PostgreSQL Integration Guide

Connect to PostgreSQL databases to read and write industrial data in your pipelines. This comprehensive guide walks you through everything from basic setup to advanced configurations.

Overview

The PostgreSQL connector is your gateway to relational database operations in MaestroHub. It enables you to:

• Read data from tables, views, or custom queries
• Write data with insert, update, or delete operations
• Use parameterized queries for dynamic, reusable data operations
• Secure connections with SSL/TLS encryption

TimescaleDB Support

The PostgreSQL connector also connects to TimescaleDB: it is PostgreSQL, so no extra setting is needed.

Connection Configuration

Creating a PostgreSQL Connection

Navigate to Connections → New Connection → PostgreSQL and configure the following:

PostgreSQL Connection Creation Fields

1. Profile Information

Field	Default	Description
Profile Name	-	A descriptive name for this connection profile (required, max 100 characters)
Description	-	Optional description for this PostgreSQL connection

2. Database Configuration

Field	Default	Description
Host	localhost	PostgreSQL server hostname or IP address - required
Port	5432	PostgreSQL server port (1-65535) - required
Connect Timeout (sec)	30	Maximum time to wait for connection establishment (0-600 seconds) - required
Schema	public	Database schema to use - required
Database	-	Database name to connect to (e.g., mydb) - required

Note: Supported PostgreSQL versions: 9.6+

3. Basic Authentication

Field	Default	Description
Username	-	PostgreSQL database user (required)
Password	-	PostgreSQL user password (required)

4. SSL Settings

4a. SSL Configuration

Field	Default	Description
Enable SSL	false	Encrypt the connection to the PostgreSQL server. When off, the connection is not encrypted.

4b. SSL Mode and Certificates

(Only displayed when SSL is enabled)

Field	Default	Description
SSL Mode	require	TLS/SSL connection mode used by the driver (require / verify-ca / verify-full)
CA Certificate	-	Trusted CA certificate (sslrootcert) in PEM format
Client Certificate	-	Optional client certificate (sslcert) in PEM format
Private Key	-	Private key for client certificate (sslkey) in PEM format

SSL Mode Options

• require: Requires SSL connection but does not verify server certificate
• verify-ca: Requires SSL and verifies that the server certificate is issued by a trusted CA; the hostname is not checked
• verify-full: Requires SSL and verifies both the CA and that the server hostname matches the certificate

5. Connection Pool Settings

Field	Default	Description
Max Open Connections	100	Maximum number of simultaneous database connections (0-1000). Higher values increase concurrency but add DB load
Max Idle Connections	25	Idle connections kept ready for reuse to reduce latency (0-1000, 0 = close idle connections immediately)
Connection Max Lifetime (sec)	900	Maximum age of a connection before it is recycled (0-86400 seconds, 0 = keep connections indefinitely). Helpful to avoid server-side timeouts
Connection Max Idle Time (sec)	300	How long an idle connection may remain unused before being closed (0-86400 seconds, 0 = disable idle timeout)

Connection pool settings help optimize database performance by balancing concurrency, resource usage, and latency across workloads.

6. Retry Configuration

Field	Default	Description
Retries	3	Retries after the first connection attempt fails (0-10, 0 = no retries). Each retry waits the Retry Delay first; the delay grows by the Backoff Multiplier after every retry
Retry Delay (ms)	100	Delay between retry attempts in milliseconds (0-3600000 ms)
Retry Backoff Multiplier	2	Exponential factor for retry delay growth (1-10, e.g., 2.0 means each retry waits twice as long)

Example Retry Behavior

• With Retry Delay = 100ms and Retry Backoff Multiplier = 2:
  • 1st retry: wait 100ms
  • 2nd retry: wait 200ms
  • 3rd retry: wait 400ms

7. Connection Labels

Field	Default	Description
Labels	-	Key-value pairs to categorize and organize this PostgreSQL connection (max 10 labels)

Example Labels

• env: prod – Environment
• team: data – Responsible team

Notes

• Required Fields: All fields described as "required" must be filled.
• SSL/TLS: When SSL is disabled, SSL Mode is automatically set to disable.
• Connection Pooling: Manages concurrency with Max Open Connections, reduces latency with Max Idle Connections, mitigates stale connections via Connection Max Lifetime, and frees resources using Connection Max Idle Time.
• Retry Logic: Implements exponential backoff—initial connection attempts use the base delay and each retry multiplies the delay by the backoff factor; ideal for transient network issues or database restarts.
• Timeout Values: Connect Timeout applies to initial connection establishment; all timeout values are in seconds unless noted (Retry Delay uses milliseconds).
• Security Best Practices: Always enable SSL for production, prefer verify-full, supply CA certificates for verify-ca or verify-full, and include client certificates for mutual TLS when possible.

