MapR Database for Next-Generation Web Applications


00:02 In this video, we're going to see MapR Database used as a back end for next generation web application. MapR Database is part of the MapR converged data platform. It's an operational database that provides the speed, scale and reliability which are inherent inside the MapR platform.

00:25 MapR Database is a database for data intensive applications. It supports multiple data models, be it graph data or JSON document store or Key-Value stores. The features that we'll see in this demo are full tech search provided by Elasticsearch, multi-cloud synchronization across the Amazon and Google clouds, as well as intelligence derived by Spark Machine Learning.

00:54 The demo is a music store which has been implemented in using standard Java interfaces to backend databases. MapR Database is providing secondary indexes to help improve performance, and the database is also replicated across data centers. So we have low latency and high synchronization across two different cloud deployments. MapR Database is configured for multi-master replication, so when a user makes changes on the web app in one cloud, it's synchronized to the other cloud very quickly, as we'll show here. When we edit the name of a album and click save on the Amazon web service side, then refresh on the Google side, then search, we can see that elastic search has updated its index to reflect that change. And we've also seen that change propagated over to the Google side. Elastic search gives us the ability to do full text searching. It maintains indices into the artists and albums in our database, and it receives synchronization with MapR Database through the change data capture stream from MapR Database, which is configured here.

02:22 You can actually use a Kafka-console-consumer to monitor the stream like this. Whenever somebody makes a change to an artist or an album, you will see that reflected in the CDC stream like this. This application, MapR music, is available on Github with documentation and tutorials that explain in detail how it has been developed. You can also run it on your laptop with the MapR Developer Container in Docker.

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