MySQL connector
Relational database for schema discovery, SQL access, and table extraction.
Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See Custom API for the shared SDK pattern and document-upload workflow.
What this connector is for
Relational database for schema discovery, SQL access, and table extraction.
A connector owns access to the source. Separate syncs shape selected resources into tables for analysis and downstream transformations using the Python SDK.
Data and tables you can pull
These are representative dataset families to consider, not a claim that the worked example implements every resource. Access, fields, and availability depend on the source account, product edition, and permissions. Use the vendor reference to select concrete resources.
| Suggested table | One row represents |
|---|---|
tables | a table |
views | a view |
columns | a column |
table_rows | a table row |
Relationships and extraction considerations
Choose stable object IDs and preserve references between related resources. Nested arrays may need child tables. The listed names are proposed output datasets; validate the actual endpoint, fields, permissions, and pagination before adding each sync.
Connecting to MySQL
Uses a database driver and a read-only database user; SQL identifiers are quoted, not interpolated unescaped. Discovery reads the configured schema from INFORMATION_SCHEMA. Uses TLS with certificate/hostname verification; supply ssl_ca for a private CA. The unbuffered cursor bounds fetch memory; closing an unfinished cursor can drain the remaining result.
Additional resources may require additional scopes. Connection settings belong to the connector; dataset filters and resource selection belong to each sync. See the CLI workflow for registration and configuration.
Connection settings used by the example
| Setting | Requirement | Purpose |
|---|---|---|
host | Required | Host |
port | Optional | Port |
database | Required | Database |
username | Required | Username |
password | Required; secret | Password |
ssl_ca | Optional | Path to trusted CA certificate on the runtime host. |
requirements.in
Save these dependencies in requirements.in alongside connector.py; the example includes packages used by its sync. Install the published vortexify package for local development. Keep sync-specific dependencies in the sync's own requirements.in when splitting the implementation into separate resources.
vortexify==0.1.1
pymysql>=1.1,<2
pandas>=2,<3PyMySQL is a Python driver; TLS trust and server authentication must still be configured for the target database.
Worked example: sync table_rows
table_rows — Reads the configured table with its actual columns; full read, no checkpoint cursor.
The complete files below implement only this example resource. They illustrate the current SDK signatures, a separate preview, bounded DataFrame output, and source-specific extraction. They do not establish complete source coverage. Live credentials and platform ingestion have not been used to validate this guide.
With default paths, place the connector files in connectors/mysql/ and sync files in connectors/mysql/syncs/table_rows/. Use your project configuration if it specifies different paths.
Connection schema: config.json
This file contains a JSON Schema, not actual credential values. Provide those through platform configuration.
{
"type": "object",
"properties": {
"host": {
"type": "string",
"minLength": 1,
"title": "Host",
"order": 0
},
"port": {
"type": "integer",
"default": 3306,
"title": "Port",
"order": 1
},
"database": {
"type": "string",
"minLength": 1,
"title": "Database",
"order": 2
},
"username": {
"type": "string",
"minLength": 1,
"title": "Username",
"order": 3
},
"password": {
"type": "string",
"minLength": 1,
"x-vortexify-secret": true,
"title": "Password",
"order": 4
},
"ssl_ca": {
"type": "string",
"title": "CA Certificate Path",
"description": "Path to trusted CA certificate on the runtime host.",
"minLength": 1,
"order": 5
}
},
"required": [
"host",
"database",
"username",
"password"
]
}Connector: connector.py
import pymysql
from contextlib import closing
from vortexify import Column, Connector, DiscoveredTable
class MysqlConnector(Connector):
vendor_domain = "mysql.com"