Function Builder

Creating PostgreSQL Functions

Once you have a connection established, you can create reusable functions for different operation types:

1. Open the connection and go to its Functions tab → New Function
2. Select one of the PostgreSQL function types: Query, Execute, or Write
3. Configure the function parameters

Zoom

PostgreSQL function creation interface showing available function types: Query, Execute, and Write

Query Function

Purpose: Execute SQL queries (SELECT) to read data from PostgreSQL. Returns rows as structured data.

Configuration Fields

Field	Type	Required	Default	Description
SQL Query	String	Yes	-	SQL SELECT statement to execute. Supports parameterized queries with ((param)) syntax.
Timeout (seconds)	Number	No	1800	Per-execution timeout in seconds (1-3600).

Use Cases:

• SELECT machine KPIs (OEE, downtime) from telemetry tables
• SELECT COUNT(*) from orders grouped by status
• SELECT with JOINs across multiple tables using $1 parameters
• SELECT with WHERE filters, ORDER BY, and LIMIT

Execute Function

Purpose: Execute DML/DDL statements (INSERT, UPDATE, DELETE, CREATE, ALTER, DROP) on PostgreSQL. Returns rowsAffected instead of row data. Use this for data modifications and schema changes.

Configuration Fields

Field	Type	Required	Default	Description
SQL Statement	String	Yes	-	DML/DDL SQL statement to execute. Supports parameterized queries with ((param)) syntax.
Timeout (seconds)	Number	No	1800	Per-execution timeout in seconds (1-3600).

Use Cases:

• INSERT production events with NOW() timestamps and $1 parameters
• UPDATE work orders, inventory counts, or maintenance schedules
• DELETE records older than NOW() - INTERVAL '30 days'
• CREATE TABLE or ALTER TABLE for schema changes

Write Function

Purpose: Write pipeline data to a PostgreSQL table with automatic schema detection. This is not a SQL editor — it loads structured data (rows/objects) from your pipeline into a target table.

Write reads the table's columns and maps the incoming data fields to them automatically. Supports batching for efficient bulk loading.

Configuration Fields

Field	Type	Required	Default	Description
Table Name	String	Yes	-	Target table to insert data into.
Data	JSON	Yes	-	Rows to write: an array of objects or a single object. Use ((data)) to take them from the pipeline, or enter static JSON.
Create Table If Not Exists	Boolean	No	false	Create the table if it does not exist, with column types from the Schema or inferred from the data.
Allow Schema Evolution	Boolean	No	false	Add a column to an existing table when the data carries a field the table has no column for. When off, such a batch fails instead of dropping the field. See step 5 below.
Schema	JSON	No	-	Column definitions (types, primary keys, indexes) for creating the table, set in the Visual Editor or as JSON. Shown when Create Table If Not Exists is on.
Batch Size	Number	No	100	Number of rows per INSERT batch (1–10,000).
Timeout (seconds)	Number	No	1800	Per-execution timeout in seconds (1-3600).

Use Cases:

• Auto-detect table schema and map pipeline data
• Create tables on-the-fly with SERIAL PRIMARY KEY columns
• Bulk insert production events with auto schema detection

How Write Works Step by Step

1. Batching

Each batch generates a single INSERT INTO ... VALUES (...), (...), (...) statement with multiple value tuples. If your pipeline sends 10 rows and Batch Size is 5, exactly 2 INSERT statements are executed (2 batches of 5 rows). If it sends 13 rows, 3 INSERT statements are executed (5 + 5 + 3).

note

PostgreSQL has a per-query parameter limit (65,535). If your configured batch size combined with the number of columns would exceed this limit, batches are automatically split into smaller chunks. This is handled transparently — the total number of inserted rows remains the same.

