Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Flipkart hiring for SQL roles
Flipkart is an Indian e-commerce company and part of the Walmart group, with businesses including Flipkart, Myntra, Cleartrip and Ekart logistics. Its engineering and analytics teams work on search, recommendations, supply chain, pricing and fraud detection. It hires for software development engineers, SDETs, business analysts, data analysts and data scientists.
For SQL roles, this Flipkart practice set focuses most on Indexes and Performance (7), Database Design (5), Joins (4), Subqueries and CTEs (4). It has 1 easy, 22 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
E-commerce & consumer tech
Headquarters
Bengaluru, India
Main India offices
Bengaluru, Hyderabad
Flipkart SQL interview process
Online coding assessment
Machine coding round
Problem solving and DSA round
Hiring manager round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for FlipkartStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
✓ 3 questions free • Unlock the rest in seconds
01
Write an upsert that inserts a row or updates it if it exists.
PostgreSQL:
INSERT INTO inventory (sku, qty)
VALUES ('A100', 10)
ON CONFLICT (sku)
DO UPDATE SET qty = inventory.qty + EXCLUDED.qty;
MySQL:
INSERT INTO inventory (sku, qty) VALUES ('A100', 10)
ON DUPLICATE KEY UPDATE qty = qty + VALUES(qty);
SQL Server and others use MERGE. A unique key on sku is required.
02
Match each employee's salary to a grade using a range table.
SELECT e.name, e.salary, g.grade
FROM employees e
JOIN salary_grades g
ON e.salary BETWEEN g.min_salary AND g.max_salary;
This is a non-equi join. Ensure grade ranges do not overlap, otherwise an employee matches more than one grade and appears twice.
03
What is the difference between OLTP and OLAP databases?
OLTP systems handle many short transactions such as orders and payments, with normalised schemas, row-based storage and focus on fast inserts and updates. OLAP systems support analysis over large history, using denormalised star or snowflake schemas, columnar storage and heavy aggregations. A bank's core system is OLTP, while its data warehouse used for monthly reports is OLAP.
04
Add a foreign key and a check constraint to an existing table.
ALTER TABLE orders
ADD CONSTRAINT fk_orders_customer
FOREIGN KEY (customer_id)
REFERENCES customers(customer_id);
ALTER TABLE orders
ADD CONSTRAINT chk_amount_positive
CHECK (amount > 0);
First clean existing rows that violate the rule, otherwise the ALTER fails. Naming constraints makes errors easier to read.
FIRST_VALUE returns the first value in the window frame, LAST_VALUE the last, and NTH_VALUE the nth. A common trap is LAST_VALUE with the default frame, which ends at the current row, so it returns the current row's value. Specify ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING to get the true last value of the partition.
06
Why is NOT IN dangerous when the subquery contains NULL?
If the subquery returns any NULL, value NOT IN (list) becomes unknown for every row, because comparing with NULL is unknown, so the query returns no rows. For example, NOT IN (SELECT manager_id FROM employees) fails when a manager_id is NULL. Use NOT EXISTS or filter out NULLs in the subquery to get correct results.
07
A reporting team wants to query the production OLTP database directly. What are the risks and alternatives?
Heavy analytical queries can lock rows, use CPU and slow customer transactions, and normalised schemas make reporting complex. Alternatives are a read replica for reporting, a data warehouse loaded by ETL or CDC with a star schema, or materialised summary tables. Grant read-only access with limits if direct access is unavoidable, and schedule heavy jobs off-peak.
08
How do you get the top 3 rows in different databases?
MySQL and PostgreSQL:
SELECT * FROM products ORDER BY price DESC LIMIT 3;
SQL Server:
SELECT TOP 3 * FROM products ORDER BY price DESC;
Standard SQL:
SELECT * FROM products ORDER BY price DESC
FETCH FIRST 3 ROWS ONLY;
Always use ORDER BY, otherwise the top rows are not guaranteed.
09
Rewrite a nested query using CTEs for readability.
WITH order_totals AS (
SELECT customer_id, SUM(amount) AS total
FROM orders GROUP BY customer_id
),
ranked AS (
SELECT customer_id, total,
NTILE(10) OVER (ORDER BY total DESC) AS decile
FROM order_totals
)
SELECT * FROM ranked WHERE decile = 1;
Each CTE is a named step, making top 10 percent customer logic easy to read.
