ElasticSearch
You Know, for Search!
About ElasticSearch
Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.
- Elasticsearch Is Fast. Really, Really Fast.
- Run It on Your Laptop. Or Hundreds of Servers with Petabytes of Data.
- Multiple Use Cases? Bring One, Bring All.
ElasticSearch Topics
The following are the things covered under ElasticSearch.
Elasticsearch - Basic Concepts
Elasticsearch is an Apache Lucene-based search server. It was developed by Shay Banon and published in 2010. It is now maintained by Elasticsearch BV. Its latest version is 2.1.0.
Elasticsearch - API Conventions
Application Programming Interface (API) in web is a group of function calls or other programming instructions to access the software component in that particular web application. For example, Facebook API helps a developer to create applications by accessing data or other functionalities from Facebook; it can be date of birth or status update.
Elasticsearch - Aggregations
This framework collects all the data selected by the search query. This framework consists of many building blocks, which help in building complex summaries of the data.
Elasticsearch - Mapping
Mapping is the outline of the documents stored in an index. It defines the data type like geo_point or string and format of the fields present in the documents and rules to control the mapping of dynamically added fields.
Elasticsearch - Analysis
When a query is processed during a search operation , the content in any index is analyzed by analysis module. This module consists of analyzer, tokenizer, tokenfilters and charfilters.
Elasticsearch - Testing
Elasticsearch provides a jar file, which can be added to any java IDE and can be used to test the code which is related to Elasticsearch.
Course Contents
The following are the course contents offered for ElasticSearch
- Basic concepts of Elasticsearch
- Document
- Index
- Type
- Cluster and node
- Shard
- Interacting with Elasticsearch
- Creating a document
- Retrieving an existing document
- Updating an existing document
- Updating a partial document
- Deleting an existing document
- How does search work?
- Importance of information retrieval
- Simple search query
- Inverted index
- Stemming
- Synonyms
- Phrase search
- Apache Lucene
- Scalability and availability
- Relation between node
- Three shards with zero replicas
- Six shards with zero replicas
- Six shards with one replica
- Distributed search
- Failure handling
- Strengths and limitations of Elasticsearch
- Installing Elasticsearch
- Installing Java
- Windows
- Starting and stopping Elasticsearch
- Mac OS X
- Starting and stopping Elasticsearch
- DEB and RPM packages
- Debian package
- RPM package
- Starting and stopping Elasticsearch
- Sample configuration files
- Verifying Elasticsearch is running
- Installing Kibana
- Mac OS X
- Starting and stopping Kibana
- Windows
- Starting and stopping Kibana
- Query format used in this book (Kibana Console)
- Using cURL or Postman
- Health of the cluster
- Mapping
- Dynamic mapping
- Create index with mapping
- Adding a new type/field
- Getting the existing mapping
- Mapping conflicts
- Data type
- Metafields
- How to handle null values
- Storing the original document
- Searching all the fields in the document
- Difference between full-text search and exact match
- Core data types
- Text
- Keyword
- Date
- Numeric
- Boolean
- Binary
- Complex data types
- Array
- Object
- Nested
- Geo data type
- Geo-point data type
- Specialized data type
- IP
- Mapping the same field with different mappings
- Handling relations between different document types
- Parent-child document relation
- How are parent-child documents stored internally?
- Nested
- Routing
- Indexing your data
- Indexing errors
- Node/shards errors
- Serialization/mapping errors
- Thread pool rejection error
- Managing an index
- What happens when you index a document?
- Updating your data
- Update using an entire document
- Partial updates
- Scripted updates
- Upsert
- NOOP
- What happens when you update a document?
- Merging segments
- Using Kibana to discover
- Using Elasticsearch in your application
- Java
- Transport client
- Dependencies
- Initializing the client
- Sniffing
- Node client
- REST client
- Third party clients
- Indexing using Java client
- Concurrency
- Translog
- Async versus sync
- CRUD from translog
- Primary and Replica shards
- Primary preference
- More replicas for query throughput
- Increasing/decreasing the number of replicas
- Bulk operations
- Bulk API
- Multi Get API
- Update by query
- Delete by query
- Reindex API
- Change mappings/settings
- Combining documents from one or more indices
- Copying only missing documents
- Copying a subset of documents into a new index
- Copying top N documents
- Copying the subset of fields into new index
- Ingest Node
- Organizing your data
- Index alias
- Index templates
- Managing time-based indices
- Shrink API
- Different types of queries
- Sample data
- Querying Elasticsearch
- Basic query (finding the exact value)
- Pagination
- Sorting based on existing fields
- Selecting the fields in the response
- Querying based on range
- Handling dates
- Analyzed versus non-analyzed fields
- Term versus Match query
- Match phrase query
- Prefix and match phrase prefix query
- Wildcard and Regular expression query
- Exists and missing queries
- Using more than one query
- Routing
- Debugging search query
- Relevance
- Queries versus Filters
- How to boost relevance based on a single field
- How to boost score based on queries
- How to boost relevance using decay functions
- Rescoring
- Debugging relevance score
- Searching for same value across multiple fields
- Best matching fields
- Most matching fields
- Cross-matching fields
- Caching
- Node Query cache
- Shard request cache
- Sample data
- Correcting typos and spelling mistakes
- Fuzzy query
- Making suggestions based on the user input
- Implementing
- Term suggester
- Phrase suggester
- Implementing the autocomplete feature
- Highlighting
- Handling document relations using parent-child
- The has_parent query
- The has_child query
- Inner hits for parent-child
- How parent-child works internally
- Handling document relations using nested
- Inner hits for nested documents
- Scripting
- Script Query
- Post Filter
- Reverse search using the percolate query
- Geo and Spatial Filtering
- Geo Distance
- Using Geolocation to rank the search results
- Geo Bounding Box
- Sorting
- Multi search
- Search templates
- Querying Elasticsearch from Java application
- Aggregation basics
- Sample data
- Query structure
- Multilevel aggregations
- Types of aggregations
- Terms aggregations (group by)
- Size and error
- Order
- Minimum document count
- Missing values
- Aggregations based on filters
- Aggregations on dates ( range
- Aggregations on numeric values (range
- Aggregations on geolocation (distance
- Geo distance
- Geo bounds
- Aggregations on child documents
- Aggregations on nested documents
- Reverse nested aggregation
- Post filter
- Using Kibana to visualize aggregations
- Caching
- Doc values
- Field data
- Configuring Elasticsearch
- The directory structure
- zip/tar.gz
- DEB/RPM
- Configuration file
- Cluster and node name
- Network configuration
- Memory configuration
- Configuring file descriptors
- Types of nodes
- Multinode cluster
- Inspecting the logs
- How nodes discover each other
- Node failures
- X-Pack
- Windows
- Mac OS X
- Debian/RPM
- Authentication
- X-Pack basic license
- Monitoring
- Monitoring Elasticsearch clusters
- Monitoring indices
- Monitoring nodes
- Thread pools
- Elasticsearch server logs
- Slow logs
- Elastic Cloud
- High availability
- Data reliability
- Security
- Authentication and roles
- Securing communications using SSL
- Graph
- Graph UI
- Alerting
Download
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