Run a Databricks notebook from another notebook

Comparison of %run and dbutils.notebook.run()

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.

The dbutils.notebook API is a complement to %run because it lets 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.

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.

Use %run to import a notebook

In this example, the first notebook defines a function, reverse, which is available in the second notebook after you use the %run magic to execute shared-code-notebook.

Because both of these notebooks are in the same directory in the workspace, use the prefix ./ in ./shared-code-notebook to indicate that the path should be resolved relative to the currently running notebook. You can organize notebooks into directories, such as %run ./dir/notebook, or use an absolute path like %run /Users/[email protected]/directory/notebook.

dbutils.notebook API

The methods available in the dbutils.notebook API 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 Databricks 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 using dbutils.notebook.run(), "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 code runs, a table appears containing a link to the running notebook:

To view the run details, click the Start time link in the table. If the run is complete, you can also view the run details by clicking the End time link.

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
spark.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)
spark.range(5).toDF("value").write.format("parquet").save("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(spark.read.format("parquet").load(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
import json

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.format("parquet").save("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.format("parquet").load(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 */

// 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)

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.

Python

# Errors throw 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 throw 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 example notebooks demonstrate how to use these constructs.

1. Download the following 4 notebooks. The notebooks are written in Scala.
2. Import the notebooks into a single folder in the workspace.
3. Run the Run concurrently notebook.

Run concurrently notebook

Get notebook

Run in parallel notebook

Get notebook

Testing notebook

Get notebook

Testing-2 notebook

Get notebook