Deferred Bulk Inserts In Frappe


One of our upcoming SaaS products handles delivery payout calculations for ecommerce companies. Here, we accept bulk imports of trip and order data for the previous day and then calculate payouts using fairly complex, dark-store specific rate cards. These imports are often multiple files of 100K+ records. Since these files are logically grouped together, we don't use Frappe's Data Import UI. And since the files are uploaded and updated by end users, we don't use bench import-csv either.

Frappe's Data Import is implemented as a wrapper for row by row insert/update.

When dealing with 100s of thousands of rows each day, row by row updates and inserts are not an option - these just take too long (think hours). So, imagine my surprise when ChatGPT suggested I try out frappe.db.bulk_insert. At first, I assumed GPT was up to its hallucinatory tricks again but I decided to see if such a function existed and what do you know!

Of Undocumented Needles In Haystacks

Frappe has a built-in ORM that serves us well for most of the common use cases. There are also a few extras that are not as commonly used in the Frappe or ERPNext code bases and perhaps not as familiar to developers:

ChatGPT found this function. I doubt I ever would have.

75 Lines Of Python

My current solution for handling the file imports consists of combining deferred_insert and bulk_insert. Here's the workflow before we dive into the code:

  1. User attaches 2-3 bulk files to a custom doctype (e.g. Trip Report).
  2. Using document hooks we parse the files row by row to look for validation errors.
  3. We create (in-memory) instances of the doctype we are importing (e.g. Trip).
  4. We serialise these documents and store them in a Redis list.
  5. Once completely parsed, we retrieve the documents from Redis and bulk write them to the DB in batches.

Steps 1 - 3 are trivial and common enough that we can skip those. To handle 4 and 5, I have a DB helper module adapted from the deferred_insert module linked above.


queue_prefix = "some_prefix_"

def get_key_name(key: str) -> str:
    return cstr(key).split("|")[1]

def deferred_insert(doc):
    Converts a Frappe document to JSON and stores it in a Redis list.
    doctype = doc.doctype
    docname =
    if not (doctype and docname):
        frappe.throw("Doctype and Docname are required")
    redis_key = f"{queue_prefix}{doctype}"
    d = doc.as_dict()
    skip = ["docstatus", "doctype", "idx"]
    for key in list(d.keys()):
        if key.startswith('_'):
        if key in skip:
    frappe.cache().rpush(redis_key, frappe.as_json(d))

def bulk_insert(doctype):
    Retrieves JSON documents from a Redis list and bulk inserts in the DB in batches.
    redis_key = f"{queue_prefix}{doctype}"
    queue_keys = frappe.cache().get_keys(redis_key)
    record_count = 0
    unique_names = set()
    records = []
    for key in queue_keys:
        queue_key = get_key_name(key)
        while frappe.cache().llen(queue_key) > 0:
            record = frappe.cache().lpop(queue_key)
            record = json.loads(record.decode("utf-8"))
            if isinstance(record, dict):
                record_count += 1
                if record['name'] in unique_names:
                print("Invalid record")
    if records:
        for batch in create_batch(records, 1000):
            fields = list(batch[0].keys())
            values = (tuple(record.values()) for record in batch)
            frappe.db.bulk_insert(doctype, fields, values)
    print(f"Inserted {record_count} records")
    return record_count

def clear_queue(doctype):
    Clear the queue in case we are reprocessing the same import file.
    redis_key = f"{queue_prefix}{doctype}"

def bulk_delete(doctype, docnames):
    Delete records in bulk. This is a wrapper around frappe.db.sql
    docnames is a list of names
    if not doctype or not docnames:
    placeholders = ', '.join(['%s'] * len(docnames))
    sql = f"""DELETE FROM `tab{doctype}` WHERE name IN ({placeholders})"""
    frappe.db.sql(sql, values=docnames)
    print(f"Deleted {len(docnames)} records")
    return len(docnames)

# usage
doctype = "Some DocType"

for row in large_file:
    doc = frappe.new_doc(doctype)
    doc.attribute = row["some_value"] = "some_name" # this is necessary as bulk_insert does not trigger autoname
    doc.creation =
    doc.modified =
    doc.owner = frappe.session.user
    doc.modified_by = frappe.session.user



How much time does this save?

For every 150K records, this reduces the import time from about 3000 seconds to ~120 seconds.

Notes & Improvements