Elasticsearch with Python

Module 1: Introduction to Elasticsearch with Python

• Introduction to Elasticsearch
• Overview of the Elastic Stack
• Elasticsearch architectures

Module 2: Getting started

• Overview of installation options
• Running Elasticsearch & Kibana in Elastic Cloud
• Setting up Elasticsearch & Kibana on macOS & Linux
• Setting up Elasticsearch & Kibana on Windows
• Understanding the basic architecture
• Inspecting the cluster
• Sending queries with cURL
• Sharding and scalability
• Sharding
• Understanding replication
• Adding more nodes to the cluster
• Overview of node roles

Module 3: Managing Documents with Python

• Creating & deleting indices
• Indexing documents
• Retrieving documents by ID
• Updating documents
• Scripted updates
• Upserts
• Replacing documents
• Deleting documents
• Understanding routing
• How Elasticsearch reads data
• How Elasticsearch writes data
• Understanding document versioning
• Optimistic concurrency control
• Update by query
• Delete by query
• Batch processing
• Importing data with cURL

Module 4: Mapping & Analysis

• Introduction to Mapping
• Introduction to analysis
• Using the Analyze API
• Understanding inverted indices
• Introduction to mapping
• Overview of data types
• How the "keyword" data type works
• Understanding type coercion
• Understanding arrays
• Adding explicit mappings
• Retrieving mappings
• Using dot notation in field names
• Adding mappings to existing indices
• How dates work in Elasticsearch
• How missing fields are handled
• Overview of mapping parameters
• Updating existing mappings
• Reindexing documents with the Reindex API
• Defining field aliases
• Multi-field mappings
• Index templates
• Introduction to the Elastic Common Schema (ECS)
• Introduction to dynamic mapping
• Combining explicit and dynamic mapping
• Configuring dynamic mapping
• Dynamic templates
• Mapping recommendations
• Stemming & stop words
• Analyzers and search queries
• Built-in analyzers
• Creating custom analyzers
• Adding analyzers to existing indices
• Updating analyzers

Module 5: Searching for Data

• Introduction to searching
• Introduction to term level queries
• Searching for terms
• Retrieving documents by IDs
• Range searches
• Prefixes, wildcards & regular expression
• Querying by field existence
• Introduction to full text queries
• The match query
• Introduction to relevance scoring
• Searching multiple fields
• Phrase searches
• Leaf and compound queries
• Querying with boolean logic
• Query execution contexts
• Boosting query
• Disjunction max (dis_max)
• Querying nested objects
• Nested inner hits
• Nested fields limitations

Module 6: Joining Queries

• Introduction to Joining Queries
• Add departments test data
• Mapping document relationships
• Adding documents
• Querying by parent ID
• Querying child documents by parent
• Querying parent by child documents
• Multi-level relations
• Parent/child inner hits
• Terms lookup mechanism
• Join limitations
• Join field performance considerations

Module 7: Controlling Query Results

• A word on document types
• Specifying the result format
• Source filtering
• Specifying the result size
• Specifying an offset
• Pagination
• Sorting results
• Sorting by multi-value fields
• Filters

Module 8: Aggregations

• Introduction to aggregations
• Metric aggregations
• Introduction to bucket aggregations
• Document counts are approximate
• Nested aggregations
• Filtering out documents
• Defining bucket rules with filters
• Range aggregations
• Histograms
• Global aggregation
• Missing field values
• Aggregating nested objects

Module 9: Improving Search Results

• Introduction to this section
• Proximity searches
• Affecting relevance scoring with proximity
• Fuzzy match query (handling typos)
• Fuzzy query
• Adding synonyms
• Adding synonyms from file
• Highlighting matches in fields
• Stemming