Prepare to excel in your Hive interviews with “The Ultimate Guide to Hive Interview Questions: Scenario-Based and Answers.” This all-encompassing guide provides a thorough collection of scenario-based questions along with detailed answers, designed to cater to both novices and seasoned professionals. Enhance your understanding of Hive, refine your problem-solving abilities, and build the confidence to tackle any interview challenge. Whether you’re aspiring to be a data engineer or a Hadoop expert, this guide offers the essential insights and strategies to help you stand out and succeed in your Hive interview. Dive in and get ready to impress your future employers.
Top Hive Interview Questions
Q1. Scenario: You have a Hive table containing web log data with columns user_id, page_id, and timestamp. You want to find the number of unique users for each page within a specific date range. How would you write this query?
Ans: You can achieve this by filtering the data within the specified date range and then using the COUNT(DISTINCT user_id) function to count unique users for each page. Here’s the query:
SELECT page_id, COUNT(DISTINCT user_id) AS unique_users
FROM web_logs
WHERE timestamp >= 'start_date' AND timestamp <= 'end_date'
GROUP BY page_id;
Q2. Scenario: You need to calculate the click-through rate (CTR) for a Hive table that contains ad click data. CTR is defined as (number of clicks / number of impressions) * 100. How can you calculate CTR and display it as a percentage?
Ans: You can calculate CTR by dividing the number of clicks by the number of impressions and multiplying the result by 100. Here’s the query:
SELECT
(SUM(clicks) / SUM(impressions)) * 100 AS CTR_percentage
FROM ad_click_data;
Q3. Scenario: You have a Hive table with sales data containing columns order_date and sales_amount. You want to calculate the rolling sum of sales for the past 7 days for each day. How can you achieve this in Hive?
Ans: You can use a window function to calculate the rolling sum of sales for the past 7 days. Here’s the query:
SELECT
order_date,
SUM(sales_amount) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_sales_sum
FROM sales_data;
Q4. Scenario: You have a Hive table that stores employee data, including their start date (start_date) and end date (end_date) of employment. You need to find the number of employees employed as of a specific date. How can you write this query?
Ans: You can find the number of employees employed as of a specific date by checking if the date falls within the range between start_date and end_date. Here’s the query:
SELECT COUNT(*) AS num_employees
FROM employee_data
WHERE 'specific_date' BETWEEN start_date AND end_date;
Q5. Scenario: You have a Hive table that contains JSON data in a column called json_data. You want to extract specific fields from the JSON data and create a new table with these fields. How can you achieve this?
Ans: You can use Hive’s GET_JSON_OBJECT function to extract specific fields from the JSON data and create a new table. Here’s the process:
- Create a new table with the desired schema.
- Use the
GET_JSON_OBJECTfunction to extract the JSON fields and insert them into the new table.
Here’s an example:
-- Create a new table with the desired schema
CREATE TABLE extracted_data (
field1 STRING,
field2 INT,
field3 DOUBLE
);
-- Insert data from the original table into the new table with JSON extraction
INSERT OVERWRITE TABLE extracted_data
SELECT
GET_JSON_OBJECT(json_data, '$.field1') AS field1,
CAST(GET_JSON_OBJECT(json_data, '$.field2') AS INT) AS field2,
CAST(GET_JSON_OBJECT(json_data, '$.field3') AS DOUBLE) AS field3
FROM original_table;
Q6. Scenario: You have a Hive table with columns user_id, login_time, and logout_time representing user login and logout times. You want to find the total time each user spent logged in. How can you calculate this?
Ans: To calculate the total time each user spent logged in, you can calculate the difference between logout_time and login_time for each user and then sum the results. Here’s the query:
SELECT
user_id,
SUM(unix_timestamp(logout_time) - unix_timestamp(login_time)) AS total_login_time_seconds
FROM user_login_data
GROUP BY user_id;
Q7. Scenario: You have a Hive table with columns order_id, order_date, and product_price. You want to find the total revenue generated on each day. How can you calculate this in Hive?
