Using docker-compose, bring up a Senzing stack.
This repository illustrates reference implementations of Senzing using docker-compose.
The instructions show how to set up a system that:
- Reads JSON lines from a file on the internet and sends each JSON line to a message queue via the Senzing stream-producer.
- Reads messages from the queue and inserts into Senzing via the Senzing stream-loader.
- Reads information from Senzing via Senzing API Server server.
- Views resolved entities in a web app.
The following diagram shows the relationship of the docker containers in this docker composition. Arrows represent data flow.
This demonstration runs on platforms that support docker and docker-compose.
docker and docker-compose
do not run in a RedHat Enterprise Linux 8 environment natively.
Likewise, docker is not a CentOS 8 supported project.
Although with user-modification it has been shown that docker and docker-compose can run in these environments,
the onus is on the user for proper operation of docker and docker networking.
The following tables indicate the instructions for variations in components.
-
Component variants:
- Queue
- RabbitMQ
- Kafka
- AWS SQS
- Database
- Postgres
- MySQL
- MS SQL
- Queue
-
Implementations of the docker formation:
Queue Database Instructions docker-compose.yaml RabbitMQ PostgreSQL instructions docker-compose-rabbitmq-postgresql.yaml RabbitMQ MySQL instructions docker-compose-rabbitmq-mysql.yaml RabbitMQ MSSQL instructions docker-compose-rabbitmq-mssql.yaml Kafka PostgreSQL instructions docker-compose-kafka-postgresql.yaml Kafka MySQL instructions docker-compose-kafka-mysql.yaml Kafka MSSQL instructions docker-compose-kafka-mssql.yaml AWS SQS PostgreSQL instructions docker-compose-sqs-postgresql.yaml -
Advanced docker formations:
Description Instructions Enhancements built upon PostgreSQL and RabbitMQ. instructions Enhancements built upon PostgreSQL and Kafka. instructions Enhancements built upon PostgreSQL and AWS SQS. instructions