use-case-shopping

Vespa sample applications - e-commerce

A sample application showcasing a simple e-commerce site built with Vespa. See Use Case - shopping for features and details:

Also included are scripts to convert data from Julian McAuley's Amazon product data set at https://cseweb.ucsd.edu/~jmcauley/datasets.html to a Vespa data feed. This repository contains a small sample of this data from the sports and outdoor category, but you can download other data from the site above and use the scripts to convert.

Requirements:

• Docker Desktop installed and running. 4 GB available memory for Docker is minimum. Refer to Docker memory for details and troubleshooting
• Alternatively, deploy using Vespa Cloud
• Operating system: Linux, macOS or Windows 10 Pro (Docker requirement)
• Architecture: x86_64 or arm64
• Homebrew to install Vespa CLI, or download a vespa cli release from GitHub releases.
• Java 17 installed.
• Apache Maven This sample app uses custom Java components and Maven is used to build the application.
• python3
• zstd: brew install zstd

See also Vespa quick start guide.

Validate environment, should be minimum 4 GB:

$ docker info | grep "Total Memory"

Install Vespa CLI:

$ brew install vespa-cli

For local deployment using Docker image:

$ vespa config set target local

Pull and start the vespa docker container image:

$ docker pull vespaengine/vespa
$ docker run --detach --name vespa --hostname vespa-container \
  --publish 8080:8080 --publish 19071:19071 \
  vespaengine/vespa

Verify that configuration service (deploy api) is ready:

$ vespa status deploy --wait 300

Download this sample application:

$ vespa clone use-case-shopping myapp && cd myapp

Build the application package:

$ mvn clean package -U

Deploy the application package:

$ vespa deploy --wait 300

Deployment note

It is possible to deploy this app to Vespa Cloud.

Run Vespa System Tests - this runs a set of basic tests to verify that the application is working as expected:

$ vespa test src/test/application/tests/system-test/product-search-test.json

First, create data feed for products:

$ curl -L -o meta_sports_20k_sample.json.zst https://data.vespa.oath.cloud/sample-apps-data/meta_sports_20k_sample.json.zst 
$ zstdcat meta_sports_20k_sample.json.zst | ./convert_meta.py > feed_items.json

Next, data feed for reviews:

$ curl -L -o reviews_sports_24k_sample.json.zst https://data.vespa.oath.cloud/sample-apps-data/reviews_sports_24k_sample.json.zst
$ zstdcat reviews_sports_24k_sample.json.zst | ./convert_reviews.py > feed_reviews.json

Next, data feed for query suggestions:

$ pip3 install spacy mmh3
$ python3 -m spacy download en_core_web_sm 
$ ./create_suggestions.py feed_items.json > feed_suggestions.json

Feed products data:

$ vespa feed feed_items.json

Feed reviews data:

$ vespa feed feed_reviews.json

Feed query suggestions data:

$ vespa feed feed_suggestions.json

Test the application:

$ vespa query "query=golf"

Browse the site: http://localhost:8080/site

Shutdown and remove the container:

$ docker rm -f vespa