Redshift JSON Extract Functions 101: Syntax and Usage Made Easy

Today, companies are dealing with tremendous volumes of data generated across various SaaS applications used for Accounting, Marketing, Sales, Human Resources, etc. To handle the growing data demands efficiently, enterprises are migrating from their Traditional Onsite Data Storage Units to Cloud-Based Data Warehouses such as Amazon Redshift. Redshift provides On-demand scaling with best-in-class query performance at a minimal cost.

Redshift also acts as a Robust Analytics platform allowing you to query structured and semi-structured data using the Standard SQL commands. One of the common ways to represent Semi-Structured Data is using the text-based format JSON. Redshift has a list of JSON commands in SQL including the Redshift JSON Extract functions.

In this article, you will learn how to effectively use the Redshift JSON Extract_Path_Text function and Redshift JSON Extract_Array_Element_Text function.

Table of Contents

• Introduction to Amazon Redshift
  • Key Features of Amazon Redshift
• Redshift JSON Extract_Path_Text Function
  • Redshift JSON Extract_Path_Text Syntax
  • Redshift JSON Extract_Path_Text Examples
• Redshift JSON Extract_Array_Element_Text Function
  • Redshift JSON Extract_Array_Element_Text Syntax
  • Redshift JSON Extract_Array_Element_Text Examples
• Conclusion

Introduction to Amazon Redshift

Redshift is a Cloud Data Warehousing and Analytics Platform launched by Amazon in 2012. Keeping in mind the growing data demands in the market, Redshift’s architecture allows on-demand scaling to store petabytes of data in easy-to-access “clusters”. Each of these clusters consists of compute nodes to query your data with great speed.

Since it uses PostgreSQL 8, Data analysts can start efficiently querying in real-time and generate reports to gain important business insights. Owing to Redshift’s Vertical Cluster Design, departments can own their individual nodes and access the data anytime, reducing wait times. Multiple users can effectively execute complex queries simultaneously and get instant results. 

Key Features of Amazon Redshift

Developers at Amazon have continuously improved Redshift over the years. Here are some of the eye-catching features that make Amazon Redshift a popular choice:

• Scalability: Giving you an edge over the Traditional Data Warehouses, Redshift allows On-demand horizontal petabyte scalability. With just a few clicks on Amazon Web Services Console or via the Cluster API, you can scale up or down according to your business requirements. You can leverage the Amazon S3 data lake to store unlimited data in open data formats. 
• Top-notch Performance: The performance-intensive workloads can be handled seamlessly by the R3 instances providing 3 times better performance compared to other alternatives. At no extra cost to you, R3 instances include Advanced Query Accelerator(AQUA) cache to get faster query results for large datasets. For repeated queries, amazon delivers the result immediately from the saved cache. 
• Flexible Pricing: If you are just starting out, you can opt for hourly plans and scale to long-term plans later on. The scaling costs are also minimal for its customers. For scaling, you can use the Concurrency Scaling Credits that you earn every day from the clusters. You also get a choice between Dense Storage Nodes and Dense Compute Nodes. This allows you to optimally select the resources for your Workload and get the best price-performance.  
• Fully Managed: It is a completely managed service with all the mundane administrative tasks computerised. Features such as Automatic Vacuum Delete, Automatic Table Sort, and Automatic Analyze reduces the manual effort, thereby providing a high-class query performance with no delays. The in-built machine learning capabilities apply complex algorithms to dynamically improve the query speed.
• Reliability: Redshift is extremely fault-tolerant. Clusters health is continuously monitored and automatic data replication from defective storage units are carried to prevent any data loss. Data is automatically backed up in Amazon S3. Within minutes you can recover your data in a few clicks on AWS Management Console or by using the Redshift APIs. 
• Secure: Amazon Redshift is a Compliant Cloud Service Provider with SOC1, SOC2, SOC3, and PCI DSS Level 1 requirements. Your data is guarded at all times with accelerated AES-256 bit security at rest and SSL Data Encryption in transit. You can also set access permissions for columns and rows for individuals and groups. You get more control as Redshift allows you to configure the firewall settings and isolate your data warehouse cluster using the Amazon Virtual Private Cloud.

For more information on these fantastic features, you can visit the Amazon Redshift Features page.

Redshift JSON Extract_Path_Text Function

Using the Redshift JSON Extract_Path_Text Function you get the VARCHAR value of the key: value pair mentioned in the path elements of the JSON string. 

Note: In a JSON String, n represents a new line, t means tab and represents backslash.

Let’s understand the Redshift JSON Extract_Path_Text function in detail via the Syntax and Examples.

