Insert Multiple Rows with Snowflake API on New Table from PostgreSQL API

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New Table from the PostgreSQL API

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Insert Multiple Rows with the Snowflake API

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Getting Started#

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

Select this integration
Configure the New Table trigger
1. Connect your PostgreSQL account
2. Configure Polling Interval
3. Select a Schema
Configure the Insert Multiple Rows action
1. Connect your Snowflake account
2. Select a Database
3. Select a Schema
4. Select a Table Name
5. Select one or more Columns
6. Configure Row Values
7. Optional- Configure Batch Size
8. Optional- Configure Max Payload Size (MB)
9. Optional- Configure Enable Batch Processing
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 Table on PostgreSQL

Description:Emit new event when a new table is added to the database. [See the documentation](https://node-postgres.com/features/queries)

Version:2.0.8

Key:postgresql-new-table

View on GitHub

PostgreSQL Overview#

On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.

Trigger Code#

import common from "../common.mjs"; export default { ...common, name: "New Table", key: "postgresql-new-table", description: "Emit new event when a new table is added to the database. [See the documentation](https://node-postgres.com/features/queries)", version: "2.0.8", type: "source", props: { ...common.props, schema: { propDefinition: [ common.props.postgresql, "schema", ], }, }, async run() { const previousTables = this._getPreviousValues() || []; const tables = await this.postgresql.getTables(this.schema); const newTables = tables.filter((table) => !previousTables.includes(table)); for (const table of newTables) { const meta = this.generateMeta(table); this.$emit(table, meta); } this._setPreviousValues(tables); }, };

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
PostgreSQL	`postgresql`	`app`	This component uses the PostgreSQL app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling Interval	`timer`	`$.interface.timer`	Pipedream will poll the API on this schedule
Schema	`schema`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

Connecting to Restricted Databases

Either enable the shared static IP for this account below, or configure a VPC to deploy any workflow in your workspace to a private network with a dedicated static IP. Learn more in our docs

SSL Setup

Configure SSL on your PostgreSQL database by providing the CA (Certificate Authority), and choosing between Full Verification, Verify Certificate Authority (CA), or Skip Verification. Skipping verification is not recommended as this has serious security implications

About PostgreSQL#

PostgreSQL is a free and open-source relational database management system emphasizing extensibility and SQL compliance.

Action#

Insert Multiple Rows on Snowflake

Description:Insert multiple rows into a table

Version:0.1.4

Key:snowflake-insert-multiple-rows

View on GitHub

Snowflake Overview#

Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture

Getting Started

1. Create a user, role and warehouse in Snowflake

Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

2. Enter those details in Pipedream

Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.

You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.

3. Build your first workflow

Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.

