   160ÔåÆ            SnowflakeConnectionError: If connection fails
   161ÔåÆ        """
   162ÔåÆ        self._check_availability()
   163ÔåÆ        self._check_configured()
   164ÔåÆ
   165ÔåÆ        # Close existing connection if any
   166ÔåÆ        if self._connection is not None:
   167ÔåÆ            self.close()
   168ÔåÆ
   169ÔåÆ        conn_cfg = self._config.connection
   170ÔåÆ        timeout_cfg = self._config.timeouts
   171ÔåÆ
   172ÔåÆ        logger.info(f"Connecting to Snowflake account: {conn_cfg.account}")
   173ÔåÆ        logger.info(f"Using warehouse: {conn_cfg.warehouse}, database: {conn_cfg.database}")
   174ÔåÆ        logger.info(f"Authenticator: {conn_cfg.authenticator}")
   175ÔåÆ        if conn_cfg.authenticator == "externalbrowser":
   176ÔåÆ            logger.info("Browser window will open for NHS SSO authentication")
   177ÔåÆ
   178ÔåÆ        start_time = time.time()
   179ÔåÆ
   180ÔåÆ        try:
   181ÔåÆ            # Build connection parameters
   182ÔåÆ            connect_params = {
   183ÔåÆ                "account": conn_cfg.account,
   184ÔåÆ                "warehouse": conn_cfg.warehouse,
   185ÔåÆ                "database": conn_cfg.database,
   186ÔåÆ                "schema": conn_cfg.schema,
   187ÔåÆ                "authenticator": conn_cfg.authenticator,
   188ÔåÆ                "login_timeout": timeout_cfg.login_timeout,
   189ÔåÆ                "network_timeout": timeout_cfg.connection_timeout,
   190ÔåÆ            }
   191ÔåÆ
   192ÔåÆ            # Optional parameters (only add if set)
   193ÔåÆ            if conn_cfg.user:
   194ÔåÆ                connect_params["user"] = conn_cfg.user
   195ÔåÆ            if conn_cfg.role:
   196ÔåÆ                connect_params["role"] = conn_cfg.role
   197ÔåÆ
   198ÔåÆ            self._connection = snowflake.connector.connect(**connect_params)
   199ÔåÆ

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

            SnowflakeConnectionError: If connection fails
        """
        self._check_availability()
        self._check_configured()

        # Close existing connection if any
        if self._connection is not None:
            self.close()

        conn_cfg = self._config.connection
        timeout_cfg = self._config.timeouts

        logger.info(f"Connecting to Snowflake account: {conn_cfg.account}")
        logger.info(f"Using warehouse: {conn_cfg.warehouse}, database: {conn_cfg.database}")
        logger.info(f"Authenticator: {conn_cfg.authenticator}")
        if conn_cfg.authenticator == "externalbrowser":
            logger.info("Browser window will open for NHS SSO authentication")

        start_time = time.time()

        try:
            # Build connection parameters
            connect_params = {
                "account": conn_cfg.account,
                "warehouse": conn_cfg.warehouse,
                "database": conn_cfg.database,
                "schema": conn_cfg.schema,
                "authenticator": conn_cfg.authenticator,
                "login_timeout": timeout_cfg.login_timeout,
                "network_timeout": timeout_cfg.connection_timeout,
            }

            # Optional parameters (only add if set)
            if conn_cfg.user:
                connect_params["user"] = conn_cfg.user
            if conn_cfg.role:
                connect_params["role"] = conn_cfg.role

            self._connection = snowflake.connector.connect(**connect_params)


I see `network_timeout` being set. Let me check the timeout defaults.


36:    connection_timeout: int = 30
38:    login_timeout: int = 120
185:        connection_timeout=timeout_data.get("connection_timeout", 30),
187:        login_timeout=timeout_data.get("login_timeout", 120),


`connection_timeout: int = 30` ÔÇö that's the network_timeout which is being passed to `snowflake.connector.connect()` as `network_timeout=30`. This is the **network-level timeout** for Snowflake operations. According to the Snowflake Python connector docs, `network_timeout` is the timeout for socket-level operations, which includes query execution time. When a query takes longer than 30 seconds, the client kills it.

This is the root cause: `network_timeout=30` limits ALL query execution to 30 seconds, regardless of the `STATEMENT_TIMEOUT_IN_SECONDS` setting.

Now I have two options to fix this:
1. **Increase `network_timeout`** ÔÇö but this would affect all queries
2. **Pass a larger timeout to `execute_dict` calls in `get_patient_indication_groups`**

Actually wait ÔÇö the `network_timeout` parameter in the Snowflake connector is for network-level operations. Let me double-check. The old query (with `QUALIFY ROW_NUMBER() = 1`) worked within 30s for 500-patient batches, but the new `GROUP BY + COUNT(*)` doesn't.

The cleanest fix is to make the `get_patient_indication_groups()` function pass a longer timeout when calling `connector.execute_dict()`. But `execute_dict` doesn't have a way to change the network timeout ÔÇö it only changes the Snowflake-side `STATEMENT_TIMEOUT_IN_SECONDS`.

The real issue is the client-side `network_timeout=30`. This was fine for the old query but not for the new one. Let me think about the best approach:

1. **Optimize the query** to run within 30s ÔÇö this is the best long-term fix
2. **Increase connection_timeout** ÔÇö easy but affects everything
3. **Create a new connection with higher timeout** for GP lookups

Let me first try to understand if the query can be optimized. The main issue is the `GROUP BY + COUNT(*)` across potentially millions of GP records. Let me check the query structure and think about optimization.

Actually, looking at this more carefully, I think the most pragmatic approach is a combination:

1. **Optimize the query** ÔÇö we can use `COUNT(DISTINCT ...)` or add better filtering
2. **Reduce batch size** from 500 to something smaller (say 100)
3. **Increase the timeout** for this specific query path

But first, let me try running the query with just a few patients to confirm it works at all.


Bash

cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
import pandas as pd
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get a few patient pseudonyms from the actual HCD data we'd process
result = connector.execute_dict('''
    SELECT DISTINCT hcd.\"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"HighCostDrug\" hcd
    WHERE hcd.\"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {patients[:3]}...')

# Test query with just 5 patients ÔÇö should be fast
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'5-patient query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:10]:
    print(f'  {r[\"PatientPseudonym\"][:8]}... | {r[\"Search_Term\"]} | freq={r[\"code_frequency\"]}')
" 2>&1

Test GP lookup query with 5 patients to check if it works at all


Error: Exit code 1
Traceback (most recent call last):
  File "<string>", line 10, in <module>
    result = connector.execute_dict('''
        SELECT DISTINCT hcd."PseudoNHSNoLinked"
    ...<2 lines>...
        LIMIT 5
    ''')
  File "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\data_processing\snowflake_connector.py", line 396, in execute_dict
    cursor.execute(query, params)
    ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\cursor.py", line 1121, in execute
    Error.errorhandler_wrapper(self.connection, self, error_class, errvalue)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 286, in errorhandler_wrapper
    handed_over = Error.hand_to_other_handler(
        connection,
    ...<2 lines>...
        error_value,
    )
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 341, in hand_to_other_handler
    cursor.errorhandler(connection, cursor, error_class, error_value)
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 217, in default_errorhandler
    raise error_class(
    ...<10 lines>...
    )
snowflake.connector.errors.ProgrammingError: 002003 (42S02): SQL compilation error:
Object 'DATA_HUB.CDM."HighCostDrug"' does not exist or not authorized.


Wrong table name. Let me check the actual Snowflake schema being used for the HCD data.


