At tuya, SQL is used all the damn time for analyzing consumer usage patterns of Tuya's smart devices and managing data related to their IoT platform's operations. So, it shouldn't surprise you that Tuya LOVES to ask SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you prep for the Tuya SQL interview, we'll cover 8 tuya SQL interview questions – scroll down to start solving them!
Tuya is an IoT platform. Let's say that Tuya has a table that logs user reviews for its IoT devices. Each row represents a review made by a user and includes the date of the review, the user ID, the device ID, and the number of stars given (from 1 to 5). Tuya would like to monitor performance over time and therefore wants to calculate the average review score by product, by month.
Write a PostgreSQL query to calculate the listed output.
review_id | user_id | submit_date | device_id | stars |
---|---|---|---|---|
1001 | 301 | 2021-09-15 | 101 | 4 |
1002 | 302 | 2021-09-18 | 101 | 5 |
1003 | 303 | 2021-09-24 | 102 | 3 |
1004 | 304 | 2021-10-05 | 102 | 2 |
1005 | 305 | 2021-10-18 | 101 | 3 |
1006 | 306 | 2021-10-26 | 102 | 5 |
month | device_id | avg_stars |
---|---|---|
9 | 101 | 4.5 |
9 | 102 | 3.0 |
10 | 101 | 3.0 |
10 | 102 | 3.5 |
This code works by extracting the month from the using the function and then grouping by the month and . The function is then used to calculate the average review score for each group. The is used to format the average with two decimal places. The final result is ordered by month, then by device_id.
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Imagine you had a table of Tuya employee salary data. Write a SQL query to find the 2nd highest salary at the company.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Try this problem and run your code right in DataLemur's online SQL environment:
You can find a detailed solution with hints here: 2nd Highest Salary.
A primary key is a column or set of columns in a table that uniquely identifies each row in the table. The primary key is used to enforce the uniqueness and non-nullability of the rows in the table.
In a SQL database, a primary key is defined using the constraint. For example, say you had a table of :
In this example, the column is the primary key of the Tuya employees table. It is defined as an integer and is marked as the primary key using the constraint.
A table can have only one primary key, but the primary key can consist of multiple columns. For example, say you had a table of Tuya customer transactions:
In the above example, the primary key of the Orders table consists of two columns: TransactionID and ProductID. This means that the combination of OrderID and ProductID must be unique for every row in the table.
As a Smart Home company, Tuya is interested in understanding the usage of their devices. They have two tables - one logging the devices that they have sold () and other logs each instance a device is turned on or off ().
The table looks like this:
device_id | selling_date | customer_id | device_type |
---|---|---|---|
101 | 2021-01-05 | 3721 | Lamp |
102 | 2021-02-07 | 1842 | TV |
103 | 2021-03-10 | 8765 | Air Conditioner |
104 | 2021-04-15 | 3921 | Lamp |
105 | 2021-05-04 | 6125 | TV |
The table looks like this:
log_id | device_id | start_time | end_time |
---|---|---|---|
10001 | 101 | 2022-06-01 08:00:00 | 2022-06-01 10:00:00 |
10002 | 103 | 2022-06-02 20:00:00 | 2022-06-02 22:00:00 |
10003 | 102 | 2022-06-03 07:00:00 | 2022-06-03 09:30:00 |
10004 | 105 | 2022-06-04 19:00:00 | 2022-06-04 23:00:00 |
10005 | 104 | 2022-06-05 18:30:00 | 2022-06-05 22:00:00 |
Tuya wants to answer the following question: What is the average usage duration for each type of device in the month of June 2022?
Here's the SQL query to achieve this:
In the solution, we join the table with on the field. We then calculate the average duration of usage for each type of device. The function is used to get the month and year from the in the table. Finally, we the to get the average usage duration for each type separately. To convert the usage duration from seconds to hours, we divide by 3600. So gives the duration of each usage record in hours. The function then computes the average of these durations, grouped by .
combines the results from two or more SELECT statements, but only returns the rows that are exactly the same between the two sets.
