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Home > Data > Enterprise Search > ElasticSearch Blueprints
ElasticSearch Blueprints
ElasticSearch Blueprints

ElasticSearch Blueprints: A practical project-based guide to generating compelling search solutions using the dynamic and powerful features of Elasticsearch

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ElasticSearch Blueprints

Chapter 2. Building Your Own E-Commerce Solution

In the first chapter, we discussed how to add documents to an Elasticsearch instance as well as how to create the fields and decide on an analyzer. All these are steps to get you started. The next two chapters help you to get accustomed to the process of creating an e-commerce search solution. E-commerce revolutionized ways to shop, and the convenience that such a trading system provides attracted people very much. Amazon, eBay, and Flipkart are all pioneers in this field. Elasticsearch offers wonderful support to build such platforms, where you can display what you have. There are numerous techniques that can push the user experience even higher, which can eventually reflect on the overall performance of such a system. This is what we deal with in this chapter. Take a look at the contents of this chapter:

  • Building the document
  • A query or filter—the right choice
  • A date-range-based filter
  • A prize-range-based filter
  • A category-based...

Data modeling in Elasticsearch

Data is modeled in documents in Elasticsearch. This means a single item irrespective of whether it is an entity like a company, country, or product has to be modeled as a single document. A document can contain any number of fields and values associated with it. This information is modeled around the JSON format that helps us to express the behavior of our data using arrays, objects, or simple values.

Note

Elasticsearch is schemaless, which means that you can index a document to Elasticsearch before you create its index or mapping. Elasticsearch guesses the best type for each field value.

Inherently, JSON supports formats such as string, numbers, object, array, and even Boolean. Elasticsearch adds various other formats on top of it, such as the date, IP and so on. You will find the list of such supported types in the following table. The use of date types to store date values will help us do effective date-specific operations, such as date range and aggregation...

Choosing between a query and a filter

The basic idea of a search is to narrow down on a subset of documents that you have. In the Elasticsearch context, this means that based on various conditions, you might want to select a set of documents from an index or a set of index. A query and filter facilitate this for you.

If you have already gone through the reference guide or some other documentation of Elasticsearch, you might have noticed that the same set of operations might be available for both queries and filters. So, what are the differentiating factors of a query and filter even when the set of operations given by them are almost the same? Let's find out.

In a query, a matched document can be a better match than another matched document. In a filter, all the matched documents are treated equally.

This means that there is a way to score or rank a document matched against a query to another document match. This is done by computing a score value that tells you how good a match a particular...

Searching your documents

To search we use a large set of documents and our interest here lies only in a subset of this document set. This can be based on a set of constraints and conditions. A search does not stop here. You might be interested in getting a snapshot view of your query result. In our case, if the user searches for dell, he/she might be interested in seeing different unique product types and their document count. This is called an aggregation. Through this, we enhance our search experience and make it more explorable. Here, we try to discover various querying options through which we can express our requirement and communicate the same to Elasticsearch.

In our search application, we expose a search box that can be used for a search. We abstract out information about which field is searched or what is the precedence of the fields that we search. Let's see the query types that would be best for this search box.

A match query

A match query is the ideal place to start your query...

Aggregating your results

Next, when I search for Lenovo, I would like to see different product types associated with it. This is more like making a report out of the results, but then, as this makes your data more explorable and easy to understand, it's safe to see this as enhancing search capabilities.

Hence, whenever I search for something, I want to see the following reports on my query results or rather, an aggregate of my results in the following information:

  • Different types of productType
  • The number of products in various predefined price ranges
  • The number of documents per year based on manufacturing dates

For this, we need to build different aggregations to capture these reports.

Note

It's very important to understand that the score of aggregation is the query. This means that the aggregations would only be done on those documents that match the query.

The format in which we should provide aggregations is as follows:

{
    "query" : { ….},
    "aggregations...

Filter your results based on a date range

With a fair idea in mind of when to use your filter and when to opt for a query, let's think of some scenarios and see how Elasticsearch enables filtering at its best. Filtering by a date range, prize, or department often pops up in use cases in an e-commerce view. Look at the left-hand side of the following figure:

Filter your results based on a date range

Checking for new arrivals or selecting an old classic song from a library may need a date-range-based filtering mechanism. Elasticsearch provides inbuilt facilities to do filtering by providing a date range filter. A term filter does the same thing for strings, which can be anything for example, a department or category. A numeric filter filters numerals and can be used for prizes and so on.

