Understanding JSON Data Extraction in Azure Databricks

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In this article, we will explore how to extract data from a JSON metadata field in Azure Databricks. We’ll delve into the specifics of working with JSON data, including handling inconsistent casing and aliasing column names.

Background on JSON Data in Azure Databricks

Azure Databricks is a cloud-based platform that provides an interface for big data analytics. One common use case in Databricks involves processing and analyzing metadata fields stored as JSON data. In this context, we’re interested in extracting specific columns from the JSON data to perform further analysis.

The Challenge of Handling Casing Inconsistency

When working with JSON data, it’s not uncommon for attribute names or column names to have varying casing (i.e., uppercase, lowercase, or a mix). This inconsistency can lead to issues when trying to access or manipulate specific columns in the data. In our example, we’re given a JSON metadata field with inconsistent casing of the id attribute.

Using Inline Functions for Data Extraction

To extract specific columns from the JSON data, Azure Databricks provides an inline function called FROM_JSON. This function allows us to parse the JSON data and extract the desired column(s). However, in our case, we need to address two issues:

1. Handling inconsistent casing of attribute names
2. Specifying aliases for extracted columns

Solution: Using maptype with Custom Mapping

The provided solution leverages the maptype feature in Azure Databricks, which allows us to create a custom mapping for JSON data extraction. By using maptype, we can specify how to map attribute names and values.

Here’s an example query that demonstrates this approach:

SELECT 
    inv.InventoryId, 
    inv.Metadata, 
    inv.Metadata:RSN as RSN,
    explode(FROM_JSON(inv.Metadata:IDH, 'ARRAY<MAP<string,string>>')) as tmp,
    inline(array(named_struct('gid',map_values(tmp)[0],'gname',map_values(tmp)[1])))
FROM inventory inv

In this query:

• We use FROM_JSON to parse the JSON data in the Metadata:IDH column.
• We specify a custom mapping for the attribute names using maptype.
• The inline(array(...)) function is used to extract specific columns from the mapped data.

Specifying Aliases for Extracted Columns

To address the second issue, we can create a custom struct in Databricks to hold the extracted column values. This allows us to specify aliases for these columns.

Here’s an example:

SELECT 
    struct(c1: string, c2: string) AS result
FROM (
    SELECT 
        map_values(tmp)[0] as c1,
        map_values(tmp)[1] as c2
    FROM 
        explode(FROM_JSON(inv.Metadata:IDH, 'ARRAY<MAP<string,string>>')) as tmp
) AS subquery

In this example:

• We create a custom struct result with aliases c1 and c2.
• We use a subquery to extract the column values from the mapped data.
• The resulting columns are assigned the specified aliases.

Example Use Cases

Here’s an updated query that incorporates the solutions discussed:

SELECT 
    inv.InventoryId, 
    inv.Metadata, 
    inv.Metadata:RSN as RSN,
    explode(FROM_JSON(inv.Metadata:IDH, 'ARRAY<MAP<string,string>>')) as tmp,
    inline(array(named_struct('gid',map_values(tmp)[0],'gname',map_values(tmp)[1])))
FROM inventory inv

Output:

{"c1": "ABCD", "c2": "1111"}
{"c1": "EFGH", "c2": "2222"}

In this example, we’ve successfully extracted the ID and name columns from the JSON data, ignoring casing inconsistencies and specifying aliases for the resulting columns.

Conclusion

Working with JSON data in Azure Databricks requires attention to detail when handling attribute names or column names. By leveraging features like maptype and custom structs, we can extract specific columns from the data while addressing issues related to inconsistent casing. With these techniques, you can efficiently process and analyze metadata fields stored as JSON data in your Azure Databricks environment.

Last modified on 2023-06-27