No matches found


611:            - PseudoNHSNoLinked: Pseudonymised NHS number (for UPID creation)
632:                "PseudoNHSNoLinked",
672:        query += ' ORDER BY "InterventionDate", "ProviderCode", "PseudoNHSNoLinked"'


   620ÔåÆ
   621ÔåÆ        Raises:
   622ÔåÆ            SnowflakeConnectionError: If not connected or query fails
   623ÔåÆ        """
   624ÔåÆ        if not self.is_connected:
   625ÔåÆ            self.connect()
   626ÔåÆ
   627ÔåÆ        # Build the query
   628ÔåÆ        table_name = 'DATA_HUB.CDM."Acute__Conmon__PatientLevelDrugs"'
   629ÔåÆ
   630ÔåÆ        query = f'''
   631ÔåÆ            SELECT
   632ÔåÆ                "PseudoNHSNoLinked",
   633ÔåÆ                "ProviderCode" AS "Provider Code",
   634ÔåÆ                "LocalPatientID" AS "PersonKey",
   635ÔåÆ                "DrugName" AS "Drug Name",
   636ÔåÆ                "InterventionDate" AS "Intervention Date",
   637ÔåÆ                "PriceActual" AS "Price Actual",
   638ÔåÆ                "ProviderName" AS "OrganisationName",
   639ÔåÆ                "TreatmentFunctionCode" AS "Treatment Function Code",
   640ÔåÆ                "TreatmentFunctionDesc" AS "Treatment Function Desc",
   641ÔåÆ                "AdditionalDetail1" AS "Additional Detail 1",
   642ÔåÆ                "AdditionalDescription1" AS "Additional Description 1",
   643ÔåÆ                "AdditionalDetail2" AS "Additional Detail 2",
   644ÔåÆ                "AdditionalDescription2" AS "Additional Description 2",
   645ÔåÆ                "AdditionalDetail3" AS "Additional Detail 3",
   646ÔåÆ                "AdditionalDescription3" AS "Additional Description 3",
   647ÔåÆ                "AdditionalDetail4" AS "Additional Detail 4",
   648ÔåÆ                "AdditionalDescription4" AS "Additional Description 4",
   649ÔåÆ                "AdditionalDetail5" AS "Additional Detail 5",
   650ÔåÆ                "AdditionalDescription5" AS "Additional Description 5"
   651ÔåÆ            FROM {table_name}
   652ÔåÆ            WHERE 1=1
   653ÔåÆ        '''
   654ÔåÆ
   655ÔåÆ        params = []
   656ÔåÆ
   657ÔåÆ        # Add date filters
   658ÔåÆ        if start_date:
   659ÔåÆ            query += ' AND "InterventionDate" >= %s'
   660ÔåÆ            params.append(start_date.isoformat())
   661ÔåÆ        if end_date:
   662ÔåÆ            query += ' AND "InterventionDate" <= %s'
   663ÔåÆ            params.append(end_date.isoformat())
   664ÔåÆ
   665ÔåÆ        # Add provider filter
   666ÔåÆ        if provider_codes:
   667ÔåÆ            placeholders = ", ".join(["%s"] * len(provider_codes))
   668ÔåÆ            query += f' AND "ProviderCode" IN ({placeholders})'
   669ÔåÆ            params.extend(provider_codes)
   670ÔåÆ
   671ÔåÆ        # Add ordering for consistent results
   672ÔåÆ        query += ' ORDER BY "InterventionDate", "ProviderCode", "PseudoNHSNoLinked"'
   673ÔåÆ
   674ÔåÆ        logger.info(f"Fetching activity data from Snowflake")
   675ÔåÆ        if start_date:
   676ÔåÆ            logger.info(f"  Date range: {start_date} to {end_date or 'now'}")
   677ÔåÆ        if provider_codes:
   678ÔåÆ            logger.info(f"  Providers: {provider_codes}")
   679ÔåÆ

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

        Raises:
            SnowflakeConnectionError: If not connected or query fails
        """
        if not self.is_connected:
            self.connect()

        # Build the query
        table_name = 'DATA_HUB.CDM."Acute__Conmon__PatientLevelDrugs"'

        query = f'''
            SELECT
                "PseudoNHSNoLinked",
                "ProviderCode" AS "Provider Code",
                "LocalPatientID" AS "PersonKey",
                "DrugName" AS "Drug Name",
                "InterventionDate" AS "Intervention Date",
                "PriceActual" AS "Price Actual",
                "ProviderName" AS "OrganisationName",
                "TreatmentFunctionCode" AS "Treatment Function Code",
                "TreatmentFunctionDesc" AS "Treatment Function Desc",
                "AdditionalDetail1" AS "Additional Detail 1",
                "AdditionalDescription1" AS "Additional Description 1",
                "AdditionalDetail2" AS "Additional Detail 2",
                "AdditionalDescription2" AS "Additional Description 2",
                "AdditionalDetail3" AS "Additional Detail 3",
                "AdditionalDescription3" AS "Additional Description 3",
                "AdditionalDetail4" AS "Additional Detail 4",
                "AdditionalDescription4" AS "Additional Description 4",
                "AdditionalDetail5" AS "Additional Detail 5",
                "AdditionalDescription5" AS "Additional Description 5"
            FROM {table_name}
            WHERE 1=1
        '''

        params = []

        # Add date filters
        if start_date:
            query += ' AND "InterventionDate" >= %s'
            params.append(start_date.isoformat())
        if end_date:
            query += ' AND "InterventionDate" <= %s'
            params.append(end_date.isoformat())

        # Add provider filter
        if provider_codes:
            placeholders = ", ".join(["%s"] * len(provider_codes))
            query += f' AND "ProviderCode" IN ({placeholders})'
            params.extend(provider_codes)

        # Add ordering for consistent results
        query += ' ORDER BY "InterventionDate", "ProviderCode", "PseudoNHSNoLinked"'

        logger.info(f"Fetching activity data from Snowflake")
        if start_date:
            logger.info(f"  Date range: {start_date} to {end_date or 'now'}")
        if provider_codes:
            logger.info(f"  Providers: {provider_codes}")


OK. Let me now directly test the GP lookup query with a small number of patients. I'll pull from the same table used in the actual pipeline.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get a few patient pseudonyms from the actual HCD data
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Test query with just 5 patients
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'5-patient query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:10]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")} | freq={r.get(\"code_frequency\")}')
" 2>&1

Test GP lookup query with 5 patients from actual HCD data


Error: Exit code 1
Traceback (most recent call last):
  File "<string>", line 36, in <module>
    results = connector.execute_dict(query, params)
  File "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\data_processing\snowflake_connector.py", line 396, in execute_dict
    cursor.execute(query, params)
    ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\cursor.py", line 1121, in execute
    Error.errorhandler_wrapper(self.connection, self, error_class, errvalue)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 286, in errorhandler_wrapper
    handed_over = Error.hand_to_other_handler(
        connection,
    ...<2 lines>...
        error_value,
    )
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 341, in hand_to_other_handler
    cursor.errorhandler(connection, cursor, error_class, error_value)
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 217, in default_errorhandler
    raise error_class(
    ...<10 lines>...
    )
snowflake.connector.errors.ProgrammingError: 000604 (57014): SQL execution was cancelled by the client due to a timeout. Error message received from the server: SQL execution canceled
Test patients: 5


Even with just 5 patients, the query times out at 30 seconds. This confirms the problem is the query execution time, not batch size. The `GROUP BY + COUNT(*)` version is fundamentally slower than the `QUALIFY ROW_NUMBER()` version.

The `PrimaryCareClinicalCoding` table is massive (national GP records). The join with the CTE + GROUP BY is too expensive. The original QUALIFY ROW_NUMBER() was fast because Snowflake could optimize it ÔÇö find the first match and stop.

Let me think about the optimization approach. The core problem is:
1. Large CTE (148 rows ÔåÆ joined with SNOMED codes ÔåÆ potentially thousands of codes)
2. Join with massive `PrimaryCareClinicalCoding` table
3. GROUP BY forces a full scan

**Strategy**: Keep the efficient QUALIFY approach but return multiple rows. Instead of `ROW_NUMBER()`, use `DENSE_RANK()` or just return distinct Search_Terms with a `COUNT(*)` computed differently.

Actually, the best approach: split the query into two steps:
1. First, find which patients have ANY match (fast with the existing join pattern)
2. Then, for matched patients, compute the code_frequency

Or better: use a different approach to get code_frequency without GROUP BY on the massive table:

```sql
-- Use QUALIFY to get distinct Search_Terms per patient (no aggregation)
SELECT DISTINCT pc."PatientPseudonym", aic.Search_Term
FROM PrimaryCareClinicalCoding pc
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
WHERE pc."PatientPseudonym" IN (...)
```

This returns distinct patient-SearchTerm pairs without the expensive GROUP BY + COUNT(*). For the tiebreaker, we could:
- Use a separate query to count codes (but that's also slow)
- Use a window function instead of GROUP BY

Let me try the DISTINCT approach first ÔÇö it should be much faster.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 5 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Try with SELECT DISTINCT instead of GROUP BY COUNT
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT DISTINCT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'DISTINCT query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:20]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")}')
" 2>&1

Test DISTINCT query (without COUNT) for GP lookup with 5 patients


Error: Exit code 1
Traceback (most recent call last):
  File "<string>", line 34, in <module>
    results = connector.execute_dict(query, params)
  File "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\data_processing\snowflake_connector.py", line 396, in execute_dict
    cursor.execute(query, params)
    ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\cursor.py", line 1121, in execute
    Error.errorhandler_wrapper(self.connection, self, error_class, errvalue)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 286, in errorhandler_wrapper
    handed_over = Error.hand_to_other_handler(
        connection,
    ...<2 lines>...
        error_value,
    )
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 341, in hand_to_other_handler
    cursor.errorhandler(connection, cursor, error_class, error_value)
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\charlwoodand\scoop\apps\python\current\Lib\site-packages\snowflake\connector\errors.py", line 217, in default_errorhandler
    raise error_class(
    ...<10 lines>...
    )
snowflake.connector.errors.ProgrammingError: 000604 (57014): SQL execution was cancelled by the client due to a timeout. Error message received from the server: SQL execution canceled
Test patients: 5


Even SELECT DISTINCT times out. The fundamental issue is the join between `PrimaryCareClinicalCoding` and the CTE-expanded SNOMED codes, filtered by patient and date. This is consistently timing out at ~30s even for 5 patients.

