The Databricks REST API allows you to programmatically access Databricks instead of going through the web UI.

This article covers REST API 1.2.



To access Databricks REST APIs, you must authenticate.

REST API use cases

  • Start Apache Spark jobs triggered from your existing production systems or from workflow systems.
  • Programmatically bring up a cluster of a certain size at a fixed time of day and then shut it down at night.

API categories

  • Execution context: create unique variable namespaces where Spark commands can be called.
  • Command execution: run commands within a specific execution context.


  • This REST API runs over HTTPS.
  • For retrieving information, use HTTP GET.
  • For modifying state, use HTTP POST.
  • For file upload, use multipart/form-data. Otherwise use application/json.
  • The response content type is JSON.
  • Basic authentication is used to authenticate the user for every API call.
  • User credentials are base64 encoded and are in the HTTP header for every API call. For example, Authorization: Basic YWRtaW46YWRtaW4=.

Get started

In the following examples, replace <databricks-instance> with the workspace URL of your Databricks deployment.

Test your connection

> telnet <databricks-instance> 443

Connected to <databricks-instance>.
Escape character is '^]'.

> nc -v -z <databricks-instance> 443
found 1 connections:
     1: flags=82<CONNECTED,PREFERRED>
    outif utun0
    src x.x.x.x port 59063
    dst y.y.y.y port 443
    rank info not available
    TCP aux info available

Connection to <databricks-instance> port 443 [TCP/HTTPS] succeeded!

You can use either tool above to test the connection. Port 443 is default HTTPS port and you can run the REST API on this port. If you cannot connect to port 443, contact with your account URL.

Sample API calls

The following examples provide some cURL commands, but you can also use an HTTP library in your programming language of choice.

GET request

If your URL has the & character in it you must quote that URL so UNIX doesn’t interpret it as a command separator:

curl -n 'https://<databricks-instance>/api/1.2/commands/status?clusterId=batVenom&contextId=35585555555555&commandId=45382422555555555'

POST request with application/json

curl -X POST -n https://<databricks-instance>/api/1.2/contexts/create -d "language=scala&clusterId=batVenom"

API endpoints by category

Execution context

  • https://<databricks-instance>/api/1.2/contexts/create – create an execution context on a specified cluster for a given programming language

    • POST request with application/json:

      • data

        {"language": "scala", "clusterId": "peaceJam"}
  • https://<databricks-instance>/api/1.2/contexts/status – show the status of an existing execution context

    • GET request:
      • Example arguments: clusterId=peaceJam&contextId=179365396413324
      • status: ["Pending", "Running", "Error"]
  • https://<databricks-instance>/api/1.2/contexts/destroy – destroy an execution context

    • POST request with application/json:

      • data

        {"contextId" : "1793653964133248955", "clusterId" : "peaceJam"}

Command execution

Known limitations: command execution does not support %run.

  • https://<databricks-instance>/api/1.2/commands/execute – run a command or file.

    • POST request with application/json:

      • data

        {"language": "scala", "clusterId": "peaceJam", "contextId" : "5456852751451433082", "command": "sc.parallelize(1 to 10).collect"}
    • POST request with multipart/form-data:

      • data

        {"language": "python", "clusterId": "peaceJam", "contextId" : "5456852751451433082"}
      • files

        {"command": "./"}
  • https://<databricks-instance>/api/1.2/commands/status – show one command’s status or result

    • GET Request
      • Example arguments: clusterId=peaceJam&contextId=5456852751451433082&commandId=5220029674192230006
      • status:["Queued", "Running", "Cancelling", "Finished", "Cancelled", "Error"]
  • https://<databricks-instance>/api/1.2/commands/cancel – cancel one command

    • POST request with application/json:

      • data

        {"clusterId": "peaceJam", "contextId" : "5456852751451433082", "commandId" : "2245426871786618466"}

Example: Upload and run a Spark JAR

Upload a JAR

Use the REST API (latest) to upload a JAR and attach it to a cluster.

Run a JAR

  1. Create an execution context.

    curl -X POST -n  https://<databricks-instance>/api/1.2/contexts/create -d "language=scala&clusterId=batVenom"
      "id": "3558513128163162828"
  2. Execute a command that uses your JAR.

    curl -X POST -n https://<databricks-instance>/api/1.2/commands/execute \
    -d 'language=scala&clusterId=batVenom&contextId=3558513128163162828&command=println(com.databricks.apps.logs.chapter1.LogAnalyzer.processLogFile(sc,null,"dbfs:/somefile.log"))'
      "id": "4538242203822083978"
  3. Check on the status of your command. It may not return immediately if you are running a lengthy Spark job.

    curl -n 'https://<databricks-instance>/api/1.2/commands/status?clusterId=batVenom&contextId=3558513128163162828&commandId=4538242203822083978'
       "id": "4538242203822083978",
       "results": {
         "data": "Content Size Avg: 1234, Min: 1234, Max: 1234",
         "resultType": "text"
       "status": "Finished"

    Allowed values for resultType include:

    • error
    • image
    • images
    • table
    • text