Run Job Now with Databricks API on New Video Created from Plainly API

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Trigger workflow on

New Video Created from the Plainly API

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Run Job Now with the Databricks API

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Getting Started#

This integration creates a workflow with a Plainly trigger and Databricks action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Video Created trigger
1. Connect your Plainly account
2. Configure timer
3. Select a Brand ID
4. Select a Article ID
Configure the Run Job Now action
1. Connect your Databricks account
2. Select a Job
3. Optional- Configure JAR Params
4. Optional- Configure Notebook Params
5. Optional- Configure Python Params
6. Optional- Configure Spark Submit Params
7. Optional- Configure Idempotency Token
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Video Created on Plainly

Description:Emit new event when a video is created or uploaded in Plainly.

Version:0.0.1

Key:plainly-new-video-created

View on GitHub

Trigger Code#

import common from "../common/base.mjs";

export default {
  ...common,
  key: "plainly-new-video-created",
  name: "New Video Created",
  description: "Emit new event when a video is created or uploaded in Plainly.",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    brandId: {
      propDefinition: [
        common.props.plainly,
        "brandId",
      ],
    },
    articleId: {
      propDefinition: [
        common.props.plainly,
        "articleId",
        (c) => ({
          brandId: c.brandId,
        }),
      ],
    },
  },
  methods: {
    ...common.methods,
    getResourceFn() {
      return this.plainly.listVideos;
    },
    getArgs() {
      return {
        brandId: this.brandId,
        articleId: this.articleId,
      };
    },
    usePagination() {
      return false;
    },
    generateMeta(item) {
      return {
        id: item.id,
        summary: `New Video Created with ID: ${item.id}`,
        ts: Date.parse(item.createdDate),
      };
    },
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Plainly	`plainly`	`app`	This component uses the Plainly app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`
Brand ID	`brandId`	`string`	Select a value from the drop down menu.
Article ID	`articleId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

Plainly uses API keys for authentication. When you connect your Plainly account, Pipedream securely stores the keys so you can easily authenticate to Plainly APIs in both code and no-code steps.

About Plainly#

Automate video creation

Action#

Run Job Now on Databricks

Description:Run a job now and return the id of the triggered run. [See the documentation](https://docs.databricks.com/en/workflows/jobs/jobs-2.0-api.html#runs-list)

Version:0.0.6

Key:databricks-run-job-now

View on GitHub

Databricks Overview#

The Databricks API allows you to interact programmatically with Databricks services, enabling you to manage clusters, jobs, notebooks, and other resources within Databricks environments. Through Pipedream, you can leverage these APIs to create powerful automations and integrate with other apps for enhanced data processing, transformation, and analytics workflows. This unlocks possibilities like automating cluster management, dynamically running jobs based on external triggers, and orchestrating complex data pipelines with ease.

Action Code#

import databricks from "../../databricks.app.mjs";