But wait ÔÇö the **old query** (with QUALIFY ROW_NUMBER()) worked! Let me verify that the old query still works, and check how long it takes.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 5 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Try OLD query with QUALIFY ROW_NUMBER
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    pc.\"EventDateTime\" AS \"EventDateTime\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
QUALIFY ROW_NUMBER() OVER (PARTITION BY pc.\"PatientPseudonym\" ORDER BY pc.\"EventDateTime\" DESC) = 1
'''

params = tuple(patients)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'OLD query (QUALIFY): {elapsed:.1f}s, {len(results)} rows')
for r in results[:20]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")} | {r.get(\"EventDateTime\")}')
" 2>&1

Test OLD query (with QUALIFY ROW_NUMBER) for comparison


Test patients: 5
OLD query (QUALIFY): 19.7s, 5 rows
  0F7C0582... | drug misuse | 2026-01-08 09:23:00
  7E0ABB65... | major depressive episodes | 2026-01-08 14:03:52
  25C9481D... | vascular disease | 2025-12-06 13:28:26
  AFB426FF... | stroke | 2025-08-28 11:40:36
  708EC81F... | influenza | 2026-01-08 12:25:36


The old query takes 19.7s for 5 patients. That's already very close to the 30s timeout! With 500 patients, it would definitely take much longer. The fact that it worked before was likely because of caching or fewer patients at the time.

Now I understand the full picture:
- Old query: ~20s for 5 patients (borderline, 500 patients would be ~30s+ with caching help)
- New query (GROUP BY): even slower, consistently >30s
- The `network_timeout=30` in the connector config kills both

The fix needs two parts:
1. **Increase the network_timeout** to allow these queries to complete
2. **Possibly optimize the query** for better performance

But the network_timeout is the immediate blocker. Let me fix that first. The cleanest approach is to pass a longer timeout when creating the connection for GP lookups. However, modifying the connector's network_timeout affects all queries.

Actually, looking at the Snowflake Python connector documentation, `network_timeout` controls the maximum time the client waits for the server response. To allow longer-running queries, we should increase this. The question is whether to increase it globally or per-connection.

The simplest approach: increase `connection_timeout` (network_timeout) in the config from 30 to 300 (or more). This is safe because the query-level `STATEMENT_TIMEOUT_IN_SECONDS` still controls how long Snowflake will run a query before killing it server-side.

Let me also check if we can pass timeout directly to `get_patient_indication_groups` ÔåÆ `execute_dict`.


    30ÔåÆ    role: str = ""
    31ÔåÆ
    32ÔåÆ
    33ÔåÆ@dataclass
    34ÔåÆclass TimeoutConfig:
    35ÔåÆ    """Timeout settings for Snowflake operations."""
    36ÔåÆ    connection_timeout: int = 30
    37ÔåÆ    query_timeout: int = 300
    38ÔåÆ    login_timeout: int = 120
    39ÔåÆ
    40ÔåÆ
    41ÔåÆ@dataclass
    42ÔåÆclass CacheConfig:
    43ÔåÆ    """Cache settings for Snowflake query results."""
    44ÔåÆ    enabled: bool = True
    45ÔåÆ    directory: str = "data/cache"
    46ÔåÆ    ttl_seconds: int = 86400  # 24 hours
    47ÔåÆ    ttl_current_data_seconds: int = 3600  # 1 hour
    48ÔåÆ    max_size_mb: int = 500
    49ÔåÆ

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

    role: str = ""

@dataclass
class TimeoutConfig:
    """Timeout settings for Snowflake operations."""
    connection_timeout: int = 30
    query_timeout: int = 300
    login_timeout: int = 120

@dataclass
class CacheConfig:
    """Cache settings for Snowflake query results."""
    enabled: bool = True
    directory: str = "data/cache"
    ttl_seconds: int = 86400  # 24 hours
    ttl_current_data_seconds: int = 3600  # 1 hour
    max_size_mb: int = 500


The most surgical fix: change `connection_timeout` from 30 to a higher value. But modifying a shared config file affects all Snowflake operations.

Actually, looking at `get_patient_indication_groups`, it calls `connector.execute_dict(query, tuple(params))` ÔÇö without passing a timeout parameter. But `execute_dict` DOES accept a `timeout` parameter which sets `STATEMENT_TIMEOUT_IN_SECONDS`. However, that only controls the server-side timeout, NOT the client-side `network_timeout`.

The real fix is to increase `network_timeout` when connecting. Let me look at whether `get_patient_indication_groups` can create its own connection with a higher timeout, or if we should just increase the default.

The pragmatic approach: increase `connection_timeout` from 30 to 600 (10 minutes). This is the **network-level** timeout (how long the client waits for any response from Snowflake). The query-level `query_timeout=300` still controls how long Snowflake will run a query. This is safe because:
- Activity data fetch already takes ~10s and uses chunked queries (separate timeout mechanism)
- GP lookup queries can take 20-30+ seconds per batch
- The query_timeout (300s) is the actual safety net against runaway queries


TodoWrite

Diagnose query timeout: network_timeout=30 kills GP lookup queries

Fix network_timeout and optimize query for GP lookups

Fixing network timeout and query performance

Re-run dry run test after fix

Mark Task 3.2 complete, commit, update progress.txt


Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

Diagnose query timeout: GROUP BY + COUNT(*) too slow for GP lookup batches

Fix query performance or timeout configuration

Re-run dry run test after fix

Mark Task 3.2 complete, commit, update progress.txt

Diagnose query timeout: network_timeout=30 kills GP lookup queries

Fix network_timeout and optimize query for GP lookups

Fixing network timeout and query performance

Re-run dry run test after fix


Now, there are two approaches to fix this:

**Option A**: Increase the global `connection_timeout` from 30 to 600 in the config. Simple but affects all queries.

**Option B**: Have `get_patient_indication_groups()` create its own connector with a higher network_timeout. More surgical but adds complexity.

I think Option A is the right call. The `connection_timeout=30` is just a network wait time, not a query execution limit. All other queries (like the activity data fetch) already work within 30s, so increasing it won't change their behavior. It just gives the GP lookup queries room to complete.

But I also need to consider query optimization. Even with a higher timeout, 20s per 5 patients ÔåÆ ~20-30 minutes for 36K patients in 500-patient batches (74 batches ├ù 20s = ~25 minutes). The old approach took ~9 minutes (according to CLAUDE.md). So the new GROUP BY query will be significantly slower.

Let me think about a better query strategy. Instead of `GROUP BY + COUNT(*)`, what about keeping the existing query pattern but removing the `QUALIFY ROW_NUMBER() = 1` to return all matches, and computing code_frequency client-side?

```sql
-- Return ALL matches per patient (like the old query but without ROW_NUMBER() = 1 filter)
SELECT DISTINCT
    pc."PatientPseudonym",
    aic.Search_Term,
    pc."SNOMEDCode"
FROM PrimaryCareClinicalCoding pc
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
WHERE pc."PatientPseudonym" IN (...)
  AND pc."EventDateTime" >= ...
```

Then compute `code_frequency` in Python: `df.groupby(['PatientPseudonym', 'Search_Term']).size()`.

Wait, that still returns all individual SNOMED codes which could be huge. And `SELECT DISTINCT` already timed out.

Let me try a different approach ÔÇö use `QUALIFY` with a higher rank instead of removing it entirely:

```sql
-- Return multiple rows per patient but use window function to get count
SELECT
    pc."PatientPseudonym",
    aic.Search_Term,
    COUNT(*) OVER (PARTITION BY pc."PatientPseudonym", aic.Search_Term) AS code_frequency
FROM PrimaryCareClinicalCoding pc
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
WHERE ...
QUALIFY ROW_NUMBER() OVER (PARTITION BY pc."PatientPseudonym", aic.Search_Term ORDER BY pc."EventDateTime" DESC) = 1
```

This uses `ROW_NUMBER() = 1` but partitioned by `PatientPseudonym + Search_Term` (not just PatientPseudonym), so it returns one row per patient-SearchTerm pair. And the `COUNT(*) OVER(...)` window function gives us the code_frequency without a GROUP BY.

This should be similar in performance to the old query because it still uses the `QUALIFY ROW_NUMBER() = 1` optimization, just with a different partition.

Let me test this.


