Understanding and Maximizing the SUBSTR Function in Looker Studio for Efficient Data Extraction and Analysis

 Understanding and Maximizing the SUBSTR Function in Looker Studio for Efficient Data Extraction and Analysis

SUBSTR functionone of the valuable and versatile tools in the Google Data Studio repertoireis a classic function used in data extraction. It serves programmers by taking a string of alphanumeric data and breaking it up into a selected sequence of characters. Anyone who requires a specific segment from a broader context of text will find SUBSTR indispensable. It’s primarily used to neatly dissect data from massive strings and arrays, thus, honing the programmer's focus on pertinent data while putting extraneous facts to rest.


Pulling out a specific combination of characters from a given text using the SUBSTR function is pretty straightforward, with a simple syntax as follows:

SUBSTR(X, start index, length)

Here, the function involves three parameters:

  1. X: This represents the source field, from which the function will extract a sub-string.
  2. Start Index: This specifies the position in the field from where the extraction will start. Notably, the first character in a sequence is at index 1. For extraction from string end, use a negative index.
  3. Length: This represents the count of characters to be extracted from the start index.

How the Function Works

Applying the SUBSTR function is comparable to using a magnifying glass to pick out specific details from a broad image. The function analyzes the assigned field (X), tracking down the specified start index. From this start point onwards, it pulls out the number of characters specified by the length parameter.

Rather ingeniously, it can work from the end of the string using negative indexes, enabling both forward and backward extraction. This bidirectional flexibility is a prime feature of the SUBSTR function.


To provide a clearer sense of how the SUBSTR function operates, consider the following example:

Imagine you manage an ecommerce portal and you sell a mix of different product categories, including electronics, furniture, and kitchen supplies. You record all transactions in a single column, but each item is labeled, e.g., "Electronics: Laptop", "Furniture: Chair", "Kitchen Supplies: Blender". Now, you need to extract just the product category for a sales performance analysis.

So, you can use the SUBSTR function to extract the category from this data string. Here's how it will look:

SUBSTR(Product, 1, FIND(Product, ":")-1)
This will chop off anything from the colon onwards, leaving just the category for your analysis.


While the SUBSTR function undoubtedly adds value in data extraction and analytics, it isn't without its constraints. For example, if the start index or length given exceeds the actual text's limits, you may encounter errors or, at best, get a truncated result. Also, any start index less than one or greater than the string's length may lead to unexpected results.


There are several hints and tricks to get the most out of the SUBSTR function.

  1. Ensure that start index and length parameters fall within the actual source field's range.
  2. Utilize the SUBSTR function in conjunction with other functions—like CONCAT, REGEXP_MATCH, and REGEXP_EXTRACT—to generate more sophisticated outcomes.
  3. Experiment with negative indexes for reverse extraction when dealing with repetitive patterns towards the end of strings.

In summary, with the consistent application of SUBSTR function, you can significantly enhance your data extraction and analysis efficiency, irrespective of the field of application.

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