This snippet shows how you can implement a date-range-based filtering in Elasticsearch:

{
"query" : 
{ "filtered" :
  { "query" :
    { "text" : { "content" : "any keywords to match" ...

Data modeling in Elasticsearch

Data is modeled in documents in Elasticsearch. This means a single item irrespective of whether it is an entity like a company, country, or product has to be modeled as a single document. A document can contain any number of fields and values associated with it. This information is modeled around the JSON format that helps us to express the behavior of our data using arrays, objects, or simple values.

Note

Elasticsearch is schemaless, which means that you can index a document to Elasticsearch before you create its index or mapping. Elasticsearch guesses the best type for each field value.

Inherently, JSON supports formats such as string, numbers, object, array, and even Boolean. Elasticsearch adds various other formats on top of it, such as the date, IP and so on. You will find the list of such supported types in the following table. The use of date types to store date values will help us do effective date-specific operations, such as date range and aggregation...

Choosing between a query and a filter

The basic idea of a search is to narrow down on a subset of documents that you have. In the Elasticsearch context, this means that based on various conditions, you might want to select a set of documents from an index or a set of index. A query and filter facilitate this for you.

If you have already gone through the reference guide or some other documentation of Elasticsearch, you might have noticed that the same set of operations might be available for both queries and filters. So, what are the differentiating factors of a query and filter even when the set of operations given by them are almost the same? Let's find out.

In a query, a matched document can be a better match than another matched document. In a filter, all the matched documents are treated equally.

This means that there is a way to score or rank a document matched against a query to another document match. This is done by computing a score value that tells you how good a match a particular...

Searching your documents

To search we use a large set of documents and our interest here lies only in a subset of this document set. This can be based on a set of constraints and conditions. A search does not stop here. You might be interested in getting a snapshot view of your query result. In our case, if the user searches for dell, he/she might be interested in seeing different unique product types and their document count. This is called an aggregation. Through this, we enhance our search experience and make it more explorable. Here, we try to discover various querying options through which we can express our requirement and communicate the same to Elasticsearch.

In our search application, we expose a search box that can be used for a search. We abstract out information about which field is searched or what is the precedence of the fields that we search. Let's see the query types that would be best for this search box.

A match query

A match query is the ideal place to start your query....

Aggregating your results

Next, when I search for Lenovo, I would like to see different product types associated with it. This is more like making a report out of the results, but then, as this makes your data more explorable and easy to understand, it's safe to see this as enhancing search capabilities.

Hence, whenever I search for something, I want to see the following reports on my query results or rather, an aggregate of my results in the following information:

  • Different types of productType

  • The number of products in various predefined price ranges

  • The number of documents per year based on manufacturing dates

For this, we need to build different aggregations to capture these reports.

Note

It's very important to understand that the score of aggregation is the query. This means that the aggregations would only be done on those documents that match the query.

The format in which we should provide aggregations is as follows:

{
    "query" : { ….},
    "aggregations" : { 
        "aggregationNameA...

Filter your results based on a date range

With a fair idea in mind of when to use your filter and when to opt for a query, let's think of some scenarios and see how Elasticsearch enables filtering at its best. Filtering by a date range, prize, or department often pops up in use cases in an e-commerce view. Look at the left-hand side of the following figure:

Checking for new arrivals or selecting an old classic song from a library may need a date-range-based filtering mechanism. Elasticsearch provides inbuilt facilities to do filtering by providing a date range filter. A term filter does the same thing for strings, which can be anything for example, a department or category. A numeric filter filters numerals and can be used for prizes and so on.

This snippet shows how you can implement a date-range-based filtering in Elasticsearch:

{
"query" : 
{ "filtered" :
  { "query" :
    { "text" : { "content" : "any keywords to match" }
  },
 "filter" : 
{ "numeric_range" :
 { "date" :
     { "lt" : ...

Implementing a prize range filter

Now, let's move on and see how to implement a prize range filter as seen in some e-commerce websites. Consider the following screenshot of an e-commerce site:

In the preceding screenshot, you can see a price range filter on the left-hand side tab. By clicking on or specifying the price ranges, the products that fall within that range would be displayed. This is nothing but a numeric range filter.

The implementation of a numeric range filter is almost similar to the date range filter in Elasticsearch. The following code snippet shows how to implement a numeric range filter. Here is the sample; assume that you have an age field that is numeric in nature:

{
    "filtered" : {
        "filter" : {
            "range" : {
                "age" : {
                    "gte": 10,
                    "lte": 20
                }
            }
        }
    }
}

After reading the implementation of filters, which is to be followed, you would understand the snippet completely...

