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Changelog

3.0.0

Breaking Changes

**Added runtime: datacustomcode.runtime.function.Runtime to function contract for codeType function.

Function now mandates runtime as arguments.

Why: runTime allows access to resources ( llm_gateway / file ) available during function execution.

Migration: use function(request: dict, runTime: Runtime) instead od function(request: dict)
```
# Before
def function(request: dict):
  pass

# After
def function(request: dict, runTime: Runtime):
  pass
```

2.0.0

Breaking Changes

Removed the row_limit parameter from read_dlo() and read_dmo().

These methods no longer accept a row_limit argument. When running locally, reads are automatically capped at 1000 rows to prevent accidentally fetching large datasets during development. When deployed to Data Cloud, no limit is applied and all records are returned.

Why: The row_limit parameter duplicated PySpark's built-in .limit() and created a behavioral difference between local and deployed environments. The 1000-row safety net is now handled internally via the default_row_limit setting in config.yaml, and deployed environments naturally omit it.

Migration: Remove any row_limit arguments from your read_dlo() and read_dmo() calls. If you need a specific number of rows, use PySpark's .limit() on the returned DataFrame:
```
# Before
df = client.read_dlo("MyObject__dll", row_limit=500)

# After
df = client.read_dlo("MyObject__dll").limit(500)
```

1.0.0

Breaking Changes

read_dlo() and read_dmo() now return DataFrames with all-lowercase column names.

Column names returned by both QueryAPIDataCloudReader and SFCLIDataCloudReader are now lowercased to match the column names produced by the deployed Data Cloud environment (e.g., unitprice__c instead of UnitPrice__c).

Why: In the deployed environment, column names are normalized to lowercase by the underlying Iceberg metadata layer. The local SDK previously returned the original API casing, causing "column does not exist" errors when scripts were deployed. This change aligns local behavior with the cloud.

Migration: Update any column references in your local scripts to use lowercase:
```
# Before
df.withColumn("Description__c", upper(col("Description__c")))
df.drop("KQ_Id__c")
df["UnitPrice__c"]

# After
df.withColumn("description__c", upper(col("description__c")))
df.drop("kq_id__c")
df["unitprice__c"]
```
Scripts already running in Data Cloud are unaffected — the cloud always returned lowercase column names.