Search Index enabled programs
The motordelears project implements the use of multiple elastic clusters at the same time. However, this connections are constructed lazily when requested for the first time.
The bookmemoiz program implements the use of a single elastic cluster.
The following designs are applied in the program:
- Modular architecture
- Separation of concerns
- SOLID Principles(However, this is applied superficially)
- 12 Factor app()
The project specific logic has been cementrized in this very repository. Al the API level interactivity comes outrightly from this backend-api.
The project requirements are specified in the requirements.txt
You can run your elastic search inside a container as per the following commands:
docker pull docker.elastic.co/elasticsearch elasticsearch:7.3.0
docker run -p 9200:9200 -e "discovery type=single-node" -e "xpack.security.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:7.3.0
NOTE: The project implements Elastic search version 8 but the provided above requires no authentication as suitabele for testing.
Elasticsearch documents are defined in a search_indexes app.
The basic units of information in the project are expressed in a ubiquitous internet data interchange format(JavaScript Object Notation). This units are indexed or assigned to a type inside an index.
In the bookmemoiz project all the respective models(Author, Publisher, Tag, Book) have been flatterned into a single BookDocument which holds all the required information.
All environments should have different indexes i.e for testing, staging and prodcution environments.
Access to the documents location should be done through a pythonic file path.
The projects use the default shards and replica provisions.
The project uses django-elasticsearch-dsl library for keeping index(es) fresh. It makes use of signals, so whenever the model(s) is changed, the correspondent index(es) is updated.
The following commands come in play when running full sync between Django’s database and Elasticsearch:
1. Create Elasticsearch indexes with:
```./manage.py search_index --create -f```
2. Sync the data with:
```./manage.py search_index --populate -f```
The project utilizes the fluid Django signals to keep indexes fresh(updated).