Getting started with Laravel & Elasticsearch
- 1. Laravel and ElasticSearch Getting started building a powerful search engine Peter Steenbergen
- 2. Who am I?
- 3. What I am about to cover • Benefits of ElasticSearch • ElasticSearch Basics • Examples of Searchengines • Installing the tools • Analyzing and feeding the data • Searching with Laravel and ElasticSearch through VueJS
- 4. What I do NOT cover • Type of queries • Analyzers • Filters • Percolator • Internals of Elasticsearch (Lucene Index) • Talk of Elze Kool - GroningenPHP https://bitbucket.org/elzekool/talk-solr-elasticsearch-internals/src The internals will cover how a score is determined in the background.
- 6. What is a “powerful search”? • Fast Search in milliseconds, not in seconds. • Relevant If you search for a Hotel in California, don’t return Hotels in Groningen. Or a black Audi, you don’t want to see a white Peugeot 107. • Scalable If your data grows, then you need more resources. It could mean by vertical scaling or (better) horizontal scaling of capacity.
- 7. MySQL Drawbacks for searching through text • LIKE %% • Fast? No, try searching for millions of records with multiple joins to combine all the information needed. • Relevant? No, Try search for “PHP programmeur” or “PHP developer”, or “Audi A8 zwart” or “zwarte Audi A8“. • Scalable? Could be, but with much hassle in configurations in production and backing up. • Facets / Counts – How many Audi’s? How many black cars? Takes multiple query’s for the calculation of those counts. NB: FULL TEXT not noted here, gave a bit better result but not great.
- 9. The Elastic Stack – formerly known as ELK stack
- 10. Usage of the Elastic stack • Time-series data Log files, searched queries, view count of document • Insights in your data • Machine-Learning capabilities • Reversal of search queries with percolator (storing search queries) • Zero-downtime* index swapping Last 7 days, Last 30 days, or products => products-20180327
- 11. Components of ElasticSearch • Node Instance of ElasticSearch. • Cluster One or more nodes that work together to serve the same data and API requests. • Document A piece of stored data you want to search for. (MySQL Row) • Index A collection of documents that have somewhat similar characteristics. (MySQL Table)
- 14. CRUD operations
- 20. Lets build that coolblue page!
- 21. First up: installation of the following tools • MySQL (ElasticSearch does not support transactions!) • ElasticSearch • Laravel • VueJS / VueX
- 22. Setting up / Starting MySQL on OSX • Default credentials User: root Password: <empty> Port: 3301 > Now create a Database called “coolshop”
- 23. Installing laravel • We use composer for the installation • If you don’t have it, get it from here: https://getcomposer.org Run the following in your terminal: Laravel Installer:
- 24. Setting up / Starting ElasticSearch on OSX Settings / name, cluster_name: /usr/local/etc/elasticsearch/elasticsearch.yml Java Virtual Machine options: /usr/local/etc/elasticsearch/jvm.options -Xms2g => -Xms256m -Xmx2g => -Xmx256m Maximum of 32GB per node is recommended NB: 256m is for small dev boxes and not production!!
- 25. Installing additional packages • ElasticSearch composer package • Guzzle (optional) • VueX for using Store • Setup default FrontEnd scaffold
- 26. Creating the Service Provider and Config • config/elasticsearch.php
- 27. Setting the ElasticSearch ServiceProvider
- 28. Register the ElasticSearch ServiceProvider NB: config/app.php
- 30. Testing the setup – we go Ping-Pong
- 31. Analyzing and Feeding the data
- 32. Analyzing the data • Image • AwardImage • Title • Rating • ReviewCount • ProductOptions • Price • PriceSecondChange • InStock • TypeRouter • Brand • CoolbluesChoice • AdministratorOptions
- 33. Mapping the data
- 34. Analyzing the data • Image => Keyword • AwardImage => Keyword • Title => Text • Rating => Float • ReviewCount => Integer • ProductOptions => Text • Price => Float • PriceSecondChange => Float • InStock => Boolean • TypeRouter => Keyword • Brand => Keyword • CoolbluesChoice => Keyword • AdministratorOptions => Keyword
- 35. Put the mapping in ElasticSearch Kibana is a no-brainer for this. It has an own Dev Console. Or with the PHP Client of ElasticSearch.
- 36. Lets inject some data in the Elasticsearch
- 44. Compile the front-end In the console run: • Development npm run dev one-time build npm run watch listener for changes • Production npm run production
- 46. Adding the endpoint – api.php
- 48. Aggregations
- 50. The end result
- 52. Questions? Twitter / Github: @petericebear Email: [email protected] Blog: http://petericebear.github.io/ COUPONCODE: ELASTICMEETING 50% OFF - FIRST MONTH – Hosting [email protected]
Editor's Notes
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