneven shards.Read hereClose

  Why shard

  One machine cannot hold or search a very large index within the latency budget. Sharding splits the index into pieces, each on its own machine, so storage and work are spread out. Replicas copy each shard for availability and extra read capacity.

  Before: one big index

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  After: sharded, scatter and gather

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  What changed

  • Storage and work are spread over machines
  • Each shard returns its own top results to be merged
  • The slowest shard decides how fast the query is

  Two ways to partition

  • Document-partitioned (local index): each shard indexes a subset of the documents and holds a complete inverted index for them. Writes touch one shard. Every query must visit every shard. This is what Elasticsearch and Solr do.
  • Term-partitioned (global index): each shard holds the postings for a subset of terms. A query touches only the shards for its terms, but a write touches many shards and long posting lists must be combined across machines. It is rare in practice.

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  Figure SDA-SAG-05-1 · Scatter, gather and merge · © forcsguy.com

  Decisions that matter

  • Shard size and count. Too many tiny shards waste memory and add fan-out; too few huge shards recover slowly and cannot spread load. Many teams aim for tens of gigabytes per shard. The primary shard count is hard to change later, so plan for growth or use time-based indexes.
  • Routing. By default a document goes to a shard by hashing its id, which spreads data evenly. Routing by tenant or user keeps one customer's data together so queries can hit a single shard, at the risk of uneven shards.
  • Tail latency. A query is as slow as its slowest shard. Use replicas, timeouts, and hedged requests (send to a second replica if the first is slow).
  • Deep pagination. Page N needs the first N x pageSize results from every shard. Use cursors (search-after) instead of large offsets.
  • Scoring across shards. Each shard computes statistics such as IDF from its own documents. Even, large shards make the difference small; otherwise gather global statistics first.
  • Rebalancing and reindexing. Plan how to add capacity without downtime, usually by indexing into a new index and switching an alias.

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  Figure SDA-SAG-05-2 · Time-based indexes · © forcsguy.com

  Keeping the index fresh

  Documents usually live in a primary database and are copied to the search index. Use a change stream or an outbox so updates are not lost, accept that search is eventually consistent, and keep a way to rebuild the whole index from the source of truth.

  Real-world reading

  Related reading

  • Elasticsearch: scalability and resilienceElasticDocumentation
  • SolrCloud: shards and indexing dataApache Solr · Apache Software FoundationDocumentation
  • Elasticsearch GuideElasticDocumentation
  • Scaling Elasticsearch Clusters at UberUber engineers · UberCase Studies

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  • Elasticsearch Performance Tuning Practice at eBayeBay engineers · eBayCase Studies
  • Solr: Improving Performance for Batch Indexing at BoxBox engineers · BoxCase Studies

  Open this lesson with its quiz and exercise

• Search, Analytics and Geo · 10 minNearby search with Redis GEOHow Redis stores locations in a sorted set using geohash scores, how GEOSEARCH finds nearby members, how to expire stale entries, and when to move to a spatial database.Read hereClose

  The problem

  "Show the nearest available drivers", "stores within 3 km", "friends nearby". The data is a large set of points that change often, and the question is always about a small neighbourhood. You want a fast, in-memory structure.

  How Redis does it

  Redis stores locations in an ordinary sorted set. Each member (a driver id) has a score that is a 52-bit geohash of its longitude and latitude. Geohash interleaves the bits of the two coordinates so that nearby places have nearby scores. A search turns a radius into a few score ranges, reads those ranges, then filters by exact distance.

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  Figure SDA-SAG-09-1 · A nearby query in Redis · © forcsguy.com

  Typical commands:

  GEOADD drivers:blr 77.5946 12.9716 driver:42
  GEOSEARCH drivers:blr FROMLONLAT 77.60 12.97 BYRADIUS 2 km ASC COUNT 20 WITHDIST
  

  Design details

  • Partition the key. One sorted set for the whole planet is a hot spot and slow to search. Use a key per city, region or grid cell so each set is small and local, and shard keys across nodes.
  • Updates are cheap. Moving a member is another GEOADD, about O(log n). 75,000 updates per second is manageable on a few nodes.
  • Freshness. Sorted set members have no individual expiry. Keep a second sorted set lastseen with the update time as score, and run a job that removes members older than, say, 30 seconds from both sets.
  • Availability filter. Store only drivers who are available, or look up status after the search; keep the hot set small.
  • Accuracy. Redis uses a spherical model of the Earth, so distances can be off by a fraction of a percent, and results near a cell boundary rely on the exact filter. That is fine for "within 2 km", not for surveying.
  • Durability. Treat Redis as a rebuildable copy. The system of record is a database, and clients re-send their location every few seconds anyway.

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  Figure SDA-SAG-09-2 · Keeping the hot set fresh · © forcsguy.com

  When Redis GEO is not enough

  Move to a spatial database such as PostGIS, or a purpose-built index, when you need polygons and fences, joins with other tables, constraints (opening hours, vehicle type) together with distance, or durable queries over history. Many systems combine them: a database for rich queries and Redis for the live, hot position data.

  Other grid systems are covered in the geofencing lesson and in the real-world reading: H3 (hexagons, good for aggregation and neighbours) and S2 (cells on a sphere).

  Real-world reading

  Related reading

  • Redis geospatial data typeRedisDocumentation
  • Redis GEOSEARCH commandRedisDocumentation
  • PostGIS documentationPostGIS project · PostGISDocumentation
  • H3: Uber's Hexagonal Hierarchical Spatial IndexUber Engineering · UberCase Studies

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  • S2 GeometryGoogle · S2 GeometryDocumentation

  Open this lesson with its quiz and exercise