Company: Pluralsight Author: Janani Ravi Full Title: Indexing Data In Elasticsearch Year: 2018 Language: English Genre: Educational: Big data Skill Level: Intermediate Price: - - Files: MP4 (+ Slides .PDF) Time: 02:46:31 Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 350 kbps Audio: AAC at 64 Kbps, 2 channels, 44.1 KHz This course explains the index distribution architecture of Elasticsearch, cluster configuration, shards and replicas, similarity models, advanced search, and mixed-language documents, all of which improve the performance of search queries. Getting Elasticsearch up and running is very simple, but tuning it to have low latency and high performance for search queries requires a deep understanding of the index distribution architecture. In this course, Indexing Data in Elasticsearch, you will understand the structure of distributed indices and advanced search constructs such as similarity models, segment merging, suggesters, fuzzy searches and working with mixed-language documents. First, you will study why shard overallocation is a good thing and how you can configure your cluster to avoid the split-brain scenario. Then, you will see how indices can be configured to use different similarity models and how to use force merging of segments to improve the performance of large indices. Next, you will explore how to cache prudently and use advanced search features. Finally, you will learn to deal with different languages in the same document with the ICU plugin. At the end of this course, you will have a deep understanding of how indexing works in Elasticsearch and be comfortable with advanced query constructs. Lessons: 1. Course Overview 01. Course Overview 2. Introducing the Index Distribution Architecture 02. Module Overview 03. Prerequisites and Course Overview 04. Demo: Elasticsearch Installation on a Local Machine 05. Demo: The Elasticsearch Head Plugin 06. Distributed Architecture 07. Demo: Configuring VMs on the Google Cloud Platform 08. The Split-brain Scenario 09. Demo: Configuring and Running a Cluster 10. Shards and Replicas 11. Demo: Shards and Data 12. Allocating Shards and Replicas 13. Demo: Routing to a Specific Shard 14. Demo: Routing Using Aliases 15. Demo: Query Preferences 3. Executing Low-level Index Control 16. Module Overview 17. The TF/IDF Relevance Algorithm 18. Understanding the BM25 Similarity Models 19. Demo: Configuring Similarity Models 20. Demo: Configuring Per-field Similarity Models 21. Demo: Custom Similarity Models 22. Merging Segments 23. Demo: Force Merge Segments 24. Caching 25. Demo: Shard Request Caching 26. Demo: Query Caching 4. Improving the User Search Experience 27. Module Overview 28. Full Text Search and Keyword Search 29. Analyzers 30. Term Queries vs. Match Queries 31. Demo: Term and Match Queries 32. Demo: Case Insensitive Term Searches with Normalizers 33. Demo: Suggesters 34. Demo: Fuzzy Search 35. Demo: Autocomplete 5. Dealing with Human Languages 36. Module Overview 37. Demo: Creating an Index Per Language 38. Demo: Setting a Per-field Language Analyzer 39. Demo: Multiple Languages in the Same Field 40. Demo: The ICU Plugin 41. Summary and Further Study Our members see more. Join us! ------------- Our members see more. Join us!
