Self-Ranking Search with Elasticsearch at Wattpad

Ideas you will meet

Distributed Systems

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

Databases

Schemas, indexes, query plans and choosing a store.

Search

Inverted indexes, relevance, autocomplete and search engines.

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 minBM25: the ranking function behind most search enginesHow BM25 adds saturation and length normalisation to TF-IDF, what k1 and b control, and how to compute a score by hand.Read hereClose

  Two fixes to TF-IDF

  BM25 ("best matching 25") keeps the idea of weighting rare terms, and adds two corrections:

  1. Saturation: the first few occurrences of a term matter a lot; the twentieth barely matters.
  2. Length normalisation: a term appearing three times in a tweet means more than three times in a book.

  The formula

  For a query with terms q, the score of document D is the sum over terms of:

  IDF(q) x f(q,D) x (k1 + 1) / ( f(q,D) + k1 x (1 - b + b x |D| / avgdl) )

  • f(q,D) is how many times the term occurs in D.
  • |D| is the length of D and avgdl the average document length in the index.
  • k1 (about 1.2) sets how fast the term saturates.
  • b (about 0.75) sets how strongly length matters.
  • A common IDF is ln(1 + (N - n + 0.5) / (n + 0.5)), which never goes negative.

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  Figure SDA-SAG-02-1 · What BM25 adds on top of IDF · © forcsguy.com

  A worked example

  With k1 = 1.2, b = 0.75 and a term appearing 3 times:

  • Average-length document: 3 x 2.2 / (3 + 1.2) = 1.57.
  • Twice the average length: 3 x 2.2 / (3 + 1.2 x 1.75) = 6.6 / 5.1 = 1.29.
  • Same document but the term appears 30 times at average length: 30 x 2.2 / (30 + 1.2) = 2.12, close to the ceiling of k1 + 1 = 2.2.

  So ten times as many occurrences is worth only 35 percent more. That is saturation.

  // Minimal BM25 term score, ready to use in a toy engine
  function bm25(f: number, len: number, avgLen: number, N: number, n: number, k1 = 1.2, b = 0.75) {
    const idf = Math.log(1 + (N - n + 0.5) / (n + 0.5));
    return idf * (f * (k1 + 1)) / (f + k1 * (1 - b + (b * len) / avgLen));
  }
  

  Tuning in practice

  • Start with the defaults; they work for general text.
  • Short fields such as titles often want a lower b (length matters less), while long bodies keep the default.
  • Fields have different value: a match in the title usually counts for more than in the body, so engines let you weight fields (BM25F style) or run separate fields and combine the scores.
  • BM25 is a first-stage ranker. Many systems retrieve a few hundred candidates with BM25 and re-rank them with a machine-learned model that also uses freshness, clicks and personalisation.
  • Measure changes with the metrics in the evaluation lesson; never tune by looking at three queries.

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  Figure SDA-SAG-02-2 · Retrieval then re-ranking · © forcsguy.com

  Real-world reading

  Related reading

  • The Probabilistic Relevance Framework: BM25 and BeyondStephen Robertson and Hugo Zaragoza · City University of LondonPapers
  • Practical BM25, part 2: the BM25 algorithm and its variablesElasticBlogs
  • Lucene BM25SimilarityApache Lucene · Apache Software FoundationDocumentation
  • Elasticsearch GuideElasticDocumentation

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  • Self-Ranking Search with Elasticsearch at WattpadWattpad engineers · WattpadCase Studies

  Open this lesson with its quiz and exercise

• Databases Deep Dive · 12 minReplication topologies and multi-region databasesSingle-leader, multi-leader and leaderless replication, how they handle conflicts and lag, and what changes across regions.Read hereClose

  Why replicate

  Replication keeps copies of data on several machines for three reasons: availability (survive a node failure), read scaling (serve reads from replicas) and latency (put data near users). The hard part is keeping copies consistent while writes continue. (Basics are in the replication and sharding lesson of the foundations course; this lesson compares the topologies.)

  Single-leader (primary and replicas)

  One node, the leader (primary or master), accepts writes and ships its changes to followers (replicas), which serve reads.

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  Figure SDA-DDV-04-1 · Single-leader replication · © forcsguy.com

  • Synchronous replication waits for a follower before acknowledging: safer, slower, and a stalled follower can block writes. Asynchronous is faster but a failover can lose the latest writes. A common compromise: one synchronous follower, others asynchronous.
  • Failover must detect failure, choose the most up-to-date replica, redirect clients and ensure the old leader does not come back believing it still leads (split brain).
  • Replication lag causes stale reads. Techniques: read-your-writes (route a user's reads to the leader shortly after they write), monotonic reads (keep a user on one replica), and checking replica position.

  Multi-leader

  Several nodes accept writes (often one per region) and replicate to each other. Benefits: local write latency and continued operation if a region is cut off. Cost: write conflicts.

  Conflict handling options:

  • Avoid them: route all writes for one record to one leader (for example by user or region).
  • Last write wins by timestamp: simple, but silently drops one of two concurrent updates and depends on clocks.
  • Merge values (union of sets, counters) using mergeable data types (CRDTs; see the collaborative editing lesson).
  • Custom resolution in application code, or ask the user.

  Multi-leader also appears in offline-capable apps (each device is a leader) and collaborative editing.

  Before: single leader

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  After: multi-leader across regions

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  What changed

  • Users in each region write to a nearby database
  • Concurrent edits to the same row need conflict handling
  • More moving parts to run and monitor

  Leaderless (Dynamo-style)

  Any replica accepts writes; clients write to several replicas and read from several, using quorums (W + R > N) to get fresh data (see the key-value store lesson). Background anti-entropy and read repair fix stale copies; versions or vector clocks detect conflicts. It favours availability and is typical of Cassandra and DynamoDB-style stores.

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  Figure SDA-DDV-04-2 · Choosing a replication model · © forcsguy.com

  Multi-region designs

  • Active-passive: one region serves writes; another is a warm standby. Simple; failover loses a little data (the replication lag) and takes minutes.
  • Active-active: several regions serve traffic. Requires either partitioning data by home region (each record has one home, cross-region reads are replicas) or true multi-leader conflict handling.
  • Global databases use consensus across regions (see the consensus lesson) for strong consistency at the cost of cross-region write latency; some offer tunable consistency per query.
  • Latency numbers matter: a cross-ocean round trip is around 100 ms or more, so synchronous cross-region replication adds that to every write.
  • Data residency laws may require keeping certain users' data in a region.

  Partitioning and replication together

  Real systems shard data into partitions and replicate each partition (a leader per partition spreads write load across nodes). Rebalancing, hot partitions and cross-partition transactions are the new problems (see sharding and the consistent-hashing lesson).

  Master/slave naming

  You will see "master/slave", "primary/replica" and "leader/follower" for the same single-leader idea; most current documentation prefers the last two.

  Checklist

  • What happens to writes if the leader or a region is lost, and how long?
  • How much data can we lose (RPO) and how long can we be down (RTO)?
  • How do we handle stale reads from replicas?
  • Who resolves conflicts, and how?
  • Have we practised failover?

  Real-world reading

  Related reading

  • Active-Active for Multi-Regional ResiliencyNetflix Engineering · NetflixCase Studies
  • Designing Data-Intensive ApplicationsMartin Kleppmann · O'ReillyBooks
  • Read Consistency with Database Replicas at ShopifyShopify engineers · ShopifyCase Studies
  • Uber Schemaless: A Scalable MySQL DatastoreUber Engineering · UberCase Studies

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  • Mitigating MySQL Replication Lag and Reducing Read Load at GithubGithub engineers · GithubCase Studies
  • Cassandra architecture overviewApache Cassandra · Apache Software FoundationDocumentation
  • How Discord Stores Trillions of MessagesBo Ingram · DiscordBlogs
  • Spanner: Google's Globally Distributed Database (paper)Corbett et al. · GooglePapers
  • Scaling Multitenant Architecture Across Multiple Data Centres at Shopify - Weingarten, Engineering Lead at ShopifyShopifyYouTube
  • MIT 6.824: Distributed SystemsRobert Morris · MITCourses

  Open this lesson with its quiz and exercise

• Databases Deep Dive · 13 minChoosing a database typeA practical map from workload to database: relational, document, key-value, wide-column, search, graph, time-series and vector stores.Read hereClose

  Start from the access pattern

  There is no best database; there is a best database for a workload. Ask:

  1. What are the main queries (by key, by range, by search, by relationship)?
  2. How much data and how fast does it grow? Read/write ratio?
  3. What consistency and transaction guarantees are needed?
  4. What latency is required, and where are users?
  5. What can your team operate?

