Verify Email Address with Verifalia API on BigQuery - New Row from Google Cloud API

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BigQuery - New Row from the Google Cloud API

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Verify Email Address with the Verifalia API

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Getting Started#

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

Select this integration
Configure the BigQuery - New Row trigger
1. Connect your Google Cloud account
2. Configure Polling interval
3. Configure Event Size
4. Select a Dataset ID
5. Select a Table Name
6. Select a Unique Key
Configure the Verify Email Address action
1. Connect your Verifalia account
2. Configure Email address
3. Optional- Select a Quality Level
4. Optional- Configure Data Retention Period
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#

BigQuery - New Row on Google Cloud

Description:Emit new events when a new row is added to a table

Version:0.1.6

Key:google_cloud-bigquery-new-row

View on GitHub

Google Cloud Overview#

The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.

Trigger Code#

import crypto from "crypto";
import { isString } from "lodash-es";
import googleCloud from "../../google_cloud.app.mjs";
import common from "../common/bigquery.mjs";

export default {
  ...common,
  key: "google_cloud-bigquery-new-row",
  // eslint-disable-next-line pipedream/source-name
  name: "BigQuery - New Row",
  description: "Emit new events when a new row is added to a table",
  version: "0.1.6",
  dedupe: "unique",
  type: "source",
  props: {
    ...common.props,
    tableId: {
      propDefinition: [
        googleCloud,
        "tableId",
        ({ datasetId }) => ({
          datasetId,
        }),
      ],
    },
    uniqueKey: {
      type: "string",
      label: "Unique Key",
      description: "The name of a column in the table to use for deduplication. See [the docs](https://github.com/PipedreamHQ/pipedream/tree/master/components/google_cloud/sources/bigquery-new-row#technical-details) for more info.",
      async options(context) {
        const { page } = context;
        if (page !== 0) {
          return [];
        }

        const columnNames = await this._getColumnNames();
        return columnNames.sort();
      },
    },
  },
  hooks: {
    ...common.hooks,
    async deploy() {
      await this._validateColumn(this.uniqueKey);
      const lastResultId = await this._getIdOfLastRow(this.getInitialEventCount());
      this._setLastResultId(lastResultId);
    },
    async activate() {
      if (this._getLastResultId()) {
        // ID of the last result has already been initialised during deploy(),
        // so we skip the rest of the activation.
        return;
      }

      await this._validateColumn(this.uniqueKey);
      const lastResultId = await this._getIdOfLastRow();
      this._setLastResultId(lastResultId);
    },
    deactivate() {
      this._setLastResultId(null);
    },
  },
  methods: {
    ...common.methods,
    _getLastResultId() {
      return this.db.get("lastResultId");
    },
    _setLastResultId(lastResultId) {
      this.db.set("lastResultId", lastResultId);
      console.log(`
        Next scan of table '${this.tableId}' will start at ${this.uniqueKey}=${lastResultId}
      `);
    },
    /**
     * Utility method to make sure that a certain column exists in the target
     * table. Useful for SQL query sanitizing.
     *
     * @param {string} columnNameToValidate The name of the column to validate
     * for existence
     */
    async _validateColumn(columnNameToValidate) {
      if (!isString(columnNameToValidate)) {
        throw new Error("columnNameToValidate must be a string");
      }

      const columnNames = await this._getColumnNames();
      if (!columnNames.includes(columnNameToValidate)) {
        throw new Error(`Nonexistent column: ${columnNameToValidate}`);
      }
    },
    async _getColumnNames() {
      const table = this.googleCloud
        .getBigQueryClient()
        .dataset(this.datasetId)
        .table(this.tableId);
      const [
        metadata,
      ] = await table.getMetadata();
      const { fields } = metadata.schema;
      return fields.map(({ name }) => name);
    },
    async _getIdOfLastRow(offset = 0) {
      const limit = offset + 1;
      const query = `
        SELECT *
        FROM \`${this.tableId}\`
        ORDER BY \`${this.uniqueKey}\` DESC
        LIMIT @limit
      `;
      const queryOpts = {
        query,
        params: {
          limit,
        },
      };
      const rows = await this.getRowsForQuery(queryOpts, this.datasetId);
      if (rows.length === 0) {
        console.log(`
          No records found in the target table, will start scanning from the beginning
        `);
        return;
      }