2. Column Matching

Write uses case-insensitive matching between incoming data field names and table column names. For example, a data field named deviceId will match a table column DEVICEID or deviceid. The original column casing from the database is preserved in the generated INSERT statement.

3. Table Exists — Normal Flow

1. The function first queries information_schema to check if the target table exists
2. If the table exists, it fetches all column metadata (name, type, nullability)
3. If the data carries fields that have no column in the table, Allow Schema Evolution decides: when on, the missing columns are added first (step 5); when off, the write fails and nothing is inserted
4. It matches incoming data fields to table columns (case-insensitive)
5. Batch INSERT statements are generated and executed using the matched columns

4. Table Does Not Exist

Create Table If Not Exists	Behavior
Disabled (default)	Returns an error immediately: "table 'X' does not exist. Enable 'Create table if not exists' or create it manually". No insert is attempted.
Enabled, no schema provided	Types are inferred from all rows in the batch using the rules below. A CREATE TABLE is executed, then all rows are inserted.
Enabled, with schema	The provided schema is used for CREATE TABLE with full column definitions (types, primary keys, indexes). After creation, all rows are inserted.

Data Type Inference — When no schema is provided, the Write function infers each column's type from all rows in the batch. For each field it uses the first row where the field has a non-null value; a field that is null in every row gets the null type below. A row that lacks a field inserts NULL for it.

Data Value	Inferred Type	Notes
true / false	BOOLEAN	JSON boolean values
42, -7, 1000	BIGINT	Whole numbers (no fractional part)
3.14, 0.5	DOUBLE PRECISION	Numbers with fractional part
"2024-01-15T10:30:00Z"	TIMESTAMPTZ	Strings matching ISO 8601 / RFC 3339 and other common timestamp formats are automatically detected
"hello", "sensor-01"	TEXT	All other strings
null	TEXT	Null values default to text
{"key": "val"}, [1,2]	TEXT	Objects and arrays are stored as JSON text

info

JSON has a single number type, so 55.0 arrives as 55 and is typed BIGINT. When rows disagree, a whole number gives way to a later number with a fractional part: a field that is 38 in one row and 38.5 in a later row gets DOUBLE PRECISION. Any other disagreement keeps the type of the first non-null value, so a field that is a string in the first row stays TEXT even if later rows send numbers. If you need a specific numeric type (e.g., NUMERIC, SMALLINT), define it in the schema using the Visual Editor.

5. Schema Evolution (Table Exists + Allow Schema Evolution On)

When Allow Schema Evolution is on and the table already exists, the function adds the missing columns before inserting:

• New columns: If the data contains fields that have no column in the table, ALTER TABLE ADD COLUMN is executed for each missing field. If a schema is defined in the Visual Editor, the specified type is used; otherwise, the type is inferred from the batch using the rules above.
• After the columns are added, column metadata is re-fetched from the database, then all rows are inserted with the updated column set.

Schema evolution only adds columns — it never changes the type of an existing column. Data that does not fit an existing column's type is not handled by schema evolution: the database rejects or converts it as it would for any INSERT.

The following changes are not made by schema evolution and require manual ALTER TABLE statements:

• Changing the type of an existing column, whether widening or narrowing it
• Changing an already-created column's type by updating the Visual Editor schema alone — the schema definition only affects new columns being added
• Dropping columns — removing a column from the Visual Editor schema does not delete it from the database table. The column remains in the table and receives NULL (or its default value) for new inserts.

tip

If you need to change an existing column's type or drop a column, use the Execute function to run ALTER TABLE ... ALTER COLUMN ... TYPE ... or ALTER TABLE ... DROP COLUMN ... manually.

Allow Schema Evolution off: if the data carries a field that has no column in the table, the write fails before anything is inserted, with an error that names the fields:

"write batch carries field(s) [pressure] with no matching column in table "sensor_readings" and schema evolution is disabled — enable 'Allow schema evolution' on this function, or add the column(s) manually"

Turn the switch on, add the column with the Execute function, or remove the field upstream. The error is marked permanent because the same batch would fail again: with Store & Forward on, the batch is not retried and goes to Failed messages.

Functions saved before the switch existed have no Allow Schema Evolution value. For them, Create Table If Not Exists decides: when on, missing columns are added; when off, fields that have no column are skipped and listed in skippedFields. When you open such a function in the editor, the switch starts in the same position as Create Table If Not Exists, and saving stores it. Once it is saved as off, a batch with an unknown field fails instead of being skipped.

