10 Splunk SQL Interview Questions (Updated 2024)
At Splunk, SQL is used all the damn time for extracting indexed machine data from Splunk's proprietary storage and performing complex structured analysis for operational intelligence. Because of this, Splunk almost always evaluates jobseekers on SQL query questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
In case you're studying for a SQL Assessment, we've collected 10 Splunk SQL interview questions to practice, which are similar to commonly asked questions at Splunk – able to answer them all?
10 Splunk SQL Interview Questions
SQL Question 1: Identify the Most Active Splunk Users
Splunk is a data platform mainly used for searching, monitoring, and examining machine-generated big data, via a web-style interface. Users log into the system and perform various tasks such as creating dashboards, running searches, or setting alerts.
One way to identify power users on Splunk can be to calculate who executes most activities in the system. For this question, assume that an activity is a search activity given this is the core function of Splunk. Power users are defined as the top 10% users who execute most searches on the platform.
Given two tables "users" and "searches", write a SQL query to list out the power users. The "users" table includes basic user info and the "searches" table contains all search activities performed by the users.
Example Input:
| user_id | first_name | last_name | role |
|---|---|---|---|
| 1 | John | Doe | Analyst |
| 2 | Jane | Smith | Manager |
| 3 | Mark | Cuban | Engineer |
Example Input:
| search_id | user_id | search_time | search_string |
|---|---|---|---|
| 100 | 1 | 06/08/2022 00:00:00 | "error code 500" |
| 101 | 2 | 06/10/2022 00:00:00 | "server down" |
| 102 | 1 | 06/18/2022 00:00:00 | "CPU usage" |
| 103 | 3 | 07/26/2022 00:00:00 | "log file not found" |
| 104 | 2 | 07/05/2022 00:00:00 | "disk space" |
| 105 | 1 | 07/08/2022 00:00:00 | "error code 404" |
| 106 | 2 | 07/10/2022 00:00:00 | "server up" |
Answer:
The query first calculates the count of searches for each user, then it calculates the 90th percentile of search counts. It uses this value to filter the list of users who have a search count greater than or equal to this percentile. These are the power users of the Splunk system.
To practice a similar problem about calculating rates, try this TikTok SQL question on DataLemur's interactive SQL code editor:
SQL Question 2: Calculate Average Login Time per User
Assume you have been provided with a database table named "user_login" which stores event logs whenever users log in to Splunk. The table columns are as follows: user_id, login_time (in datetime format).
Your task is to write a SQL window function to calculate the average time between consecutive logins for each user. For simplicity, let's assume it's sufficient to calculate the average over all users' second-to-last login to their most recent login.
Example Input:
| login_id | user_id | login_time |
|---|---|---|
| 1 | 001 | 01/08/2022 08:25:10 |
| 2 | 002 | 01/08/2022 09:10:00 |
| 3 | 001 | 01/09/2022 07:30:15 |
| 4 | 003 | 01/09/2022 07:30:17 |
| 5 | 002 | 01/10/2022 10:00:00 |
| 6 | 001 | 01/10/2022 08:30:20 |
Example Output:
| user_id | avg_login_interval |
|---|---|
| 001 | 1 days 00:02:35 |
| 002 | 2 days 00:50:00 |
Answer:
The above query uses the window function to compute the interval between the current login and the previous login per user (ordered by login_time). For the first login of each user, the login interval is NULL. Therefore, we disregard these records from the average calculation.
Subsequently, the window function calculates the average login interval for each user. Note that results are presented in 'DD HH:MI:SS' format, displaying the interval as 'days hours:minutes:seconds'.
To solve another question about calculating rates, try this TikTok SQL Interview Question within DataLemur's interactive coding environment:
SQL Question 3: What does it mean to perform a self-join?
A self-join is a operation in which a single table is joined to itself. To perform a self-join, you must specify the table name twice in the clause, giving each instance a different alias. You can then join the two instances of the table using a clause, and specify the relationship between the rows in a clause.
