Shopify connector
Commerce platform for products, customers, orders, inventory, and fulfillment.
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
Commerce platform for products, customers, orders, inventory, and fulfillment.
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 |
|---|---|
products | a product |
product_variants | a product variant |
customers | a customer |
orders | a order |
inventory_levels | a inventory level |
fulfillments | a fulfillment |
Relationships and extraction considerations
Use Shopify GraphQL IDs to join products, variants, inventory, and orders. Nested connections can require their own pagination. Orders and protected customer data may require additional scopes/approval. The example reads products through the GraphQL Admin API and propagates GraphQL errors.
Connecting to Shopify
Uses an Admin API access token with read_products. Uses GraphQL rather than legacy REST resources. Provide a supported quarterly API version; API throttling errors propagate to the platform.
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 |
|---|---|---|
access_token | Required; secret | Platform-supplied access token. Refresh or rotate before expiry. |
shop | Required | Store subdomain without .myshopify.com. |
api_version | Required | Supported quarterly version, e.g. 2026-10. |
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
httpx>=0.27,<1
pandas>=2,<3No separately installed database driver is needed by this HTTP example.
Worked example: sync products
products — Full read using the API's native pagination.
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/shopify/ and sync files in connectors/shopify/syncs/products/. 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": {
"access_token": {
"type": "string",
"title": "Access Token",
"description": "Platform-supplied access token. Refresh or rotate before expiry.",
"minLength": 1,
"x-vortexify-secret": true,
"order": 0
},
"shop": {
"type": "string",
"title": "Shop",
"description": "Store subdomain without .myshopify.com.",
"minLength": 1,
"order": 1
},
"api_version": {
"type": "string",
"title": "Admin API Version",
"description": "Supported quarterly version, e.g. 2026-10.",
"minLength": 1,
"order": 2
}
},
"required": [
"access_token",
"shop",
"api_version"
]
}Connector: connector.py
import httpx
from vortexify import Column, Connector, DiscoveredTable
class ShopifyConnector(Connector):
vendor_domain = "shopify.com"
def client(self):
if self._client is None or self._client.is_closed:
self._client = httpx.Client(
base_url="https://"
+ self.config["shop"]
+ ".myshopify.com/admin/api/"
+ self.config["api_version"]
+ "/",
headers={"X-Shopify-Access-Token": self.config["access_token"]},
timeout=60,
follow_redirects=False,
)
return self._client
def check(self) -> bool:
cfg = self.config
page_size = 1
path = "graphql.json"
body = {
"query": "query($first:Int!,$after:String){products(first:$first,after:$after){nodes{id title handle status updatedAt} pageInfo{hasNextPage endCursor}}}",
"variables": {"first": page_size, "after": None},
}
response = self.client().post(path, json=body)
response.raise_for_status()
payload = response.json()
if payload.get("errors"):
raise ValueError("GraphQL error: " + json.dumps(payload["errors"]))
return True
def discover(self) -> list[DiscoveredTable]:
return [
DiscoveredTable(
name="products",
schema=[
Column(name="id", type="string"),
Column(name="title", type="string"),
Column(name="handle", type="string"),
Column(name="status", type="string"),
Column(name="updated_at", type="timestamp"),
Column(name="document", type="string"),
],
)
]
def close(self) -> None:
if self._client is not None:
try:
self._client.close()
finally:
self._client = NoneSync schema: syncs/products/config.json
{
"type": "object",
"properties": {},
"required": []
}Sync: syncs/products/sync.py
from collections.abc import Iterator
from math import isfinite
import json
import pandas as pd
from vortexify import Sync
class ShopifyProductsSync(Sync):
def preview(self) -> pd.DataFrame:
cfg = self.connector.config
page_size = 100
path = "graphql.json"
body = {
"query": "query($first:Int!,$after:String){products(first:$first,after:$after){nodes{id title handle status updatedAt} pageInfo{hasNextPage endCursor}}}",
"variables": {"first": page_size, "after": None},
}
response = self.client.post(path, json=body)
response.raise_for_status()
payload = response.json()
if payload.get("errors"):
raise ValueError("GraphQL error: " + json.dumps(payload["errors"]))
records = payload["data"]["products"]["nodes"]
if not isinstance(records, list) or any(
(not isinstance(record, dict) for record in records)
):
raise ValueError(
"Unexpected API record shape; refusing a partial successful sync"
)
records = records[:100]
rows = []
for record in records:
row = {
"id": record.get("id"),
"title": record.get("title"),
"handle": record.get("handle"),
"status": record.get("status"),
"updated_at": record.get("updatedAt"),
"document": json.dumps(record, ensure_ascii=False, default=str),
}
rows.append(
{
key: (
json.dumps(value, ensure_ascii=False, default=str)
if isinstance(value, (dict, list))
else value
)
for key, value in row.items()
}
)
return pd.DataFrame(
rows,
columns=["id", "title", "handle", "status", "updated_at", "document"],
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, 250)
cfg = self.connector.config
page_size = batch_size
path = "graphql.json"
body = {
"query": "query($first:Int!,$after:String){products(first:$first,after:$after){nodes{id title handle status updatedAt} pageInfo{hasNextPage endCursor}}}",
"variables": {"first": page_size, "after": None},
}
seen_pages = set()
while True:
response = self.client.post(path, json=body)
response.raise_for_status()
payload = response.json()
if payload.get("errors"):
raise ValueError("GraphQL error: " + json.dumps(payload["errors"]))
records = payload["data"]["products"]["nodes"]
if not isinstance(records, list) or any(
(not isinstance(record, dict) for record in records)
):
raise ValueError(
"Unexpected API record shape; refusing a partial successful sync"
)
rows = []
for record in records:
row = {
"id": record.get("id"),
"title": record.get("title"),
"handle": record.get("handle"),
"status": record.get("status"),
"updated_at": record.get("updatedAt"),
"document": json.dumps(record, ensure_ascii=False, default=str),
}
rows.append(
{
key: (
json.dumps(value, ensure_ascii=False, default=str)
if isinstance(value, (dict, list))
else value
)
for key, value in row.items()
}
)
for start in range(0, len(rows), batch_size):
yield pd.DataFrame(
rows[start : start + batch_size],
columns=[
"id",
"title",
"handle",
"status",
"updated_at",
"document",
],
dtype=object,
)
info = payload["data"]["products"]["pageInfo"]
if not info["hasNextPage"]:
break
next_token = info["endCursor"]
if not next_token or next_token in seen_pages:
raise ValueError("Invalid Shopify cursor")
seen_pages.add(next_token)
body["variables"]["after"] = next_tokenRunning and extending the example
Register the connector before the sync and commit each resource separately:
vortexify create connector shopify
vortexify create sync shopify/productsEdit 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.