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Solr Cookbook - Third Edition

Solr Cookbook - Third Edition

By : Rafal Kuc
3.8 (6)
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Solr Cookbook - Third Edition

Solr Cookbook - Third Edition

3.8 (6)
By: Rafal Kuc

Overview of this book

This book is for intermediate Solr Developers who are willing to learn and implement Pro-level practices, techniques, and solutions. This edition will specifically appeal to developers who wish to quickly get to grips with the changes and new features of Apache Solr 5.
Table of Contents (12 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Apache Solr Configuration
1. Apache Solr Configuration
Introduction
Running Solr on a standalone Jetty
Installing ZooKeeper for SolrCloud
Migrating configuration from master-slave to SolrCloud
Choosing the proper directory configuration
Configuring the Solr spellchecker
Using Solr in a schemaless mode
Limiting I/O usage
Using core discovery
Configuring SolrCloud for NRT use cases
Configuring SolrCloud for high-indexing use cases
Configuring SolrCloud for high-querying use cases
Configuring the Solr heartbeat mechanism
Changing similarity
2
2. Indexing Your Data
2. Indexing Your Data
Introduction
Indexing PDF files
Counting the number of fields
Using parsing update processors to parse data
Using scripting update processors to modify documents
Indexing data from a database using Data Import Handler
Incremental imports with DIH
Transforming data when using DIH
Indexing multiple geographical points
Updating document fields
Detecting the document language during indexation
Optimizing the primary key indexation
Handling multiple currencies
3
3. Analyzing Your Text Data
3. Analyzing Your Text Data
Introduction
Using the enumeration type
Removing HTML tags during indexing
Storing data outside of Solr index
Using synonyms
Stemming different languages
Using nonaggressive stemmers
Using the n-gram approach to do performant trailing wildcard searches
Using position increment to divide sentences
Using patterns to replace tokens
4
4. Querying Solr
4. Querying Solr
Introduction
Understanding and using the Lucene query language
Using position aware queries
Using boosting with autocomplete
Phrase queries with shingles
Handling user queries without errors
Handling hierarchies with nested documents
Sorting data on the basis of a function value
Controlling the number of terms needed to match
Affecting document score using function queries
Using simple nested queries
Using the Solr document query join functionality
Handling typos with n-grams
Rescoring query results
5
5. Faceting
5. Faceting
Introduction
Getting the number of documents with the same field value
Getting the number of documents with the same value range
Getting the number of documents matching the query and subquery
Removing filters from faceting results
Using decision tree faceting
Calculating faceting for relevant documents in groups
Improving faceting performance for low cardinality fields
6
6. Improving Solr Performance
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6. Improving Solr Performance
Introduction
Handling deep paging efficiently
Configuring the document cache
Configuring the query result cache
Configuring the filter cache
Improving Solr query performance after the start and commit operations
Lowering the memory consumption of faceting and sorting
Speeding up indexing with Solr segment merge tuning
Avoiding caching of rare filters to improve the performance
Controlling the filter execution to improve expensive filter performance
Configuring numerical fields for high-performance sorting and range queries
7
7. In the Cloud
7. In the Cloud
Introduction
Creating a new SolrCloud cluster
Setting up multiple collections on a single cluster
Splitting shards
Having more than a single shard from a collection on a node
Creating a collection on defined nodes
Adding replicas after collection creation
Removing replicas
Moving shards between nodes
Using aliasing
Using routing
8
8. Using Additional Functionalities
8. Using Additional Functionalities
Introduction
Finding similar documents
Highlighting fragments found in documents
Efficient highlighting
Using versioning
Retrieving information about the index structure
Altering the index structure on a live collection
Grouping documents by the field value
Grouping documents by the query value
Grouping documents by the function value
Efficient documents grouping using the post filter
9
9. Dealing with Problems
9. Dealing with Problems
Introduction
Dealing with the too many opened files exception
Diagnosing and dealing with memory problems
Configuring sorting for non-English languages
Migrating data to another collection
SolrCloud read-side fault tolerance
Using the check index functionality
Adjusting the Jetty configuration to avoid deadlocks
Tuning segment merging
Avoiding swapping
10
10. Real-life Situations
10. Real-life Situations
Introduction
Implementing the autocomplete functionality for products
Implementing the autocomplete functionality for categories
Handling time-sliced data using aliases
Boosting words closer to each other
Using the Solr spellchecking functionality
Using the Solr administration panel for monitoring
Automatically expiring Solr documents
Exporting whole query results
11
Index
Index

Improving Solr query performance after the start and commit operations

Almost everyone who has some experience with Solr would have noticed one thing—right after startup or searcher reopening (such as a soft autocommit), Solr doesn't have such query performance as after running for a while. This is happening because Solr doesn't have any information stored in caches, the I/O is not optimized, and so on. Can we do something about it? Of course we can, and this recipe will show you how to do it.

How to do it...

  1. First of all, we need to identify the most common and the heaviest queries that we send to Solr. I have two ways of doing this: first, I analyze the logs that Solr produces and see how queries are behaving. I tend to choose those queries that are run often and those that run slowly. The second way of choosing the right queries is analyzing the applications that use Solr and see what queries they produce, what queries will be the most crucial, and so on. Based on my experience, the log...

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