1. **Problem Statement:** We are performing several MongoDB operations on a large collection named `bigdata`. 2. **Insert Documents:** We insert 200,000 documents where each document has: - `account_no`: auto-incrementing integer from 1 to 200,000 - `balance`: a random integer between 0 and 1,000,000 (simulating bank balances) 3. **Insert Code Explanation:** The loop: $$ \text{for } i=1 \text{ to } 200000 $$ Inside the loop: $$ \text{insert document } \{ \"account_no\": i, \"balance\": \text{Math.round(Math.random()*1000000)} \} $$ 4. **Verify Insert:** Count total documents: $$ db.bigdata.count() = 200000 $$ 5. **Create Index:** Create index on `account_no` to speed up queries: $$ db.bigdata.createIndex(\{ \"account_no\": 1 \}) $$ 6. **Query and Explain:** Query the document where $account\\_no=69271$: $$ db.bigdata.find(\{\"account_no\":69271\}).explain(\"executionStats\").executionStats.executionTimeMillis $$ Record the milliseconds taken as $t_1$. 7. **Exercise 6 - Delete Index:** Delete the index on `account_no`: $$ db.bigdata.dropIndex(\{\"account_no\":1\}) $$ 8. **Create Index on `balance`:** Create new index: $$ db.bigdata.createIndex(\{\"balance\":1\}) $$ 9. **Query on `balance=10000`:** Query and record time taken as $t_2$: $$ db.bigdata.find(\{\"balance\":10000\}) $$ 10. **Drop `balance` index:** $$ db.bigdata.dropIndex(\{\"balance\":1\}) $$ 11. **Query again `balance=10000`:** Query and record time as $t_3$. 12. **Comparison and Learning:** - Observe $t_1$ should be much less than query time without any index (from Exercise 3). - Observe $t_2$ should be less than $t_3$ showing index on `balance` speeds up queries. This exercise shows how creating indexes on fields enhances query speed, especially for large datasets.