Deploy as an application with durable state

Paragraphs must be ordered sequentially. When you deploy your application, all paragraphs within a note will be executed sequentially (left-to-right, top-to-bottom) as they appear in your note. You can check this order by choosing Run All Paragraphs in your note.

Your code is a combination of Python and SQL or Scala and SQL. We do not support Python and Scala together at this time for deploy-as-application.

Your note should have only the following interpreters: %flink, %flink.ssql, %flink.pyflink, %flink.ipyflink, %md.

The use of the Zeppelin context object z is not supported. Methods that return nothing will do nothing except log a warning. Other methods will raise Python exceptions or fail to compile in Scala.

A note must result in a single Apache Flink job.

Notes with dynamic forms are unsupported for deploying as an application.

%md (Markdown) paragraphs will be skipped in deploying as an application, as these are expected to contain human-readable documentation that is unsuitable for running as part of the resulting application.

Paragraphs disabled for running within Zeppelin will be skipped in deploying as an application. Even if a disabled paragraph uses an incompatible interpreter, for example, %flink.ipyflink in a note with %flink and %flink.ssql interpreters, it will be skipped while deploying the note as an application, and will not result in an error.

There must be at least one paragraph present with source code (Flink SQL, PyFlink or Flink Scala) that is enabled for running for the application deployment to succeed.

Setting parallelism in the interpreter directive within a paragraph (e.g. %flink.ssql(parallelism=32)) will be ignored in applications deployed from a note. Instead, you can update the deployed application through the AWS Management Console, AWS Command Line Interface or AWS API to change the Parallelism and/or ParallelismPerKPU settings according to the level of parallelism your application requires, or you can enable autoscaling for your deployed application.

If you are deploying as an application with durable state your VPC must have internet access. If your VPC does not have internet access, see Deploy as an application with durable state in a VPC with no internet access.

In your Scala or Python code, use the Blink planner (senv, stenv for Scala; s_env, st_env for Python) and not the older "Flink" planner (stenv_2 for Scala, st_env_2 for Python). The Apache Flink project recommends the use of the Blink planner for production use cases, and this is the default planner in Zeppelin and in Flink.

Your Python paragraphs must not use shell invocations/assignments using ! or IPython magic commands like %timeit or %conda in notes meant to be deployed as applications.

You can't use Scala case classes as parameters of functions passed to higher-order dataflow operators like map and filter. For information about Scala case classes, see CASE CLASSES in the Scala documentation.

Simple SELECT statements are not permitted, as there’s nowhere equivalent to a paragraph’s output section where the data can be delivered.

In any given paragraph, DDL statements (USE, CREATE, ALTER, DROP, SET, RESET) must precede DML (INSERT) statements. This is because DML statements in a paragraph must be submitted together as a single Flink job.

There should be at most one paragraph that has DML statements in it. This is because, for the deploy-as-application feature, we only support submitting a single job to Flink.