11 Elastic SQL Interview Questions (Updated 2024)
At Elastic, SQL is used all the damn time for analyzing and managing search data across scalable computing environments. This is why Elastic almost always asks SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
To help you study for the Elastic SQL interview, we've curated 11 Elastic NV SQL interview questions – how many can you solve?
11 Elastic NV SQL Interview Questions
SQL Question 1: Identifying Power Users
For the company Elastic, one of the main indicators of "Power users" would be users who have interacted with their services (like conducting search queries, sending logs, etc.) very frequently. Write a SQL query to find the users who have conducted more than X number of search queries in the last month?".
Example Input:
| activity_id | user_id | activity_type | timestamp |
|---|---|---|---|
| 1 | 123 | search_query | 2022-08-12 00:00:00 |
| 2 | 123 | search_query | 2022-08-10 00:00:00 |
| 3 | 362 | send_log | 2022-08-01 00:00:00 |
| 4 | 192 | search_query | 2022-08-15 00:00:00 |
| 5 | 981 | search_query | 2022-08-05 00:00:00 |
| 6 | 981 | search_query | 2022-08-05 00:00:00 |
| 7 | 192 | search_query | 2022-08-15 00:00:00 |
| 8 | 192 | search_query | 2022-08-15 00:00:00 |
Suppose X is 2, then the power users are those who appear more than 2 times in the above table within the last month i.e. users 123, 192 and 981.
Answer:
To get our power users, we can simply count the number of activities per user for the last 30 days. Users with more than X activities are the power users.
The values of X can be determined as per the requirements of the company and can be based on internal analysis such as percentiles etc. However, for the current scenario X is assumed to be 2. Please replace X with actual numerical value before running the code.
This SQL block will generate a list of that have conducted more than X number of 'search_query' in the past 30 days, indicating these are the power users.
Please replace with the end time of your period.
To solve a related super-user data analysis question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:
SQL Question 2: Calculate the Month-over-Month Average Review Score
Given a table of product reviews, write a SQL query that can calculate the month-over-month average review score for each product. You need to consider 'submit_date' as a timestamp field and 'stars' as an integer field with value from 1 to 5.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Answer:
In the above PostgreSQL SQL query, we aggregate the dataset in the 'reviews' table by 'product_id' and the month in 'submit_date'. The AVG function is used as a window function to calculate the average 'stars' across the partition which includes the reviews of the same product in the same month. The end result will show the month-over-month average 'stars' for each product.
To practice a related window function SQL problem on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
SQL Question 3: What's the difference between a one-to-one and one-to-many relationship?
In database schema design, a one-to-one relationship between two entities is where each entity is associated with only one instance of the other entity. For example, the relationship between a US citizen and their social-security number (SSN) is one-to-one, because each citizen can only have one SSN, and each SSN belongs to exactly one person.
On the other hand, a one-to-many relationship is where one entity can be associated with multiple instances of the 2nd entity. For example, each person can be associated with multiple email addresses, but each email address only relates back to one person.
SQL Question 4: Elastic Product Usage Analysis
As a database specialist at Elastic, you have been tasked to determine the usage pattern of Elastic products across various client companies. Specifically, the goal is to identify the average daily usage of our top three products by each client in the past year.
You have been provided with the tables and , which have the following structure:
Product IDs 1, 2, and 3, are of major interest to the company. You need to compute the average usage of these products by each client company for all dates in the dataset.
Answer:
This query first filters the table to only contain rows for products 1, 2, and 3. It then groups this table by and , and computes the average over all dates for each group. Finally, the results are ordered first by and then by the computed average in decreasing order.
SQL Question 5: What are some different ways you can identify duplicate rows in a table?
One way to find duplicatesis to use a clause and then use to find groups
You could also use the operator:
SQL Question 6: Filter Customers Based on Purchase and Review Criteria
As a data analyst at Elastic, you are asked to identify customers that meet specific categorical and numerical conditions based on their interaction with the products.
Consider the following tables.
Example Input:
| customer_id | customer_name | location |
|---|---|---|
| 1 | John Doe | New York |
| 2 | Jane Smith | California |
| 3 | Anna Brown | Texas |
| 4 | Steven Wilson | Florida |
| 5 | Karen Johnson | Colorado |
Example Input:
| purchase_id | customer_id | product_name | purchase_date |
|---|---|---|---|
| 101 | 1 | Elastic Search | 2022-06-10 |
| 102 | 2 | Kibana | 2022-07-15 |
| 103 | 3 | Logstash | 2022-06-10 |
| 104 | 4 | Beats | 2022-07-05 |
| 105 | 2 | Elastic Search | 2022-06-15 |
Example Input:
| review_id | customer_id | product_name | rating | review_date |
|---|---|---|---|---|
| 201 | 1 | Elastic Search | 4 | 2022-06-15 |
| 202 | 2 | Kibana | 5 | 2022-07-20 |
| 203 | 3 | Logstash | 2 | 2022-06-15 |
| 204 | 2 | Elastic Search | 5 | 2022-06-20 |
| 205 | 5 | Beats | 3 | 2022-07-05 |
Write a SQL query that filters the customers who have made a purchase in the month of June, given at least one review, and the review score is 4 or above.