Bash

cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 5 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Try NEW approach: window function for count + QUALIFY for dedup
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) OVER (PARTITION BY pc.\"PatientPseudonym\", aic.Search_Term) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
QUALIFY ROW_NUMBER() OVER (PARTITION BY pc.\"PatientPseudonym\", aic.Search_Term ORDER BY pc.\"EventDateTime\" DESC) = 1
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'Window+QUALIFY query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:20]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")} | freq={r.get(\"code_frequency\")}')
" 2>&1

Test optimized query with window function + QUALIFY for dedup


Still timing out at 30s. The window function approach is just as slow. The fundamental problem is the `network_timeout=30` is too low for ANY variant of this query. The old query barely squeaked through at 19.7s for 5 patients.

The key insight: even the OLD working query takes ~20s per batch. When we ran the full pipeline in earlier iterations (according to CLAUDE.md, "~9 minutes for 37K patients"), that's ~74 batches ├ù ~7s per batch with Snowflake query caching helping subsequent batches. But the first batch is always the slowest.

The fix is clear: **increase the connection_timeout** and then also increase it for `get_patient_indication_groups`. Let me also check if there's a way to make the query faster.

Actually, I realize the old query that took 19.7s was for the FIRST 5 random patients. In production, Snowflake caches the query plan and data after the first batch, so subsequent batches are much faster. This matches the "9 minutes for 37K patients" ÔÇö that's ~7-8 seconds per batch on average, with the first batch being much slower.

The fix is straightforward: increase `connection_timeout` to give the first batch enough time to complete. Let me do that.


The file C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\config\__init__.py has been updated successfully.

"""
Configuration module for Patient Pathway Analysis.

This module provides access to configuration settings loaded from TOML files.
Primary configuration file: config/snowflake.toml

Usage:
    from config import load_snowflake_config, SnowflakeConfig

    config = load_snowflake_config()
    print(config.connection.account)
    print(config.cache.ttl_seconds)
"""

from pathlib import Path
from dataclasses import dataclass, field
from typing import Optional
import tomllib  # Python 3.11+ built-in TOML parser

@dataclass
class ConnectionConfig:
    """Snowflake connection settings."""
    account: str = ""
    warehouse: str = "ANALYST_WH"
    database: str = "DATA_HUB"
    schema: str = "DWH"
    authenticator: str = "externalbrowser"
    user: str = ""
    role: str = ""

@dataclass
class TimeoutConfig:
    """Timeout settings for Snowflake operations."""
    connection_timeout: int = 30
    query_timeout: int = 300
    login_timeout: int = 120

@dataclass
class CacheConfig:
    """Cache settings for Snowflake query results."""
    enabled: bool = True
    directory: str = "data/cache"
    ttl_seconds: int = 86400  # 24 hours
    ttl_current_data_seconds: int = 3600  # 1 hour
    max_size_mb: int = 500

@dataclass
class TableReference:
    """Reference to a Snowflake table or view."""
    database: str = ""
    schema: str = ""
    view: str = ""
    table: str = ""
    key_columns: list = field(default_factory=list)

    @property
    def fully_qualified_name(self) -> str:
        """Return the fully qualified table/view name."""
        obj_name = self.table or self.view
        if not obj_name:
            return ""
        if self.database and self.schema:
            return f'"{self.database}"."{self.schema}"."{obj_name}"'
        elif self.schema:
            return f'"{self.schema}"."{obj_name}"'
        else:
            return f'"{obj_name}"'

@dataclass
class TablesConfig:
    """Configuration for commonly used tables."""
    activity: TableReference = field(default_factory=TableReference)
    patient: TableReference = field(default_factory=TableReference)
    medication: TableReference = field(default_factory=TableReference)
    organization: TableReference = field(default_factory=TableReference)

@dataclass
class QueryConfig:
    """Query execution settings."""
    quote_identifiers: bool = True
    test_limit: int = 20
    max_rows: int = 100000
    chunk_size: int = 10000

@dataclass
class SnowflakeConfig:
    """Complete Snowflake configuration."""
    connection: ConnectionConfig = field(default_factory=ConnectionConfig)
    timeouts: TimeoutConfig = field(default_factory=TimeoutConfig)
    cache: CacheConfig = field(default_factory=CacheConfig)
    tables: TablesConfig = field(default_factory=TablesConfig)
    query: QueryConfig = field(default_factory=QueryConfig)

    def validate(self) -> list[str]:
        """
        Validate the configuration.

        Returns:
            List of error messages (empty if valid).
        """
        errors = []

        if not self.connection.account:
            errors.append("Snowflake account is not configured (connection.account)")

        if not self.connection.warehouse:
            errors.append("Snowflake warehouse is not configured (connection.warehouse)")

        if self.connection.authenticator not in ("externalbrowser", "snowflake", "oauth", "okta"):
            errors.append(f"Invalid authenticator: {self.connection.authenticator}")

        if self.cache.ttl_seconds < 0:
            errors.append("Cache TTL must be non-negative")

        if self.query.max_rows < 1:
            errors.append("max_rows must be at least 1")

        return errors

    @property
    def is_configured(self) -> bool:
        """Return True if minimum required settings are present."""
        return bool(self.connection.account)

def _parse_table_reference(data: dict) -> TableReference:
    """Parse a table reference from TOML data."""
    return TableReference(
        database=data.get("database", ""),
        schema=data.get("schema", ""),
        view=data.get("view", ""),
        table=data.get("table", ""),
        key_columns=data.get("key_columns", []),
    )

def load_snowflake_config(config_path: Optional[Path] = None) -> SnowflakeConfig:
    """
    Load Snowflake configuration from TOML file.

    Args:
        config_path: Path to the TOML config file. Defaults to config/snowflake.toml
                     relative to the project root.

    Returns:
        SnowflakeConfig dataclass with all settings.

    Raises:
        FileNotFoundError: If the config file doesn't exist.
        tomllib.TOMLDecodeError: If the TOML is invalid.
    """
    if config_path is None:
        # Default to config/snowflake.toml relative to this file's directory
        config_path = Path(__file__).parent / "snowflake.toml"

    if not config_path.exists():
        # Return default config if file doesn't exist
        return SnowflakeConfig()

    with open(config_path, "rb") as f:
        data = tomllib.load(f)

    # Parse connection settings
    conn_data = data.get("connection", {})
    connection = ConnectionConfig(
        account=conn_data.get("account", ""),
        warehouse=conn_data.get("warehouse", "ANALYST_WH"),
        database=conn_data.get("database", "DATA_HUB"),
        schema=conn_data.get("schema", "DWH"),
        authenticator=conn_data.get("authenticator", "externalbrowser"),
        user=conn_data.get("user", ""),
        role=conn_data.get("role", ""),
    )

    # Parse timeout settings
    timeout_data = data.get("timeouts", {})
    timeouts = TimeoutConfig(
        connection_timeout=timeout_data.get("connection_timeout", 30),
        query_timeout=timeout_data.get("query_timeout", 300),
        login_timeout=timeout_data.get("login_timeout", 120),
    )

    # Parse cache settings
    cache_data = data.get("cache", {})
    cache = CacheConfig(
        enabled=cache_data.get("enabled", True),
        directory=cache_data.get("directory", "data/cache"),
        ttl_seconds=cache_data.get("ttl_seconds", 86400),
        ttl_current_data_seconds=cache_data.get("ttl_current_data_seconds", 3600),
        max_size_mb=cache_data.get("max_size_mb", 500),
    )

    # Parse table references
    tables_data = data.get("tables", {})
    tables = TablesConfig(
        activity=_parse_table_reference(tables_data.get("activity", {})),
        patient=_parse_table_reference(tables_data.get("patient", {})),
        medication=_parse_table_reference(tables_data.get("medication", {})),
        organization=_parse_table_reference(tables_data.get("organization", {})),
    )

    # Parse query settings
    query_data = data.get("query", {})
    query = QueryConfig(
        quote_identifiers=query_data.get("quote_identifiers", True),
        test_limit=query_data.get("test_limit", 20),
        max_rows=query_data.get("max_rows", 100000),
        chunk_size=query_data.get("chunk_size", 10000),
    )

    return SnowflakeConfig(
        connection=connection,
        timeouts=timeouts,
        cache=cache,
        tables=tables,
        query=query,
    )

# Module-level cached config (loaded on first access)
_cached_config: Optional[SnowflakeConfig] = None

def get_snowflake_config() -> SnowflakeConfig:
    """
    Get the Snowflake configuration (cached after first load).

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    if _cached_config is None:
        _cached_config = load_snowflake_config()
    return _cached_config

def reload_snowflake_config() -> SnowflakeConfig:
    """
    Reload the Snowflake configuration from disk.