Ans: To calculate the total revenue generated on each day, you can group the data by order_date and sum the product_price for each group. Here’s the query:
SELECT
order_date,
SUM(product_price) AS total_revenue
FROM sales_data
GROUP BY order_date;
Q8. Scenario: You have a Hive table that stores temperature data with columns location, timestamp, and temperature. You want to find the maximum temperature recorded for each location on a specific date. How can you write this query?
Ans: You can find the maximum temperature recorded for each location on a specific date by filtering the data for that date, grouping by location, and finding the maximum temperature within each group. Here’s the query:
SELECT
location,
MAX(temperature) AS max_temperature
FROM temperature_data
WHERE timestamp >= 'specific_date' AND timestamp < 'next_day'
GROUP BY location;
Q9. Scenario: You have a Hive table with columns user_id, timestamp, and action_type representing user actions. You want to find the number of users who performed a specific action within a specified time frame. How can you achieve this?
Ans: You can find the number of users who performed a specific action within a specified time frame by filtering the data for that time frame and counting the distinct user_ids who performed the action. Here’s the query:
SELECT COUNT(DISTINCT user_id) AS num_users
FROM user_actions
WHERE action_type = 'specific_action'
AND timestamp >= 'start_time' AND timestamp <= 'end_time';
Q10. Scenario: You have a Hive table containing sales data with columns order_id, product_id, and quantity_sold. You want to find the top-selling product in terms of total quantity sold. How can you write this query?
Ans: You can find the top-selling product by grouping the data by product_id, summing the quantity_sold for each product, and ordering the results in descending order. Here’s the query:
SELECT
product_id,
SUM(quantity_sold) AS total_quantity_sold
FROM sales_data
GROUP BY product_id
ORDER BY total_quantity_sold DESC
LIMIT 1;
Q11. Scenario: You have a Hive table with columns user_id, timestamp, and purchase_amount representing user purchases. You want to calculate the average purchase amount per user. How can you achieve this in Hive?
Ans: You can calculate the average purchase amount per user by grouping the data by user_id and calculating the average purchase_amount for each user. Here’s the query:
SELECT
user_id,
AVG(purchase_amount) AS avg_purchase_amount
FROM user_purchases
GROUP BY user_id;
Q12. Scenario: You have a Hive table with columns user_id, timestamp, and action_type representing user actions. You want to find the most frequent action type for each user. How can you achieve this in Hive?
Ans: You can find the most frequent action type for each user by using the MODE() function within a subquery to calculate the mode (most frequent) action type for each user. Here’s the query:
SELECT
user_id,
action_type AS most_frequent_action
FROM (
SELECT
user_id,
MODE() WITHIN GROUP (ORDER BY action_type) AS action_type
FROM user_actions
GROUP BY user_id
) subquery;
Q13. Scenario: You have a Hive table with columns employee_id, salary, and department_id representing employee data. You want to find the employees with the highest salary in each department. How can you write this query?
Ans: You can find the employees with the highest salary in each department by using a window function to rank employees within each department based on their salary and selecting the top-ranked employee for each department. Here’s the query:
SELECT
employee_id,
salary,
department_id
FROM (
SELECT
employee_id,
salary,
department_id,
ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
FROM employee_data
) ranked_data
WHERE rank = 1;
Q14. Scenario: You have a Hive table with columns user_id, timestamp, and event_type representing user events. You want to find users who have a continuous sequence of a specific event type occurring at least three times in a row. How can you achieve this in Hive?
Ans: You can find users with a continuous sequence of a specific event type occurring at least three times in a row by using a window function to calculate a running count of consecutive events within each user’s partition. Here’s the query:
WITH consecutive_events AS (
SELECT
user_id,
event_type,
timestamp,
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY timestamp) -
ROW_NUMBER() OVER (PARTITION BY user_id, event_type ORDER BY timestamp) AS consecutive_group
FROM user_events
)
SELECT DISTINCT user_id
FROM consecutive_events
WHERE event_type = 'specific_event_type'
AND consecutive_group >= 2;
Q15. Scenario: You have a Hive table with columns user_id, timestamp, and action_type representing user actions. You want to find users who have performed a specific sequence of actions within a specific time frame. How can you achieve this in Hive?
