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Unlocking the Power of JSON_QUERY in IBM Event Processing

By Bibin S posted 8 days ago

  

Introduction 

In today’s fast-paced digital world, businesses rely on real-time data processing to drive operational efficiency and make informed decisions. IBM Event Processing empowers users with an intuitive, event-driven canvas to design workflows that handle streaming data with precision and speed.
At its core, IBM Event Processing is powered by Apache Flink®, a high-performance stream processing engine known for its scalability and low-latency capabilities. However, a common challenge arises when JSON data is embedded within string fields, making direct extraction complex and inefficient.
With Apache Flink 1.20®, a new RETURNING clause has been introduced to the JSON_QUERY function, allowing results to be returned as either a string or  an array of strings. This addition makes JSON processing more efficient within IBM Event Processing.
 
In this blog, we explore how JSON_QUERY simplifies data extraction in the upcoming release of IBM Event Processing, tackling key challenges associated with handling JSON-formatted strings. Additionally, we present a real-world shipment tracking use case, demonstrating how businesses can leverage JSON_QUERY to optimize their event-driven data processing workflows.
The Challenge: Extracting Data from JSON Strings
 
IBM Event Processing seamlessly flattens JSON data when it is properly formatted, enabling users to reference JSON attributes directly in their processing workflows. However, when JSON data is encapsulated within a string, this automatic flattening does not take place.

Example: Shipment Tracking Data

Imagine a logistics platform where an event source ingests shipment tracking records. Certain fields, such as tracking_history and items, are stored as stringified JSON, posing challenges for efficient processing.

Here’s an example of such a record:

{
  "tracking_id": "ABC123XYZ",
  "status": "In Transit",
  "origin": "New York, USA",
  "destination": "Los Angeles, USA",
  "tracking_history": "[{\"date\":\"2025-02-05\",\"location\":\"New York, USA\",\"status\":\"Picked Up\"},{\"date\":\"2025-02-06\",\"location\":\"Philadelphia, USA\",\"status\":\"In Transit\"},{\"date\":\"2025-02-07\",\"location\":\"Chicago, USA\",\"status\":\"Arrived at Facility\"}]",
  "items": "[{\"item_id\":\"12345\",\"name\":\"Laptop\",\"quantity\":1},{\"item_id\":\"67890\",\"name\":\"Wireless Mouse\",\"quantity\":2}]",
  "delivery_estimate": "2025-02-10"
}
The accompanying diagram demonstrates the challenge of processing shipment tracking records within a logistics platform. It showcases how the Event Source node reads the data but treats fields like tracking history and items as stringified JSON. This results in these fields being handled as plain strings rather than structured JSON objects. The diagram also highlights how the Transform node is unable to extract meaningful insights from these fields, ultimately showing that the JSON data remains unprocessed and inaccessible for detailed analysis.
Visualizing JSON Challenges: Stringified Data in Event Processing
                                                                                                    Visualizing JSON Challenges: Stringified Data in Event Processing
The result below showcases how stringified JSON fields appear as plain strings, making it challenging to extract values without JSON_QUERY.

tracking_id

status

origin

destination

tracking_history

items

failure_reason

retry_attempts

event_time

ABC123XYZ

In Transit

New York, USA

Los Angeles, USA

[{"date":"2025-02-05","location":"New York, USA","status":"Picked Up"},{"date":"2025-02-06","location":"Philadelphia, USA","status":"In Transit"},{"date":"2025-02-07","location":"Chicago, USA","status":"Arrived at Facility"}]

[{"item_id":"12345","name":"Laptop","quantity":1},{"item_id":"67890","name":"Wireless Mouse","quantity":2}]

null

null

2025-02-09 10:55:34.359

Business & Technical Impact

  • Without JSON_QUERY, developers must implement complex string manipulation techniques, increasing development effort and maintenance overhead.
  • Business users face difficulties extracting actionable insights from nested JSON data, limiting operational efficiency.
  • Real-time data processing is slowed, affecting timely decision-making and business analytics.
Solution: Using JSON_QUERY to extract structured data efficiently.
What is JSON_QUERY?

JSON_QUERY® in Apache Flink simplifies structured data extraction from JSON stored as a string. While available in earlier versions, Flink 1.20 introduces the RETURNING clause, enhancing flexibility for both business users and developers.