Implementing a category filter

Normally, the category fields are of the type string. We need to index this unanalyzed field to match the exact term. In fact, Elasticsearch uses a term filter for the purpose of matching strings:

The following snippet shows you how to use a terms filter:

{
    "filtered" : {
        "filter" : {
            "term" : {
                "category" : "Books"
        
            }
        }
    }
}

Implementation of filters in Elasticsearch

This section sheds some light on the implementation of filters in Elasticsearch. All filters are not the same. We will see how each one differs from another and how we can implement these with Elasticsearch. First, let's see all the three implementation types of filters in Elasticsearch.

We have already showed you the most basic type of usage of a filter in the previous examples. You would have noticed the term constant score or filtered every time we used a filter. These are queries and they wrap the filter inside and apply it against the normal query's result. This is the first type of filter implementation of Elasticsearch.

Note that the filtered/constant score implementation of filters will affect both the results of the query and aggregations; some of the examples are shown as follows:

curl -XPOST 'http://localhost:9200/products/_search?pretty' -d '{
   "aggregations": {
     "department": {
       "terms": {
         "field": "color"
       ...

Searching with multiple conditions

Often, we find ourselves in a position where we need to search with more than one criterion, where each of these criteria could be expressed as an individual query. In this section, let's see how we can make such a combination of search.

One simple way to express multiple search criteria is using a bool query. The bool query lets you use a combination of other queries, where each of the conditions can be put using a Boolean clause. There are three Boolean clauses:

  • must

  • should

  • must_not

The must clause suggests that the constituent query must appear in the matching documents. If there is no must clause in the query, a combination of should clauses can be given. Also, you may set the minimum number of should clauses that should be matched. As the name indicates, must_not means that this clause should not match the document.

Let's see how we can use the bool query in Elasticsearch:

{
    "bool" : {
        "must" : [{
            "term" : { "productType" : ...
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Description

If you are a data enthusiast and would like to explore and specialize on search technologies based on Elasticsearch, this is the right book for you. A compelling case-to-case mapping of features and implementation of Elasticsearch to solve many real-world use cases makes this book the right choice to start and specialize on Elasticsearch.

Who is this book for?

If you are a data enthusiast and would like to explore and specialize on search technologies based on Elasticsearch, this is the right book for you. A compelling case-to-case mapping of features and implementation of Elasticsearch to solve many real-world use cases makes this book the right choice to start and specialize on Elasticsearch.

What you will learn

  • Build a simple scalable server for effective searching in Elasticsearch
  • Design a scalable ecommerce search solution to generate accurate search results using various filters such as filters based on date range and price range
  • Improve the relevancy and scoring of your searches
  • Manage realworld, complex data using various techniques, including parentchild search and searching questions based on the criteria of questions and answers
  • Use the excellent data crunching and aggregation capability of Elasticsearch to analyze your data
  • Generate realtime visualizations of your data using compelling visualization techniques, such as time graphs, pie charts, and stacked graphs
  • Enhance the quality of your search and widen the scope of matches using various analyzer techniques, such as lower casing, stemming, and synonym matching
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Publication date : Jul 24, 2015
Length: 192 pages
Edition : 1st
Language : English
ISBN-13 : 9781783984923
Vendor :
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Category :
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Concepts :
Enterprise Search
Tools :
Elasticsearch

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Publication date : Jul 24, 2015
Length: 192 pages
Edition : 1st
Language : English
ISBN-13 : 9781783984923
Vendor :
Elastic
Category :
Data
Concepts :
Enterprise Search
Tools :
Elasticsearch

