MySQL Integration Guide

Connect MaestroHub pipelines to MySQL databases for flexible, high-performance data ingestion and orchestration. This guide covers connection setup, security configuration, and query functions tailored for MySQL workloads.

Overview

The MySQL connector brings relational database capabilities to your MaestroHub pipelines:

• Read and write data using standard SQL queries with full parameter support
• Secure connections with five SSL/TLS modes including mutual TLS (mTLS)
• Broad compatibility with MySQL 5.7+, MariaDB 10.2+, and Aurora MySQL

Compatibility

The MySQL connector is validated with MySQL 5.7+, MariaDB 10.2+, and Amazon Aurora MySQL.

Connection Configuration

Creating a MySQL Connection

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

MySQL 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 MySQL connection

2. Database Configuration

Field	Default	Description
Host	localhost	MySQL server hostname or IP address (required)
Port	3306	MySQL server port (1-65535)
Database	-	Database name to connect to (required)
Connect Timeout (sec)	30	Maximum time to wait for connection establishment (0-600 seconds)

Note: Compatible with MySQL 5.7+, MariaDB 10.2+, and Aurora MySQL.

3. Basic Authentication

Field	Default	Description
Username	-	MySQL database user
Password	-	MySQL user password

4. SSL/TLS Settings

4a. SSL Mode

Field	Default	Description
SSL Mode	preferred	TLS/SSL connection mode (disabled / preferred / required / verify-ca / verify-identity)

SSL Mode Options

• disabled: No encryption — data transmitted in plain text
• preferred: Use TLS if the server supports it; fall back to unencrypted otherwise (default)
• required: Enforce TLS encryption; fail if the server does not support it
• verify-ca: Enforce TLS and verify the server certificate against a trusted CA (the hostname is not checked)
• verify-identity: Enforce TLS, verify CA, and also verify the server hostname matches the certificate

4b. Certificate Configuration

(Only displayed when SSL Mode is verify-ca or verify-identity)

Field	Default	Description
CA Certificate	-	Trusted CA certificate in PEM format. Only required for self-signed or private CA certificates; leave empty for public CAs
Client Certificate	-	Optional client certificate in PEM format for mutual TLS (mTLS)
Private Key	-	Private key corresponding to the client certificate in PEM format

5. Connection Pool Settings

Field	Default	Description
Max Open Connections	100	Maximum number of simultaneous database connections (1-1000). 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). Helps 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)	200	Initial delay between retry attempts in milliseconds (0-3,600,000 ms)
Retry Backoff Multiplier	2	Exponential factor for retry delay growth (1.0-10.0, e.g., 2 means delay doubles each retry)

Retry Behavior Example

• 1st retry: wait 200ms
• 2nd retry: wait 400ms (200 × 2)
• 3rd retry: wait 800ms (400 × 2)

7. Connection Labels

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

Example Labels

• env: prod — Environment
• team: data — Responsible team
• region: us-east — Geographical region

Notes

• Required Fields: All fields described as "required" must be filled.
• SSL/TLS: When SSL Mode is set to disabled, no encryption is used. Use preferred for opportunistic encryption or required and above for enforced encryption.
• Certificate Fields: CA Certificate, Client Certificate, and Private Key are only shown when SSL Mode is verify-ca or verify-identity.
• 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 use required or stricter SSL modes for production. Prefer verify-identity and supply CA certificates for maximum security. Use client certificates for mutual TLS when possible.

Function Builder

Creating MySQL 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 MySQL function types: Query, Execute, or Write
3. Configure the function parameters

Zoom

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

Query Function

Purpose: Execute SQL queries (SELECT) to read data from MySQL. 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
• SELECT with WHERE filters and ORDER BY

Execute Function

Purpose: Execute DML/DDL statements (INSERT, UPDATE, DELETE, CREATE, ALTER, DROP) on MySQL. 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 (start/stop, shift logs, quality checks)
• UPDATE work orders, inventory counts, or maintenance schedules
• DELETE or archive old telemetry and audit logs
• CREATE TABLE or ALTER TABLE for schema changes

Write Function

Purpose: Write pipeline data to a MySQL 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 from pipeline data
• Bulk insert production events with auto schema detection

How Write Works Step by Step

1. Batching

Each batch generates a single INSERT INTO `table` (`col1`, `col2`) 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).

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	TINYINT(1)	JSON boolean values
42, -7, 1000	BIGINT	Whole numbers (no fractional part)
3.14, 0.5	DOUBLE	Numbers with fractional part
"2024-01-15T10:30:00Z"	DATETIME	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. 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., DECIMAL, INT), 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 ... MODIFY COLUMN ... 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

MaestroHub detects the ((parameterName)) syntax and exposes each parameter for validation and runtime binding for Query and Execute functions.

Configuration	Description	Example
Type	Enforce data types for incoming values	string, number, boolean, date, array
Required	Mark parameters as mandatory or optional	Required / Optional
Default Value	Fallback value applied when callers omit a parameter	NOW(), 0, 'active'
Description	Add guidance for downstream users	"Start date for the report"

Zoom

Parameter configuration with validation rules and helper text

Pipeline Integration

Use the MySQL functions you create here as nodes inside the Pipeline Designer to integrate your MySQL databases with 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.

Common Use Cases

Query: Reading Production Metrics

Scenario: Generate hourly production reports with efficiency metrics from MySQL tables.

SELECT
    DATE_FORMAT(event_time, '%Y-%m-%d %H:00:00') AS hour_bucket,
    line_id,
    COUNT(*) AS event_count,
    ROUND(AVG(efficiency), 2) AS avg_efficiency,
    MIN(efficiency) AS min_efficiency,
    MAX(efficiency) AS max_efficiency
FROM production_events
WHERE event_time >= ((startDate)) AND event_time < ((endDate))
GROUP BY hour_bucket, line_id
ORDER BY hour_bucket DESC;

Pipeline Integration: 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)) / total_units * 100, 2),
    updated_at = NOW(),
    updated_by = ((userId))
WHERE order_id = ((orderId));

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.

INSERT INTO sensor_readings_archive
SELECT * FROM sensor_readings
WHERE recorded_at < DATE_SUB(NOW(), INTERVAL ((retentionDays)) DAY)
LIMIT 10000;

DELETE FROM sensor_readings
WHERE recorded_at < DATE_SUB(NOW(), INTERVAL ((retentionDays)) DAY)
LIMIT 10000;

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 MySQL 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.