Think of using a self-joins whenever your data analysis involves analyzie pairs of the same things, like comparing the salaries of employees within the same department, or identifying pairs of products that are frequently purchased together (which you can do in this real SQL question from a Walmart interview).
For another self-join example, suppose you were conducting an HR analytics project and needed to examine how frequently employees within the same department at Splunk interact with one another, you could use a self-join query like the following to retrieve all pairs of Splunk employees who work in the same department:
This query returns all pairs of Splunk employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Splunk employee being paired with themselves).
SQL Question 4: Filter Splunk Customers Based on Various Conditions
Splunk Inc. provides innovative software solutions to manage and analyze data. As part of their operations, they want to filter their customers who are based in the US and have a lifetime subscription with the company. They also want to exclude customers with active support tickets.
Given the table, write an SQL query to achieve this. Filters should be on the , , and fields.
Example Input:
| customer_id | first_name | last_name | country | subscription_type | active_tickets |
|---|---|---|---|---|---|
| 101 | Robert | Smith | US | Monthly | 0 |
| 102 | Jane | Doe | US | Lifetime | 0 |
| 103 | Tom | Brown | CA | Lifetime | 1 |
| 104 | Julia | Cook | US | Lifetime | 0 |
| 105 | John | Green | US | Lifetime | 3 |
Example Output:
| customer_id | first_name | last_name | country | subscription_type | active_tickets |
|---|---|---|---|---|---|
| 102 | Jane | Doe | US | Lifetime | 0 |
| 104 | Julia | Cook | US | Lifetime | 0 |
Answer:
This query is filtering the relevant records from the table. It includes only customers based in the US (), with a lifetime subscription (), and no active support tickets ().
To practice another question about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:
SQL Question 5: What's the difference between and ?
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing Splunk sales data:
This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $500k.
SQL Question 6: Average Number of Logs per Server
In the company Splunk, which primarily deals with big data and its analysis through their proprietary tool, you might be asked to find the average number of logs generated per server on a daily basis. This is a realistic problem, as understanding how much data your servers are generating can be important for optimizing resources, spotting anomalies, and understanding the health of your systems.
Assume you have access to a 'log_data' table, which contains information about every log entry generated by their system.
Example Input:
| log_id | server_id | log_timestamp | log_type | |
|---|---|---|---|---|
| 10001 | 1 | 2022-07-10T08:30:00Z | Error | |
| 10002 | 1 | 2022-07-10T08:45:00Z | Warning | |
| 10003 | 2 | 2022-07-10T09:00:00Z | Information | |
| 10004 | 2 | 2022-07-10T09:15:00Z | Error | |
| 10005 | 2 | 2022-07-10T09:30:00Z | Information | |
| 10006 | 3 | 2022-07-10T09:45:00Z | Warning | |
| 10007 | 3 | 2022-07-10T10:00:00Z | Error | |
| 10008 | 3 | 2022-07-10T10:15:00Z | Warning | |
| 10009 | 1 | 2022-07-10T10:30:00Z | Information |
The task:
Write a SQL query which calculates the average number of logs generated by each server on a daily basis.
Answer:
This query first groups logs by their server_id and the day they were generated on, then takes the average of the counts of these groupings (which represent the number of logs generated per day per server). The OVER clause allows us to compute this average while maintaining the original rows of our table, which lets us see not just the average number of logs per day, but how this number varies for each day.
To practice a related problem on DataLemur's free online SQL code editor, solve this Facebook SQL Interview question:
SQL Question 7: Can you explain the distinction between a clustered and a non-clustered index?
Here is an example of a clustered index on the column of a table of Splunk customer transactions:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 8: Calculate Average Data Ingested
As a data engineer at Splunk, you primarily deal with log management and analysis for clients. You have a table recording the daily amount of data ingested from various client's systems. Your task is to compute the average daily data ingested per client, per month, and sorted by client name and month.