Syntax:

JSON_QUERY(jsonValue, path
  [RETURNING dataType] 
  [{WITHOUT | WITH CONDITIONAL | WITH UNCONDITIONAL} [ARRAY] WRAPPER] 
  [{NULL | EMPTY ARRAY | EMPTY OBJECT | ERROR} ON EMPTY] 
  [{NULL | EMPTY ARRAY | EMPTY OBJECT | ERROR} ON ERROR]
)
  • jsonValue – The input JSON stored as a string.
  • path – Defines the JSON path expression to extract specific data.
  • RETURNING dataType – (New in Flink 1.20) Specifies the output type as STRING or ARRAY<STRING>.
  • WRAPPER options – Controls how the result is wrapped:
    • WITHOUT ARRAY WRAPPER: Returns the extracted value as is, without wrapping it in an array. If the result is an array, it remains unchanged.
    • WITH CONDITIONAL ARRAY WRAPPER: Wraps the extracted value in an array only if it is not already an array.
    • WITH UNCONDITIONAL ARRAY WRAPPER: Always wraps the extracted value in an array, even if it is already an array.
    • ARRAY is optional: Including or omitting ARRAY in the WRAPPER clause does not change the result.
  • ON EMPTY & ON ERROR – Defines the behavior when no match is found or an error occurs:
    • ON EMPTY: Specifies the result when the JSON path expression evaluates to an empty value. Options are returning NULL (default), an empty object ({}), an empty array ([]), or raising an error.
    • ON ERROR: Determines the behavior when an error occurs during path evaluation. It can return NULL (default), an empty object ({}), an empty array ([]), or raise an error.
    • Both parameters provide error handling and missing data management to ensure smooth JSON processing.

Key Business Benefits

Simplifies Data Processing – Extracts nested JSON values effortlessly, reducing development effort.
Enhances Flexibility – Adapts to dynamic JSON structures, enabling seamless integration into event-driven workflows.
Optimizes Performance – Eliminates manual string operations, improving real-time data processing speed.

Extracting Shipment Insights with JSON Query

JSON_QUERY streamlines data extraction, enabling business users to define precise paths for retrieving structured data effortlessly. The json_query method can be seamlessly integrated into the Transform node using a Custom SQL Processor node, ensuring efficient and flexible event processing.

JSON_QUERY(tracking_history, '$[*].date') RETURNING ARRAY<STRING>

Extracts all dates from tracking_history and returns them as an array of strings.

JSON_QUERY(tracking_history, '$[0].location') RETURNING STRING

Retrieves the first recorded location from tracking_history (e.g., "New York, USA").

JSON_QUERY(items, '$[*].name') RETURNING ARRAY<STRING>

Extracts all item names from the items field as an array of strings.

The diagram below demonstrates how JSON_QUERY is applied within the Transform node using a Custom SQL Processor. This enables seamless extraction of structured data from stringified JSON, allowing businesses to efficiently access and utilize key insights in real time.

Applying JSON Query in the Transform Node for Seamless Data Extraction
                                                                   Applying JSON Query in the Transform Node for Seamless Data Extraction

The results below showcase the successful extraction of structured data from stringified JSON. The transaction history and items fields, previously stored as strings, are now parsed, enabling seamless access to key insights for real-time processing.

tracking_id

status

origin

destination

dates

location

itemNames

statuses

failure_reason

retry_attempts

event_time

ABC123XYZ

In Transit

New York, USA

Los Angeles, USA

["2025-02-05","2025-02-06","2025-02-07"]

["New York, USA"]

["Laptop","Wireless Mouse"]

["Picked Up","In Transit","Arrived at Facility"]

null

null

2025-02-09 12:19:35.487

Handling Empty or Invalid JSON Data Gracefully

JSON Query ensures smooth processing even when JSON data is missing or malformed.

The behavior of empty JSON results can be controlled using the ON EMPTY parameter, which allows returning NULL, {}, [], or raising an error.

Similarly, the ON ERROR parameter lets users define the response to path evaluation errors by returning NULL, {}, [], or raising an error.

Example 1: Handling Empty Fields

If the tracking_history field is missing or empty, JSON_QUERY returns an empty array ([]) instead of causing errors, ensuring seamless data processing.

Example 2: Handling Invalid JSON Formats

When a corrupted JSON string is received, JSON_QUERY can return null or a predefined error message based on the configuration, preventing disruptions in data processing.

