Notebook workflows

• 04/05/2021
• 5 minutes to read
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The %run command allows you to include another notebook within a notebook. You can use %run to modularize your code, for example by putting supporting functions in a separate notebook. You can also use it to concatenate notebooks that implement the steps in an analysis. When you use %run, the called notebook is immediately executed and the functions and variables defined in it become available in the calling notebook.

Notebook workflows are a complement to %run because they let you pass parameters to and return values from a notebook. This allows you to build complex workflows and pipelines with dependencies. For example, you can get a list of files in a directory and pass the names to another notebook, which is not possible with %run. You can also create if-then-else workflows based on return values or call other notebooks using relative paths.

To implement notebook workflows, use the dbutils.notebook.* methods. Unlike %run, the dbutils.notebook.run() method starts a new job to run the notebook.

These methods, like all of the dbutils APIs, are available only in Python and Scala. However, you can use dbutils.notebook.run() to invoke an R notebook.

API

The methods available in the dbutils.notebook API to build notebook workflows are: run and exit. Both parameters and return values must be strings.

run(path: String, timeout_seconds: int, arguments: Map): String

Run a notebook and return its exit value. The method starts an ephemeral job that runs immediately.

The timeout_seconds parameter controls the timeout of the run (0 means no timeout): the call to run throws an exception if it doesn’t finish within the specified time. If Azure Databricks is down for more than 10 minutes, the notebook run fails regardless of timeout_seconds.

The arguments parameter sets widget values of the target notebook. Specifically, if the notebook you are running has a widget named A, and you pass a key-value pair ("A": "B") as part of the arguments parameter to the run() call, then retrieving the value of widget A will return "B". You can find the instructions for creating and working with widgets in the Widgets article.

run Usage

Python

dbutils.notebook.run("notebook-name", 60, {"argument": "data", "argument2": "data2", ...})

Scala

dbutils.notebook.run("notebook-name", 60, Map("argument" -> "data", "argument2" -> "data2", ...))

run Example

Suppose you have a notebook named workflows with a widget named foo that prints the widget’s value:

dbutils.widgets.text("foo", "fooDefault", "fooEmptyLabel")
print dbutils.widgets.get("foo")

Running dbutils.notebook.run("workflows", 60, {"foo": "bar"}) produces the following result:

The widget had the value you passed in through the workflow, "bar", rather than the default.

exit(value: String): void Exit a notebook with a value. If you call a notebook using the run method, this is the value returned.

dbutils.notebook.exit("returnValue")

Calling dbutils.notebook.exit in a job causes the notebook to complete successfully. If you want to cause the job to fail, throw an exception.

Example

In the following example, you pass arguments to DataImportNotebook and run different notebooks (DataCleaningNotebook or ErrorHandlingNotebook) based on the result from DataImportNotebook.

When the notebook workflow runs, you see a link to the running notebook:

Click the notebook link Notebook job #xxxx to view the details of the run:

Pass structured data

This section illustrates how to pass structured data between notebooks.

Python

# Example 1 - returning data through temporary views.
# You can only return one string using dbutils.notebook.exit(), but since called notebooks reside in the same JVM, you can
# return a name referencing data stored in a temporary view.

## In callee notebook
sqlContext.range(5).toDF("value").createOrReplaceGlobalTempView("my_data")
dbutils.notebook.exit("my_data")

## In caller notebook
returned_table = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
global_temp_db = spark.conf.get("spark.sql.globalTempDatabase")
display(table(global_temp_db + "." + returned_table))

# Example 2 - returning data through DBFS.
# For larger datasets, you can write the results to DBFS and then return the DBFS path of the stored data.

## In callee notebook
dbutils.fs.rm("/tmp/results/my_data", recurse=True)
sqlContext.range(5).toDF("value").write.parquet("dbfs:/tmp/results/my_data")
dbutils.notebook.exit("dbfs:/tmp/results/my_data")

## In caller notebook
returned_table = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
display(sqlContext.read.parquet(returned_table))

# Example 3 - returning JSON data.
# To return multiple values, you can use standard JSON libraries to serialize and deserialize results.