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    _cached_config = load_snowflake_config()
    return _cached_config

# Export public API
__all__ = [
    "SnowflakeConfig",
    "ConnectionConfig",
    "TimeoutConfig",
    "CacheConfig",
    "TableReference",
    "TablesConfig",
    "QueryConfig",
    "load_snowflake_config",
    "get_snowflake_config",
    "reload_snowflake_config",
]


Now let me test again with the new timeout. Let me try the window function + QUALIFY approach first.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 5 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Test GROUP BY + COUNT(*) with longer timeout
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'GROUP BY query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:20]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")} | freq={r.get(\"code_frequency\")}')
" 2>&1

Test GROUP BY query with increased timeout (600s)


Still timing out. The config change didn't take effect. The connector might be caching its configuration or reusing a connection. Let me check if the connector singleton is caching the old config.


9:    from data_processing.snowflake_connector import SnowflakeConnector, get_connector
12:    with get_connector() as conn:
735:# Module-level singleton for convenience
736:_default_connector: Optional[SnowflakeConnector] = None
739:def get_connector(config: Optional[SnowflakeConfig] = None) -> SnowflakeConnector:
741:    Get a Snowflake connector (creates singleton on first call).
763:    """Reset the default connector (closes connection and clears singleton)."""
768:        _default_connector = None
792:    "get_connector",


   735ÔåÆ# Module-level singleton for convenience
   736ÔåÆ_default_connector: Optional[SnowflakeConnector] = None
   737ÔåÆ
   738ÔåÆ
   739ÔåÆdef get_connector(config: Optional[SnowflakeConfig] = None) -> SnowflakeConnector:
   740ÔåÆ    """
   741ÔåÆ    Get a Snowflake connector (creates singleton on first call).
   742ÔåÆ
   743ÔåÆ    Args:
   744ÔåÆ        config: Optional configuration. If provided, creates new connector
   745ÔåÆ                with this config. If None, uses/creates default connector.
   746ÔåÆ
   747ÔåÆ    Returns:
   748ÔåÆ        SnowflakeConnector instance
   749ÔåÆ    """
   750ÔåÆ    global _default_connector
   751ÔåÆ
   752ÔåÆ    if config is not None:
   753ÔåÆ        # Custom config requested, create new connector
   754ÔåÆ        return SnowflakeConnector(config)
   755ÔåÆ
   756ÔåÆ    if _default_connector is None:
   757ÔåÆ        _default_connector = SnowflakeConnector()
   758ÔåÆ
   759ÔåÆ    return _default_connector
   760ÔåÆ
   761ÔåÆ
   762ÔåÆdef reset_connector() -> None:
   763ÔåÆ    """Reset the default connector (closes connection and clears singleton)."""
   764ÔåÆ    global _default_connector
   765ÔåÆ
   766ÔåÆ    if _default_connector is not None:
   767ÔåÆ        _default_connector.close()
   768ÔåÆ        _default_connector = None
   769ÔåÆ

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

# Module-level singleton for convenience
_default_connector: Optional[SnowflakeConnector] = None

def get_connector(config: Optional[SnowflakeConfig] = None) -> SnowflakeConnector:
    """
    Get a Snowflake connector (creates singleton on first call).

    Args:
        config: Optional configuration. If provided, creates new connector
                with this config. If None, uses/creates default connector.

    Returns:
        SnowflakeConnector instance
    """
    global _default_connector

    if config is not None:
        # Custom config requested, create new connector
        return SnowflakeConnector(config)

    if _default_connector is None:
        _default_connector = SnowflakeConnector()

    return _default_connector

def reset_connector() -> None:
    """Reset the default connector (closes connection and clears singleton)."""
    global _default_connector

    if _default_connector is not None:
        _default_connector.close()
        _default_connector = None


Let me check how the config is loaded and passed to the connector.


85:class SnowflakeConnector:
104:    def __init__(self, config: Optional[SnowflakeConfig] = None):
112:        self._config = config or get_snowflake_config()
119:        return self._config
141:        if not self._config.is_configured:
169:        conn_cfg = self._config.connection
170:        timeout_cfg = self._config.timeouts
349:        effective_timeout = timeout or self._config.timeouts.query_timeout
386:        effective_timeout = timeout or self._config.timeouts.query_timeout
434:        effective_timeout = timeout or self._config.timeouts.query_timeout
435:        effective_chunk_size = chunk_size or self._config.query.chunk_size
436:        effective_max_rows = max_rows if max_rows is not None else self._config.query.max_rows
499:        effective_timeout = timeout or self._config.timeouts.query_timeout
500:        effective_chunk_size = chunk_size or self._config.query.chunk_size
501:        effective_max_rows = max_rows if max_rows is not None else self._config.query.max_rows
564:        effective_timeout = timeout or self._config.timeouts.query_timeout
565:        effective_max_rows = max_rows if max_rows is not None else self._config.query.max_rows
680:        effective_max_rows = max_rows if max_rows is not None else self._config.query.max_rows
681:        effective_timeout = timeout or self._config.timeouts.query_timeout


49:from config import get_snowflake_config, SnowflakeConfig
110:                    config/snowflake.toml using get_snowflake_config().
112:        self._config = config or get_snowflake_config()
779:        config = get_snowflake_config()


231:def get_snowflake_config() -> SnowflakeConfig:
232-    """
233-    Get the Snowflake configuration (cached after first load).
234-
235-    Returns:
236-        SnowflakeConfig dataclass with all settings.


   231ÔåÆdef get_snowflake_config() -> SnowflakeConfig:
   232ÔåÆ    """
   233ÔåÆ    Get the Snowflake configuration (cached after first load).
   234ÔåÆ
   235ÔåÆ    Returns:
   236ÔåÆ        SnowflakeConfig dataclass with all settings.
   237ÔåÆ    """
   238ÔåÆ    global _cached_config
   239ÔåÆ    if _cached_config is None:
   240ÔåÆ        _cached_config = load_snowflake_config()
   241ÔåÆ    return _cached_config
   242ÔåÆ
   243ÔåÆ
   244ÔåÆdef reload_snowflake_config() -> SnowflakeConfig:
   245ÔåÆ    """
   246ÔåÆ    Reload the Snowflake configuration from disk.
   247ÔåÆ
   248ÔåÆ    Returns:
   249ÔåÆ        SnowflakeConfig dataclass with all settings.
   250ÔåÆ    """
   251ÔåÆ    global _cached_config
   252ÔåÆ    _cached_config = load_snowflake_config()
   253ÔåÆ    return _cached_config
   254ÔåÆ
   255ÔåÆ
   256ÔåÆ# Export public API
   257ÔåÆ__all__ = [
   258ÔåÆ    "SnowflakeConfig",
   259ÔåÆ    "ConnectionConfig",
   260ÔåÆ    "TimeoutConfig",

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

def get_snowflake_config() -> SnowflakeConfig:
    """
    Get the Snowflake configuration (cached after first load).

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    if _cached_config is None:
        _cached_config = load_snowflake_config()
    return _cached_config

def reload_snowflake_config() -> SnowflakeConfig:
    """
    Reload the Snowflake configuration from disk.

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    _cached_config = load_snowflake_config()
    return _cached_config

# Export public API
__all__ = [
    "SnowflakeConfig",
    "ConnectionConfig",
    "TimeoutConfig",


The config is cached. Let me check what `load_snowflake_config` does ÔÇö it might read from snowflake.toml and override the defaults.


144:def load_snowflake_config(config_path: Optional[Path] = None) -> SnowflakeConfig:


   144ÔåÆdef load_snowflake_config(config_path: Optional[Path] = None) -> SnowflakeConfig:
   145ÔåÆ    """
   146ÔåÆ    Load Snowflake configuration from TOML file.
   147ÔåÆ
   148ÔåÆ    Args:
   149ÔåÆ        config_path: Path to the TOML config file. Defaults to config/snowflake.toml
   150ÔåÆ                     relative to the project root.
   151ÔåÆ
   152ÔåÆ    Returns:
   153ÔåÆ        SnowflakeConfig dataclass with all settings.
   154ÔåÆ
   155ÔåÆ    Raises:
   156ÔåÆ        FileNotFoundError: If the config file doesn't exist.
   157ÔåÆ        tomllib.TOMLDecodeError: If the TOML is invalid.
   158ÔåÆ    """
   159ÔåÆ    if config_path is None:
   160ÔåÆ        # Default to config/snowflake.toml relative to this file's directory
   161ÔåÆ        config_path = Path(__file__).parent / "snowflake.toml"
   162ÔåÆ
   163ÔåÆ    if not config_path.exists():
   164ÔåÆ        # Return default config if file doesn't exist
   165ÔåÆ        return SnowflakeConfig()
   166ÔåÆ
   167ÔåÆ    with open(config_path, "rb") as f:
   168ÔåÆ        data = tomllib.load(f)
   169ÔåÆ
   170ÔåÆ    # Parse connection settings
   171ÔåÆ    conn_data = data.get("connection", {})
   172ÔåÆ    connection = ConnectionConfig(
   173ÔåÆ        account=conn_data.get("account", ""),
   174ÔåÆ        warehouse=conn_data.get("warehouse", "ANALYST_WH"),
   175ÔåÆ        database=conn_data.get("database", "DATA_HUB"),
   176ÔåÆ        schema=conn_data.get("schema", "DWH"),
   177ÔåÆ        authenticator=conn_data.get("authenticator", "externalbrowser"),
   178ÔåÆ        user=conn_data.get("user", ""),
   179ÔåÆ        role=conn_data.get("role", ""),
   180ÔåÆ    )
   181ÔåÆ
   182ÔåÆ    # Parse timeout settings
   183ÔåÆ    timeout_data = data.get("timeouts", {})
   184ÔåÆ    timeouts = TimeoutConfig(
   185ÔåÆ        connection_timeout=timeout_data.get("connection_timeout", 30),
   186ÔåÆ        query_timeout=timeout_data.get("query_timeout", 300),
   187ÔåÆ        login_timeout=timeout_data.get("login_timeout", 120),
   188ÔåÆ    )
   189ÔåÆ
   190ÔåÆ    # Parse cache settings
   191ÔåÆ    cache_data = data.get("cache", {})
   192ÔåÆ    cache = CacheConfig(
   193ÔåÆ        enabled=cache_data.get("enabled", True),
   194ÔåÆ        directory=cache_data.get("directory", "data/cache"),
   195ÔåÆ        ttl_seconds=cache_data.get("ttl_seconds", 86400),
   196ÔåÆ        ttl_current_data_seconds=cache_data.get("ttl_current_data_seconds", 3600),
   197ÔåÆ        max_size_mb=cache_data.get("max_size_mb", 500),
   198ÔåÆ    )
   199ÔåÆ
   200ÔåÆ    # Parse table references
   201ÔåÆ    tables_data = data.get("tables", {})
   202ÔåÆ    tables = TablesConfig(
   203ÔåÆ        activity=_parse_table_reference(tables_data.get("activity", {})),
   204ÔåÆ        patient=_parse_table_reference(tables_data.get("patient", {})),
   205ÔåÆ        medication=_parse_table_reference(tables_data.get("medication", {})),
   206ÔåÆ        organization=_parse_table_reference(tables_data.get("organization", {})),
   207ÔåÆ    )
   208ÔåÆ
   209ÔåÆ    # Parse query settings
   210ÔåÆ    query_data = data.get("query", {})
   211ÔåÆ    query = QueryConfig(
   212ÔåÆ        quote_identifiers=query_data.get("quote_identifiers", True),
   213ÔåÆ        test_limit=query_data.get("test_limit", 20),
   214ÔåÆ        max_rows=query_data.get("max_rows", 100000),
   215ÔåÆ        chunk_size=query_data.get("chunk_size", 10000),
   216ÔåÆ    )
   217ÔåÆ
   218ÔåÆ    return SnowflakeConfig(
   219ÔåÆ        connection=connection,
   220ÔåÆ        timeouts=timeouts,
   221ÔåÆ        cache=cache,
   222ÔåÆ        tables=tables,
   223ÔåÆ        query=query,
   224ÔåÆ    )
   225ÔåÆ
   226ÔåÆ
   227ÔåÆ# Module-level cached config (loaded on first access)
   228ÔåÆ_cached_config: Optional[SnowflakeConfig] = None

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

def load_snowflake_config(config_path: Optional[Path] = None) -> SnowflakeConfig:
    """
    Load Snowflake configuration from TOML file.

    Args:
        config_path: Path to the TOML config file. Defaults to config/snowflake.toml
                     relative to the project root.

    Returns:
        SnowflakeConfig dataclass with all settings.

    Raises:
        FileNotFoundError: If the config file doesn't exist.
        tomllib.TOMLDecodeError: If the TOML is invalid.
    """
    if config_path is None:
        # Default to config/snowflake.toml relative to this file's directory
        config_path = Path(__file__).parent / "snowflake.toml"

    if not config_path.exists():
        # Return default config if file doesn't exist
        return SnowflakeConfig()

    with open(config_path, "rb") as f:
        data = tomllib.load(f)

    # Parse connection settings
    conn_data = data.get("connection", {})
    connection = ConnectionConfig(
        account=conn_data.get("account", ""),
        warehouse=conn_data.get("warehouse", "ANALYST_WH"),
        database=conn_data.get("database", "DATA_HUB"),
        schema=conn_data.get("schema", "DWH"),
        authenticator=conn_data.get("authenticator", "externalbrowser"),
        user=conn_data.get("user", ""),
        role=conn_data.get("role", ""),
    )

    # Parse timeout settings
    timeout_data = data.get("timeouts", {})
    timeouts = TimeoutConfig(
        connection_timeout=timeout_data.get("connection_timeout", 30),
        query_timeout=timeout_data.get("query_timeout", 300),
        login_timeout=timeout_data.get("login_timeout", 120),
    )

    # Parse cache settings
    cache_data = data.get("cache", {})
    cache = CacheConfig(
        enabled=cache_data.get("enabled", True),
        directory=cache_data.get("directory", "data/cache"),
        ttl_seconds=cache_data.get("ttl_seconds", 86400),
        ttl_current_data_seconds=cache_data.get("ttl_current_data_seconds", 3600),
        max_size_mb=cache_data.get("max_size_mb", 500),
    )

    # Parse table references
    tables_data = data.get("tables", {})
    tables = TablesConfig(
        activity=_parse_table_reference(tables_data.get("activity", {})),
        patient=_parse_table_reference(tables_data.get("patient", {})),
        medication=_parse_table_reference(tables_data.get("medication", {})),
        organization=_parse_table_reference(tables_data.get("organization", {})),
    )

    # Parse query settings
    query_data = data.get("query", {})
    query = QueryConfig(
        quote_identifiers=query_data.get("quote_identifiers", True),
        test_limit=query_data.get("test_limit", 20),
        max_rows=query_data.get("max_rows", 100000),
        chunk_size=query_data.get("chunk_size", 10000),
    )

    return SnowflakeConfig(
        connection=connection,
        timeouts=timeouts,
        cache=cache,
        tables=tables,
        query=query,
    )

# Module-level cached config (loaded on first access)
_cached_config: Optional[SnowflakeConfig] = None


I see ÔÇö line 185 has a hardcoded fallback: `connection_timeout=timeout_data.get("connection_timeout", 30)`. The TOML file overrides the dataclass default. Let me check the TOML file.


     1ÔåÆ# Snowflake Configuration for NHS Patient Pathway Analysis
     2ÔåÆ#
     3ÔåÆ# This file contains connection settings for the Snowflake data warehouse.
     4ÔåÆ# IMPORTANT: This file should NOT be committed to version control if it contains
     5ÔåÆ# sensitive information. However, with externalbrowser auth, no passwords are stored.
     6ÔåÆ#
     7ÔåÆ# For NHS SSO authentication, the 'externalbrowser' authenticator opens a browser
     8ÔåÆ# window for authentication via NHS identity management.
     9ÔåÆ
    10ÔåÆ[connection]
    11ÔåÆ# Snowflake account identifier (e.g., "xy12345.uk-south.azure")
    12ÔåÆ# Ask your Snowflake administrator for the correct account name
    13ÔåÆaccount = "ZK91403.uk-south.azure"
    14ÔåÆ
    15ÔåÆ# Default warehouse to use for queries
    16ÔåÆ# Common options: ANALYST_WH, COMPUTE_WH
    17ÔåÆwarehouse = "WH__XSMALL"
    18ÔåÆ
    19ÔåÆ# Default database for queries
    20ÔåÆ# DATA_HUB is the primary analyst-curated data warehouse
    21ÔåÆdatabase = "DATA_HUB"
    22ÔåÆ
    23ÔåÆ# Default schema (optional, can be overridden per query)
    24ÔåÆschema = "DWH"
    25ÔåÆ
    26ÔåÆ# Authentication method
    27ÔåÆ# "externalbrowser" opens browser for NHS SSO (required for NHS environments)
    28ÔåÆ# Other options: "snowflake" (username/password), "oauth", "okta"
    29ÔåÆauthenticator = "externalbrowser"
    30ÔåÆ
    31ÔåÆ# User principal (email address for externalbrowser auth)
    32ÔåÆ# Leave empty to use current Windows user or prompt
    33ÔåÆuser = "ANDREW.CHARLWOOD@NHS.NET"
    34ÔåÆ
    35ÔåÆ# Role to use (optional, uses default role if empty)
    36ÔåÆrole = ""
    37ÔåÆ
    38ÔåÆ[timeouts]
    39ÔåÆ# Connection timeout in seconds
    40ÔåÆconnection_timeout = 30
    41ÔåÆ
    42ÔåÆ# Query execution timeout in seconds (for long-running queries)
    43ÔåÆ# Set to 0 for no timeout
    44ÔåÆquery_timeout = 300
    45ÔåÆ
    46ÔåÆ# Login timeout in seconds (for SSO browser auth)
    47ÔåÆlogin_timeout = 120
    48ÔåÆ
    49ÔåÆ[cache]
    50ÔåÆ# Enable result caching
    51ÔåÆenabled = true
    52ÔåÆ
    53ÔåÆ# Cache directory (relative to project root or absolute path)
    54ÔåÆ# Defaults to data/cache/ if not specified
    55ÔåÆdirectory = "data/cache"
    56ÔåÆ
    57ÔåÆ# Time-to-live for cached results in seconds
    58ÔåÆ# 24 hours for historical data (86400 seconds)
    59ÔåÆttl_seconds = 86400
    60ÔåÆ
    61ÔåÆ# TTL for data that includes today's date (shorter)
    62ÔåÆttl_current_data_seconds = 3600
    63ÔåÆ
    64ÔåÆ# Maximum cache size in MB (oldest entries removed when exceeded)
    65ÔåÆmax_size_mb = 500
    66ÔåÆ
    67ÔåÆ[databases]
    68ÔåÆ# Quick reference for database purposes (read-only documentation)
    69ÔåÆ# DATA_HUB = "Analyst-curated data warehouse - primary source for most queries"
    70ÔåÆ# PRIMARY_CARE = "Raw extracts from EMIS and TPP clinical systems"
    71ÔåÆ# NATIONAL = "NHS England national datasets (SUS, ECDS, MHSDS, etc.)"
    72ÔåÆ# FACTS_AND_DIMENSIONS_ALL_DATA = "External reference data (BNF, SNOMED, QOF clusters)"
    73ÔåÆ# REPORTING_DATASETS_ICB = "Reporting outputs and analyst workspaces"
    74ÔåÆ
    75ÔåÆ# Tables commonly used for high-cost drug analysis
    76ÔåÆ[tables.activity]
    77ÔåÆ# Main activity data source (high-cost drug interventions)
    78ÔåÆ# Acute__Conmon__PatientLevelDrugs contains patient-level high-cost drug data
    79ÔåÆdatabase = "DATA_HUB"
    80ÔåÆschema = "CDM"
    81ÔåÆtable = "Acute__Conmon__PatientLevelDrugs"
    82ÔåÆkey_columns = [
    83ÔåÆ    "PseudoNHSNoLinked",    # Pseudonymised NHS number for patient linking
    84ÔåÆ    "ProviderCode",          # NHS provider code (e.g., RM1, RGP)
    85ÔåÆ    "LocalPatientID",        # Local patient identifier within provider
    86ÔåÆ    "InterventionDate",      # Date of drug intervention
    87ÔåÆ    "DrugName",              # Drug name (raw, needs standardization)
    88ÔåÆ    "DrugSNOMEDCode",        # SNOMED code for drug
    89ÔåÆ    "PriceActual",           # Actual cost of intervention
    90ÔåÆ    "TreatmentFunctionCode", # NHS treatment function code
    91ÔåÆ    "TreatmentFunctionDesc", # Treatment function description
    92ÔåÆ    "AdditionalDetail1",     # Additional details (used for directory identification)
    93ÔåÆ]
    94ÔåÆ
    95ÔåÆ[tables.patient]
    96ÔåÆ# Patient demographics
    97ÔåÆdatabase = "DATA_HUB"
    98ÔåÆschema = "DWH"
    99ÔåÆview = "DimPerson"
   100ÔåÆkey_columns = ["PatientPseudonym", "PersonKey", "CurrentGeneralPractice"]
   101ÔåÆ
   102ÔåÆ[tables.medication]
   103ÔåÆ# Medication reference data
   104ÔåÆdatabase = "DATA_HUB"
   105ÔåÆschema = "DWH"
   106ÔåÆview = "DimMedicineAndDevice"
   107ÔåÆkey_columns = ["ProductSnomedCode", "TherapeuticMoietySnomedCode", "ProductDescription"]
   108ÔåÆ
   109ÔåÆ[tables.organization]
   110ÔåÆ# NHS organizations and GP practices
   111ÔåÆdatabase = "DATA_HUB"
   112ÔåÆschema = "DWH"
   113ÔåÆview = "DimOrganisationAndSite"
   114ÔåÆkey_columns = ["SiteCode", "OrganisationName"]
   115ÔåÆ
   116ÔåÆ[query]
   117ÔåÆ# Default query behaviors
   118ÔåÆ# Always double-quote identifiers for case-sensitivity
   119ÔåÆquote_identifiers = true
   120ÔåÆ
   121ÔåÆ# Default row limit for test queries
   122ÔåÆtest_limit = 20
   123ÔåÆ
   124ÔåÆ# Maximum rows to fetch in a single query (prevents runaway queries)
   125ÔåÆmax_rows = 100000
   126ÔåÆ
   127ÔåÆ# Chunk size for large result sets
   128ÔåÆchunk_size = 10000
   129ÔåÆ

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

# Snowflake Configuration for NHS Patient Pathway Analysis
#
# This file contains connection settings for the Snowflake data warehouse.
# IMPORTANT: This file should NOT be committed to version control if it contains
# sensitive information. However, with externalbrowser auth, no passwords are stored.
#
# For NHS SSO authentication, the 'externalbrowser' authenticator opens a browser
# window for authentication via NHS identity management.

[connection]
# Snowflake account identifier (e.g., "xy12345.uk-south.azure")
# Ask your Snowflake administrator for the correct account name
account = "ZK91403.uk-south.azure"

# Default warehouse to use for queries
# Common options: ANALYST_WH, COMPUTE_WH
warehouse = "WH__XSMALL"

# Default database for queries
# DATA_HUB is the primary analyst-curated data warehouse
database = "DATA_HUB"

# Default schema (optional, can be overridden per query)
schema = "DWH"

# Authentication method
# "externalbrowser" opens browser for NHS SSO (required for NHS environments)
# Other options: "snowflake" (username/password), "oauth", "okta"
authenticator = "externalbrowser"

# User principal (email address for externalbrowser auth)
# Leave empty to use current Windows user or prompt
user = "ANDREW.CHARLWOOD@NHS.NET"

# Role to use (optional, uses default role if empty)
role = ""

[timeouts]
# Connection timeout in seconds
connection_timeout = 30

# Query execution timeout in seconds (for long-running queries)
# Set to 0 for no timeout
query_timeout = 300

# Login timeout in seconds (for SSO browser auth)
login_timeout = 120

[cache]
# Enable result caching
enabled = true

# Cache directory (relative to project root or absolute path)
# Defaults to data/cache/ if not specified
directory = "data/cache"

# Time-to-live for cached results in seconds
# 24 hours for historical data (86400 seconds)
ttl_seconds = 86400

# TTL for data that includes today's date (shorter)
ttl_current_data_seconds = 3600

# Maximum cache size in MB (oldest entries removed when exceeded)
max_size_mb = 500

[databases]
# Quick reference for database purposes (read-only documentation)
# DATA_HUB = "Analyst-curated data warehouse - primary source for most queries"
# PRIMARY_CARE = "Raw extracts from EMIS and TPP clinical systems"
# NATIONAL = "NHS England national datasets (SUS, ECDS, MHSDS, etc.)"
# FACTS_AND_DIMENSIONS_ALL_DATA = "External reference data (BNF, SNOMED, QOF clusters)"
# REPORTING_DATASETS_ICB = "Reporting outputs and analyst workspaces"

# Tables commonly used for high-cost drug analysis
[tables.activity]
# Main activity data source (high-cost drug interventions)
# Acute__Conmon__PatientLevelDrugs contains patient-level high-cost drug data
database = "DATA_HUB"
schema = "CDM"
table = "Acute__Conmon__PatientLevelDrugs"
key_columns = [
    "PseudoNHSNoLinked",    # Pseudonymised NHS number for patient linking
    "ProviderCode",          # NHS provider code (e.g., RM1, RGP)
    "LocalPatientID",        # Local patient identifier within provider
    "InterventionDate",      # Date of drug intervention
    "DrugName",              # Drug name (raw, needs standardization)
    "DrugSNOMEDCode",        # SNOMED code for drug
    "PriceActual",           # Actual cost of intervention
    "TreatmentFunctionCode", # NHS treatment function code
    "TreatmentFunctionDesc", # Treatment function description
    "AdditionalDetail1",     # Additional details (used for directory identification)
]

[tables.patient]
# Patient demographics
database = "DATA_HUB"
schema = "DWH"
view = "DimPerson"
key_columns = ["PatientPseudonym", "PersonKey", "CurrentGeneralPractice"]

[tables.medication]
# Medication reference data
database = "DATA_HUB"
schema = "DWH"
view = "DimMedicineAndDevice"
key_columns = ["ProductSnomedCode", "TherapeuticMoietySnomedCode", "ProductDescription"]

[tables.organization]
# NHS organizations and GP practices
database = "DATA_HUB"
schema = "DWH"
view = "DimOrganisationAndSite"
key_columns = ["SiteCode", "OrganisationName"]

[query]
# Default query behaviors
# Always double-quote identifiers for case-sensitivity
quote_identifiers = true

# Default row limit for test queries
test_limit = 20

# Maximum rows to fetch in a single query (prevents runaway queries)
max_rows = 100000

# Chunk size for large result sets
chunk_size = 10000


Found it. Line 40: `connection_timeout = 30` in `config/snowflake.toml`. This is what gets loaded. The dataclass default I changed doesn't matter because the TOML overrides it. Let me update the TOML file.


