Python Job Runner

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Python Job Runner

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Introduction¶

Joblib is a set of tools to provide lightweight pipelining inPython. In particular:

  1. transparent disk-caching of functions and lazy re-evaluation(memoize pattern)
  2. easy simple parallel computing

Joblib is optimized to be fast and robust on largedata in particular and has specific optimizations for numpy arrays. It isBSD-licensed.

Documentation:https://joblib.readthedocs.io
Download:https://pypi.python.org/pypi/joblib#downloads
Source code:https://github.com/joblib/joblib
Report issues:https://github.com/joblib/joblib/issues

Vision¶

The vision is to provide tools to easily achieve better performance andreproducibility when working with long running jobs.

  • Avoid computing the same thing twice: code is often rerun again andagain, for instance when prototyping computational-heavy jobs (as inscientific development), but hand-crafted solutions to alleviate thisissue are error-prone and often lead to unreproducible results.
  • Persist to disk transparently: efficiently persistingarbitrary objects containing large data is hard. Usingjoblib’s caching mechanism avoids hand-written persistence andimplicitly links the file on disk to the execution context ofthe original Python object. As a result, joblib’s persistence isgood for resuming an application status or computational job, egafter a crash.

Joblib addresses these problems while leaving your code and your flowcontrol as unmodified as possible (no framework, no new paradigms).

Main features¶

  1. Transparent and fast disk-caching of output value: a memoize ormake-like functionality for Python functions that works well forarbitrary Python objects, including very large numpy arrays. Separatepersistence and flow-execution logic from domain logic or algorithmiccode by writing the operations as a set of steps with well-definedinputs and outputs: Python functions. Joblib can save theircomputation to disk and rerun it only if necessary:

  2. Embarrassingly parallel helper: to make it easy to write readableparallel code and debug it quickly:

  3. Fast compressed Persistence: a replacement for pickle to workefficiently on Python objects containing large data (joblib.dump & joblib.load ).

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This article contains examples that demonstrate how to use the Azure Databricks REST API 2.0.

In the following examples, replace <databricks-instance> with the workspace URL of your Azure Databricks deployment. <databricks-instance> should start with adb-. Do not use the deprecated regional URL starting with <azure-region-name>. It may not work for new workspaces, will be less reliable, and will exhibit lower performance than per-workspace URLs.

Authentication

To learn how to authenticate to the REST API, review Authentication using Azure Databricks personal access tokens and Authenticate using Azure Active Directory tokens.

The examples in this article assume you are using Azure Databricks personal access tokens. In the following examples, replace <your-token> with your personal access token. The curl examples assume that you store Azure Databricks API credentials under .netrc. The Python examples use Bearer authentication. Although the examples show storing the token in the code, for leveraging credentials safely in Azure Databricks, we recommend that you follow the Secret management user guide.

For examples that use Authenticate using Azure Active Directory tokens, see the articles in that section.

Get a gzipped list of clusters

Upload a big file into DBFS

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The amount of data uploaded by single API call cannot exceed 1MB. To upload a file that is larger than 1MB to DBFS, use the streaming API, which is a combination of create, addBlock, and close.

Here is an example of how to perform this action using Python.

Python job runner tutorial

Create a Python 3 cluster (Databricks Runtime 5.5 LTS and higher)

Note

Python 3 is the default version of Python in Databricks Runtime 6.0 and above.

The following example shows how to launch a Python 3 cluster usingthe Databricks REST API and the requests Python HTTP library:

Create a High Concurrency cluster

The following example shows how to launch a High Concurrency mode cluster usingthe Databricks REST API:

Jobs API examples

This section shows how to create Python, spark submit, and JAR jobs and run the JAR job and view its output.

Create a Python job

This example shows how to create a Python job. It uses the Apache Spark Python Spark Pi estimation.

  1. Download the Python file containing the example and upload it to Databricks File System (DBFS) using the Databricks CLI.

  2. Create the job.

    The following examples demonstrate how to create a job using Databricks Runtime and Databricks Light.

    Databricks Runtime

    Databricks Light

Create a spark-submit job

This example shows how to create a spark-submit job. It uses the Apache Spark SparkPi example.