6. Partial Failure

Each batch executes independently — there is no transaction wrapping all batches. If batch 3 of 5 fails, batches 1 and 2 have already been committed. The error response still includes rowsInserted showing how many rows succeeded before the failure.

Response Metadata

On success, the response includes:

Field	Description
rowsInserted	Total number of rows successfully inserted
matchedColumns	Column names that had matching data fields (when the table already existed)
skippedFields	Data fields that had no matching column and were skipped — only for functions saved before Allow Schema Evolution existed (if any)
schemaEvolution	Columns added by schema evolution, each with its type (if any)
tableCreated	true if the table was created during this call
columns	Columns written to (when the table was created during this call)
batchSize	Batch size used
totalRows	Total number of input rows

Using Parameters

The ((parameterName)) syntax creates dynamic, reusable queries for Query and Execute functions. Parameters are automatically detected and can be configured with:

Configuration	Description	Example
Type	Data type validation	string, number, boolean, date, array
Required	Make parameters mandatory or optional	Required / Optional
Default Value	Fallback value if not provided	NOW(), 0, active
Description	Help text for users	"Start date for the report"

Zoom

Parameter configuration interface showing type validation, required flags, default values, and descriptions

Pipeline Integration

Use the PostgreSQL functions you create here as nodes inside the Pipeline Designer to move data in and out of your database alongside the rest of your operations stack. Drag the appropriate node type onto the canvas, bind its parameters to upstream outputs or constants, and configure connection-level options without leaving the designer.

Each function type maps to a dedicated pipeline node — Query, Execute, and Write — so you can clearly separate read, write, and data-loading operations in your flows.

For end-to-end orchestration ideas, such as combining database reads with MQTT, REST, or analytics steps, explore the Connector Nodes page to see how SQL nodes complement other automation patterns.

Zoom

PostgreSQL Query node in the pipeline designer

Common Use Cases

Query: Reading Production Metrics

Scenario: Generate hourly production reports with efficiency metrics.

SELECT
    DATE_TRUNC('hour', timestamp) as hour,
    machine_id,
    COUNT(*) as event_count,
    AVG(efficiency) as avg_efficiency,
    MIN(efficiency) as min_efficiency,
    MAX(efficiency) as max_efficiency
FROM production_events
WHERE timestamp >= ((startDate)) AND timestamp < ((endDate))
GROUP BY hour, machine_id
ORDER BY hour DESC;

Pipeline Integration: Use in a pipeline to feed data to visualization dashboards, BI tools, or reporting nodes.

---

Execute: Updating Work Order Status

Scenario: Track manufacturing progress by updating work order status in real-time.

UPDATE work_orders
SET
    status = ((newStatus)),
    completed_units = ((completedUnits)),
    completion_percentage = ROUND((((completedUnits))::numeric / total_units * 100), 2),
    updated_at = NOW(),
    updated_by = ((userId))
WHERE order_id = ((orderId))
RETURNING order_id, status, completion_percentage;

Pipeline Integration: Trigger this function based on production events, barcode scans, or manual workflows.

---

Execute: Data Retention and Cleanup

Scenario: Maintain database performance by archiving or deleting old data.

WITH archived AS (
    INSERT INTO sensor_readings_archive
    SELECT * FROM sensor_readings
    WHERE timestamp < NOW() - INTERVAL '((retentionDays)) days'
      AND archived = false
    RETURNING id
)
DELETE FROM sensor_readings
WHERE id IN (SELECT id FROM archived);

Pipeline Integration: Schedule this function to run daily or weekly via pipeline triggers (cron jobs).

---

Write: Bulk Loading Sensor Data

Scenario: Load real-time sensor readings from IoT devices into a PostgreSQL table.

Configure a Write function with:

• Table Name: sensor_readings
• Create Table If Not Exists: enabled
• Allow Schema Evolution: enabled, so a new field from the devices becomes a new column instead of failing the batch

Connect it after data collection nodes (MQTT, OPC UA, Modbus, etc.) in your pipeline. The Write function automatically maps incoming fields (sensor_id, temperature, pressure, vibration) to table columns and inserts them in batches. If the table doesn't exist, it is created automatically with inferred types.