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Lagom is a Swedish word meaning “just the right amount”. Microservices have often been categorised as small services. However, Lightbend wants to emphasize that finding the right boundaries between services, aligning them with bounded contexts, business capabilities, and isolation requirements are the most important aspects when architecting a microservice-based system. Therefore, it fits very well in a Domain-Driven Design focused mindset. Following this will help in building a scalable and resilient system that is easy to deploy and manage. According to Lightbend the focus should not be on how small the services are, but instead they should be just the right size , “Lagom” size services. Lagom, being an opinionated framework, provides a “golden path” from which the developer can deviate if necessary. Being based on the reactive principles as defined in the Reactive Manifesto , Lagom provides the developer a guard-railed approach with good defaults while also allowing to deviate if...

Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.ml currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.ml uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.ml has the following parameters: numBlocks is the number of blocks the users and items will be partitioned into in order to parallelize computation (defaults to 10). rank is the number of latent factors in the model (defaults to 10). maxIter is the maximum number of iterations to run (defaults to 10). regParam specifies the regularization parameter in ALS (defaults to 1.0). implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data (defaults to false...