Ans: To find users who have performed a specific sequence of actions within a specific time frame, you can use subqueries to filter and join the data based on the specified conditions. Here’s a general approach:
WITH filtered_actions AS (
SELECT
user_id,
timestamp,
action_type
FROM user_actions
WHERE timestamp >= 'start_time' AND timestamp <= 'end_time'
),
sequence_match AS (
SELECT DISTINCT
fa1.user_id
FROM filtered_actions fa1
JOIN filtered_actions fa2 ON fa1.user_id = fa2.user_id AND fa1.timestamp < fa2.timestamp
JOIN filtered_actions fa3 ON fa2.user_id = fa3.user_id AND fa2.timestamp < fa3.timestamp
WHERE fa1.action_type = 'action1'
AND fa2.action_type = 'action2'
AND fa3.action_type = 'action3'
)
SELECT DISTINCT user_id
FROM sequence_match;
This query finds users who have performed ‘action1’, ‘action2’, and ‘action3’ in sequence within the specified time frame.
Q16. Scenario: You have a Hive table containing stock price data with columns stock_symbol, date, and closing_price. You want to calculate the 7-day moving average of closing prices for each stock symbol. How can you write this query?
Ans: To calculate the 7-day moving average of closing prices for each stock symbol, you can use a window function to calculate the average over a rolling window of 7 days for each stock symbol. Here’s the query:
SELECT
stock_symbol,
date,
closing_price,
AVG(closing_price) OVER (PARTITION BY stock_symbol ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS 7_day_moving_avg
FROM stock_data;
Q17. Scenario: You have a Hive table containing user data with columns user_id, join_date, and last_activity_date. You want to find the users who have been inactive for more than 90 days since they joined. How can you write this query?
Ans: You can find the users who have been inactive for more than 90 days since they joined by calculating the difference between last_activity_date and join_date, and then filtering the results. Here’s the query:
SELECT user_id, join_date, last_activity_date
FROM user_data
WHERE DATEDIFF(last_activity_date, join_date) > 90;
Q18. Scenario: You have a Hive table with columns user_id, timestamp, and event_type. You want to identify users who have a specific event type followed by another specific event type within a certain time frame. How can you achieve this in Hive?
Ans: To identify users who have a specific event type followed by another specific event type within a certain time frame, you can use subqueries and window functions. Here’s the query:
WITH event_sequence AS (
SELECT
user_id,
timestamp,
event_type,
LEAD(event_type) OVER (PARTITION BY user_id ORDER BY timestamp) AS next_event_type
FROM user_events
)
SELECT DISTINCT user_id
FROM event_sequence
WHERE event_type = 'first_event_type'
AND next_event_type = 'second_event_type'
AND timestamp <= date_add('start_time', 7);
This query finds users who have ‘first_event_type’ followed by ‘second_event_type’ within a 7-day time frame.
Q19. Scenario: You have a Hive table with columns user_id, timestamp, and transaction_amount. You want to find the users who have made transactions on at least three different days. How can you achieve this in Hive?
Ans: To find users who have made transactions on at least three different days, you can use subqueries and aggregation. Here’s the query:
WITH user_transaction_counts AS (
SELECT
user_id,
COUNT(DISTINCT date(timestamp)) AS unique_days_with_transactions
FROM transaction_data
GROUP BY user_id
)
SELECT DISTINCT user_id
FROM user_transaction_counts
WHERE unique_days_with_transactions >= 3;
Q20. Scenario: You have a Hive table with columns user_id, timestamp, and product_id representing user interactions with products. You want to find the users who have interacted with at least three different products within a specific time frame. How can you write this query?
Ans: To find users who have interacted with at least three different products within a specific time frame, you can use subqueries and aggregation. Here’s the query:
WITH user_product_counts AS (
SELECT
user_id,
COUNT(DISTINCT product_id) AS unique_products_interacted
FROM user_product_interactions
WHERE timestamp >= 'start_time' AND timestamp <= 'end_time'
GROUP BY user_id
)
SELECT DISTINCT user_id
FROM user_product_counts
WHERE unique_products_interacted >= 3;
This query identifies users who have interacted with at least three different products within the specified time frame.
These scenario-based Hive interview questions and detailed answers cover a range of practical scenarios that experienced candidates may encounter in their roles.
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