  The families

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  Figure SDA-DDV-05-1 · Map from need to database family · © forcsguy.com

  Relational (PostgreSQL, MySQL)

  Tables, SQL, joins, constraints, ACID transactions. The default for most business data. PostgreSQL has rich features (JSON, full-text, extensions, many index types); MySQL is widely deployed and simple to operate. They scale vertically very far, and read replicas and sharding extend them. Choose when data is structured, relationships matter and correctness is critical.

  Document (MongoDB and similar)

  Stores JSON-like documents; flexible schema; related data can be embedded in one document so a single read gets everything. Good for content, catalogues and objects read whole. Watch for duplicated data, joins you must do in code, and weaker multi-document transaction habits.

  Key-value (Redis, Memcached; DynamoDB as a key-value/wide hybrid)

  Lookup by key at very low latency. Redis adds data structures (hashes, sorted sets, lists, bitmaps), persistence and clustering (see the Redis lesson). Ideal for caches, sessions, counters, rate limits, leaderboards. Not for ad-hoc queries.

  Wide-column and partitioned key stores (Cassandra, DynamoDB, HBase)

  Data is partitioned by a partition key across many nodes; you design tables around queries. Excellent write throughput, horizontal scale and availability. Cost: limited query flexibility, careful key design (hot partitions), eventual consistency by default. Good for event logs, messaging history, time-ordered user data.

  Search engines (Elasticsearch, OpenSearch, Solr)

  Build an inverted index (word to documents) for full-text search, relevance scoring, filters and facets. Usually a secondary copy of data from a primary database, kept in sync. Not a system of record: treat it as a derived index that can be rebuilt.

  Graph (Neo4j and others)

  Stores nodes and relationships and answers questions that follow connections: friends of friends, fraud rings, recommendations, dependency analysis. Where many-hop traversals would need expensive recursive joins in SQL, a graph database is natural.

  Time-series (InfluxDB, TimescaleDB, Prometheus)

  Optimised for append-heavy, timestamped data with time-range queries, downsampling and retention. Used for metrics, IoT and finance ticks (see the metrics monitoring lesson). Keep tag cardinality low.

  Vector databases (and vector indexes in other databases)

  Store embeddings (arrays of numbers representing meaning) and find the nearest neighbours, using approximate indexes (graph-based or clustering) for speed. Used for semantic search and retrieval for AI applications. Many general databases now offer vector indexes, which can be enough for moderate scale and avoids another system.

  A quick chooser

  Need	First choice
  Business data with relationships and transactions	Relational
  Flexible, self-contained records	Document
  Cache, session, counter, leaderboard	Redis
  Massive write volume, simple access by key	Wide-column / DynamoDB-style
  Full-text search and facets	Search engine
  Many-hop relationship queries	Graph
  Metrics and sensor data	Time-series
  Semantic similarity	Vector index
  Analytics on large history	Warehouse or lakehouse (see the big data course)

  Polyglot persistence and its price

  Many systems use several stores, each for what it does best: a relational database as the source of truth, a cache, a search index and a metrics store fed from it. The cost is keeping them in sync (change events, outbox pattern, rebuild jobs), more to operate and more failure modes. Add a store only when a measured need justifies it.

  SQL versus NoSQL, honestly

  "NoSQL" is a family of different designs, not one thing. Relational databases scale further than many assume; NoSQL stores often give up joins and some consistency for scale and flexibility. Decide on requirements, not fashion. If in doubt, start relational and specialise later.

  Questions before committing

  • Can we model our top ten queries easily?
  • What happens at 10 times the data? At a regional failure?
  • How do we back up, restore, migrate and monitor it?
  • Does the team know it, and is it supported well on our platform?

  Real-world reading

  Related reading

  • Redis UniversityRedis · Redis Ltd.Courses
  • RedisRedis · Redis Ltd.GitHub
  • Redis DocumentationRedis · Redis Ltd.Documentation
  • Build Your Own XCodeCrafters · GitHubGitHub

  Show 6 moreShow fewer

  • Redis persistenceRedis · Redis Ltd.Documentation
  • Moving Food Feed from Redis to Cassandra at ZomatoZomato engineers · ZomatoCase Studies
  • Scale with Redis ClusterRedis · Redis Ltd.Documentation
  • Improving key expiration in Redis at TwitterTwitter engineers · TwitterCase Studies
  • Migrating MySQL from 5.6 to 8.0 at FacebookFacebook engineers · FacebookCase Studies
  • Decreasing RAM Usage by 40% Using jemalloc with Python & Celery at ZapierZapier engineers · ZapierCase Studies

  Open this lesson with its quiz and exercise