      const startingRow = rows.pop();
      return startingRow[this.uniqueKey];
    },
    getQueryOpts() {
      const lastResultId = this._getLastResultId();
      const query = `
        SELECT *
        FROM \`${this.tableId}\`
        WHERE \`${this.uniqueKey}\` >= @lastResultId
        ORDER BY \`${this.uniqueKey}\` ASC
      `;
      const params = {
        lastResultId,
      };
      return {
        query,
        params,
      };
    },
    generateMeta(row, ts) {
      const id = row[this.uniqueKey];
      const summary = `New row: ${id}`;
      return {
        id,
        summary,
        ts,
      };
    },
    generateMetaForCollection(rows, ts) {
      const hash = crypto.createHash("sha1");
      rows
        .map((i) => i[this.uniqueKey])
        .map((i) => i.toString())
        .forEach((i) => hash.update(i));
      const id = hash.digest("base64");

      const rowCount = rows.length;
      const entity = rowCount === 1
        ? "row"
        : "rows";
      const summary = `${rowCount} new ${entity}`;

      return {
        id,
        summary,
        ts,
      };
    },
  },
};

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
Google Cloud	`googleCloud`	`app`	This component uses the Google Cloud app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling interval	`timer`	`$.interface.timer`	How often to run your query
Event Size	`eventSize`	`integer`	The number of rows to include in a single event (by default, emits 1 event per row)
Dataset ID	`datasetId`	`string`	Select a value from the drop down menu.
Table Name	`tableId`	`string`	Select a value from the drop down menu.
Unique Key	`uniqueKey`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

Create a service account in GCP and set the permissions you need for Pipedream workflows.
Generate a service account key
Download the key details in JSON format
Open the JSON in a text editor, and copy and paste its contents here.

About Google Cloud#

The Google Cloud Platform, including BigQuery

Action#

Verify Email Address on Verifalia

Description:Verify an email address and check if it is properly formatted, really exists and can accept mails, flagging spam traps, disposable emails and much more. [See the docs](https://verifalia.com/developers#email-validations-creating) for more information

Version:1.1.0

Key:verifalia-verify-email

View on GitHub

Verifalia Overview#

Verifalia's API provides robust email validation and verification services, ensuring that email addresses in your lists are accurate and deliverable. Leveraging Verifalia within Pipedream workflows can automate the process of cleaning up email lists, improve email marketing efficiency, and maintain communication channel integrity. By integrating Verifalia's capabilities, you can cut down on bounces, identify disposable email addresses, and segment lists based on quality scores.

Action Code#

import { axios } from "@pipedream/platform";
import verifalia from "../../verifalia.app.mjs";
import common from "../../common.mjs";

import {
  CancellationToken,
  OperationCanceledError,
} from "verifalia";

export default {
  name: "Verify Email Address",
  description: "Verify an email address and check if it is properly formatted, really exists and can accept mails, " +
        "flagging spam traps, disposable emails and much more. [See the docs](https://verifalia.com/developers#email-validations-creating) for more information",
  key: "verifalia-verify-email",
  version: "1.1.0",
  type: "action",
  props: {
    verifalia,
    emailAddress: {
      type: "string",
      label: "Email address",
      description: "Enter the email address to verify (e.g. `batman@gmail.com`)",
      optional: false,
    },
    quality: {
      type: "string",
      label: "Quality Level",
      description: "The higher the quality level the longer it could take to complete the verification; by default, we use the configured default quality level for your Verifalia user.",
      optional: true,
      options: common.qualityLevelOptions,
    },
    retention: {
      type: "string",
      label: "Data Retention Period",
      description: "The data retention period to observe for the validation job, expressed in the format `dd.hh:mm:ss` " +
                "(where dd: days, hh: hours, mm: minutes, ss: seconds); the initial `dd.` part is added only for periods of " +
                "more than 24 hours. The value has a minimum of 5 minutes (`0:5:0`) and a maximum of 30 days (`30.0:0:0`): " +
                "Verifalia will delete the job and its data once its data retention period is over, starting to count when " +
                "it gets completed.",
      optional: true,
    },
  },

  async run({ $ }) {
    const { run } = $.context;
    const verifaliaClient = this.verifalia.buildVerifaliaRestClient();

    // This component takes advantage of the new Pipedream's flow suspension: we handle
    // the first run vs. callback scenarios hereafter.

    if (run.runs === 1) {
      // Check the format of the data retention period and report a user-friendly error

      if (this.retention && !this.verifalia.isValidTimeSpan(this.retention)) {
        throw new Error(`The specified data retention period '${this.retention}' is incorrect: must be in the ` +
                    "format dd.hh:mm:ss (where dd: days, hh: hours, mm: minutes, ss: seconds).");
      }

