shadow VS Apache Hive

So: I am making a JavaFX application to act as a remote console for my server. I'm the only person that will have a copy of it, so it doesn't need to be snazzy or 100% efficient. I have turned the project into a jar via ShadowJar, turned that into an EXE via Launch4j, and yesterday turned that into an installer via Inno.

I’ve never tried this and I I’m not libgdx expert, but you can try running a Uber jar with shadow. it’s the Gradle equivalent of the shade plug-in for maven builds.

When you get into a situation where you need to sandbox a dependency within another, including this example, where you want each of your dependencies to get its own unique instance of their dependency, you can "shadow" the dependency that is to be sandboxed this way. Look into https://github.com/johnrengelman/shadow.

Try using the shadow plugin for gradle https://github.com/johnrengelman/shadow

and also apparently, it's a problem with shadowJar itself https://github.com/johnrengelman/shadow/issues/713

Behind the scenes, Shadow creates a FatJar with all the things we may need in the future.

Note: **According to Serverless documentation you can use the command **serverless deploy -v. However, with Java-based-projects that’s not true because it needs to create a Fatjar with all the needed requirements (so the command will fail). By default, Serverless will set **shadow** as a dependency in the gradle file for those purposes. The deploy command will generate a build file called -all.jar that is the one that’s going to be uploaded to the AWS Lambda function.

The result .jar is going to submitted to Apache Spark cluster (e.g. spark-submit command). So, it should contain all runtime artefacts. Unfortunately, the standard Spring Boot packaging does not put the dependencies in the way Apache Spark expects it. So, we'll use shadow-jar Gradle plugin. Take a look at the example below.

I've writen many Kotlin+Gradle projects that produce jars without the shadow plugin. They're not fat, shaded, standalone executable jars though - is that what you need?

Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.

Hive, A data warehouse infrastructure that provides data summarization and ad hoc querying.

Apache Drill, Druid, Flink, Hive, Kafka, Spark

In this article, I'm showing you how to create a Spring Boot app that loads data from Apache Hive via Apache Spark to the Aerospike Database. More than that, I'm giving you a recipe for writing integration tests for such scenarios that can be run either locally or during the CI pipeline execution. The code examples are taken from this repository.

ListItem(name='Apache Hive', website='https://hive.apache.org/', category='Interactive Query', short_description='Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.'),

Apache Hive takes in a specific SQL dialect and converts it to map-reduce.

Apache Hive

Apache Calcite is a popular parser/optimizer that is used in popular databases and query engines like Apache Hive, BlazingSQL and many others.