10
Your running total jumps by large amounts on some dates instead of row by row. Why?
With ORDER BY and no explicit frame, the default frame is RANGE UNBOUNDED PRECEDING, which treats all rows with the same order value as peers and includes them together. When several rows share the same date, the running total adds them all at once. Specify ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, or add a unique tiebreaker to ORDER BY.
Avoid indexes on small tables where scans are cheap, on columns with very low selectivity such as a boolean flag used alone, on tables with heavy writes and few reads, and duplicates of existing indexes. Every index slows inserts and updates and uses memory and storage. Review unused indexes periodically with database statistics and drop them after checking dependencies.
12
What are dirty reads, non-repeatable reads and phantom reads?
A dirty read sees uncommitted changes from another transaction that may roll back. A non-repeatable read gets different values when reading the same row twice because another transaction updated and committed it. A phantom read gets a different set of rows for the same query because another transaction inserted or deleted matching rows. Higher isolation levels prevent more of these.
13
You need each customer's latest address from a history table. How do you query it?
Use ROW_NUMBER partitioned by customer and ordered by updated date descending, then keep rn = 1: WITH r AS (SELECT *, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY updated_at DESC) AS rn FROM address_history) SELECT * FROM r WHERE rn = 1. Add a tiebreaker such as id DESC in case two updates share the same timestamp.
14
Rewrite a non-SARGable date filter to use an index.
Slow:
SELECT * FROM orders
WHERE YEAR(order_date) = 2025
AND MONTH(order_date) = 3;
Fast:
SELECT * FROM orders
WHERE order_date >= '2025-03-01'
AND order_date < '2025-04-01';
The second form compares the raw column, so an index on order_date can be used with a range seek.
15
Show all dates in a month with sales, including days with zero sales.
WITH days AS (
SELECT generate_series(DATE '2025-01-01',
DATE '2025-01-31', INTERVAL '1 day')::date AS d
)
SELECT d.d, COALESCE(SUM(s.amount), 0) AS sales
FROM days d
LEFT JOIN sales s ON s.sale_date = d.d
GROUP BY d.d
ORDER BY d.d;
This PostgreSQL example uses a date series; other databases use a calendar table.
16
Product prices change often, but old orders must keep the price at the time of purchase. How do you design it?
Store the unit price, discount and tax in order_items when the order is created instead of reading the current product price, so history is not affected by updates. Maintain a product_price_history table with effective_from and effective_to dates for analysis. Reports on past orders use order_items values, while catalogue pages use the current price.
17
A query with many OR conditions across different columns is slow. How can you optimise it?
OR across different columns often prevents a single index seek. Rewrite it as UNION ALL of separate queries, each using its own index, removing duplicates carefully if needed, or use IN for multiple values of the same column. Some optimisers can combine indexes with bitmap or index merge plans. Compare plans and timings to choose the best approach.
18
Someone ran an UPDATE without WHERE on production. What would you do?
Stop further changes and inform the lead and DBA immediately. If the session is still in an open transaction, roll back. Otherwise, restore affected rows from the latest backup or point-in-time recovery to a separate database and update the production table from it, or use audit or history tables if available. Afterwards, add safeguards such as safe-update mode, reviews and restricted write access.
19
Average order value looks too low in a report. What could be wrong?
Check the grain: if the query averages order items instead of orders, each line counts separately and the average drops. Joins can also duplicate rows. Verify whether cancelled or zero-value orders are included and how NULL amounts are treated. Compute SUM(amount) / COUNT(DISTINCT order_id) at order level and compare with business definitions before changing the report.
20
Inserts into a heavily used table have become slow. What could be causing it?
Too many indexes increase write cost, so audit and remove unused ones. Random UUID primary keys cause page splits and fragmentation. Triggers, foreign key checks without indexes on child columns, lock contention from long transactions, and log disk bottlenecks also slow inserts. Use batch inserts, sequential keys, proper indexing of foreign keys and monitor waits to find the actual bottleneck.
21
A table stores phone numbers as 'ph1, ph2, ph3' in one column. What is wrong and how do you fix it?
The column violates first normal form because it stores multiple values in one field, making search, validation and updates difficult. Create a separate customer_phones table with customer_id, phone and type columns, one row per number, with a foreign key to customers. Migrate data by splitting the strings, then drop the old column after applications are updated.
22
You need customers who bought both product A and product B. How would you write it?
