udata search service
Project description
udata-search-service
A search service for udata. The idea is to have search service separated from the udata MongoDB. The indexation update is made using real-time messages with Kafka.
See the following architecture schema:
Getting started
You can follow this recommended architecture for your code:
$WORKSPACE
├── fs
├── udata
│ ├── ...
│ └── setup.py
│ └── udata.cfg
├── udata-front
│ ├── ...
│ └── setup.py
└── udata-search-service
├── ...
└── pyproject.toml
Clone the repository:
cd $WORKSPACE
git clone git@github.com:opendatateam/udata-search-service.git
Start the different services using docker-compose:
cd udata-search-service
docker-compose up
This will start:
- an elasticsearch
- a kafka broker
- a zookeper
- a kafka consumer
- a search app
Initialize the elasticsearch indices on setup.
# Locally
udata-search-service init-es
# In the docker context
docker-compose run --entrypoint /bin/bash web -c 'udata-search-service init-es'
This will create the following indices:
- {UDATA_INSTANCE_NAME}-dataset-{yyyy}-{mm}-{dd}-{HH}-{MM}
- {UDATA_INSTANCE_NAME}-reuse-{yyyy}-{mm}-{dd}-{HH}-{MM}
- {UDATA_INSTANCE_NAME}-organization-{yyyy}-{mm}-{dd}-{HH}-{MM}
Configure your udata to use the search service, by updating the following variables in your udata.cfg. Ex in local:
SEARCH_SERVICE_API_URL = 'http://127.0.0.1:5000/api/1/'
KAFKA_URI = 'localhost:9092'
You can feed the elasticsearch by publishing messages to Kafka. Using udata, when you modify objects, indexation messages will be sent and will be consumed by the kafka consumer. If you want to reindex your local mongo base in udata, you can run:
cd $WORKSPACE/udata/
source ./venv/bin/activate
udata search index
Make sure to have the corresponding UDATA_INSTANCE_NAME specified in your udata settings.
After a reindexation, you'll need to change the alias by using the following command:
# Locally
udata-search-service set-alias <index-suffix>
# In the docker context
docker-compose run --entrypoint /bin/bash web -c 'udata-search-service set-alias <index-suffix>'
You can query the search service with the search service api, ex: http://localhost:5000/api/1/datasets/?q=toilettes%20à%20rennes
Development
You can create a virtualenv, activate it and install the requirements with the following commands.
python3 -m venv venv
source venv/bin/activate
make deps
make install
You can start the Elasticsearch and Kafak broker and zookeper using the docker compose. You can start the consumer locally with the following command:
udata-search-service consume-kafka
You can start the web search service with the following command:
udata-search-service run
Deployment
The project depends on Kafka and ElasticSearch 7.16.
Elasticsearch requires the Analysis ICU plugin for your specific version. On Debian, you can take a look at these instructions for installation.
You will need a Kafka broker and zookeeper. You can follow the quick-start instructions to start all services in correct order.
You will need to start a search service app a kafka consumer. You can start these using uWSGI.
Troubleshooting
- If the elasticsearch service exits with an error 137, it is killed due to out of memory error. You should read the following points.
- If you are short on RAM, you can limit heap memory by setting
ES_JAVA_OPTS=-Xms750m -Xmx750mas environment variable when starting the elasticsearch service. - If you are on MAC and still encounter RAM memory issues, you should increase Docker limit memory to 4GB instead of default 2GB.
- If you are on Linux, you may need to double the vm.max_map_count. You can set it with the following command:
sysctl -w vm.max_map_count=262144. - If you are on Linux, you may encounter permissions issues. You can either create the volume or change the user to the current user using
chown.
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