Example Input:
| log_id | client_name | ingest_date | data_size_GB |
|---|---|---|---|
| 101 | Client_A | 04/02/2022 | 20.5 |
| 102 | Client_A | 04/05/2022 | 23.1 |
| 103 | Client_B | 04/04/2022 | 19.2 |
| 104 | Client_B | 04/10/2022 | 24.3 |
| 105 | Client_A | 05/01/2022 | 22.6 |
| 106 | Client_B | 05/03/2022 | 21.5 |
| 107 | Client_A | 05/10/2022 | 25.7 |
| 108 | Client_B | 05/15/2022 | 20.4 |
Example Output:
| month | client_name | avg_data_size_GB |
|---|---|---|
| 4 | Client_A | 21.8 |
| 4 | Client_B | 21.75 |
| 5 | Client_A | 24.15 |
| 5 | Client_B | 20.95 |
Answer:
This SQL query groups our data by client_name and month (extracted from the date using the EXTRACT function). We calculate the average data_size_GB with the AVG function in our SELECT statement. The result is then ordered by client_name and month.
To practice a similar problem about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL code editor:
SQL Question 9: Analyze Revenue and Customer Activities
Analyze the revenue and customer activities of Splunk. Assume we have two tables: and . The table contains records of all purchases, with the of the customer who made the purchase, the , and the of the product the customer bought. The table contains information about each user, with fields for , and .
Write a SQL query that retrieves all the purchases made by customers whose last names start with the letter 'S', and return the first name, last name, total number of purchases and total revenue for each of these customers, ordered by in descending order.
Sample Data:
| purchase_id | user_id | purchase_date | price |
|---|---|---|---|
| 101 | 001 | 01/10/2022 | 99.99 |
| 102 | 002 | 01/12/2022 | 49.50 |
| 103 | 003 | 01/14/2022 | 29.90 |
| 104 | 001 | 01/17/2022 | 79.99 |
| 105 | 003 | 01/20/2022 | 69.90 |
Sample Data:
| user_id | first_name | last_name |
|---|---|---|
| 001 | Steve | Smith |
| 002 | Raj | Singh |
| 003 | Millie | Salzano |
Answer:
This query first joins the table with the table using a SQL JOIN clause on the columns of both tables. Then, it filters the records to only include those where the last name in the table starts with 'S' using the WHERE clause with a wild card (the % character). Next, it groups the filtered records by user_id, calculates the total number of unique purchase_ids and the sum of price for all purchases for each user_id as and respectively. Finally, it sorts the resulting aggregated records in descending order of .
To practice a similar problem about calculating rates, try this SQL interview question from TikTok on DataLemur's interactive coding environment:
SQL Question 10: What are stored procedures, and why are they useful?
Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.
For example, if you worked as a Data Analyst in support of the Marketing Analytics team at Splunk, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:
To call this stored procedure, you'd execute the following query:
Preparing For The Splunk SQL Interview
The key to acing a Splunk SQL interview is to practice, practice, and then practice some more!
Besides solving the earlier Splunk SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive SQL code editor so you can right online code up your SQL query and have it checked.
To prep for the Splunk SQL interview it is also wise to practice SQL questions from other tech companies like:
But if your SQL query skills are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as grouping by multiple columns and LEAD window function – both of which come up routinely during SQL job interviews at Splunk.
Splunk Data Science Interview Tips
What Do Splunk Data Science Interviews Cover?
For the Splunk Data Science Interview, besides SQL questions, the other types of questions to practice:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Open-Ended Data Case Studies
- Machine Learning and Predictive Modeling Questions
- Resume-Based Behavioral Questions
How To Prepare for Splunk Data Science Interviews?
The best way to prepare for Splunk Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google, & Amazon
- A Crash Course on Stats, SQL & ML
- Great Reviews (900+ reviews, 4.5-star rating)