      // HACK: Pausing a workflow is not supported in test mode, as the editor would just
      // output the message "Would have paused until...". To overcome this, we are *not*
      // suspending runs in test mode and, in that case, proceed with a standard polling
      // to the API, backed by the usual Promise support. If the execution time approaches
      // the one we are allowed by Pipedream, we just let the user know.

      let resumeUrl = null;
      let waitForResults = false;
      let cancellationToken = null;

      if ($.context.test) {
        // In test mode, we will wait for the verification results during this execution
        // and cancel the token after 20s (the actual lowest limit is 30s).
        // See: https://pipedream.com/docs/limits/#time-per-execution

        waitForResults = true;
        cancellationToken = new CancellationToken();
        setTimeout(() => cancellationToken.cancel(), 20 * 1000);
      } else {
        // In deployment mode, we won't wait for the verification results during this
        // execution and, instead, rely on the Verifalia completion callback support and
        // on the Pipedream's new ability to suspend execution flows.

        resumeUrl = $.flow.rerun(86400 * 1000, {}, 1).resume_url;
      }

      // Submit the verification to Verifalia

      let job;

      try {
        job = await this.verifalia.wrapVerifaliaApiInvocation(async () => {
          return await verifaliaClient
            .emailValidations
            .submit({
              quality: this.quality,
              entries: [
                {
                  inputData: this.emailAddress,
                },
              ],
              retention: this.retention,
              callback: {
                url: resumeUrl,
              },
            },
            waitForResults,
            cancellationToken);
        });
      } catch (error) {
        // If the error is an OperationCanceledError it means that we are in test mode and
        // have manually cancelled the submission: just let the user know why that happened.

        if (error instanceof OperationCanceledError) {
          // Had to replace the word req*ire with a synonym because of https://github.com/PipedreamHQ/pipedream/issues/3187 :)

          throw new Error("This operation would need some more time to complete and that would not work properly in " +
                        "Pipedream's test mode. Please deploy your workflow to get a meaningful email verification result.");
        }

        throw error;
      }

      // Handle the verification outcome, if available

      if (job.overview.status === "Completed") {
        if (!resumeUrl) {
          // We are in test mode and the validation just completed in a single request

          return job;
        }

        // TODO: If the validation completed, abort the suspension and return the results
        // Blocked by https://github.com/PipedreamHQ/pipedream/issues/3151

        // We are *not* in test mode and the job is complete: simulate an instantaneous
        // webhook completion callback, as the external one may take a few seconds to
        // fire and we wish to continue the flow as fast as possible.

        return axios($, {
          method: "POST",
          url: resumeUrl,
          data: {
            event: {
              type: "email-validation.completed",
              data: {
                id: job.overview.id,
              },
            },
          },
          returnFullResponse: true,
        });
      }

      // If we are here, the validation is not yet completed and the flow is going to
      // be suspended by Pipedream.
    } else {
      const callbackRequest = run.callback_request;

      // We are only interested in a specific callback type

      if (callbackRequest.body.event.type === "email-validation.completed") {
        // Retrieve and return the job

        const jobId = callbackRequest.body.event.data.id;

        return await this.verifalia.wrapVerifaliaApiInvocation(() => verifaliaClient
          .emailValidations
          .get(jobId));
      }

      // We have just received a callback we weren't waiting for :/

      console.log(callbackRequest);
      throw new Error("An error occurred while processing your request, sorry.");
    }
  },
};

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
Verifalia	`verifalia`	`app`	This component uses the Verifalia app.
Email address	`emailAddress`	`string`	Enter the email address to verify (e.g. `batman@gmail.com`)
Quality Level	`quality`	`string`	Select a value from the drop down menu:`{ "label": "Standard quality", "value": "standard" }{ "label": "High quality", "value": "high" }{ "label": "Extreme quality", "value": "extreme" }`
Data Retention Period	`retention`	`string`	The data retention period to observe for the validation job, expressed in the format `dd.hh:mm:ss` (where dd: days, hh: hours, mm: minutes, ss: seconds); the initial `dd.` part is added only for periods of more than 24 hours. The value has a minimum of 5 minutes (`0:5:0`) and a maximum of 30 days (`30.0:0:0`): Verifalia will delete the job and its data once its data retention period is over, starting to count when it gets completed.

Action Authentication#

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

In order to connect Verifalia with Pipedream, enter your Verifalia username and password below.

To learn more about how Verifalia handles authentication, see their documentation.
Make sure to use a strong and unique password for Verifalia.

About Verifalia#

Verifalia is a fast and accurate email verification service which identifies deliverable, invalid, or otherwise risky email addresses in real-time: it stops bad and low-quality emails getting into your systems, reduces bounce rates and keeps your campaigns deliverable.