A) Redshift JSON Extract_Path_Text Syntax

json_extract_path_text('json_string', 'path_elem' [,'path_elem'[, …] ] [, null_if_invalid ] )

The above-mentioned syntax has the following parameters:

• json_string: The properly formatted JSON string from which you want to extract the path elements. 
• path_elem: The desired path element you require. Redshift allows nested path elements, up to five levels deep.
• null_if_invalid: A boolean value which when set to true, will return NULL if the JSON is invalid. By default, it is set to false and will return an error if the JSON is invalid.

B) Redshift JSON Extract_Path_Text Examples

• For Nested path elements ‘g4’,’g6’

select json_extract_path_text('{"g2":{"g3":1},"g4":{"g5":99,"g6":"delta"}}','g4', 'g6');

Output: 

json_extract_path_text
----------------------
delta 

In this example, the JSON string has nested path elements. Here, for the path element “g4”, the value is  “g5″:99,”g6″:”delta”. Now inside this value, “g6” is key with “delta” as the value.

• An invalid JSON String

select json_extract_path_text('{"g2":{"g3":1},"g4":{"g5":99,"g6":"delta"}','g4', ‘g6');

Output:  

An error occurred when executing the SQL command:
select json_extract_path_text('{"g2":{"g3":1},"g4":{"g5":99,"g6":"delta"}','g4', 'g6')

Here, the JSON String is invalid as a ‘}’ is missing at the end. By default the null_if_invalid is false, thereby showing the error message in the output.

• Setting null_if_invalid as true

select json_extract_path_text('{"g2":{"g3":1},"g4":{"g5":99,"g6":"delta"}',’g4', 'g6',true);

Output: 

json_extract_path_text
-------------------------------

Similar to the previous example the JSON String is invalid. Since the null_if_invalid is passed as true, the output is NULL instead of an error message.

Redshift JSON Extract_Array_Element_Text Function

Using the JSON Extract_Array_Element_Text Function, you get the VARCHAR array element from the outermost array of the JSON string.

Let’s understand the Redshift JSON Extract_Array_Element_Text function in detail via the Syntax and Examples.

A) Redshift JSON Extract_Array_Element_Text Syntax

json_extract_array_element_text('json string', pos [, null_if_invalid ] )

The following parameters are present in the above mentioned syntax:

• json_string: The neatly formatted JSON string.
• pos: This represents the integer position of the array element. The array position starts with 0. The function will return an empty string if the position entered is negative or out of bounds.
• null_if_invalid: If set to “true”, the function will return NULL is the JSON string is invalid. To get an error message in the output for the invalid JSON string, set the null_if_invalid to “false”.

B) Redshift JSON Extract_Array_Element_Text Examples

• Returns the value of array element at position 1

select json_extract_array_element_text('[121,122,123]', 1);

Output: 

json_extract_array_element_text 
-------------------------------
122

Since the index starts at 0, 112 is at position 1. 

• An invalid JSON String

select json_extract_array_element_text('["a",["b",2,["c",3,4,null,]]]',1);

Output:

An error occurred when executing the SQL command:
select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1)

By default, the null_if_invalid parameter is set to “false”. Hence, you get an error message in the output for the invalid string.

• Setting null_if_invalid as true

select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1,true);

Output: 

json_extract_array_element_text
-------------------------------

Since the null_if_valid is set to true, the function returns NULL in the output for the invalid JSON string.

For more details on the Redshift JSON Extract functions, visit the Amazon Documentation.

Conclusion

In this article, you have learned how to accurately use the Redshift JSON Extract_Path_Text function and Redshift JSON Extract_Array_Element_Text function. Amazon Redshift offers a unified solution for all your Data Warehousing and Analytics challenges. The on-demand petabyte scaling, flexible pricing, user-friendly interface, and excellent security features make Amazon Redshift one of the most preferred solutions among various Organisations. The Standard SQL environment allows Data analysts and business teams to efficiently query the data and harness insights into the business performance in real-time.

As the demand for your products and services rises in the market, your customer base will rise gradually. Massive amounts of data are generated at an exponential rate related to the customers, orders, etc. Regularly handling and integrating all this data across all the different applications used in your firm can be a cumbersome task. You would require to invest a part of your engineering bandwidth to Integrate, Clean, Transform and Load your data into Redshift Data Warehouse for further analysis. All this can be seamlessly automated by a Cloud-based ETL Tool like Hevo Data.

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Tell us about your experience of working with Redshift JSON Extract functions! Share your thoughts with us in the comments section below.