See how JSON_QUERY effectively handles empty and invalid JSON fields, maintaining smooth and error-free data processing.

How JSON Query Manages Missing and Malformed Data
                                                                                                                                            Example Output Event After JSON_QUERY Processing in Event Processing

Step-by-Step Explanation with Before & After Comparison

To illustrate the business impact of leveraging JSON Query in IBM Event Processing, let's examine how data transformation enhances efficiency. Initially, raw JSON data is embedded within a string field, making it difficult to extract meaningful business insights. By applying JSON Query within a Transform Node (via a Custom SQL Processor), we can efficiently structure and extract relevant data, optimizing real-time analytics and decision-making.

This comparison highlights:

  1. Before Processing: JSON data is stored as an unstructured string, requiring additional effort for extraction and processing.
  2. Applying JSON Query: A structured query extracts key attributes directly, eliminating the need for complex string parsing.
  3. After Processing: The extracted data is presented in a structured format, enabling seamless analysis, improved business intelligence, and operational efficiency.

Now, let’s review the transformation through a before-and-after output comparison.

Before After

tracking_history

items

[

  {

    "date": "2025-02-05",

    "location": "New York, USA",

    "status": "Picked Up"

  },

  {

    "date": "2025-02-06",

    "location": "Philadelphia, USA",

    "status": "In Transit"

  },

  {

    "date": "2025-02-07",

    "location": "Chicago, USA",

    "status": "Arrived at Facility"

  }

]

[

  {

    "item_id": "12345",

    "name": "Laptop",

    "quantity": 1

  },

  {

    "item_id": "67890",

    "name": "Wireless Mouse",

    "quantity": 2

  }

]

dates

location

itemNames

statuses

["2025-02-05","2025-02-06","2025-02-07"]

["New York, USA"]

["Laptop","Wireless Mouse"]

["Picked Up","In Transit","Arrived at Facility"]

This table highlights the transformation of shipment tracking data using JSON_QUERY in IBM Event Processing.

  • Before Processing: Shipment tracking details and item lists are stored as stringified JSON, making it difficult to extract insights efficiently.
  • After Processing: JSON_QUERY seamlessly structures the data, enabling real-time access to key business attributes such as shipment dates, locations, item names, and statuses.

This transformation enhances operational visibility, accelerates decision-making, and improves overall efficiency in event-driven business workflows.

Expanding Business Applications of JSON_QUERY

JSON Query is invaluable in event processing, particularly when handling JSON data stored as strings. This is common in:

Databases with JSON Stored as Strings – Many event-driven applications store JSON within string columns in databases. JSON_QUERY enables efficient extraction of nested values for downstream processing.

Streaming Pipelines Handling Unstructured JSON – Real-time event streams often contain JSON messages formatted as strings. JSON_QUERY allows seamless extraction of relevant fields without additional transformations.

Log and Audit Data Analysis – Security logs and audit trails frequently store metadata in JSON format within string fields. JSON_QUERY simplifies attribute retrieval for compliance checks and anomaly detection.

IoT Sensor Data Processing – Sensor data ingested as JSON strings can be efficiently parsed using JSON_QUERY, optimizing real-time monitoring and analytics.

Financial Transactions & E-commerce Records – Payment logs, order details, and user activity events often contain JSON-stored data. JSON_QUERY enables structured extraction for fraud detection, personalization, and operational intelligence.

By applying JSON_QUERY, businesses can streamline data access, drive smarter decision-making, and enhance real-time analytics across diverse use cases.

Conclusion

JSON_QUERY in IBM Event Processing revolutionizes real-time data processing by enabling seamless extraction of structured values from JSON strings.

By leveraging JSON_QUERY in IBM Event Processing, businesses can:
Simplify Event Data Processing – Eliminate complex string parsing for faster insights.
Optimize Operational Efficiency – Extract meaningful data effortlessly from JSON-stored strings.
Enhance Decision-Making – Gain structured, real-time insights for better business outcomes.
Ensure Robust Data Handling – Gracefully manage errors and empty JSON fields.

IBM Event Processing empowers organizations to transform raw event streams into actionable intelligence, driving agility and precision in event-driven architectures.

Next Steps

Unlock the full potential of IBM Event Processing by leveraging JSON_QUERY for seamless data extraction. Enhance operational efficiency, drive real-time insights, and accelerate business decision-making today! 🚀

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