Working with Elasticsearch

Install and configure Elasticsearch by choosing packages, ensuring Java, and setting cluster name, master and data nodes, unicast discovery, and heap memory for a scalable, highly available cluster.

Create, retrieve, update, and delete documents in elastic search, indexing video metadata with an index, type, and id; learn about shards, lucene indexing, and inverted indexes.

Learn to run searches and aggregations in Elasticsearch, indexing data, querying the video search index, and using aggregations to reveal uploader and view insights.

Define mappings to serve as schemas for fields across document types in an index. Understand fields such as source and all, type and id to identify and search documents.

Explore core field types in Elasticsearch—numbers, dates, strings, booleans—and learn how index options, not analyzed vs analyzed, and multi fields enable precise search, range queries, and aggregations.

Explore predefined and custom analyzers with the analyzer API, including language options, stopword removal, and stemming. Build custom analyzers using character filters, tokenizers, and filters like engrams, shingles, and synonyms.

Learn how to query Elasticsearch to return specific fields, sort by a field, and paginate results using from and size, while understanding ranking with term frequency inverse document frequency.

Explore full-text search with elasticsearch by using match and multi-match queries across fields, leveraging analyzers, scoring, fuzziness, and phrase matching to improve relevance.

Explore how the lucene query syntax powers query strings and simple query strings in Elasticsearch, covering operators, phrase queries, fuzziness, ranges, field targeting, and boosting.

Learn to combine full-text and term queries with the bool query, using must, must not, should, and filter clauses plus minimum should match.

Tune relevance by applying function score techniques like qwerty, boosting views and likes, and using filters. Explore decay, script, and random functions to shape score mode and final rankings.

Explore how queries and aggregations run together in a cluster, build uploader buckets, apply post filters, and use global and multi-filter aggregations across multiple charts.

Explore combining terms, bucket, and pipeline aggregations in Elasticsearch to create meaningful analytics, including global scope, date histogram, moving averages, and metrics like sum and average.

Learn about Elasticsearch aggregation types, including significant terms, terms, cardinality, and percentiles, and how background sets, sampling, and top aggregations shape results and performance.

Index videos with comments as objects or nested documents to enforce per-comment boundaries. Query with nested paths and apply aggregations, including the parent option and score modes.

Learn how to model videos and comments as separate documents with parent-child relations in Elasticsearch, enabling independent updates, efficient joins, and targeted queries and aggregations.

Explore denormalization and application-side joins in Elasticsearch, balancing fast queries with larger indexes and update complexity, versus compact indices but slower, more complex searches.

Learn to optimize indexing and searching in Elasticsearch by using the bulk API for batch index, update, and delete, paging with scrolls, and managing segment merges, flushes, and refreshes.

Tune Elasticsearch node settings to balance heap and OS caches, optimize disk throughput with multiple disks, and configure caches and circuit breakers to reduce latency for large aggregations.

Configure shards and replicas to balance tolerance and performance, allocate shards across nodes, and scale with more nodes; learn dedicated master and data roles and cross-cluster search with tribunal.

Explore how to scale elasticsearch with strategies like overshared indices, routing, and shared allocation, including time-based indices for logs and multi-tenant data.

explore how aliases mimic independent indices for users within a shared index, enabling routing and filtering, and learn to use index templates for time-based management and versioning.

Balance shards and improve stability in Elasticsearch by enabling rack or availability zones awareness, updating transient or persistent cluster settings, and tuning recovery, concurrency, and thread pool parameters.

Monitor and configure Elasticsearch logs and metrics by adjusting logging levels, slow logs, and thresholds. Centralize logs with ELK stack and track cluster, node, and JVM metrics to diagnose performance.

Learn to back up Elasticsearch configurations using file system or other repositories, manage incremental snapshots, restore indices, and perform rolling or full upgrades with proper stop indexing and allocation controls.

Explore how Elasticsearch ranks and returns top results, with guidance on indexing, mapping, and analysis, plus production considerations like scaling, caching, and monitoring.