Transaction Data

Adding multiple data records to the system in a single batch operation to improve efficiency and maintain consistency.

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🧩 Overview

Transaction Data workflows are designed to handle the insertion or update of multiple records as a single logical unit of work. This ensures that either all changes succeed or none do, maintaining data integrity and system reliability.

Typical use cases include:

• Batch processing of invoices, test results, or orders
• Importing CSV/Excel data
• Multi-table insertions (e.g., order header and items)

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🔁 Key Concepts

Term	Definition
Atomicity	All operations within the transaction complete successfully or none do
Consistency	Ensures database remains in a valid state before and after execution
Isolation	Concurrent transactions don't interfere with each other
Durability	Once committed, changes are permanent even if system fails

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📥 Example: Batch Insert (SQL)

START TRANSACTION;

INSERT INTO patients (name, dob) VALUES ('Alice', '1992-01-01');
INSERT INTO patients (name, dob) VALUES ('Bob', '1985-04-03');
INSERT INTO patients (name, dob) VALUES ('Carol', '1978-09-12');

COMMIT;

🔄 Use ROLLBACK; instead of COMMIT; if an error occurs during processing.

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🧪 Use Case: Lab Billing Entry

When creating a lab bill, multiple test line items are recorded together with the header:

1. Insert bill_header
2. Insert multiple bill_items linked by bill_id

START TRANSACTION;

INSERT INTO lab_bills (patient_id, doctor_id, total_amount) VALUES (1, 3, 1500);
SET @bill_id = LAST_INSERT_ID();

INSERT INTO lab_bill_items (bill_id, test_name, amount) VALUES (@bill_id, 'Blood Sugar', 500);
INSERT INTO lab_bill_items (bill_id, test_name, amount) VALUES (@bill_id, 'Thyroid Panel', 1000);

COMMIT;

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⚙️ API Pattern (Node.js Example)

const conn = await db.getConnection();
try {
  await conn.beginTransaction();

  const [billResult] = await conn.query("INSERT INTO lab_bills ...");
  const billId = billResult.insertId;

  await conn.query("INSERT INTO lab_bill_items ...", [billId, ...]);
  await conn.commit();

  res.json({ success: true });
} catch (err) {
  await conn.rollback();
  res.status(500).json({ error: "Transaction failed" });
} finally {
  conn.release();
}

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📊 Bulk Data Upload

Steps:

1. Parse file (CSV, Excel)
2. Validate entries
3. Group into batches (e.g., 100 per insert)
4. Begin transaction
5. Insert batch
6. Rollback on any error or commit if all succeed

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🧠 Best Practices

• Use transactions for grouped inserts and multi-table operations
• Validate data before the transaction begins to avoid partial commits
• Use batch sizes appropriate to database limits (e.g., 500–1000 rows)
• Log failed batches with reasons for review
• For extremely large imports, consider asynchronous processing with status tracking

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🧩 UI Integration Tips

• Show progress indicators for large imports
• Allow user to download failed entries for correction
• Display transaction results: success count, error count, skipped

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🛡️ Error Handling & Rollback

Scenario	Action
Validation failure	Abort entire batch
Partial insert error	Rollback transaction
Database constraint violation	Log & rollback
Success	Commit transaction

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📚 Applications of Transaction Data

Domain	Batch Action Example
Healthcare	Entering multiple lab test results
Finance	Importing ledger entries or bulk payments
Education	Uploading exam results
Logistics	Updating multiple inventory records
HR	Adding new employee batches

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🔚 Summary

The Transaction Data workflow ensures high integrity and performance when processing bulk records. It reduces manual effort, increases efficiency, and safeguards your database with controlled batch operations.

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