Group by customer and count distinct matching products: SELECT customer_id FROM order_items WHERE product_id IN ('A', 'B') GROUP BY customer_id HAVING COUNT(DISTINCT product_id) = 2. Alternatively use INTERSECT of two queries, or two EXISTS conditions. The HAVING approach is concise, scans the table once and is easy to extend to three or more products.
23
Write a query to show each employee with their manager's name.
SELECT e.emp_id,
e.name AS employee,
m.name AS manager
FROM employees e
LEFT JOIN employees m
ON e.manager_id = m.emp_id;
The same table is used twice with different aliases. LEFT JOIN ensures the CEO, who has no manager, still appears with a NULL manager name.
24
A query uses SELECT * on a table with 80 columns. Why is that a problem?
SELECT * fetches every column, increasing disk reads, memory, network transfer and application processing. It prevents covering indexes from being used and can break applications when columns are added or reordered. Select only required columns, which lets the optimiser use narrower indexes and makes the intent clear. SELECT * is acceptable only for quick exploration, not production code.
25
How do you balance performance tuning with delivery deadlines?
Situation: a release was due in two days, but a key query was slow under load testing. Task: fix the risk without delaying the release. Action: I identified the top two bottlenecks from the plan, applied an index and a query rewrite that were low risk, and logged deeper redesign work as a follow-up ticket with data. Result: the release went live on time and the redesign was completed in the next sprint.
A covering index contains all columns a query needs, either as key columns or included columns, so the database answers the query from the index alone without visiting the table. For SELECT order_date, amount FROM orders WHERE customer_id = 5, an index on (customer_id) INCLUDE (order_date, amount) avoids lookups. It speeds reads but increases index size and write cost.
27
A query with a correlated subquery in SELECT runs for every row and is slow. How do you improve it?
Rewrite the correlated scalar subquery as a join to a pre-aggregated derived table or CTE, for example aggregate order counts per customer once and LEFT JOIN it. Alternatively use a window function. Ensure the correlation column is indexed if you keep the subquery. Compare execution plans and timing before and after to confirm the improvement.
28
Your application logs frequent deadlock errors at peak hours. How do you investigate and reduce them?
Capture deadlock graphs or logs to see which statements and resources are involved. Common fixes are accessing tables and rows in the same order in all code paths, keeping transactions short, adding indexes so updates lock fewer rows, avoiding user interaction inside transactions, and using lower isolation where safe. Add retry logic for deadlock victims since some deadlocks cannot be fully eliminated.
29
How do you approach a SQL problem you have never seen before in an interview?
Situation: in a campus interview I got a problem about consecutive login days that I had not practised. Task: solve it while explaining my thinking. Action: I clarified the table and expected output, wrote a small example, broke it into steps with CTEs, and talked through each step before writing the final query. Result: I solved it with a small hint and the interviewer appreciated the structured approach.
30
A daily aggregation over 2 billion rows is too slow for a dashboard. What would you propose?
Avoid recalculating raw data on every request. Build a summary table or materialised view at daily grain refreshed incrementally, partition the fact table by date, and use columnar storage if available. Pre-compute common metrics, index the summary on dimensions used in filters, and let the dashboard query the summary. Validate summary totals against raw data periodically.
Tips to crack the Flipkart SQL interview
Practise machine coding problems where you build a small working system, such as a parking lot or splitwise, in about 90 minutes.
For analyst roles, prepare guesstimates and cases on topics like Big Billion Days sales, returns or delivery delays.
Learn about Flipkart's supply chain and Ekart operations, as many data problems relate to logistics in Indian conditions.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Flipkart interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Flipkart SQL interview?
Candidates usually go through 5 stages: Online coding assessment; Machine coding round; Problem solving and DSA round; Hiring manager round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Flipkart SQL interview?
This practice set leans on Indexes and Performance (7), Database Design (5), Joins (4), Subqueries and CTEs (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Flipkart interviewers ask you to walk through real work.
Are these the exact questions asked at Flipkart?
No. These are curated practice questions that match the skills and difficulty usually tested in e-commerce & consumer tech interviews like Flipkart's. They are not a leaked or verified list of past questions.
Where does Flipkart hire SQL professionals in India?
Flipkart has large teams in Bengaluru, Hyderabad. Openings and locations change often, so check the Flipkart careers page and filter by city and experience.