The below diagram represents the components involved and their arrangement in Search service.
- 1.The search service module has a play application and its internal actor module.
- 2.By leveraging Elasticsearch, we can deliver an efficient and robust search experience to our users, ensuring that our application performs exceptionally well and remains flexible to adapt to future needs.
- 3.By leveraging Flink(Async Jobs), we enable real-time and batch data processing with low latency, high throughput, and fault tolerance.
With the content/collection APIs, we have the ability to create or update assets (content/collection). These assets are stored in Neo4j, and to track any transactions, we've implemented custom neo4j-extensions that generate transaction logs and store them in log files associated with asset changes in Neo4j.
To process these transaction logs and ensure their real-time availability, we employ Logstash to read the logs from the files and push them to the transaction events Kafka topic. Subsequently, we use a Flink job to consume and process these events, finally storing them in Elasticsearch for further analysis and querying.
Search Service flow diagram