Learn more at Pipedream University

Action Code#

import snowflake from "../../snowflake.app.mjs"; import { ConfigurationError } from "@pipedream/platform"; export default { type: "action", key: "snowflake-insert-multiple-rows", name: "Insert Multiple Rows", description: "Insert multiple rows into a table", version: "0.1.4", annotations: { destructiveHint: false, openWorldHint: true, readOnlyHint: false, }, props: { snowflake, database: { propDefinition: [ snowflake, "database", ], }, schema: { propDefinition: [ snowflake, "schema", (c) => ({ database: c.database, }), ], }, tableName: { propDefinition: [ snowflake, "tableName", (c) => ({ database: c.database, schema: c.schema, }), ], description: "The table where you want to add rows", }, columns: { propDefinition: [ snowflake, "columns", (c) => ({ tableName: c.tableName, }), ], }, values: { propDefinition: [ snowflake, "values", ], }, batchSize: { type: "integer", label: "Batch Size", description: "Number of rows to process per batch. Automatically calculated based on data size if not specified. Recommended: `50-200` for wide tables, `100-500` for narrow tables.", optional: true, default: 100, min: 10, max: 1000, }, maxPayloadSizeMB: { type: "integer", label: "Max Payload Size (MB)", description: "Maximum payload size per batch in MB. Helps prevent `413 Payload Too Large` errors.", optional: true, default: 5, min: 1, max: 10, }, enableBatching: { type: "boolean", label: "Enable Batch Processing", description: "Enable automatic batch processing for large datasets. Disable only for small datasets (< 50 rows) or troubleshooting.", optional: true, default: true, }, }, async run({ $ }) { let rows = this.values; let inputValidated = true; if (!Array.isArray(rows)) { try { rows = JSON.parse(rows); } catch (parseError) { throw new ConfigurationError("The row data could not be parsed as JSON. Please ensure it's a valid JSON array of arrays."); } } if (!rows || !rows.length || !Array.isArray(rows)) { inputValidated = false; } else { rows.forEach((row, index) => { if (!Array.isArray(row)) { console.log(`Row ${index + 1} is not an array:`, row); inputValidated = false; } }); } // Throw an error if input validation failed if (!inputValidated) { throw new ConfigurationError("The row data you passed is not an array of arrays. Please enter an array of arrays in the `Values` parameter above. If you're trying to add a single row to Snowflake, select the **Insert Single Row** action."); } const expectedColumnCount = this.columns.length; const invalidRows = rows.filter((row, index) => { if (row.length !== expectedColumnCount) { console.error(`Row ${index + 1} has ${row.length} values but ${expectedColumnCount} columns specified`); return true; } return false; }); if (invalidRows.length > 0) { throw new ConfigurationError(`${invalidRows.length} rows have a different number of values than the specified columns. Each row must have exactly ${expectedColumnCount} values to match the selected columns.`); } // Add batch processing options const batchOptions = { batchSize: this.batchSize, maxPayloadSizeMB: this.maxPayloadSizeMB, enableBatching: this.enableBatching, }; try { const response = await this.snowflake.insertRows( this.tableName, this.columns, rows, batchOptions, ); // Handle different response formats (batched vs single insert) if (response.summary) { // Batched response const { summary } = response; $.export("$summary", `Successfully inserted ${summary.totalRowsProcessed} rows into ${this.tableName} using ${summary.totalBatches} batches`); // Export detailed batch information $.export("batchDetails", { totalRows: summary.totalRows, totalBatches: summary.totalBatches, successfulBatches: summary.successfulBatches, failedBatches: summary.failedBatches, batchSize: summary.batchSize, processingTime: new Date().toISOString(), }); // Export batch results for debugging if needed $.export("batchResults", summary.results); return response; } else { // Single insert response (small dataset or batching disabled) $.export("$summary", `Successfully inserted ${rows.length} rows into ${this.tableName}`); return response; } } catch (error) { // Enhanced error handling for batch processing if (error.summary) { // Partial failure in batch processing const { summary } = error; $.export("$summary", `Partial success: ${summary.totalRowsProcessed}/${summary.totalRows} rows inserted. ${summary.failedBatches} batches failed.`); $.export("batchDetails", summary); $.export("failedBatches", summary.results.filter((r) => !r.success)); } // Re-throw the error with additional context if (error.message.includes("413") || error.message.includes("Payload Too Large")) { throw new ConfigurationError( `Payload too large error detected. Try reducing the batch size (current: ${this.batchSize}) or enable batching if disabled. ` + `You're trying to insert ${rows.length} rows with ${this.columns.length} columns each. ` + `Original error: ${error.message}`, ); } throw error; } }, };

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
Snowflake	`snowflake`	`app`	This component uses the Snowflake app.
Database	`database`	`string`	Select a value from the drop down menu.
Schema	`schema`	`string`	Select a value from the drop down menu.
Table Name	`tableName`	`string`	Select a value from the drop down menu.
Columns	`columns`	`string[]`	Select a value from the drop down menu.
Row Values	`values`	`string`	Provide an array of arrays. Each nested array should represent a row, with each element of the nested array representing a value (e.g., passing `[["Foo",1,2],["Bar",3,4]]` will insert two rows of data with three columns each). The most common pattern is to reference an array of arrays exported by a previous step (e.g., `{{steps.foo.$return_value}}`). You may also enter or construct a string that will `JSON.parse()` to an array of arrays.
Batch Size	`batchSize`	`integer`	Number of rows to process per batch. Automatically calculated based on data size if not specified. Recommended: `50-200` for wide tables, `100-500` for narrow tables.
Max Payload Size (MB)	`maxPayloadSizeMB`	`integer`	Maximum payload size per batch in MB. Helps prevent `413 Payload Too Large` errors.
Enable Batch Processing	`enableBatching`	`boolean`	Enable automatic batch processing for large datasets. Disable only for small datasets (< 50 rows) or troubleshooting.