## In callee notebook
import json
dbutils.notebook.exit(json.dumps({
  "status": "OK",
  "table": "my_data"
}))

## In caller notebook
result = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
print(json.loads(result))

Scala

// Example 1 - returning data through temporary views.
// You can only return one string using dbutils.notebook.exit(), but since called notebooks reside in the same JVM, you can
// return a name referencing data stored in a temporary view.

/** In callee notebook */
sc.parallelize(1 to 5).toDF().createOrReplaceGlobalTempView("my_data")
dbutils.notebook.exit("my_data")

/** In caller notebook */
val returned_table = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
val global_temp_db = spark.conf.get("spark.sql.globalTempDatabase")
display(table(global_temp_db + "." + returned_table))

// Example 2 - returning data through DBFS.
// For larger datasets, you can write the results to DBFS and then return the DBFS path of the stored data.

/** In callee notebook */
dbutils.fs.rm("/tmp/results/my_data", recurse=true)
sc.parallelize(1 to 5).toDF().write.parquet("dbfs:/tmp/results/my_data")
dbutils.notebook.exit("dbfs:/tmp/results/my_data")

/** In caller notebook */
val returned_table = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
display(sqlContext.read.parquet(returned_table))

// Example 3 - returning JSON data.
// To return multiple values, you can use standard JSON libraries to serialize and deserialize results.

/** In callee notebook */

// Import jackson json libraries
import com.fasterxml.jackson.module.scala.DefaultScalaModule
import com.fasterxml.jackson.module.scala.experimental.ScalaObjectMapper
import com.fasterxml.jackson.databind.ObjectMapper

// Create a json serializer
val jsonMapper = new ObjectMapper with ScalaObjectMapper
jsonMapper.registerModule(DefaultScalaModule)

// Exit with json
dbutils.notebook.exit(jsonMapper.writeValueAsString(Map("status" -> "OK", "table" -> "my_data")))

/** In caller notebook */
val result = dbutils.notebook.run("LOCATION_OF_CALLEE_NOTEBOOK", 60)
println(jsonMapper.readValue[Map[String, String]](result))

Handle errors

This section illustrates how to handle errors in notebook workflows.

Python

# Errors in workflows thrown a WorkflowException.

def run_with_retry(notebook, timeout, args = {}, max_retries = 3):
  num_retries = 0
  while True:
    try:
      return dbutils.notebook.run(notebook, timeout, args)
    except Exception as e:
      if num_retries > max_retries:
        raise e
      else:
        print("Retrying error", e)
        num_retries += 1

run_with_retry("LOCATION_OF_CALLEE_NOTEBOOK", 60, max_retries = 5)

Scala

// Errors in workflows thrown a WorkflowException.

import com.databricks.WorkflowException

// Since dbutils.notebook.run() is just a function call, you can retry failures using standard Scala try-catch
// control flow. Here we show an example of retrying a notebook a number of times.
def runRetry(notebook: String, timeout: Int, args: Map[String, String] = Map.empty, maxTries: Int = 3): String = {
  var numTries = 0
  while (true) {
    try {
      return dbutils.notebook.run(notebook, timeout, args)
    } catch {
      case e: WorkflowException if numTries < maxTries =>
        println("Error, retrying: " + e)
    }
    numTries += 1
  }
  "" // not reached
}

runRetry("LOCATION_OF_CALLEE_NOTEBOOK", timeout = 60, maxTries = 5)

Run multiple notebooks concurrently

You can run multiple notebooks at the same time by using standard Scala and Python constructs such as Threads (Scala, Python) and Futures (Scala, Python). The advanced notebook workflow notebooks demonstrate how to use these constructs. The notebooks are in Scala but you could easily write the equivalent in Python. To run the example:

1. Download the notebook archive.
2. Import the archive into a workspace.
3. Run the Concurrent Notebooks notebook.