def client(self):
if self._client is None:
self._client = pymysql.connect(
host=self.config["host"],
port=self.config.get("port", 3306),
database=self.config["database"],
user=self.config["username"],
password=self.config["password"],
ssl={"ca": self.config.get("ssl_ca"), "check_hostname": True},
cursorclass=pymysql.cursors.SSCursor,
autocommit=True,
connect_timeout=10,
read_timeout=60,
)
return self._client
def check(self) -> bool:
with closing(self.client().cursor()) as cursor:
cursor.execute("SELECT 1")
return cursor.fetchone()[0] == 1
def discover(self) -> list[DiscoveredTable]:
resources = {}
with closing(self.client().cursor()) as cursor:
cursor.execute(
"SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE, IS_NULLABLE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA=%s ORDER BY TABLE_NAME, ORDINAL_POSITION",
(self.config["database"],),
)
for table, name, data_type, nullable in cursor:
if table not in resources:
resources[table] = DiscoveredTable(
name=str(self.config["database"]) + "." + table, schema=[]
)
resources[table].schema.append(
Column(name=name, type=data_type, nullable=nullable == "YES")
)
return list(resources.values())
def close(self) -> None:
if self._client is not None:
try:
self._client.close()
finally:
self._client = NoneSync schema: syncs/table_rows/config.json
{
"type": "object",
"properties": {
"table": {
"type": "string",
"title": "Table",
"description": "Table name within the configured schema/database.",
"minLength": 1,
"order": 0
}
},
"required": [
"table"
]
}Sync: syncs/table_rows/sync.py
from collections.abc import Iterator
from math import isfinite
import pandas as pd
from vortexify import Sync
from contextlib import closing
class MysqlTableRowsSync(Sync):
def preview(self) -> pd.DataFrame:
parts = [self.connector.config["database"], self.config["table"]]
qualified = ".".join(("`" + part.replace("`", "``") + "`" for part in parts))
with closing(self.client.cursor()) as cursor:
cursor.execute("SELECT * FROM " + qualified + " LIMIT 100")
columns = [column[0] for column in cursor.description]
return pd.DataFrame(cursor.fetchall(), columns=columns, dtype=object)
def extract(
self, *, avg_row_size_bytes: float | None = None
) -> Iterator[pd.DataFrame]:
memory_mib = self.compute.memory if self.compute else None
memory_mib = memory_mib if memory_mib is not None and memory_mib > 0 else 1024
target_bytes = min(16 * 1024 * 1024, max(1, memory_mib * 1024 * 1024 // 20))
batch_size = 100
if avg_row_size_bytes is not None:
if not isfinite(avg_row_size_bytes) or avg_row_size_bytes <= 0:
raise ValueError("avg_row_size_bytes must be finite and positive")
batch_size = max(1, int(target_bytes / max(1.0, avg_row_size_bytes) / 4))
batch_size = min(batch_size, 10000)
parts = [self.connector.config["database"], self.config["table"]]
qualified = ".".join(("`" + part.replace("`", "``") + "`" for part in parts))
with closing(self.client.cursor()) as cursor:
cursor.execute("SELECT * FROM " + qualified)
columns = [column[0] for column in cursor.description]
while rows := cursor.fetchmany(batch_size):
yield pd.DataFrame(rows, columns=columns, dtype=object)Running and extending the example
Register the connector before the sync and commit each resource separately:
vortexify create connector mysql
vortexify create sync mysql/table_rowsEdit the files, inspect vortexify status, and use the CLI guide to commit and configure them. Registration alone does not run extraction or deploy the connector.
Preview must run with a fresh connector and disposable state. The extraction implementation determines full/incremental behavior; merely setting a mode does not implement change capture. API/driver failures propagate unless explicitly handled in the code. Check source limits and single-record/response sizes as well as output batch size.
Sources and further reading
- Vendor API and data reference — Resource definitions, authentication, fields, and source-specific limits. Check the selected resource's reference when extending the example.
- Vortexify connector and sync contracts — Client ownership, discovery, preview isolation, and DataFrame extraction.
- Vortexify CLI workflow — Project paths, resource registration, source commits, and credential configuration.