# Deploy as an application with durable state


You can build your code and export it to Amazon S3. You can promote the code that you wrote in your note to a continuously running stream processing application. There are two modes of running an Apache Flink application on Managed Service for Apache Flink: With a Studio notebook, you have the ability to develop your code interactively, view results of your code in real time, and visualize it within your note. After you deploy a note to run in streaming mode, Managed Service for Apache Flink creates an application for you that runs continuously, reads data from your sources, writes to your destinations, maintains long-running application state, and autoscales automatically based on the throughput of your source streams. 

**Note**  
The S3 bucket to which you export your application code must be in the same Region as your Studio notebook.

You can only deploy a note from your Studio notebook if it meets the following criteria:
+ 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](https://zeppelin.apache.org/docs/0.9.0/usage/other_features/zeppelin_context.html) 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](https://zeppelin.apache.org/docs/0.9.0/usage/dynamic_form/intro.html) are unsupported for deploying as an application.
+ %md ([Markdown](https://zeppelin.apache.org/docs/0.9.0/interpreter/markdown.html)) 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](how-zeppelin-troubleshooting.md#how-zeppelin-troubleshooting-deploying-no-internet). 

## Scala/Python criteria

+ In your Scala or Python code, use the [Blink planner](https://nightlies.apache.org/flink/flink-docs-release-1.15/dev/table/#dependency-structure) (`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](https://ipython.readthedocs.io/en/stable/interactive/python-ipython-diff.html#shell-assignment) using `!` or [IPython magic commands](https://ipython.readthedocs.io/en/stable/interactive/magics.html) 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](https://docs.scala-lang.org/overviews/scala-book/case-classes.html) in the Scala documentation.

## SQL criteria

+ 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.

For more information and an example, see [ Translate, redact and analyze streaming data using SQL functions with Amazon Managed Service for Apache Flink, Amazon Translate, and Amazon Comprehend](https://aws.amazon.com/blogs/machine-learning/translate-redact-and-analyze-streaming-data-using-sql-functions-with-amazon-kinesisanalytics-MyApplicatioamazon-translate-and-amazon-comprehend/).