How to split a string based on a delimiter that appears more than once?

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Summary

The user asks how to extract a substring from a URL in a DB2 database, specifically the value following the final equals sign (=) in the query string. The requested operation is a string manipulation task: parsing a URL to isolate a parameter value at the end of the string. This is a common data cleaning scenario where raw text fields contain key-value pairs that need to be normalized into structured columns.

Root Cause

The data is stored as unstructured text in a VARCHAR column. The URL contains a query string with multiple parameters delimited by &. The specific value needed (source) is always the last parameter but is embedded within a larger string. Without specific parsing logic, the database engine treats the column as an atomic string rather than a collection of key-value pairs.

Why This Happens in Real Systems

  1. Legacy Data Ingestion: Systems often dump raw logs or API responses directly into database text fields without pre-processing the query string.
  2. Flexibility over Structure: Storing parameters in a URL string allows for dynamic fields without altering the database schema, but it complicates downstream analytics.
  3. String-Based APIs: Some older systems rely on concatenating parameters into a string rather than using JSON or XML, making extraction necessary for reporting.

Real-World Impact

  • Reporting Inaccuracy: Aggregating data by the unique identifier (e.g., source) becomes impossible without proper parsing.
  • Performance Overhead: Extracting values on the fly using string functions in SELECT queries adds CPU load, especially on large tables.
  • Data Integrity Risks: Manual string splitting is error-prone; if the URL structure changes slightly (e.g., the source parameter moves to the middle), simple string functions may fail or return incorrect data.

Example or Code

To extract the text after the final = in DB2, you can use a combination of REVERSE, LOCATE, and SUBSTR. This method works regardless of whether the parameter is in the middle or at the end, as long as it follows the last =.

SELECT 
    ID,
    SUBSTR(
        URL, 
        LENGTH(URL) - LOCATE('=', REVERSE(URL)) + 2
    ) AS URL_end
FROM 
    YOUR_TABLE_NAME;

Note on the Input Data:
In the user’s input, the HTML entity & appears. If the data in the database actually contains & (literal characters) instead of &, the delimiter changes. The query would need to look for =amp; or handle the replacement first.

If the data is stored with literal & (common if scraped from HTML sources), use this logic:

SELECT 
    ID,
    SUBSTR(
        URL, 
        LENGTH(URL) - LOCATE('=amp;', REVERSE(URL)) + 6
    ) AS URL_end
FROM 
    YOUR_TABLE_NAME;

How Senior Engineers Fix It

Senior engineers prioritize robustness and maintainability over quick hacks.

  1. Schema Normalization: Instead of parsing strings on every read, they create a new column (e.g., source_id) and backfill the data using a one-time script. This moves the compute cost to a write operation.
  2. Regex or JSON Functions: If the DB2 version supports it (DB2 11.1+), they use REGEXP_EXTRACT or JSON parsing functions if the data can be standardized. This is more readable and less prone to off-by-one errors than string slicing.
  3. ETL Layer: If the logic is complex, they move the extraction to an ETL tool (like Python or Spark) where string manipulation is more expressive and testable, rather than relying on obscure SQL functions.
  4. Validation: They validate the extraction logic against a sample set to ensure edge cases (missing parameters, malformed URLs) are handled.

Why Juniors Miss It

  1. Lack of DB2 Knowledge: Juniors often rely on generic SQL syntax (like SPLIT_PART available in PostgreSQL) which does not exist in DB2. They struggle to map logic to DB2’s specific function set (SUBSTR, LOCATE, REVERSE).
  2. Procedural Thinking: They might try to loop through rows in a stored procedure rather than using set-based SQL operations, leading to poor performance.
  3. Overlooking Data Quality: Juniors often assume the data is perfectly formatted. They might hardcode the delimiter to = and not check for & or other variations, causing the query to break on dirty data.
  4. Hardcoding Logic: They write the extraction logic directly into the SELECT statement for the report. This makes the logic hidden and difficult to reuse or maintain in other queries.