export default {
  key: "databricks-run-job-now",
  name: "Run Job Now",
  description: "Run a job now and return the id of the triggered run. [See the documentation](https://docs.databricks.com/en/workflows/jobs/jobs-2.0-api.html#runs-list)",
  version: "0.0.6",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  type: "action",
  props: {
    databricks,
    jobId: {
      propDefinition: [
        databricks,
        "jobId",
      ],
    },
    jarParams: {
      type: "string[]",
      label: "JAR Params",
      description: "A list of parameters for jobs with JAR tasks, e.g. \"jar_params\": [\"john doe\", \"35\"]. The parameters will be used to invoke the main function of the main class specified in the Spark JAR task. If not specified upon run-now, it will default to an empty list. jar_params cannot be specified in conjunction with notebook_params. The JSON representation of this field (i.e. {\"jar_params\":[\"john doe\",\"35\"]}) cannot exceed 10,000 bytes.",
      optional: true,
    },
    notebookParams: {
      type: "object",
      label: "Notebook Params",
      description: "A map from keys to values for jobs with notebook task, e.g. \"notebook_params\": {\"name\": \"john doe\", \"age\":  \"35\"}. The map is passed to the notebook and is accessible through the dbutils.widgets.get function. If not specified upon run-now, the triggered run uses the job’s base parameters. You cannot specify notebook_params in conjunction with jar_params. The JSON representation of this field (i.e. {\"notebook_params\":{\"name\":\"john doe\",\"age\":\"35\"}}) cannot exceed 10,000 bytes.",
      optional: true,
    },
    pythonParams: {
      type: "string[]",
      label: "Python Params",
      description: "A list of parameters for jobs with Python tasks, e.g. \"python_params\": [\"john doe\", \"35\"]. The parameters will be passed to Python file as command-line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The JSON representation of this field (i.e. {\"python_params\":[\"john doe\",\"35\"]}) cannot exceed 10,000 bytes.",
      optional: true,
    },
    sparkSubmitParams: {
      type: "string[]",
      label: "Spark Submit Params",
      description: "A list of parameters for jobs with spark submit task, e.g. \"spark_submit_params\": [\"--class\", \"org.apache.spark.examples.SparkPi\"]. The parameters will be passed to spark-submit script as command-line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The JSON representation of this field cannot exceed 10,000 bytes.",
      optional: true,
    },
    idempotencyToken: {
      type: "string",
      label: "Idempotency Token",
      description: "An optional token to guarantee the idempotency of job run requests. If a run with the provided token already exists, the request does not create a new run but returns the ID of the existing run instead. If a run with the provided token is deleted, an error is returned. If you specify the idempotency token, upon failure you can retry until the request succeeds. Databricks guarantees that exactly one run is launched with that idempotency token. This token must have at most 64 characters. For more information, see [How to ensure idempotency for jobs](https://kb.databricks.com/jobs/jobs-idempotency.html).",
      optional: true,
    },
  },
  async run({ $ }) {
    const response = await this.databricks.runJobNow({
      data: {
        job_id: this.jobId,
        jar_params: this.jarParams,
        notebook_params: this.notebookParams,
        python_params: this.pythonParams,
        spark_submit_params: this.sparkSubmitParams,
        idempotency_token: this.idempotencyToken,
      },
      $,
    });

    if (response) {
      $.export("$summary", `Successfully initiated job with ID ${this.jobId}.`);
    }

    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Databricks	`databricks`	`app`	This component uses the Databricks app.
Job	`jobId`	`string`	Select a value from the drop down menu.
JAR Params	`jarParams`	`string[]`	A list of parameters for jobs with JAR tasks, e.g. "jar_params": ["john doe", "35"]. The parameters will be used to invoke the main function of the main class specified in the Spark JAR task. If not specified upon run-now, it will default to an empty list. jar_params cannot be specified in conjunction with notebook_params. The JSON representation of this field (i.e. {"jar_params":["john doe","35"]}) cannot exceed 10,000 bytes.
Notebook Params	`notebookParams`	`object`	A map from keys to values for jobs with notebook task, e.g. "notebook_params": {"name": "john doe", "age": "35"}. The map is passed to the notebook and is accessible through the dbutils.widgets.get function. If not specified upon run-now, the triggered run uses the job’s base parameters. You cannot specify notebook_params in conjunction with jar_params. The JSON representation of this field (i.e. {"notebook_params":{"name":"john doe","age":"35"}}) cannot exceed 10,000 bytes.
Python Params	`pythonParams`	`string[]`	A list of parameters for jobs with Python tasks, e.g. "python_params": ["john doe", "35"]. The parameters will be passed to Python file as command-line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The JSON representation of this field (i.e. {"python_params":["john doe","35"]}) cannot exceed 10,000 bytes.
Spark Submit Params	`sparkSubmitParams`	`string[]`	A list of parameters for jobs with spark submit task, e.g. "spark_submit_params": ["--class", "org.apache.spark.examples.SparkPi"]. The parameters will be passed to spark-submit script as command-line parameters. If specified upon run-now, it would overwrite the parameters specified in job setting. The JSON representation of this field cannot exceed 10,000 bytes.
Idempotency Token	`idempotencyToken`	`string`	An optional token to guarantee the idempotency of job run requests. If a run with the provided token already exists, the request does not create a new run but returns the ID of the existing run instead. If a run with the provided token is deleted, an error is returned. If you specify the idempotency token, upon failure you can retry until the request succeeds. Databricks guarantees that exactly one run is launched with that idempotency token. This token must have at most 64 characters. For more information, see How to ensure idempotency for jobs

Action Authentication#

Databricks uses API keys for authentication. When you connect your Databricks account, Pipedream securely stores the keys so you can easily authenticate to Databricks APIs in both code and no-code steps.