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"}
- data =
- POST request with application/x-www-form-urlencoded:
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”]
- Example arguments:
- GET request:
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"}
- data =
- POST request with application/x-www-form-urlencoded:
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"}
- data =
- POST request with multipart/form-data:
- data =
{"language": "python", "clusterId": "peaceJam", "contextId" : "5456852751451433082"}
- files =
{"command": "./myfile.py"}
- data =
- POST request with application/x-www-form-urlencoded:
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”]
- Example arguments:
- GET Request
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"}
- data =
- POST request with application/x-www-form-urlencoded:
Example: Upload and run a Spark JAR¶
Upload a JAR¶
- Use the REST API 2.0 to upload a JAR and attach it to a cluster.
Run a JAR¶
Create an execution context.
curl -X POST -n https://<databricks-instance>/api/1.2/contexts/create -d "language=scala&clusterId=batVenom"
{ "id": "3558513128163162828" }
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" }
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" }