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Series · Day 100 of 100

Elasticsearch & Search Engineering

  1. 01
    Why Elasticsearch for search: what developers should know
    Fast, relevant full-text search that SQL LIKE queries cannot match.
  2. 02
    Elasticsearch core concepts: what developers should know
    Indexes, documents, shards and replicas.
  3. 03
    Running Elasticsearch locally: best practices explained
    Start with Docker in minutes.
  4. 04
    How the inverted index works for Elasticsearch developers
    The data structure behind fast search.
  5. 05
    Elasticsearch mapping explained: lessons from production
    Define how fields are stored and searched.
  6. 06
    text vs keyword fields: a practical guide for developers
    The most common mapping mistake.
  7. 07
    Dynamic mapping pitfalls: a hands-on guide for engineers
    Let Elasticsearch guess and it will guess wrong.
  8. 08
    Analyzers explained in Elasticsearch & Search Engineering
    How text becomes searchable terms.
  9. 09
    Custom analyzers: Elasticsearch best practices explained
    Tune text processing for your data.
  10. 10
    Indexing documents in Elasticsearch & Search Engineering
    Add and update data.
  11. 11
    Bulk indexing best practices for Elasticsearch developers
    Load data fast without overloading the cluster.
  12. 12
    Query DSL basics: Elasticsearch best practices explained
    How to write Elasticsearch queries.
  13. 13
    Bool queries: concepts, trade-offs and production lessons
    Combine conditions the right way.
  14. 14
    Query vs filter context: a practical Elasticsearch guide
    Scoring costs time.
  15. 15
    multi_match queries in Elasticsearch & Search Engineering
    Search across several fields.
  16. 16
    Relevance tuning: Elasticsearch best practices explained
    Make the best results appear first.
  17. 17
    BM25 explained: Elasticsearch best practices explained
    How Elasticsearch scores relevance.
  18. 18
    Synonyms in Elasticsearch: practical tips and trade-offs
    Match what users mean.
  19. 19
    Fuzzy search and typos: a practical guide for developers
    Find results despite misspellings.
  20. 20
    Autocomplete with Elasticsearch: lessons from production
    Search-as-you-type suggestions.
  21. 21
    Highlighting search results for Elasticsearch developers
    Show users why a result matched.
  22. 22
    Aggregations explained: a practical guide for developers
    Analytics and filters from search data.
  23. 23
    Faceted search: Elasticsearch best practices explained
    Filters with counts, like online stores.
  24. 24
    Date histograms: Elasticsearch best practices explained
    Time-based analytics.
  25. 25
    Cardinality aggregation: a practical Elasticsearch guide
    Count unique values approximately.
  26. 26
    Pagination in Elasticsearch: what developers should know
    from/size, search_after and PIT.
  27. 27
    Sorting results: Elasticsearch best practices explained
    Order by fields or score.
  28. 28
    Nested vs object fields: a practical Elasticsearch guide
    Model arrays of objects correctly.
  29. 29
    Parent-child relationships: patterns, pitfalls and fixes
    Join-like modeling in Elasticsearch.
  30. 30
    Data modeling for search: a hands-on guide for engineers
    Design documents for queries, not normalization.
  31. 31
    Syncing a database to Elasticsearch: a step-by-step guide
    Keep search up to date.
  32. 32
    Index aliases: concepts, trade-offs and production lessons
    Change indexes without changing code.
  33. 33
    Zero-downtime reindexing: a hands-on guide for engineers
    Change mappings safely.
  34. 34
    Index templates: Elasticsearch best practices explained
    Consistent settings for new indexes.
  35. 35
    Index lifecycle management: patterns, pitfalls and fixes
    Manage logs and time-series data.
  36. 36
    Data streams: concepts, trade-offs and production lessons
    Append-only time-series indexes.
  37. 37
    Shard sizing: concepts, trade-offs and production lessons
    Too many shards hurt clusters.
  38. 38
    Replicas and availability: practical tips and trade-offs
    Survive node failures.
  39. 39
    Cluster health explained: a hands-on guide for engineers
    Green, yellow and red.
  40. 40
    Node roles: everything engineers need to know in practice
    Master, data, ingest and coordinating.
  41. 41
    Elasticsearch performance tuning: lessons from production
    Faster queries and indexing.
  42. 42
    Search slow logs: Elasticsearch best practices explained
    Find slow queries.
  43. 43
    Caching in Elasticsearch: a hands-on guide for engineers
    Built-in caches that help.
  44. 44
    JVM heap sizing: Elasticsearch best practices explained
    Memory settings for nodes.
  45. 45
    Ingest pipelines: Elasticsearch best practices explained
    Transform documents before indexing.
  46. 46
    The ELK stack explained: a practical Elasticsearch guide
    Elasticsearch, Logstash and Kibana.
  47. 47
    Kibana Discover and dashboards: best practices explained
    Explore logs quickly.
  48. 48
    Centralized logging with Elasticsearch, explained simply
    All logs in one place.
  49. 49
    Elasticsearch with Node.js: patterns, pitfalls and fixes
    The official client in practice.
  50. 50
    Elasticsearch with .NET: real-world lessons and examples
    Elastic.Clients.Elasticsearch in C#.