For example, let's use to find all of Tuya's Facebook video ads with more than 10k views that are also being run on YouTube:
As a data scientist in Tuya, a global leading IoT platform, you are asked to help the product team understand the usage of their IoT devices in different homes. The team specifically wants to know the average energy consumption per home for each type of device in their product line for a given period of time.
We have a table that logs every device's energy consumption every hour. Our table looks like this:
stat_id | home_id | device_type | log_time | energy_consumption |
---|---|---|---|---|
101 | 5501 | Smart Bulb | 2022-06-08 00:00:00 | 1.5 |
102 | 5501 | Smart Bulb | 2022-06-08 01:00:00 | 2.0 |
103 | 5502 | Smart Plug | 2022-06-08 00:00:00 | 0.8 |
104 | 5501 | Smart Bulb | 2022-06-09 00:00:00 | 1.3 |
105 | 5502 | Smart Plug | 2022-06-09 00:00:00 | 0.7 |
home_id | device_type | avg_energy_consumption |
---|---|---|
5501 | Smart Bulb | 1.60 |
5502 | Smart Plug | 0.75 |
To find the average energy consumption per home for each device type, you can use the function in PostgreSQL as follows:
In the SQL query above, we are grouping the data by both and . For each group, we compute the average energy consumption with the function. The function is used to limit the result to two decimal places for readability. The result gives us the average energy consumption for each IoT device at each home, thus providing useful insights for the product team at Tuya.
To practice a very similar question try this interactive Google Odd and Even Measurements Question which is similar for needing to analyze IoT device measurements or this Amazon Server Utilization Time Question which is similar for calculating total usage over a given period of time.
Denormalization is a technique used to improve the read performance of a database, typically at the expense of some write performance.
By adding redundant copies of data or grouping data together in a way that does not follow normalization rules, denormalization improves the performance and scalability of a database by eliminating costly join operations, which is important for OLAP use cases that are read-heavy and have minimal updates/inserts.
You have been provided with a list of customers in the company's database. Your task is to write a SQL command to retrieve all customer records where the customer's city contains the word "York" within it. Assume that the cities can be New York, Yorkville, Yorkton, etc.
customer_id | first_name | last_name | city | |
---|---|---|---|---|
5671 | John | Doe | john_doe@example.com | New York |
9102 | Jane | Smith | jane_smith@example.com | Yorkville |
3048 | Alice | Johnson | alice_johnson@example.com | London |
4536 | Charlie | Brown | charlie_brown@example.com | Yorkton |
7890 | Bob | Davis | bob_davis@example.com | San Francisco |
customer_id | first_name | last_name | city | |
---|---|---|---|---|
5671 | John | Doe | john_doe@example.com | New York |
9102 | Jane | Smith | jane_smith@example.com | Yorkville |
4536 | Charlie | Brown | charlie_brown@example.com | Yorkton |
This query will select all records from the table where the field contains the string 'York'. The '%' symbol is a wildcard in SQL, matching any sequence of characters. Therefore, '%York%' will match any string that contains 'York'. The keyword LIKE is used in the WHERE clause to search for the specified pattern in a column.
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the earlier Tuya SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each exercise has multiple hints, detailed solutions and best of all, there's an interactive coding environment so you can right online code up your SQL query and have it checked.
To prep for the Tuya SQL interview you can also be wise to solve SQL questions from other tech companies like:
In case your SQL query skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.
This tutorial covers SQL topics like window functions like RANK() and ROW_NUMBER() and WHERE vs. HAVING – both of which pop up frequently in SQL job interviews at Tuya.
In addition to SQL interview questions, the other types of questions covered in the Tuya Data Science Interview are:
I'm sorta biased, but I believe the optimal way to prep for Tuya Data Science interviews is to read the book Ace the Data Science Interview.
The book solves 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google). It also has a refresher on Stats, ML, & Data Case Studies. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.