REST API 1.2

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

This topic covers REST API 1.2. For most use cases, we recommend using the REST API 2.0. It supports most of the functionality of the 1.2 API, as well as additional functionality.

REST API use cases

  • Start 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.

Details

  • 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 application/x-www-form-urlencoded is used.
  • 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 <REGION>.azuredatabricks.net domain name of your Databricks deployment.

Test your connection

> telnet <databricks-instance> 443

Trying 52.11.163.202...
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 support@databricks.com with your account URL.

Sample API calls

We cover some sample cURL commands below, but you can also use an HTTP library in your programming language of choice.

GET request

Note

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/x-www-form-urlencoded
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 – creates an execution context on a specified cluster for a given programming language
    • POST request with application/x-www-form-urlencoded:
      • data = {"language": "scala", "clusterId": "peaceJam"}
  • https://<databricks-instance>/api/1.2/contexts/status – shows the status of an existing execution context
    • GET request:
      • Example arguments: clusterId=peaceJam&contextId=179365396413324
      • Status is one of [“Pending”, “Running”, “Error”]
  • https://<databricks-instance>/api/1.2/contexts/destroy – destroys an execution context
    • POST request with application/x-www-form-urlencoded:
      • data = {"contextId" : "1793653964133248955", "clusterId" : "peaceJam"}

Command execution

Known limitations: command execution does not support %run.

  • https://<databricks-instance>/api/1.2/commands/execute – runs a command or file.
    • POST request with application/x-www-form-urlencoded:
      • 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": "./myfile.py"}
  • https://<databricks-instance>/api/1.2/commands/status – shows one command’s status or result
    • GET Request
      • Example arguments: clusterId=peaceJam&contextId=5456852751451433082&commandId=5220029674192230006
      • status can be [“Queued”, “Running”, “Cancelling”, “Finished”, “Cancelled”, “Error”]
  • https://<databricks-instance>/api/1.2/commands/cancel – cancels one command
    • POST request with application/x-www-form-urlencoded:
      • data = {"clusterId": "peaceJam", "contextId" : "5456852751451433082", "commandId" : "2245426871786618466"}

Example: Upload and run a Spark JAR

Upload a JAR

  1. Use the REST API 2.0 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"
}