  1. Download the JAR containing the example and upload the JAR to Databricks File System (DBFS) using the Databricks CLI.

  2. Create the job.

Create and run a spark-submit job for R scripts

This example shows how to create a spark-submit job to run R scripts.

  1. Upload the R file to Databricks File System (DBFS) using the Databricks CLI.

    If the code uses SparkR, it must first install the package. Databricks Runtime contains the SparkR source code. Install the SparkR package from its local directory as shown in the following example:

    Databricks Runtime installs the latest version of sparklyr from CRAN. If the code uses sparklyr, You must specify the Spark master URL in spark_connect. To form the Spark master URL, use the SPARK_LOCAL_IP environment variable to get the IP, and use the default port 7077. For example:

  2. Create the job.

    This returns a job-id that you can then use to run the job.

  3. Run the job using the job-id.

Create and run a JAR job

This example shows how to create and run a JAR job. It uses the Apache Spark SparkPi example.

  1. Download the JAR containing the example.

  2. Upload the JAR to your Azure Databricks instance using the API:

    A successful call returns {}. Otherwise you will see an error message.

  3. Get a list of all Spark versions prior to creating your job.

    This example uses 7.3.x-scala2.12. See Runtime version strings for more information about Spark cluster versions.

  4. Create the job. The JAR is specified as a library and the main class name is referenced in the Spark JAR task.

    This returns a job-id that you can then use to run the job.

  5. Run the job using run now:

  6. Navigate to https://<databricks-instance>/#job/<job-id> and you’ll be able to see your job running.

  7. You can also check on it from the API using the information returned from the previous request.

    Which should return something like:

  8. To view the job output, visit the job run details page.

Create cluster enabled for table access control example

To create a cluster enabled for table access control, specify the following spark_conf property in your request body:

Cluster log delivery examples

While you can view the Spark driver and executor logs in the Spark UI, Azure Databricks can also deliver the logs to DBFS destinations.See the following examples.

Create a cluster with logs delivered to a DBFS location

The following cURL command creates a cluster named cluster_log_dbfs and requests Azure Databricks tosends its logs to dbfs:/logs with the cluster ID as the path prefix.

The response should contain the cluster ID:

After cluster creation, Azure Databricks syncs log files to the destination every 5 minutes.It uploads driver logs to dbfs:/logs/1111-223344-abc55/driver and executor logs todbfs:/logs/1111-223344-abc55/executor.

Check log delivery status

You can retrieve cluster information with log delivery status via API:

If the latest batch of log upload was successful, the response should contain only the timestampof the last attempt:

In case of errors, the error message would appear in the response:

Workspace examples

Here are some examples for using the Workspace API to list, get info about, create, delete, export, and import workspace objects.

List a notebook or a folder

The following cURL command lists a path in the workspace.

The response should contain a list of statuses:

Python

If the path is a notebook, the response contains an array containing the status of the input notebook.

Get information about a notebook or a folder

The following cURL command gets the status of a path in the workspace.

The response should contain the status of the input path:

Create a folder

The following cURL command creates a folder. It creates the folder recursively like mkdir -p.If the folder already exists, it will do nothing and succeed.

If the request succeeds, an empty JSON string will be returned.

Delete a notebook or folder

The following cURL command deletes a notebook or folder. You can enable recursive torecursively delete a non-empty folder.

If the request succeeds, an empty JSON string is returned.

Python Job Runner Online

Export a notebook or folder

The following cURL command exports a notebook. Notebooks can be exported in the following formats:SOURCE, HTML, JUPYTER, DBC. A folder can be exported only as DBC.

The response contains base64 encoded notebook content.

Alternatively, you can download the exported notebook directly.

The response will be the exported notebook content.

Import a notebook or directory

The following cURL command imports a notebook in the workspace. Multiple formats (SOURCE, HTML, JUPYTER, DBC) are supported.If the format is SOURCE, you must specify language. The content parameter contains base64 encodednotebook content. You can enable overwrite to overwrite the existing notebook.

If the request succeeds, an empty JSON string is returned.

Python Job Runner

Alternatively, you can import a notebook via multipart form post.