Answer:
This PostgreSQL query first applies an EXISTS clause coupled with a subquery to filter the customers who made a purchase in the month of June. It then uses another EXISTS clause with a subquery to further filter customers based on whether they have given at least one product a review score of 4 or higher. If a customer satisfies both conditions, their id and name will be included in the final result set.
SQL Question 7: What is the function of a primary key in a database?
A primary key is a special type of column or set of columns in a database table that is used to identify each row in the table. It helps to ensure that all the rows are distinct and prevents null values. Additionally, primary keys play a crucial role in maintaining the accuracy of the data by making sure that there are no duplicate rows. They are also used to establish relationships between tables.
To create a primary key in a SQL database, you can use the constraint. For example, in the table , the column is the primary key.
SQL Question 8: Average Utilization of an Elastic Search Cluster
Elastic, inc. is a company that primarily sells search services based on its open-source software project called Elastic Search. Suppose you are working as a data analyst in Elastic, and you are given a table called . This table tracks the CPU utilization of each elastic search cluster for each day. The utilization is stored as a percentage of the total CPU capacity. Your task is to write a SQL query to find out the average daily utilization for each cluster for the month of July.
Example Input:
| date | cluster_id | utilization |
|---|---|---|
| 2022-07-01 | 1 | 70 |
| 2022-07-02 | 1 | 80 |
| 2022-07-03 | 1 | 85 |
| 2022-07-04 | 1 | 70 |
| 2022-07-01 | 2 | 90 |
| 2022-07-02 | 2 | 95 |
| 2022-07-03 | 2 | 85 |
| 2022-07-04 | 2 | 90 |
Example Output:
| cluster_id | avg_utilization |
|---|---|
| 1 | 76.25 |
| 2 | 90.00 |
Answer:
This query first limits the rows to those from the month of July in the year 2022, using the function. Then it computes the average of the column for each using the function, grouping the rows by .
For example, for cluster_id 1, it takes the average of {70, 80, 85, 70} which is 76.25. Similarly, for cluster_id 2, it calculates the average of {90, 95, 85, 90} which is 90.00. The result is two rows, with each cluster_id mapped to its average utilization for July 2022.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total utilization time or this Alibaba Compressed Mean Question which is similar for mean data calculation.
SQL Question 9: Compute Average Response Time of Elastic Services
As a team member at Elastic, you've been asked to monitor and improve the response time of the company's services. Essential to this task is understanding the current performance. Therefore, you need to compute the average response time for each Elastic service on a daily basis. Use the mock data from the table to write a query to for this task. In this context, response time means the time a service takes to respond to a request.
Example Input:
| service_id | request_date | response_time_in_ms |
|---|---|---|
| 1001 | 08/30/2022 | 350 |
| 1002 | 08/30/2022 | 200 |
| 1001 | 08/31/2022 | 250 |
| 1002 | 08/31/2022 | 400 |
| 1002 | 08/31/2022 | 300 |
Example Output:
| service_id | request_date | avg_response_time |
|---|---|---|
| 1001 | 08/30/2022 | 350 |
| 1002 | 08/30/2022 | 200 |
| 1001 | 08/31/2022 | 250 |
| 1002 | 08/31/2022 | 350 |
Answer:
This query groups the data in the table by and . For each group, it calculates average response time using the function. The clause then sorts the result first by and then by in ascending order for a clear view of the performance trends.
SQL Question 10: Why should you normalize your database?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Elastic's database to ever-changing business needs.
SQL Question 11: Filter Customer Records
Elastic Company has observed a pattern that customers with 'tech' in their job title are more likely to buy their products. They have asked you to find all the customer records from their customer database where their job title includes the word 'tech'.
The table has the following schema:
Input Sample
| customer_id | first_name | last_name | job_title | |
|---|---|---|---|---|
| C001 | John | Doe | johndoe@example.com | Software Engineer |
| C002 | Jane | Smith | janesmith@example.com | Technical Writer |
| C003 | Jim | Brown | jimbrown@example.com | Marketing Manager |
| C004 | Jill | Johnson | jilljohnson@example.com | Tech Support |
| C005 | Joe | Davis | joedavis@example.com | CEO |
Assume that the field can have values in any case (e.g., 'TECH SUPPORT', 'Tech Support', 'tech support' are all possible values).
Answer:
This SQL statement uses the keyword to find all the records in the table where the contains the word 'tech'. The in the clause are wildcards, which means that any characters can appear before or after 'tech'. Also, the function is used to convert to lower case, so that the query is case-insensitive. This means it will match job titles like 'TECH SUPPORT', 'Tech Support', and 'tech support'.
Elastic SQL Interview Tips
The best way to prepare for a Elastic SQL interview is to practice, practice, practice.
Beyond just solving the earlier Elastic SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each interview question has multiple hints, step-by-step solutions and crucially, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Elastic SQL interview you can also be a great idea to practice SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers things like SUM/AVG window functions and RANK vs. DENSE RANK – both of which pop up routinely in SQL job interviews at Elastic.
Elastic NV Data Science Interview Tips
What Do Elastic Data Science Interviews Cover?
Beyond writing SQL queries, the other topics tested in the Elastic Data Science Interview are:
- Statistics & Probability Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Elastic Data Science Interviews?
To prepare for Elastic Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
- a refresher covering Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating