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MySQL
MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Redis
Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
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We implemented the database as a MySQL server running on an 8GB 2 CPU VPS. We hosted the backend application and the database in the same server farm, but they do not share the same VPS. Since the sports data is simple but highly heterogeneous, the database was structured to avoid duplication and encourage standardization. This structure is why we chose a relational database. As it stands, the database involves hundreds of tables, and I cannot present it entirely here due to an NDA.
We developed the backend in Kotlin with the Spring Boot 2.5.3 framework and the Hibernate 5.4.32.Final ORM (Object Relational Mapping). We deployed it on an 8GB 4 CPU VPS through a Docker container managed by Dokku. The initial heap size was set to 2GB and limited to 7GB, while we allocated the remaining GB of RAM to a Redis-based caching system. We built the web application with performance in mind. Specifically, it's based on the multi-layered Spring Boot architecture described here and involves multi-thread processing.
We developed the backend in Kotlin with the Spring Boot 2.5.3 framework and the Hibernate 5.4.32.Final ORM (Object Relational Mapping). We deployed it on an 8GB 4 CPU VPS through a Docker container managed by Dokku. The initial heap size was set to 2GB and limited to 7GB, while we allocated the remaining GB of RAM to a Redis-based caching system. We built the web application with performance in mind. Specifically, it's based on the multi-layered Spring Boot architecture described here and involves multi-thread processing.
We developed the backend in Kotlin with the Spring Boot 2.5.3 framework and the Hibernate 5.4.32.Final ORM (Object Relational Mapping). We deployed it on an 8GB 4 CPU VPS through a Docker container managed by Dokku. The initial heap size was set to 2GB and limited to 7GB, while we allocated the remaining GB of RAM to a Redis-based caching system. We built the web application with performance in mind. Specifically, it's based on the multi-layered Spring Boot architecture described here and involves multi-thread processing.