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Table of Contents

9 Chapters
1. Google-like Web Search Chevron down icon Chevron up icon
1. Google-like Web Search
Deploying Elasticsearch
Communicating with the Elasticsearch server
Setting the analyzer
Using phrase query to search
Using the highlighting feature
Pagination
Summary
2. Building Your Own E-Commerce Solution Chevron down icon Chevron up icon
2. Building Your Own E-Commerce Solution
Data modeling in Elasticsearch
Choosing between a query and a filter
Searching your documents
Aggregating your results
Filter your results based on a date range
Implementing a prize range filter
Implementing a category filter
Implementation of filters in Elasticsearch
Searching with multiple conditions
Sorting results
Using the scroll API for consistent pagination
Autocomplete in Elasticsearch
Hotel suggester using autocomplete
Summary
3. Relevancy and Scoring Chevron down icon Chevron up icon
3. Relevancy and Scoring
How scoring works
The Ebola outbreak
Summary
4. Managing Relational Content Chevron down icon Chevron up icon
4. Managing Relational Content
The product-with-tags search problem
Nested types to the rescue
Limitations on a query on nested fields
Using a parent-child approach
Schema design to store questions and answers
Searching questions based on a criteria of answers
Searching answers based on a criteria of questions
The score of questions based on the score of each answer
Filtering questions with more than four answers
Summary
5. Analytics Using Elasticsearch Chevron down icon Chevron up icon
5. Analytics Using Elasticsearch
A flight ticket analytics scenario
Summary
6. Improving the Search Experience Chevron down icon Chevron up icon
6. Improving the Search Experience
News search
A case-insensitive search
Effective e-mail or URL link search inside text
Prioritizing a title match over content match
Terms aggregation giving weird results
Using a lowercased analyzer
Improving the search experience using stemming
A synonym-aware search
The holy box of search
Boolean operations
Words with similar sounds
Substring matching
Summary
7. Spicing Up a Search Using Geo Chevron down icon Chevron up icon
7. Spicing Up a Search Using Geo
Restaurant search
Data modeling for restaurants
The nearest hotel problem
The maximum distance covered
Inside the city limits
Distance values between the current point and each restaurant
Restaurant categorization based on distance
Aggregating restaurants based on their nearness
Summary
8. Handling Time-based Data Chevron down icon Chevron up icon
8. Handling Time-based Data
Overriding default mapping and settings in Elasticsearch
Index template creation
Searching for time-based data
Archiving time-based data
Closing older indices
Snapshot creation
Restoring a snapshot
Restoring multiple indices
The curator
Shard allocation using curator
Optimization
Summary
Index Chevron down icon Chevron up icon
Index

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Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
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1 star 50%
Quentin FAYET Sep 22, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I read that book to improve my knowledge about Elasticsearch.After reading "Elasticsearch Server" from the same editor, I was searching for a book with complete examples, from basic stuff to more complicated topics (such as Geolocalized requests).Well, this book did the job perfectly.Going from "basic" stuff, such as "Google style" full-text search, to more complex notions such as geolocalized requests, this book is reviewing every important feature of the amazing Elasticsearch.Other interesting such as data analysis, or even seach experience and search optimizations are exposed in this book.I would say this book is really complete, and I would definitely recommand it.
Amazon Verified review Amazon
Jeremy McLain Dec 16, 2016
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This would have been a good book if it had been written in intelligible English. It's riddled with incoherent sentences, grammar mistakes, and misspellings. I don't mean a few dozen mistakes - nearly every paragraph has a major sentence construction or spelling error. It's obvious that English is not the first language of both the author and the editor. I applaud their effort to attempt to write a book in a non-native language, but Packt Publishing should have had it reviewed by a native English speaker before it was published.
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Generative AI with LangChain
Generative AI with LangChain
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Dec 2023 368 pages
Full star icon 4 (34)
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eBook
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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
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May 2023 456 pages
Full star icon 4.5 (49)
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About the author

Profile icon Mohan
Mohan
Vineeth Mohan is an architect and developer. He currently works as the CTO at Factweavers Technologies and is also an Elasticsearch-certified trainer. He loves to spend time studying emerging technologies and applications related to data analytics, data visualizations, machine learning, natural language processing, and developments in search analytics. He began coding during his high school days, which later ignited his interest in computer science, and he pursued engineering at Model Engineering College, Cochin. He was recruited by the search giant Yahoo! during his college days. After 2 years of work at Yahoo! on various big data projects, he joined a start-up that dealt with search and analytics. Finally, he started his own big data consulting company, Factweavers. Under his leadership and technical expertise, Factweavers is one of the early adopters of Elasticsearch and has been engaged with projects related to end-to-end big data solutions and analytics for the last few years. There, he got the opportunity to learn various big-data-based technologies, such as Hadoop, and high-performance data ingress systems and storage. Later, he moved to a start-up in his hometown, where he chose Elasticsearch as the primary search and analytic engine for the project assigned to him. Later in 2014, he founded Factweavers Technologies along with Jalaluddeen; it is consultancy that aims at providing Elasticsearch-based solutions. He is also an Elasticsearch-certified corporate trainer who conducts trainings in India. Till date, he has worked on numerous projects that are mostly based on Elasticsearch and has trained numerous multinationals on Elasticsearch.
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Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela