Migrating to duckdb

Reviewed-on: #3

.gitignore

@@ -8,3 +8,5 @@ __pycache__/*
 */__pycache__/*
 *.pbix
 /logs/*
+.vscode/*
+*.coverage

.python-version

@@ -0,0 +1 @@
+3.13

config/custom_json_logger.py

@@ -3,6 +3,7 @@ import json
 import logging
 from typing import override
 
+
 class custom_json_logger(logging.Formatter):
     def __init__(
         self,
@@ -21,7 +22,7 @@ class custom_json_logger(logging.Formatter):
         always_fields = {
             "message" : record.getMessage(),
             "timestamp" : dt.datetime.fromtimestamp(
-                record.created, tz=dt.timezone.utc
+                record.created, tz=dt.UTC
             ).isoformat(),
         }
         if record.exc_info is not None:

config/exit_codes.py

@@ -11,3 +11,6 @@ CONFLICT = 9
 MOVE_FILE_ERROR = 10
 DUPLICATE_RESOLUTION_ERROR = 11
 UNIQUE_ID_NOT_FOUND = 12
+NO_DATA_PRODUCED = 13
+MISSING_DATA_FILES = 14
+BAD_JOIN = 15

data_check.py

@@ -1,17 +1,18 @@
+import duckdb
+import os
 import polars as pl
 
-df = pl.read_parquet('data/warehouse/transactions.parquet')
-print("Data loaded from Parquet file:")
-print(df)
 
-relevant_data = df.sql('''
+db = duckdb.connect('/mnt/bulk/data/ynab_data.duckdb')
+
+    # SELECT * FROM gold.transactions
+relevant_data = db.sql('''
     SELECT
-        date,
+        transaction_date,
         sum(transaction_amount) as total
-    FROM self
-    GROUP BY date
-    ORDER BY date DESC
-    '''
-)
+    FROM gold.transactions
+    GROUP BY transaction_date
+    ORDER BY transaction_date DESC
+''')
 print("Data after SQL query:")
 print(relevant_data)

docs/ERD.md

@@ -34,23 +34,29 @@ erDiagram
     }
     
     DATES {
-        int date_id
-        string date
+        string date_id
+        date date
         int year
         int month
         int day
+        boolean is_weekday
+        int weekday
     }
     
     TRANSACTIONS {
-        int transaction_id
+        str transaction_id
         int account_id
         int category_id
         int payee_id
-        int date_id
+        int transaction_date
         decimal amount
         boolean cleared
         boolean approved
         boolean deleted
+        string memo
+        string flag_color
+        str transfer_account_id
+
     }
     
     SCHEDULED_TRANSACTIONS {
@@ -58,10 +64,14 @@ erDiagram
         int account_id
         int category_id
         int payee_id
-        int date_id
+        str date_first
+        str date_next
         decimal amount
         string frequency
         boolean deleted
+        text memo
+        string flag_color
+        str transfer_account_id
     }
     
     TRANSACTIONS ||--o{ ACCOUNTS : "belongs to"
@@ -71,6 +81,6 @@ erDiagram
     SCHEDULED_TRANSACTIONS ||--o{ ACCOUNTS : "belongs to"
     SCHEDULED_TRANSACTIONS ||--o{ CATEGORIES : "belongs to"
     SCHEDULED_TRANSACTIONS ||--o{ PAYEES : "belongs to"
-    SCHEDULED_TRANSACTIONS ||--o{ DATES : "scheduled on"
+    SCHEDULED_TRANSACTIONS ||--o{ DATES : "First Scheduled"
+    SCHEDULED_TRANSACTIONS ||--o{ DATES : "Next Scheduled"
 ```
-

docs/Get_Started.md

@@ -25,7 +25,7 @@ For the `BUDGET_ID`, you can get it from the URL of your budget page on the YNAB
 ### Clone the repository
 
 ```bash
-git clone #link tbc
+git clone https://github.com/Jake-Pullen/data_pipeline_for_YNAB.git
 ```
 
 ### Install dependencies

docs/dataflow.md

@@ -28,3 +28,7 @@ The Data Warehouse is the data after it has been aggregated and transformed. It
 ## Processed Archive
 
 The Processed Archive is the data after it has been processed and stored in the base tables. It is the raw json files in the `data/processed/` directory with a folder for each entity and file for each load that has been processed.
+
+## Visualisation datasets
+
+When preparing the data for visualisation, we create dataframes in memory that are used to create the visualisations. These are not stored on disk.

main.py

@@ -1,17 +1,17 @@
-import os
-import dotenv
-import logging
-import yaml
-import sys
 import atexit
+import logging
 import logging.config
 import logging.handlers
+import os
+import sys
+
+import dotenv
+import yaml
 
 import config.exit_codes as ec
-from pipeline.ingest import Ingest
-from pipeline.raw_to_base import RawToBase
-from pipeline.dimensions import DimAccounts, DimCategories, DimPayees, DimDate
-from pipeline.facts import FactTransactions, FactScheduledTransactions
+from pipeline.pipeline_main import pipeline_main
+
+logger = logging.getLogger("data_pipeline_for_ynab")
 
 def set_up_logging():
     try:
@@ -27,55 +27,48 @@ def set_up_logging():
         queue_handler.listener.start()
         atexit.register(queue_handler.listener.stop)
 
-logger = logging.getLogger("data_pipeline_for_ynab")
+def load_config(logger):
+    try:
+        with open('config/config.yaml', 'r') as file:
+            config = yaml.safe_load(file)
+        return config
+    except FileNotFoundError:
+        logger.error('config.yaml file not found')
+        sys.exit(ec.MISSING_CONFIG_FILE)
+    except yaml.YAMLError as e:
+        logger.error(f'Error loading config.yaml: {e}')
+        sys.exit(ec.CORRUPTED_CONFIG_FILE)
+
 os.makedirs('logs', exist_ok=True)
 set_up_logging()
 
-# Load environment variables
 dotenv.load_dotenv()
 
 API_TOKEN = os.getenv('API_TOKEN')
 BUDGET_ID = os.getenv('BUDGET_ID')
 
-def main():
-    if not API_TOKEN or not BUDGET_ID:
-        logging.error('API_TOKEN or BUDGET_ID is not set in .env file')
-        sys.exit(ec.MISSING_ENV_VARS)
-
-    try:
-        with open('config/config.yaml', 'r') as file:
-            config = yaml.safe_load(file)
-    except FileNotFoundError:
-        logging.error('config.yaml file not found')
-        sys.exit(ec.MISSING_CONFIG_FILE)
-    except yaml.YAMLError as e:
-        logging.error(f'Error loading config.yaml: {e}')
-        sys.exit(ec.CORRUPTED_CONFIG_FILE)
-
-    config['API_TOKEN'] = API_TOKEN
-    config['BUDGET_ID'] = BUDGET_ID
-
-    logging.info('Starting data pipeline')
-
-    Ingest(config)
-    RawToBase(config)
-    DimAccounts(config)
-    DimCategories(config)
-    DimPayees(config)
-    DimDate(config)
-    FactTransactions(config)
-    FactScheduledTransactions(config)
-
-    logging.info('Data pipeline completed successfully')
-    sys.exit(ec.SUCCESS)
+if not API_TOKEN or not BUDGET_ID:
+    logger.error('API_TOKEN or BUDGET_ID is not set in .env file')
+    sys.exit(ec.MISSING_ENV_VARS)
 
 if __name__ == '__main__':
+    config = load_config(logger)
+    config['API_TOKEN'] = API_TOKEN
+    config['BUDGET_ID'] = BUDGET_ID
     try:
-        main()
+        pipeline_main(config, logger)
+
+        data_exists = os.path.exists('data/processed') and os.listdir('data/processed')
+        if data_exists:
+            logger.info('Processing Successful')
+            sys.exit(ec.SUCCESS)
+        else:
+            logger.error('Data pipeline did not produce any data. Dash app will not run.')
+            sys.exit(ec.NO_DATA_PRODUCED)
     except SystemExit as e:
         exit_code = e.code
         if exit_code == ec.SUCCESS:
-            logging.info('Program exited successfully')
+            logger.info('Program exited successfully')
         else:
-            logging.error(f'Program exited with code {exit_code}')
+            logger.error(f'Program exited with code {exit_code}')
         raise

pipeline/dimensions.py

@@ -1,88 +1,97 @@
-import polars as pl
-import logging
 import os
 from datetime import date
 
+import polars as pl
+
+
 class Dimensions:
-    def __init__(self, config):
+    def __init__(self, config,logger):
         self.config = config
         self.base_file_path = self.config['base_data_path']
         os.makedirs(self.config['warehouse_data_path'], exist_ok=True)
+        self.logger = logger
 
     def get_full_file_path(self, file_name):
         return f"{self.base_file_path}/{file_name}"
 
 
 class DimAccounts(Dimensions):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.file_path = self.get_full_file_path('accounts.parquet')
         self.transform()
 
     def transform(self):
         # Read the parquet file into a polars DataFrame
         try:
-            accounts_df = pl.read_parquet(self.file_path)
+            source_accounts = pl.read_parquet(self.file_path)
         except Exception as e:
-            logging.error(f"Failed to read the base accounts parquet file: {e}")
+            self.logger.error(f"Failed to read the base accounts parquet file: {e}")
             return
 
-        # Transform the DataFrame
-        logging.info("Transforming the accounts DataFrame")
+        self.logger.info("Transforming the accounts DataFrame")
         try:
-            accounts_df = (
-                accounts_df
-                .with_columns([
-                    pl.col("id").alias("account_id"),
-                    pl.col("name").alias("account_name"),
-                    pl.col("type").alias("account_type"),
-                    pl.col("on_budget").alias("on_budget"),
-                    pl.col("closed").alias("closed"),
-                    pl.col("note").alias("note"),
-                    pl.col("balance").alias("balance"),
-                    pl.col("cleared_balance").alias("cleared_balance"),
-                    pl.col("uncleared_balance").alias("uncleared_balance"),
-                    pl.col("deleted").alias("deleted"),
-                ])
-                .with_columns([
-                    pl.col("note").fill_null("unknown"),
-                    (pl.col("balance") / 100).alias("balance"),
-                    (pl.col("cleared_balance") / 100).alias("cleared_balance"),
-                    (pl.col("uncleared_balance") / 100).alias("uncleared_balance"),
-                ])
-                .drop([
-                    "transfer_payee_id", "direct_import_linked", "direct_import_in_error",
-                    "last_reconciled_at", "debt_original_balance", "debt_interest_rates",
-                    "debt_minimum_payments", "debt_escrow_amounts", "ingestion_date"
+            base_accounts = (
+                source_accounts.select([
+                    "id",
+                    "name",
+                    "type",
+                    "on_budget",
+                    "closed",
+                    "note",
+                    "balance",
+                    "cleared_balance",
+                    "uncleared_balance",
+                    "deleted"
                 ])
             )
         except Exception as e:
-            logging.error(f"Failed to transform the accounts DataFrame: {e}")
+            self.logger.error(f"Failed to select columns from the categories DataFrame: {e}")
             return
-        # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed accounts DataFrame to parquet file")
+
         try:
-            accounts_df.write_parquet(self.config['warehouse_data_path'] + '/accounts.parquet')
+            add_accounts_prefix = base_accounts.with_columns([
+                pl.col("id").alias("account_id"),
+                pl.col("name").alias("account_name"),
+                pl.col("type").alias("account_type")
+            ])
+            fill_accounts_null_values = add_accounts_prefix.with_columns([
+                pl.col('note').fill_null('none')
+            ])
+            fix_accounts_values = fill_accounts_null_values.with_columns([
+                (pl.col("balance") / 1000).alias("balance"),
+                (pl.col("cleared_balance") / 1000).alias("cleared_balance"),
+                (pl.col("uncleared_balance") / 1000).alias("uncleared_balance"),
+            ])
+            drop_accounts_columns = fix_accounts_values.drop([
+                "id", "name", "type"
+            ])
         except Exception as e:
-            logging.error(f"Failed to write the transformed accounts DataFrame to parquet file: {e}")
+            self.logger.error(f"Failed to transform the accounts DataFrame: {e}")
+            return
+
+        self.logger.info("Writing the transformed accounts DataFrame to parquet file")
+        try:
+            drop_accounts_columns.write_parquet(self.config['warehouse_data_path'] + '/accounts.parquet')
+        except Exception as e:
+            self.logger.error(f"Failed to write the transformed accounts DataFrame to parquet file: {e}")
             return
 
 class DimCategories(Dimensions):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.file_path = self.get_full_file_path('categories.parquet')
         self.transform()
 
     def transform(self):
-        # Read the parquet file into a polars DataFrame
         try:
-            categories_df = pl.read_parquet(self.file_path)
+            source_categories = pl.read_parquet(self.file_path)
         except Exception as e:
-            logging.error(f"Failed to read the base categories parquet file: {e}")
+            self.logger.error(f"Failed to read the base categories parquet file: {e}")
             return
-        logging.info("Transforming the categories DataFrame")
+        self.logger.info("Transforming the categories DataFrame")
         try:
-            categories_df = categories_df.select([
+            base_categories = source_categories.select([
                 'id',
                 'name',
                 'category_group_name',
@@ -94,75 +103,82 @@ class DimCategories(Dimensions):
                 'deleted'
             ])
         except Exception as e:
-            logging.error(f"Failed to select columns from the categories DataFrame: {e}")
+            self.logger.error(f"Failed to select columns from the categories DataFrame: {e}")
             return
 
         try:
-            # Rename the columns
-            categories_df = categories_df.with_columns(pl.col('id').alias('category_id'))
-            categories_df = categories_df.with_columns(pl.col('name').alias('category_name'))
-
-            # Fill null values in the note column
-            categories_df = categories_df.with_columns(pl.col('note').fill_null('unknown'))
-
-            # Convert the balance, budgeted, and activity columns to decimal
-            categories_df = categories_df.with_columns(pl.col('balance') / 100)
-            categories_df = categories_df.with_columns(pl.col('budgeted') / 100)
-            categories_df = categories_df.with_columns(pl.col('activity') / 100)
+            add_categories_prefix = base_categories.with_columns([
+                pl.col('id').alias('category_id'),
+                pl.col('name').alias('category_name')
+            ])
+            fill_null_category_values = add_categories_prefix.with_columns([
+                pl.col('note').fill_null('none')
+            ])
+            fix_categories_values = fill_null_category_values.with_columns([
+                (pl.col('balance') / 1000),
+                (pl.col('budgeted') / 1000),
+                (pl.col('activity') / 1000)
+            ])
+            drop_categories_columns = fix_categories_values.drop([
+                'id', 'name'
+            ])
         except Exception as e:
-            logging.error(f"Failed to transform the categories DataFrame: {e}")
+            self.logger.error(f"Failed to transform the categories DataFrame: {e}")
             return
 
-        # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed categories DataFrame to parquet file")
+        self.logger.info("Writing the transformed categories DataFrame to parquet file")
         try:
-            categories_df.write_parquet(self.config['warehouse_data_path'] + '/categories.parquet')
+            drop_categories_columns.write_parquet(self.config['warehouse_data_path'] + '/categories.parquet')
         except Exception as e:
-            logging.error(f"Failed to write the transformed categories DataFrame to parquet file: {e}")
+            self.logger.error(f"Failed to write the transformed categories DataFrame to parquet file: {e}")
             return
 
 class DimPayees(Dimensions):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.file_path = self.get_full_file_path('payees.parquet')
         self.transform()
 
     def transform(self):
-        # Read the parquet file into a polars DataFrame
         try:
-            payees_df = pl.read_parquet(self.file_path)
+            source_payees = pl.read_parquet(self.file_path)
         except Exception as e:
-            logging.error(f"Failed to read the base payees parquet file: {e}")
+            self.logger.error(f"Failed to read the base payees parquet file: {e}")
             return
-        logging.info("Transforming the payees DataFrame")
+        self.logger.info("Transforming the payees DataFrame")
         try:
-            payees_df = payees_df.select([
+            base_payees = source_payees.select([
                 'id',
                 'name',
                 'deleted'
             ])
         except Exception as e:
-            logging.error(f"Failed to select columns from the payees DataFrame: {e}")
+            self.logger.error(f"Failed to select columns from the payees DataFrame: {e}")
             return
+
         try:
-            # Rename the columns
-            payees_df = payees_df.with_columns(pl.col('id').alias('payee_id'))
-            payees_df = payees_df.with_columns(pl.col('name').alias('payee_name'))
+            add_payees_prefix = base_payees.with_columns([
+                pl.col('id').alias('payee_id'),
+                pl.col('name').alias('payee_name')
+            ])
+            drop_payees_columns = add_payees_prefix.drop([
+                'id', 'name'
+            ])
         except Exception as e:
-            logging.error(f"Failed to rename columns in the payees DataFrame: {e}")
+            self.logger.error(f"Failed to rename columns in the payees DataFrame: {e}")
             return
 
         # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed payees DataFrame to parquet file")
+        self.logger.info("Writing the transformed payees DataFrame to parquet file")
         try:
-            payees_df.write_parquet(self.config['warehouse_data_path'] + '/payees.parquet')
+            drop_payees_columns.write_parquet(self.config['warehouse_data_path'] + '/payees.parquet')
         except Exception as e:
-            logging.error(f"Failed to write the transformed payees DataFrame to parquet file: {e}")
+            self.logger.error(f"Failed to write the transformed payees DataFrame to parquet file: {e}")
             return
 
 class DimDate(Dimensions):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.transform()
 
     def transform(self):
@@ -170,7 +186,7 @@ class DimDate(Dimensions):
         try:
             dates_df = pl.DataFrame({'date':pl.date_range(date(2020, 1, 1), date(2030, 12, 31), "1d", eager=True)})
         except Exception as e:
-            logging.error(f"Failed to create a DataFrame with dates: {e}")
+            self.logger.error(f"Failed to create a DataFrame with dates: {e}")
             return
         # Extract year, month, day, and weekday from the date column
         try:
@@ -181,21 +197,32 @@ class DimDate(Dimensions):
                 pl.col('date').dt.weekday().alias('weekday')
             ])
         except Exception as e:
-            logging.error(f"Failed to extract year, month, day, and weekday from the date column: {e}")
+            self.logger.error(f"Failed to extract year, month, day, and weekday from the date column: {e}")
             return
         try:
             # Create a new column to indicate if the date is a weekday or weekend
             dates_df = dates_df.with_columns([
-                (pl.col('weekday') < 5).alias('is_weekday')  # True for weekdays (Monday to Friday), False for weekends (Saturday and Sunday)
+                (pl.col('weekday') < 6).alias('is_weekday')  # True for weekdays (Monday to Friday), False for weekends (Saturday and Sunday)
             ])
         except Exception as e:
-            logging.error(f"Failed to create a new column to indicate if the date is a weekday or weekend: {e}")
+            self.logger.error(f"Failed to create a new column to indicate if the date is a weekday or weekend: {e}")
+            return
+
+        # Create a primary key by concatenating year, month, and day with no separators
+        try:
+            dates_df = dates_df.with_columns([
+                (pl.col('year').cast(pl.Utf8) +
+                 pl.col('month').cast(pl.Utf8).str.zfill(2) +
+                 pl.col('day').cast(pl.Utf8).str.zfill(2)
+                ).alias('date_id')
+            ])
+        except Exception as e:
+            self.logger.error(f"Failed to create the primary key column: {e}")
             return
         # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed dates DataFrame to parquet file")
+        self.logger.info("Writing the transformed dates DataFrame to parquet file")
         try:
             dates_df.write_parquet(self.config['warehouse_data_path'] + '/dates.parquet')
         except Exception as e:
-            logging.error(f"Failed to write the transformed dates DataFrame to parquet file: {e}")
+            self.logger.error(f"Failed to write the transformed dates DataFrame to parquet file: {e}")
             return
-

pipeline/duckdb_layer.py

@@ -0,0 +1,241 @@
+"""DuckDB-based silver and gold layer for the YNAB data pipeline."""
+
+import os
+from datetime import date, timedelta
+
+import duckdb
+
+
+class DuckDBLayer:
+    """Orchestrates silver (external base) and gold (views + materialized) layers via DuckDB."""
+
+    def __init__(self, base_path: str, warehouse_path: str, logger):
+        self.base_path = base_path
+        self.warehouse_path = warehouse_path
+        self.logger = logger
+        self.db = None
+        os.makedirs(self.warehouse_path, exist_ok=True)
+        self._init()
+
+    def _silver_parquet_expr(self, entity: str) -> str:
+        return f"read_parquet('{self.base_path}/{entity}.parquet')"
+
+    def _init(self):
+        self.db = duckdb.connect('/mnt/bulk/data/ynab_data.duckdb')
+        self._setup_schemas()
+        self._create_silver_external_tables()
+        self._drop_gold()
+        self._create_gold_views()
+        self._create_gold_dates()
+        self._create_gold_facts()
+
+    def _setup_schemas(self):
+        self.db.execute("CREATE SCHEMA IF NOT EXISTS silver")
+        self.db.execute("CREATE SCHEMA IF NOT EXISTS gold")
+
+    def _create_silver_external_tables(self):
+        entities = ['accounts', 'categories', 'payees', 'transactions', 'scheduled_transactions']
+        for entity in entities:
+            sql = f"CREATE TABLE IF NOT EXISTS silver.{entity} AS SELECT * FROM {self._silver_parquet_expr(entity)}"
+            try:
+                self.db.execute(sql)
+                self.logger.info(f"Created silver.{entity}")
+            except Exception as e:
+                self.logger.error(f"Failed to create silver.{entity}: {e}")
+        self.logger.info("Silver layer initialized")
+
+    def _drop_gold(self):
+        for name in ['accounts', 'categories', 'payees']:
+            self.db.execute(f"DROP VIEW IF EXISTS gold.{name}")
+        self.db.execute("DROP TABLE IF EXISTS gold.dates")
+        self.db.execute("DROP TABLE IF EXISTS gold.transactions")
+        self.db.execute("DROP TABLE IF EXISTS gold.scheduled_transactions")
+
+    # ── gold views (dimensions) ─────
+
+    def _create_gold_accounts_view(self):
+        self.logger.info("Transforming and creating gold.accounts view")
+        sql = """
+            SELECT
+                id AS account_id,
+                name AS account_name,
+                "type" AS account_type,
+                on_budget,
+                closed,
+                COALESCE(CAST(note AS VARCHAR), 'none') AS note,
+                balance / 1000.0 AS balance,
+                cleared_balance / 1000.0 AS cleared_balance,
+                uncleared_balance / 1000.0 AS uncleared_balance,
+                deleted
+            FROM silver.accounts
+        """
+        try:
+            self.db.execute(f"CREATE VIEW gold.accounts AS {sql}")
+            self.logger.info("Created gold.accounts view")
+        except Exception as e:
+            self.logger.error(f"Failed to create gold.accounts view: {e}")
+
+    def _create_gold_categories_view(self):
+        self.logger.info("Transforming and creating gold.categories view")
+        sql = """
+            SELECT
+                id AS category_id,
+                name AS category_name,
+                category_group_name,
+                hidden,
+                COALESCE(CAST(note AS VARCHAR), 'none') AS note,
+                budgeted / 1000.0 AS budgeted,
+                activity / 1000.0 AS activity,
+                balance / 1000.0 AS balance,
+                deleted
+            FROM silver.categories
+        """
+        try:
+            self.db.execute(f"CREATE VIEW gold.categories AS {sql}")
+            self.logger.info("Created gold.categories view")
+        except Exception as e:
+            self.logger.error(f"Failed to create gold.categories view: {e}")
+
+    def _create_gold_payees_view(self):
+        self.logger.info("Transforming and creating gold.payees view")
+        sql = """
+            SELECT
+                id AS payee_id,
+                name AS payee_name,
+                deleted
+            FROM silver.payees
+        """
+        try:
+            self.db.execute(f"CREATE VIEW gold.payees AS {sql}")
+            self.logger.info("Created gold.payees view")
+        except Exception as e:
+            self.logger.error(f"Failed to create gold.payees view: {e}")
+
+    def _create_gold_views(self):
+        self._create_gold_accounts_view()
+        self._create_gold_categories_view()
+        self._create_gold_payees_view()
+
+    # ── gold dates (materialized as Parquet, loaded into DuckDB) ─────
+
+    def _create_gold_dates(self):
+        dates_path = os.path.join(self.warehouse_path, 'dates.parquet')
+        self.logger.info("Creating gold.dates dimension")
+        try:
+            start = date(2020, 1, 1)
+            end = date(2030, 12, 31)
+            days = (end - start).days + 1
+            rows = []
+            for i in range(days):
+                d = start + timedelta(days=i)
+                month_z = str(d.month).zfill(2)
+                day_z = str(d.day).zfill(2)
+                rows.append({
+                    'date_id': f"{d.year}{month_z}{day_z}",
+                    'date': d,
+                    'year': int(d.year),
+                    'month': int(d.month),
+                    'day': int(d.day),
+                    'weekday': int(d.isoweekday()),
+                    'is_weekday': 1 if d.isoweekday() < 6 else 0,
+                })
+
+            import pyarrow as pa
+            import pyarrow.parquet as pq
+            dates_df = pa.Table.from_pylist(rows)
+            dates_df = dates_df.cast(pa.schema([
+                ('date_id', pa.string()),
+                ('date', pa.date32()),
+                ('year', pa.int32()),
+                ('month', pa.int8()),
+                ('day', pa.int8()),
+                ('weekday', pa.int8()),
+                ('is_weekday', pa.int8()),
+            ]))
+            pq.write_table(dates_df, dates_path)
+
+            self.db.execute(f"CREATE TABLE gold.dates AS SELECT * FROM read_parquet('{dates_path}')")
+            self.logger.info(f"Created gold.dates and persisted to {dates_path}")
+        except Exception as e:
+            self.logger.error(f"Failed to create gold.dates: {e}")
+
+    # ── gold facts (materialized as Parquet) ─────
+
+    def _create_gold_transactions_fact(self):
+        transactions_path = os.path.join(self.warehouse_path, 'transactions.parquet')
+        self.logger.info("Transforming and persisting gold.transactions fact")
+        sql = f"""
+            COPY (
+                SELECT
+                    memo,
+                    cleared,
+                    approved,
+                    COALESCE(CAST(flag_color AS VARCHAR), 'none') AS flag_color,
+                    account_id,
+                    payee_id,
+                    category_id,
+                    transfer_account_id,
+                    id AS transaction_id,
+                    CAST(YEAR(CAST(date AS DATE)) AS VARCHAR)
+                        || LPAD(CAST(MONTH(CAST(date AS DATE)) AS VARCHAR), 2, '0')
+                        || LPAD(CAST(DAY(CAST(date AS DATE)) AS VARCHAR), 2, '0') AS transaction_date,
+                    amount / 1000.0 AS transaction_amount,
+                    deleted
+                FROM silver.transactions
+            ) TO '{transactions_path}' (FORMAT PARQUET)
+        """
+        try:
+            self.db.execute(sql)
+            self.logger.info(f"Persisted transactions.fact to {transactions_path}")
+        except Exception as e:
+            self.logger.error(f"Failed to create transactions.fact: {e}")
+
+    def _create_gold_scheduled_transactions_fact(self):
+        st_path = os.path.join(self.warehouse_path, 'scheduled_transactions.parquet')
+        self.logger.info("Transforming and persisting gold.scheduled_transactions fact")
+        sql = f"""
+            COPY (
+                SELECT
+                    CAST(date_first AS DATE) AS date_first,
+                    CAST(date_next AS DATE) AS date_next,
+                    frequency,
+                    COALESCE(memo, 'none') AS memo,
+                    COALESCE(CAST(flag_color AS VARCHAR), 'none') AS flag_color,
+                    account_id,
+                    payee_id,
+                    category_id,
+                    transfer_account_id,
+                    id AS scheduled_transaction_id,
+                    amount / 1000.0 AS scheduled_transaction_amount
+                FROM silver.scheduled_transactions
+            ) TO '{st_path}' (FORMAT PARQUET)
+        """
+        try:
+            self.db.execute(sql)
+            self.logger.info(f"Persisted scheduled_transactions.fact to {st_path}")
+        except Exception as e:
+            self.logger.error(f"Failed to create scheduled_transactions.fact: {e}")
+
+    def _create_gold_facts(self):
+        self._create_gold_transactions_fact()
+        self._create_gold_scheduled_transactions_fact()
+        # Create DuckDB tables from the persistent Parquet files so they're queryable
+        self._register_facts()
+
+    def _register_facts(self):
+        """Register the fact Parquet files as DuckDB tables."""
+        try:
+            self.db.execute(f"CREATE TABLE gold.transactions AS SELECT * FROM read_parquet('{os.path.join(self.warehouse_path, 'transactions.parquet')}')")
+            self.db.execute(f"CREATE TABLE gold.scheduled_transactions AS SELECT * FROM read_parquet('{os.path.join(self.warehouse_path, 'scheduled_transactions.parquet')}')")
+            self.logger.info("Registered fact tables in DuckDB")
+        except Exception as e:
+            self.logger.error(f"Failed to register fact tables: {e}")
+
+    def close(self):
+        if self.db:
+            self.db.close()
+
+
+def get_duckdb_layer(base_path: str, warehouse_path: str, logger) -> DuckDBLayer:
+    """Factory function to create a DuckDB layer and perform all transformations."""
+    return DuckDBLayer(base_path, warehouse_path, logger)

pipeline/facts.py

@@ -1,116 +1,152 @@
-import polars as pl
-import logging
 import os
 
+import polars as pl
+
+
 class Facts:
-    def __init__(self, config):
+    def __init__(self, config,logger):
         self.config = config
         self.base_file_path = self.config['base_data_path']
+        self.logger = logger
         os.makedirs(self.config['warehouse_data_path'], exist_ok=True)
 
     def get_full_file_path(self, file_name):
         return f"{self.base_file_path}/{file_name}"
 
 class FactTransactions(Facts):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.file_path = self.get_full_file_path('transactions.parquet')
         self.transform()
 
     def transform(self):
-        # Read the parquet file into a polars DataFrame
         try:
-            transactions_df = pl.read_parquet(self.file_path)
+            source_transactions = pl.read_parquet(self.file_path)
         except FileNotFoundError:
-            logging.error("The transactions DataFrame does not exist")
+            self.logger.error("The transactions DataFrame does not exist")
             return
 
-        # Transform the DataFrame
-        logging.info("Transforming the transactions DataFrame")
         try:
-            transactions_df = (
-                transactions_df
-                .with_columns([
-                    pl.col("id").alias("transaction_id"),
-                    pl.col("date").alias("transaction_date"),
-                    pl.col("amount").alias("transaction_amount"),
-                    pl.col("memo").alias("transaction_memo"),
-                    pl.col("cleared").alias("transaction_cleared"),
-                    pl.col("approved").alias("transaction_approved"),
-                    pl.col("flag_color").alias("transaction_flag_color"),
-                    pl.col("account_id").alias("account_id"),
-                    pl.col("payee_id").alias("payee_id"),
-                    pl.col("category_id").alias("category_id"),
-                    pl.col("transfer_account_id").alias("transfer_account_id"),
-                ])
-                .with_columns([
-                    pl.col("memo").fill_null("unknown"),
-                    (pl.col("amount") / 100).alias("transaction_amount"),
-                ])
-                .drop([
-                    "transfer_transaction_id", "matched_transaction_id", "import_id",
-                    "subtransactions", "deleted","flag_name","account_name",
-                    "payee_name","category_name","import_payee_name","import_payee_name_original",
-                    "debt_transaction_type","ingestion_date"
-                ])
-            )
+            base_transactions = source_transactions.select([
+                "id",
+                "date",
+                "amount",
+                "memo",
+                "cleared",
+                "approved",
+                "flag_color",
+                "account_id",
+                "payee_id",
+                "category_id",
+                "transfer_account_id"
+            ])
         except Exception as e:
-            logging.error(f"Failed to transform the transactions DataFrame: {e}")
+            self.logger.error(f"Failed to select columns from the transactions DataFrame: {e}")
+            return
+
+        self.logger.info("Transforming the transactions DataFrame")
+        try:
+            resolve_transaction_dates = base_transactions.with_columns([
+                pl.col("date").str.strptime(pl.Date, format="%Y-%m-%d").alias("date")
+            ])
+        except Exception as e:
+            self.logger.error(f"Failed to covert the date to date format: {e}")
+            return
+
+        try:
+            add_transaction_prefix = resolve_transaction_dates.with_columns([
+                pl.col("id").alias("transaction_id"),
+                (pl.col("date").dt.year().cast(pl.Utf8) +
+                    pl.col("date").dt.month().cast(pl.Utf8).str.zfill(2) +
+                    pl.col("date").dt.day().cast(pl.Utf8).str.zfill(2)).alias("transaction_date"),
+            ])
+            fix_transaction_nulls = add_transaction_prefix.with_columns([
+                pl.col("memo").fill_null("none"),
+                pl.col("flag_color").fill_null("none"),
+                pl.col("transfer_account_id").fill_null("none"),
+                pl.col("category_id").fill_null("none"),
+            ])
+            fix_transaction_values = fix_transaction_nulls.with_columns([
+                (pl.col("amount") / 1000).alias("transaction_amount")
+            ])
+            drop_transaction_columns = fix_transaction_values.drop([
+                "id", "date", "amount"
+            ])
+
+        except Exception as e:
+            self.logger.error(f"Failed to transform the transactions DataFrame: {e}")
             return
         # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed transactions DataFrame to parquet file")
+        self.logger.info("Writing the transformed transactions DataFrame to parquet file")
         try:
-            transactions_df.write_parquet(self.config['warehouse_data_path'] + '/transactions.parquet')
+            drop_transaction_columns.write_parquet(
+                self.config['warehouse_data_path'] + '/transactions.parquet'
+            )
         except Exception as e:
-            logging.error(f"Failed to write the transformed transactions DataFrame: {e}")
+            self.logger.error(f"Failed to write the transformed transactions DataFrame: {e}")
 
 class FactScheduledTransactions(Facts):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config,logger):
+        super().__init__(config,logger)
         self.file_path = self.get_full_file_path('scheduled_transactions.parquet')
         self.transform()
 
     def transform(self):
-        # Read the parquet file into a polars DataFrame
         try:
-            scheduled_transactions_df = pl.read_parquet(self.file_path)
+            source_scheduled = pl.read_parquet(self.file_path)
         except FileNotFoundError:
-            logging.error("The scheduled transactions DataFrame does not exist")
+            self.logger.error("The scheduled transactions DataFrame does not exist")
             return
 
-        # Transform the DataFrame
-        logging.info("Transforming the scheduled transactions DataFrame")
         try:
-            scheduled_transactions_df = (
-                scheduled_transactions_df
-                .with_columns([
-                    pl.col("id").alias("scheduled_transaction_id"),
-                    pl.col("date_first").alias("scheduled_transaction_first_date"),
-                    pl.col("date_next").alias("scheduled_transaction_next_date"),
-                    pl.col("frequency").alias("scheduled_transaction_frequency"),
-                    pl.col("amount").alias("scheduled_transaction_amount"),
-                    pl.col("memo").alias("scheduled_transaction_memo"),
-                    pl.col("flag_color").alias("scheduled_transaction_flag_color"),
-                    pl.col("account_id").alias("account_id"),
-                    pl.col("payee_id").alias("payee_id"),
-                    pl.col("category_id").alias("category_id"),
-                    pl.col("transfer_account_id").alias("transfer_account_id"),
-                ])
-                .with_columns([
-                    pl.col("memo").fill_null("unknown"),
-                    (pl.col("amount") / 100).alias("scheduled_transaction_amount"),
-                ])
-                .drop([
-                    "subtransactions", "deleted","flag_name","account_name",
-                    "payee_name","category_name","ingestion_date"
-                ])
-            )
+            base_scheduled = source_scheduled.select([
+                "id",
+                "date_first",
+                "date_next",
+                "frequency",
+                "amount",
+                "memo",
+                "flag_color",
+                "account_id",
+                "payee_id",
+                "category_id",
+                "transfer_account_id"
+            ])
         except Exception as e:
-            logging.error(f"Failed to transform the scheduled transactions DataFrame: {e}")
+            self.logger.error(f"Failed to select columns from the scheduled transactions DataFrame: {e}")
             return
-        # Write the DataFrame to a new parquet file
-        logging.info("Writing the transformed scheduled transactions DataFrame to parquet file")
+
         try:
-            scheduled_transactions_df.write_parquet(self.config['warehouse_data_path'] + '/scheduled_transactions.parquet')
+            resolve_scheduled_dates = base_scheduled.with_columns([
+                pl.col("date_first").str.strptime(pl.Date, format="%Y-%m-%d").alias("date_first"),
+                pl.col("date_next").str.strptime(pl.Date, format="%Y-%m-%d").alias("date_next")
+            ])
         except Exception as e:
-            logging.error(f"Failed to write the transformed scheduled transactions DataFrame: {e}")
+            self.logger.error(f"Failed to covert the date to date format: {e}")
+            return
+
+        self.logger.info("Transforming the scheduled transactions DataFrame")
+        try:
+            add_scheduled_prefix = resolve_scheduled_dates.with_columns([
+                pl.col("id").alias("scheduled_transaction_id")
+            ])
+            fix_sheduled_nulls = add_scheduled_prefix.with_columns([
+                pl.col("memo").fill_null("none"),
+                pl.col("flag_color").fill_null("none"),
+                pl.col("transfer_account_id").fill_null("none"),
+                pl.col("category_id").fill_null("none"),
+            ])
+            fix_scheduled_values = fix_sheduled_nulls.with_columns([
+                (pl.col("amount") / 1000).alias("scheduled_transaction_amount"),
+            ])
+            drop_scheduled_columns = fix_scheduled_values.drop([
+                "id", "amount"
+            ])
+        except Exception as e:
+            self.logger.error(f"Failed to transform the scheduled transactions DataFrame: {e}")
+            return
+        self.logger.info("Writing the transformed scheduled transactions DataFrame to parquet file")
+        try:
+            drop_scheduled_columns.write_parquet(self.config['warehouse_data_path'] + '/scheduled_transactions.parquet')
+        except Exception as e:
+            self.logger.error(f"Failed to write the transformed scheduled transactions DataFrame: {e}")

pipeline/ingest.py

@@ -1,17 +1,17 @@
-import os
-import time
 import json
-import logging
-import requests
+import os
 import sys
-import yaml
-from typing import Dict, Any
+import time
+from typing import Any
+
+import requests
+
 import config.exit_codes as ec
 
+
 class Ingest:
 
-
-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: dict[str, Any],logger):
         """
         Initialize the Ingest class with the provided configuration.
         """
@@ -22,21 +22,12 @@ class Ingest:
         self.entities = config['entities']
         self.raw_data_path = config['raw_data_path']
         self.headers = {'Authorization': f'Bearer {self.api_token}'}
-        self.knowledge_cache = self.load_knowledge_cache()
         self.MAX_RETRIES = config['REQUESTS_MAX_RETRIES']
         self.RETRY_DELAY = config['REQUESTS_RETRY_DELAY']
-        self.fetch_and_cache_entity_data()
+        self.logger = logger
 
-    def load_knowledge_cache(self) -> Dict[str, Any]:
-        """
-        Load the knowledge cache from the file if it exists.
-        """
-        if os.path.exists(self.knowledge_file):
-            with open(self.knowledge_file, 'r') as f:
-                return json.load(f)
-        return {}
 
-    def save_entity_data_to_raw(self, entity: str, data: Dict[str, Any]):
+    def save_entity_data_to_raw(self, entity: str, data: dict[str, Any]):
         """
         Save the data for a specific entity to a new cache file.
         """
@@ -45,13 +36,23 @@ class Ingest:
         if not os.path.exists(directory):
             os.makedirs(directory)
         entity_file = f'{directory}/{current_time}.json'
-        logging.info(f"Saving {entity} data to {entity_file}")
+        self.logger.info(f"Saving {entity} data to {entity_file}")
         try:
             with open(entity_file, 'w') as f:
                 json.dump(data, f, indent=4)
         except Exception as e:
-            logging.error(f"Error saving {entity} data: {e}")
+            self.logger.error(f"Failed to save data for {entity} to {entity_file}")
+            raise e
 
+    def load_knowledge_cache(self) -> dict[str, Any]:
+        """
+        Load the knowledge cache from the file if it exists.
+        """
+        if not os.path.exists(self.knowledge_file):
+            os.makedirs(os.path.dirname(self.knowledge_file),exist_ok=True)
+            return {}
+        with open(self.knowledge_file, 'r') as f:
+            return json.load(f)
 
     def update_server_knowledge_cache(self, entity: str, server_knowledge: Any):
         """
@@ -61,7 +62,7 @@ class Ingest:
             with open(self.knowledge_file, 'r') as f:
                 knowledge_cache = json.load(f)
         except FileNotFoundError:
-            logging.info(f"Knowledge file not found. Creating a new one at {self.knowledge_file}. This is normal for the first run.")
+            self.logger.info(f"Knowledge file not found. Creating a new one at {self.knowledge_file}. This is normal for the first run.")
             os.makedirs(os.path.dirname(self.knowledge_file), exist_ok=True)
             knowledge_cache = {}
 
@@ -70,69 +71,61 @@ class Ingest:
         with open(self.knowledge_file, 'w') as f:
             json.dump(knowledge_cache, f, indent=4)
 
-    def check_rate_limit(self, response: requests.Response):
-        """
-        Check and handle the rate limit based on the response headers.
-        """
-        rate_limit_header = response.headers.get('X-Rate-Limit')
-        if rate_limit_header:
-            requests_made, limit = map(int, rate_limit_header.split('/'))
-            remaining_requests = limit - requests_made
-            logging.info(f"Rate Limit: {remaining_requests}/{limit} requests remaining.")
-            if remaining_requests < 20:
-                logging.warning("Approaching rate limit. Consider pausing further requests.")
-                # Implement pause or delay logic here if necessary
-            if remaining_requests == 1:
-                logging.error("Rate limit exceeded. ending requests here and moving on with what we have.")
-                return True #returning True here to break out of any more ingestions
-                
-        else:
-            logging.warning("X-Rate-Limit header is missing.")
+        knowledge_cache = self.load_knowledge_cache()
+        knowledge_cache[entity] = server_knowledge
+        try:
+            with open(self.knowledge_file, 'w') as f:
+                json.dump(knowledge_cache, f, indent=4)
+        except Exception as e:
+            self.logger.error(f"Failed to update knowledge cache for {entity} in {self.knowledge_file}")
+            raise e
 
     def handle_response(self, response) -> bool:
         if response.status_code == 400:
-            logging.error("Bad request. The request could not be understood by the API due to malformed syntax or validation errors.")
+            self.logger.error("Bad request. The request could not be understood by the API due to malformed syntax or validation errors.")
             sys.exit(ec.BAD_REQUEST)
         elif response.status_code == 401:
-            logging.error("Unauthorized. Please check your API token.")
+            self.logger.error("Unauthorized. Please check your API token.")
             sys.exit(ec.UNAUTHORIZED_API_TOKEN)
         elif response.status_code == 403:
-            logging.error("Forbidden. Access is denied.")
+            self.logger.error("Forbidden. Access is denied.")
             sys.exit(ec.FORBIDDEN)
         elif response.status_code == 404:
-            logging.error("Not found. The specified URI does not exist.")
+            self.logger.error("Not found. The specified URL does not exist.")
             sys.exit(ec.NOT_FOUND)
         elif response.status_code == 409:
-            logging.error("Conflict. The resource cannot be saved due to a conflict.")
+            self.logger.error("Conflict. The resource cannot be saved due to a conflict.")
             sys.exit(ec.CONFLICT)
         elif response.status_code == 429:
-            logging.error("Too many requests. You have made too many requests in a short amount of time.")
+            self.logger.error("Too many requests. You have made too many requests in a short amount of time.")
             return True
         elif response.status_code == 500:
-            logging.error("Internal server error. The API experienced an unexpected error.")
+            self.logger.error("Internal server error. The API experienced an unexpected error.")
             return True
         elif response.status_code == 503:
-            logging.error("Service unavailable. The API is temporarily disabled or a request timeout occurred.")
+            self.logger.error("Service unavailable. The API is temporarily disabled or a request timeout occurred.")
             return True
         else:
             response.raise_for_status()
             return False
 
-    def fetch_and_cache_entity_data(self):
+    def start_ingestion(self):
         """
         Fetch and cache data for all entities.
         """
         for entity in self.entities:
             file_path = f'data/raw/{entity}'
             if os.path.exists(file_path) and os.listdir(file_path):
-                logging.warning(f"Raw data exists for {entity} processing any raw data we already have.")
+                self.logger.warning(f"Raw data exists for {entity} processing any raw data we already have.")
                 break # break here instead of continue as we dont want to update our server knowledge cache and potentially miss data.
 
-            last_knowledge = self.knowledge_cache.get(entity, 0)
+            knowledge_cache = self.load_knowledge_cache()
+            last_knowledge = knowledge_cache.get(entity, 0)
             #logging.debug(f'Last Knowledge of {entity}: {last_knowledge}')
-            logging.info(f'Fetching {entity} data since last knowledge: {last_knowledge}')
-            url = f'{self.base_url}/{self.budget_id}/{entity}?last_knowledge_of_server={last_knowledge}'
 
+            self.logger.info(f'Fetching {entity} data since last knowledge: {last_knowledge}')
+            url = f'{self.base_url}/{self.budget_id}/{entity}?last_knowledge_of_server={last_knowledge}'
+            response = None
             for attempt in range(self.MAX_RETRIES):
                 try:
                     response = requests.get(url, headers=self.headers)
@@ -140,16 +133,17 @@ class Ingest:
                     if not should_retry:
                         break  # Exit the loop if the request is successful
                 except requests.exceptions.RequestException as e:
-                    logging.error(f"Error fetching {entity} data (attempt {attempt + 1}/{self.MAX_RETRIES}): {e}")
+                    self.logger.error(f"Error fetching {entity} data (attempt {attempt + 1}/{self.MAX_RETRIES}): {e}")
                     if attempt < self.MAX_RETRIES - 1:
                         time.sleep(self.RETRY_DELAY)  # Wait before retrying
                     else:
-                        logging.error("Max retries reached. Exiting.")
+                        self.logger.error("Max retries reached. Exiting.")
                         sys.exit(ec.REQUESTS_ERROR)
 
             data = response.json()
+            self.logger.debug(f'response data: {data}')
             server_knowledge = data['data'].get('server_knowledge')
-            logging.debug(f'{entity} new server knowledge: {server_knowledge}')
+            self.logger.debug(f'{entity} new server knowledge: {server_knowledge}')
 
             if server_knowledge is not None and server_knowledge != last_knowledge:
                 self.update_server_knowledge_cache(entity, server_knowledge)
@@ -157,7 +151,4 @@ class Ingest:
                 entity_data.pop('server_knowledge', None)
                 self.save_entity_data_to_raw(entity, entity_data)
             else:
-                logging.info(f"No new data for {entity}. Skipping cache update.")
-            
-            if self.check_rate_limit(response):
-                break # break out here and continue processing the data we have.
+                self.logger.info(f"No new data for {entity}. Skipping cache update.")

pipeline/pipeline_main.py

@@ -0,0 +1,19 @@
+'''Module to run the data pipeline'''
+
+from pipeline import duckdb_layer, ingest, raw_to_base
+
+
+def pipeline_main(config, logger):
+    '''Run the data pipeline'''
+    logger.info('Starting data pipeline')
+
+    ingest.Ingest(config, logger).start_ingestion()
+    raw_to_base.RawToBase(config, logger)
+
+    duckdb_layer.get_duckdb_layer(
+        config['base_data_path'],
+        config['warehouse_data_path'],
+        logger
+    )
+
+    logger.info('Data pipeline completed successfully')

pipeline/raw_to_base.py

@@ -1,14 +1,16 @@
-import os
 import json
-import logging
+import os
 import sys
 from datetime import datetime
-from typing import Dict, Any
-import config.exit_codes as ec
+from typing import Any
+
 import polars as pl
 
+import config.exit_codes as ec
+
+
 class RawToBase:
-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: dict[str, Any],logger):
         self.entities = config['entities']
         self.primary_keys = config['primary_keys']
         self.raw_data_path = config['raw_data_path']
@@ -16,39 +18,40 @@ class RawToBase:
         self.base_data_path = config['base_data_path']
         self.data = {}
         self.base_data = {}
+        self.logger = logger
         self.process_entities()
 
     def process_entities(self):
         for entity in self.entities:
-            logging.info(f"Processing entity: {entity}")
+            self.logger.info(f"Processing entity: {entity}")
             # check the file is in the raw data path, if not skip the entity
             folder_path = os.path.join(self.raw_data_path, entity)
             folder_contents = os.listdir(folder_path)
             if not folder_contents:
-                logging.warning(f"The folder {folder_path} is empty skipping {entity}.")
+                self.logger.warning(f"The folder {folder_path} is empty skipping {entity}.")
                 continue
             if not self._load_raw_data(entity):
-                logging.warning(f"Skipping processing for entity: {entity} due to empty data.")
+                self.logger.warning(f"Skipping processing for entity: {entity} due to empty data.")
                 continue
             self._load_existing_base_data(entity)
             self._combine_data(entity)
             if not self._save_base_data(entity):
-                logging.error(f"Skipping processing for entity: {entity} due to failed saving base data.")
+                self.logger.error(f"Skipping processing for entity: {entity} due to failed saving base data.")
                 continue
             if not self._move_raw_to_processed(entity):
-                logging.error(f"entity: {entity} has been processed, but we could not move the file out of the raw folder, please clear the raw folder for {entity}.")
+                self.logger.error(f"entity: {entity} has been processed, but we could not move the file out of the raw folder, please clear the raw folder for {entity}.")
                 sys.exit(ec.MOVE_FILE_ERROR)
-            logging.info(f"Successfully processed entity: {entity}")
+            self.logger.info(f"Successfully processed entity: {entity}")
 
     def _load_raw_data(self, entity):
         entity_path = os.path.join(self.raw_data_path, entity)
         self.data[entity] = []
-        logging.debug(f"Loading data for entity: {entity} from path: {entity_path}")
+        self.logger.debug(f"Loading data for entity: {entity} from path: {entity_path}")
 
         files = [f for f in os.listdir(entity_path) if f.endswith('.json')]
 
         if len(files) > 1:
-            logging.error(f"""More than one file found in path: {entity_path}. Skipping processing for entity: {entity}.
+            self.logger.error(f"""More than one file found in path: {entity_path}. Skipping processing for entity: {entity}.
 recommended actions is to move the newest file(s) out, re-run main.py.
 Then move the files back in one at a time oldest to newest and run again for each file""")
             return False
@@ -56,44 +59,50 @@ Then move the files back in one at a time oldest to newest and run again for eac
         if len(files) == 1:
             file_name = files[0]
             file_path = os.path.join(entity_path, file_name)
-            logging.debug(f"Reading file: {file_path}")
+            self.logger.debug(f"Reading file: {file_path}")
             try:
                 with open(file_path, 'r') as f:
                     data = json.load(f)
             except Exception as e:
-                logging.error(f"Failed to load data from file: {file_path}, error: {e}")
+                self.logger.error(f"Failed to load data from file: {file_path}, error: {e}")
                 return False
 
             if self._is_data_empty(entity, data, file_path):
                 return False
 
             modified_data = self._add_ingestion_date(entity, data, file_name)
+            for index, record in enumerate(modified_data):
+                self.logger.debug(f"processing record: {record}")
+                filtered_record = {k: v for k, v in record.items() if not k.startswith('debt_')}
+                modified_data[index] = filtered_record
+                self.logger.debug(f"filtered record: {filtered_record}")
+            self.logger.debug(f"modified data: {modified_data}")
 
             self.data[entity].append(modified_data)
-            logging.debug(f"Successfully loaded data from file: {file_path}")
+            self.logger.debug(f"Successfully loaded data from file: {file_path}")
         return True
 
     def _is_data_empty(self, entity, data, file_path):
-        logging.debug(f"Checking if data is empty for entity: {entity}")
+        self.logger.debug(f"Checking if data is empty for entity: {entity}")
         if entity == "categories":
             has_categories = any(group.get("categories") for group in data.get("category_groups", []))
             if not has_categories:
-                logging.warning(f"Received empty data for entity: {entity} in file: {file_path}, deleting file.")
+                self.logger.warning(f"Received empty data for entity: {entity} in file: {file_path}, deleting file.")
                 os.remove(file_path)
                 return True
         else:
             if not data.get(entity, []):
-                logging.warning(f"Received empty data for entity: {entity} in file: {file_path}, deleting file.")
+                self.logger.warning(f"Received empty data for entity: {entity} in file: {file_path}, deleting file.")
                 os.remove(file_path)
                 return True
-        logging.debug(f"Data is not empty for entity: {entity}")
+        self.logger.debug(f"Data is not empty for entity: {entity}")
         return False
 
     def _add_ingestion_date(self, entity, data, file_name):
         modified_data = []
         ingestion_date = datetime.strptime(file_name.split('.')[0], '%Y%m%d%H%M%S').date()
 
-        logging.debug(f"Adding ingestion date to data for entity: {entity}")
+        self.logger.debug(f"Adding ingestion date to data for entity: {entity}")
         if entity == 'categories':
             for group in data.get('category_groups', []):
                 for category in group.get('categories', []):
@@ -106,22 +115,22 @@ Then move the files back in one at a time oldest to newest and run again for eac
                     modified_data.append(record)
                 else:
                     modified_data.append({'record': record, 'ingestion_date': ingestion_date})
-        logging.debug(f"Successfully added ingestion date to data for entity: {entity}")
+        self.logger.debug(f"Successfully added ingestion date to data for entity: {entity}")
         return modified_data
 
     def _load_existing_base_data(self, entity):
         base_path = os.path.join(self.base_data_path, f'{entity}.parquet')
         if os.path.exists(base_path):
-            logging.debug(f"Loading existing base data for entity: {entity} from path: {base_path}")
+            self.logger.debug(f"Loading existing base data for entity: {entity} from path: {base_path}")
             try:
                 self.base_data[entity] = pl.read_parquet(base_path)
             except Exception as e:
-                logging.error(f"Failed to load existing base data for entity: {entity}, error: {e}, Creating an empty DataFrame")
+                self.logger.error(f"Failed to load existing base data for entity: {entity}, error: {e}, Creating an empty DataFrame")
                 self.base_data[entity] = pl.DataFrame()
-            logging.debug(f"Successfully loaded existing base data for entity: {entity}")
+            self.logger.debug(f"Successfully loaded existing base data for entity: {entity}")
         else:
             self.base_data[entity] = pl.DataFrame()
-            logging.debug(f"No existing base data found for entity: {entity}, starting with an empty DataFrame")
+            self.logger.debug(f"No existing base data found for entity: {entity}, starting with an empty DataFrame")
 
     #Function to cast null Struct({'': Null}) columns to String
     def _cast_struct_to_string(self,df):
@@ -130,13 +139,13 @@ Then move the files back in one at a time oldest to newest and run again for eac
                 df = df.with_columns(
                     pl.when(pl.col(col).is_null())
                     .then(pl.lit("null"))
-                    .otherwise(pl.col(col).map_elements(lambda x: str(x) if x is not None else "null"))
+                    .otherwise(pl.col(col).map_elements(lambda x: str(x) if x is not None else "null", return_dtype=pl.Utf8))
                     .alias(col)
                 )
         return df
 
     def _combine_data(self, entity):
-        logging.debug(f"Combining data for entity: {entity}")
+        self.logger.debug(f"Combining data for entity: {entity}")
         combined_data = []
 
         # Combine data from the entity
@@ -153,8 +162,8 @@ Then move the files back in one at a time oldest to newest and run again for eac
         # Ensure the unique id column is preserved
         unique_id = self.primary_keys[entity]['unique_id']
         if unique_id not in new_data_df.columns:
-            logging.error(f"Unique ID column '{unique_id}' not found in the combined data for entity: {entity}")
-            exit(ec.UNIQUE_ID_NOT_FOUND)
+            self.logger.error(f"Unique ID column '{unique_id}' not found in the combined data for entity: {entity}")
+            sys.exit(ec.UNIQUE_ID_NOT_FOUND)
 
         # Cast columns in new_data_df
         new_data_df = self._cast_struct_to_string(new_data_df)
@@ -182,7 +191,7 @@ Then move the files back in one at a time oldest to newest and run again for eac
         else:
             self.base_data[entity] = new_data_df
 
-        logging.debug(f"Successfully combined data for entity: {entity}")
+        self.logger.debug(f"Successfully combined data for entity: {entity}")
 
     def _save_base_data(self, entity):
         os.makedirs(self.base_data_path, exist_ok=True)
@@ -190,9 +199,9 @@ Then move the files back in one at a time oldest to newest and run again for eac
         try:
             self.base_data[entity].write_parquet(file_path)
         except Exception as e:
-            logging.error(f"Failed to save base data for entity: {entity}, error: {e}")
+            self.logger.error(f"Failed to save base data for entity: {entity}, error: {e}")
             return False
-        logging.debug(f"Saved base data for entity: {entity} to path: {file_path}")
+        self.logger.debug(f"Saved base data for entity: {entity} to path: {file_path}")
         return True
 
     def _move_raw_to_processed(self, entity):
@@ -204,24 +213,24 @@ Then move the files back in one at a time oldest to newest and run again for eac
         try:
             files = [f for f in os.listdir(raw_entity_path) if f.endswith('.json')]
             if len(files) != 1:
-                logging.error(f"Expected exactly one file in path: {raw_entity_path}, but found {len(files)}")
+                self.logger.error(f"Expected exactly one file in path: {raw_entity_path}, but found {len(files)}")
                 return False
 
             file_name = files[0]
             raw_file_path = os.path.join(raw_entity_path, file_name)
             processed_file_path = os.path.join(processed_path, file_name)
 
-            logging.debug(f"Moving file: {raw_file_path} to {processed_file_path}")
+            self.logger.debug(f"Moving file: {raw_file_path} to {processed_file_path}")
 
             os.rename(raw_file_path, processed_file_path)
-            logging.debug(f"Moved file: {file_name} to processed")
+            self.logger.debug(f"Moved file: {file_name} to processed")
 
         except FileNotFoundError as e:
-            logging.error(f"File not found: {e}")
+            self.logger.error(f"File not found: {e}")
             return False
         except Exception as e:
-            logging.error(f"Failed to move file for entity: {entity}, error: {e}")
+            self.logger.error(f"Failed to move file for entity: {entity}, error: {e}")
             return False
 
-        logging.debug(f"Moved processed file for entity: {entity} to path: {processed_path}")
+        self.logger.debug(f"Moved processed file for entity: {entity} to path: {processed_path}")
         return True

pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "data-pipeline-for-ynab"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "duckdb>=1.5.5",
+    "pandas>=3.0.5",
+    "polars>=1.43.0",
+    "pyarrow>=25.0.0",
+    "pytest>=9.1.1",
+    "python-dotenv>=1.2.2",
+    "pyyaml>=6.0.3",
+    "requests>=2.34.2",
+    "ruff>=0.16.0",
+]

requirements.txt

@@ -1,4 +0,0 @@
-python-dotenv
-polars
-requests
-pyyaml

tests/test_ingest.py

@@ -0,0 +1,306 @@
+import json
+import os
+from unittest.mock import MagicMock, mock_open, patch
+
+import pytest
+
+import config.exit_codes as ec
+from pipeline.ingest import Ingest
+
+# Mock configuration for initializing the Ingest class
+mock_config = {
+    'API_TOKEN': 'test_token',
+    'BUDGET_ID': 'test_budget_id',
+    'base_url': 'http://test_base_url',
+    'knowledge_file': 'data/test_knowledge_file.json',
+    'entities': ['entity1', 'entity2'],
+    'raw_data_path': 'test_raw_data_path',
+    'REQUESTS_MAX_RETRIES': 3,
+    'REQUESTS_RETRY_DELAY': 1
+}
+
+# Test for load_knowledge_cache method
+def test_load_knowledge_cache_file_exists():
+    mock_data = {"key": "value"}
+    with patch('os.path.exists', return_value=True), \
+         patch('builtins.open', mock_open(read_data=json.dumps(mock_data))) as mock_file:
+        
+        ingest_instance = Ingest(mock_config)
+        result = ingest_instance.load_knowledge_cache()
+        
+        mock_file.assert_called_once_with(mock_config['knowledge_file'], 'r')
+        assert result == mock_data
+
+def test_load_knowledge_cache_file_not_exists():
+    with patch('os.path.exists', return_value=False):
+        
+        ingest_instance = Ingest(mock_config)
+        result = ingest_instance.load_knowledge_cache()
+        
+        assert result == {}
+
+# Test for save_entity_data_to_raw method
+def test_save_entity_data_to_raw_success():
+    entity = 'entity1'
+    data = {"key": "value"}
+    current_time = '20230101123000'
+    directory = os.path.join(mock_config['raw_data_path'], entity)
+    entity_file = f'{directory}/{current_time}.json'
+
+    with patch('os.path.exists', return_value=False), \
+         patch('os.makedirs') as mock_makedirs, \
+         patch('builtins.open', mock_open()) as mock_file, \
+         patch('time.strftime', return_value=current_time), \
+         patch('logging.info') as mock_logging_info:
+        
+        ingest_instance = Ingest(mock_config)
+        ingest_instance.save_entity_data_to_raw(entity, data)
+        
+        mock_makedirs.assert_called_once_with(directory)
+        mock_file.assert_called_once_with(entity_file, 'w')
+        
+        # Get the file handle and check the written content
+        handle = mock_file()
+        handle.write.assert_called()
+        written_content = ''.join(call.args[0] for call in handle.write.call_args_list)
+        assert written_content == json.dumps(data, indent=4)
+        
+        mock_logging_info.assert_called_once_with(f"Saving {entity} data to {entity_file}")
+
+def test_save_entity_data_to_raw_existing_directory():
+    entity = 'entity1'
+    data = {"key": "value"}
+    current_time = '20230101123000'
+    directory = os.path.join(mock_config['raw_data_path'], entity)
+    entity_file = f'{directory}/{current_time}.json'
+
+    with patch('os.path.exists', return_value=True), \
+         patch('os.makedirs') as mock_makedirs, \
+         patch('builtins.open', mock_open()) as mock_file, \
+         patch('time.strftime', return_value=current_time), \
+         patch('logging.info') as mock_logging_info:
+        
+        ingest_instance = Ingest(mock_config)
+        ingest_instance.save_entity_data_to_raw(entity, data)
+        
+        mock_makedirs.assert_not_called()
+        mock_file.assert_called_once_with(entity_file, 'w')
+        
+        # Get the file handle and check the written content
+        handle = mock_file()
+        handle.write.assert_called()
+        written_content = ''.join(call.args[0] for call in handle.write.call_args_list)
+        assert written_content == json.dumps(data, indent=4)
+        
+        mock_logging_info.assert_called_once_with(f"Saving {entity} data to {entity_file}")
+
+def test_save_entity_data_to_raw_error():
+    entity = 'entity1'
+    data = {"key": "value"}
+    current_time = '20230101123000'
+    directory = os.path.join(mock_config['raw_data_path'], entity)
+    entity_file = f'{directory}/{current_time}.json'
+
+    with patch('os.path.exists', return_value=True), \
+         patch('builtins.open', mock_open()) as mock_file, \
+         patch('time.strftime', return_value=current_time), \
+         patch('logging.info') as mock_logging_info, \
+         patch('logging.error') as mock_logging_error:
+
+        mock_file.side_effect = Exception("Test error")
+
+        ingest_instance = Ingest(mock_config)
+
+        with pytest.raises(Exception, match="Test error"):
+            ingest_instance.save_entity_data_to_raw(entity, data)
+
+        mock_logging_error.assert_called_once_with(f"Failed to save data for {entity} to {entity_file}")
+
+def test_update_server_knowledge_cache_file_exists():
+    entity = 'entity1'
+    server_knowledge = {"key": "value"}
+    existing_cache = {"entity2": {"key": "old_value"}}
+    updated_cache = {"entity2": {"key": "old_value"}, "entity1": {"key": "value"}}
+
+    with patch('builtins.open', mock_open(read_data=json.dumps(existing_cache))) as mock_file, \
+         patch('os.path.exists', return_value=True), \
+         patch('logging.error') as mock_logging_error:
+
+        ingest_instance = Ingest(mock_config)
+        ingest_instance.update_server_knowledge_cache(entity, server_knowledge)
+
+        mock_file.assert_called_with(mock_config['knowledge_file'], 'w')
+        handle = mock_file()
+        handle.write.assert_called()
+        written_content = ''.join(call.args[0] for call in handle.write.call_args_list)
+        assert json.loads(written_content) == updated_cache
+        mock_logging_error.assert_not_called()
+
+def test_update_server_knowledge_cache_file_not_exists():
+    entity = 'entity1'
+    server_knowledge = {"key": "value"}
+    updated_cache = {"entity1": {"key": "value"}}
+
+    with patch('builtins.open', mock_open()) as mock_file, \
+         patch('os.path.exists', return_value=False), \
+         patch('os.makedirs') as mock_makedirs, \
+         patch('logging.info') as mock_logging_info, \
+         patch('logging.error') as mock_logging_error:
+
+        # Ensure the side_effect list has enough elements to cover all calls to open
+        mock_file.side_effect = [FileNotFoundError(), mock_open().return_value]
+
+        ingest_instance = Ingest(mock_config)
+        
+        with pytest.raises(FileNotFoundError):
+            ingest_instance.update_server_knowledge_cache(entity, server_knowledge)
+
+        mock_makedirs.assert_called_once_with(os.path.dirname(mock_config['knowledge_file']), exist_ok=True)
+        mock_file.assert_called_with(mock_config['knowledge_file'], 'w')
+        mock_logging_error.assert_called_once_with(f"Failed to update knowledge cache for {entity} in {mock_config['knowledge_file']}")
+
+def test_update_server_knowledge_cache_write_error():
+    entity = 'entity1'
+    server_knowledge = {"key": "value"}
+
+    with patch('builtins.open', mock_open()) as mock_file, \
+         patch('logging.error') as mock_logging_error:
+
+        mock_file.side_effect = Exception("Test error")
+
+        ingest_instance = Ingest(mock_config)
+
+        with pytest.raises(Exception, match="Test error"):
+            ingest_instance.update_server_knowledge_cache(entity, server_knowledge)
+
+        mock_logging_error.assert_called_once_with(f"Failed to update knowledge cache for {entity} in {mock_config['knowledge_file']}")
+
+def test_check_rate_limit_above_threshold():
+    response = MagicMock()
+    response.headers = {'X-Rate-Limit': '10/100'}
+    
+    ingest_instance = Ingest(mock_config)
+    result = ingest_instance.check_rate_limit(response)
+    
+    assert result is None
+
+def test_check_rate_limit_below_threshold():
+    response = MagicMock()
+    response.headers = {'X-Rate-Limit': '90/100'}
+    
+    ingest_instance = Ingest(mock_config)
+    result = ingest_instance.check_rate_limit(response)
+    
+    assert result is None
+
+def test_check_rate_limit_exceeded():
+    response = MagicMock()
+    response.headers = {'X-Rate-Limit': '100/100'}
+    
+    ingest_instance = Ingest(mock_config)
+    result = ingest_instance.check_rate_limit(response)
+    
+    assert result is True
+
+def test_check_rate_limit_header_missing():
+    response = MagicMock()
+    response.headers = {}
+    
+    ingest_instance = Ingest(mock_config)
+    result = ingest_instance.check_rate_limit(response)
+    
+    assert result is None
+
+def test_handle_response_bad_request():
+    response = MagicMock()
+    response.status_code = 400
+    
+    ingest_instance = Ingest(mock_config)
+    
+    with pytest.raises(SystemExit) as e:
+        ingest_instance.handle_response(response)
+    assert e.type == SystemExit
+    assert e.value.code == ec.BAD_REQUEST
+
+def test_handle_response_unauthorized():
+    response = MagicMock()
+    response.status_code = 401
+    
+    ingest_instance = Ingest(mock_config)
+    
+    with pytest.raises(SystemExit) as e:
+        ingest_instance.handle_response(response)
+    assert e.type == SystemExit
+    assert e.value.code == ec.UNAUTHORIZED_API_TOKEN
+
+def test_handle_response_forbidden():
+    response = MagicMock()
+    response.status_code = 403
+    
+    ingest_instance = Ingest(mock_config)
+    
+    with pytest.raises(SystemExit) as e:
+        ingest_instance.handle_response(response)
+    assert e.type == SystemExit
+    assert e.value.code == ec.FORBIDDEN
+
+def test_handle_response_not_found():
+    response = MagicMock()
+    response.status_code = 404
+    
+    ingest_instance = Ingest(mock_config)
+    
+    with pytest.raises(SystemExit) as e:
+        ingest_instance.handle_response(response)
+    assert e.type == SystemExit
+    assert e.value.code == ec.NOT_FOUND
+
+def test_handle_response_conflict():
+    response = MagicMock()
+    response.status_code = 409
+    
+    ingest_instance = Ingest(mock_config)
+    
+    with pytest.raises(SystemExit) as e:
+        ingest_instance.handle_response(response)
+    assert e.type == SystemExit
+    assert e.value.code == ec.CONFLICT
+
+def test_handle_response_too_many_requests():
+    response = MagicMock()
+    response.status_code = 429
+    
+    ingest_instance = Ingest(mock_config)
+    
+    result = ingest_instance.handle_response(response)
+    assert result is True
+
+def test_handle_response_internal_server_error():
+    response = MagicMock()
+    response.status_code = 500
+    
+    ingest_instance = Ingest(mock_config)
+    
+    result = ingest_instance.handle_response(response)
+    assert result is True
+
+def test_handle_response_service_unavailable():
+    response = MagicMock()
+    response.status_code = 503
+    
+    ingest_instance = Ingest(mock_config)
+    
+    result = ingest_instance.handle_response(response)
+    assert result is True
+
+def test_handle_response_ok():
+    response = MagicMock()
+    response.status_code = 200
+    
+    ingest_instance = Ingest(mock_config)
+    
+    result = ingest_instance.handle_response(response)
+    assert result is False
+
+if __name__ == "__main__":
+    pytest.main()

tests/test_main.py

@@ -0,0 +1,70 @@
+import logging
+from unittest.mock import MagicMock, mock_open, patch
+
+import yaml
+
+import config.exit_codes as ec
+from main import load_config, set_up_logging
+
+
+# Test for set_up_logging function
+def test_set_up_logging_success():
+    with patch('builtins.open', mock_open(read_data="handlers:\n  queue_handler:\n    class: logging.handlers.QueueHandler")), \
+         patch('yaml.safe_load', return_value={"handlers": {"queue_handler": {"class": "logging.handlers.QueueHandler"}}}), \
+         patch('logging.config.dictConfig') as mock_dict_config, \
+         patch('logging.getHandlerByName', return_value=MagicMock(listener=MagicMock(start=MagicMock(), stop=MagicMock()))), \
+         patch('atexit.register') as mock_atexit_register:
+        
+        set_up_logging()
+        
+        mock_dict_config.assert_called_once_with({"handlers": {"queue_handler": {"class": "logging.handlers.QueueHandler"}}})
+        mock_atexit_register.assert_called_once()
+
+def test_set_up_logging_yaml_error():
+    with patch('builtins.open', mock_open(read_data="invalid_yaml")), \
+         patch('yaml.safe_load', side_effect=yaml.YAMLError("Error")), \
+         patch('logging.basicConfig') as mock_basic_config:
+        
+        set_up_logging()
+        
+        mock_basic_config.assert_called_once_with(level=logging.INFO)
+
+def test_set_up_logging_no_queue_handler():
+    with patch('builtins.open', mock_open(read_data="handlers:\n  queue_handler:\n    class: logging.handlers.QueueHandler")), \
+         patch('yaml.safe_load', return_value={"handlers": {"queue_handler": {"class": "logging.handlers.QueueHandler"}}}), \
+         patch('logging.config.dictConfig') as mock_dict_config, \
+         patch('logging.getHandlerByName', return_value=None):
+        
+        set_up_logging()
+        
+        mock_dict_config.assert_called_once_with({"handlers": {"queue_handler": {"class": "logging.handlers.QueueHandler"}}})
+
+# Test for load_config function
+def test_load_config_success():
+    with patch('builtins.open', mock_open(read_data="key: value")), \
+         patch('yaml.safe_load', return_value={"key": "value"}):
+        
+        config = load_config()
+        
+        assert config == {"key": "value"}
+
+def test_load_config_file_not_found():
+    with patch('builtins.open', side_effect=FileNotFoundError), \
+         patch('logging.error') as mock_logging_error, \
+         patch('sys.exit') as mock_sys_exit:
+        
+        load_config()
+        
+        mock_logging_error.assert_called_once_with('config.yaml file not found')
+        mock_sys_exit.assert_called_once_with(ec.MISSING_CONFIG_FILE)
+
+def test_load_config_yaml_error():
+    with patch('builtins.open', mock_open(read_data="invalid_yaml")), \
+         patch('yaml.safe_load', side_effect=yaml.YAMLError("Error")), \
+         patch('logging.error') as mock_logging_error, \
+         patch('sys.exit') as mock_sys_exit:
+        
+        load_config()
+        
+        mock_logging_error.assert_called_once()
+        mock_sys_exit.assert_called_once_with(ec.CORRUPTED_CONFIG_FILE)

uv.lock

@@ -0,0 +1,447 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+resolution-markers = [
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+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "certifi"
+version = "2026.7.22"
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