Edit

[timeouts]
# Connection timeout in seconds
connection_timeout = 30

[timeouts]
# Network timeout in seconds (how long client waits for Snowflake response)
# Must be high enough for GP record lookups which can take 30-60s per batch
connection_timeout = 600


The file C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\config\snowflake.toml has been updated successfully.

[timeouts]
# Network timeout in seconds (how long client waits for Snowflake response)
# Must be high enough for GP record lookups which can take 30-60s per batch
connection_timeout = 600

# Snowflake Configuration for NHS Patient Pathway Analysis
#
# This file contains connection settings for the Snowflake data warehouse.
# IMPORTANT: This file should NOT be committed to version control if it contains
# sensitive information. However, with externalbrowser auth, no passwords are stored.
#
# For NHS SSO authentication, the 'externalbrowser' authenticator opens a browser
# window for authentication via NHS identity management.

[connection]
# Snowflake account identifier (e.g., "xy12345.uk-south.azure")
# Ask your Snowflake administrator for the correct account name
account = "ZK91403.uk-south.azure"

# Default warehouse to use for queries
# Common options: ANALYST_WH, COMPUTE_WH
warehouse = "WH__XSMALL"

# Default database for queries
# DATA_HUB is the primary analyst-curated data warehouse
database = "DATA_HUB"

# Default schema (optional, can be overridden per query)
schema = "DWH"

# Authentication method
# "externalbrowser" opens browser for NHS SSO (required for NHS environments)
# Other options: "snowflake" (username/password), "oauth", "okta"
authenticator = "externalbrowser"

# User principal (email address for externalbrowser auth)
# Leave empty to use current Windows user or prompt
user = "ANDREW.CHARLWOOD@NHS.NET"

# Role to use (optional, uses default role if empty)
role = ""

[timeouts]
# Connection timeout in seconds
connection_timeout = 30

# Query execution timeout in seconds (for long-running queries)
# Set to 0 for no timeout
query_timeout = 300

# Login timeout in seconds (for SSO browser auth)
login_timeout = 120

[cache]
# Enable result caching
enabled = true

# Cache directory (relative to project root or absolute path)
# Defaults to data/cache/ if not specified
directory = "data/cache"

# Time-to-live for cached results in seconds
# 24 hours for historical data (86400 seconds)
ttl_seconds = 86400

# TTL for data that includes today's date (shorter)
ttl_current_data_seconds = 3600

# Maximum cache size in MB (oldest entries removed when exceeded)
max_size_mb = 500

[databases]
# Quick reference for database purposes (read-only documentation)
# DATA_HUB = "Analyst-curated data warehouse - primary source for most queries"
# PRIMARY_CARE = "Raw extracts from EMIS and TPP clinical systems"
# NATIONAL = "NHS England national datasets (SUS, ECDS, MHSDS, etc.)"
# FACTS_AND_DIMENSIONS_ALL_DATA = "External reference data (BNF, SNOMED, QOF clusters)"
# REPORTING_DATASETS_ICB = "Reporting outputs and analyst workspaces"

# Tables commonly used for high-cost drug analysis
[tables.activity]
# Main activity data source (high-cost drug interventions)
# Acute__Conmon__PatientLevelDrugs contains patient-level high-cost drug data
database = "DATA_HUB"
schema = "CDM"
table = "Acute__Conmon__PatientLevelDrugs"
key_columns = [
    "PseudoNHSNoLinked",    # Pseudonymised NHS number for patient linking
    "ProviderCode",          # NHS provider code (e.g., RM1, RGP)
    "LocalPatientID",        # Local patient identifier within provider
    "InterventionDate",      # Date of drug intervention
    "DrugName",              # Drug name (raw, needs standardization)
    "DrugSNOMEDCode",        # SNOMED code for drug
    "PriceActual",           # Actual cost of intervention
    "TreatmentFunctionCode", # NHS treatment function code
    "TreatmentFunctionDesc", # Treatment function description
    "AdditionalDetail1",     # Additional details (used for directory identification)
]

[tables.patient]
# Patient demographics
database = "DATA_HUB"
schema = "DWH"
view = "DimPerson"
key_columns = ["PatientPseudonym", "PersonKey", "CurrentGeneralPractice"]

[tables.medication]
# Medication reference data
database = "DATA_HUB"
schema = "DWH"
view = "DimMedicineAndDevice"
key_columns = ["ProductSnomedCode", "TherapeuticMoietySnomedCode", "ProductDescription"]

[tables.organization]
# NHS organizations and GP practices
database = "DATA_HUB"
schema = "DWH"
view = "DimOrganisationAndSite"
key_columns = ["SiteCode", "OrganisationName"]

[query]
# Default query behaviors
# Always double-quote identifiers for case-sensitivity
quote_identifiers = true

# Default row limit for test queries
test_limit = 20

# Maximum rows to fetch in a single query (prevents runaway queries)
max_rows = 100000

# Chunk size for large result sets
chunk_size = 10000

+# Network timeout in seconds (how long client waits for Snowflake response)

+# Must be high enough for GP record lookups which can take 30-60s per batch


Also revert the dataclass default change since the TOML is the authoritative source.


"""
Configuration module for Patient Pathway Analysis.

This module provides access to configuration settings loaded from TOML files.
Primary configuration file: config/snowflake.toml

Usage:
    from config import load_snowflake_config, SnowflakeConfig

    config = load_snowflake_config()
    print(config.connection.account)
    print(config.cache.ttl_seconds)
"""

from pathlib import Path
from dataclasses import dataclass, field
from typing import Optional
import tomllib  # Python 3.11+ built-in TOML parser

@dataclass
class ConnectionConfig:
    """Snowflake connection settings."""
    account: str = ""
    warehouse: str = "ANALYST_WH"
    database: str = "DATA_HUB"
    schema: str = "DWH"
    authenticator: str = "externalbrowser"
    user: str = ""
    role: str = ""

@dataclass
class TimeoutConfig:
    """Timeout settings for Snowflake operations."""
    connection_timeout: int = 600
    query_timeout: int = 300
    login_timeout: int = 120

@dataclass
class CacheConfig:
    """Cache settings for Snowflake query results."""
    enabled: bool = True
    directory: str = "data/cache"
    ttl_seconds: int = 86400  # 24 hours
    ttl_current_data_seconds: int = 3600  # 1 hour
    max_size_mb: int = 500

@dataclass
class TableReference:
    """Reference to a Snowflake table or view."""
    database: str = ""
    schema: str = ""
    view: str = ""
    table: str = ""
    key_columns: list = field(default_factory=list)

    @property
    def fully_qualified_name(self) -> str:
        """Return the fully qualified table/view name."""
        obj_name = self.table or self.view
        if not obj_name:
            return ""
        if self.database and self.schema:
            return f'"{self.database}"."{self.schema}"."{obj_name}"'
        elif self.schema:
            return f'"{self.schema}"."{obj_name}"'
        else:
            return f'"{obj_name}"'

@dataclass
class TablesConfig:
    """Configuration for commonly used tables."""
    activity: TableReference = field(default_factory=TableReference)
    patient: TableReference = field(default_factory=TableReference)
    medication: TableReference = field(default_factory=TableReference)
    organization: TableReference = field(default_factory=TableReference)

@dataclass
class QueryConfig:
    """Query execution settings."""
    quote_identifiers: bool = True
    test_limit: int = 20
    max_rows: int = 100000
    chunk_size: int = 10000

@dataclass
class SnowflakeConfig:
    """Complete Snowflake configuration."""
    connection: ConnectionConfig = field(default_factory=ConnectionConfig)
    timeouts: TimeoutConfig = field(default_factory=TimeoutConfig)
    cache: CacheConfig = field(default_factory=CacheConfig)
    tables: TablesConfig = field(default_factory=TablesConfig)
    query: QueryConfig = field(default_factory=QueryConfig)

    def validate(self) -> list[str]:
        """
        Validate the configuration.

        Returns:
            List of error messages (empty if valid).
        """
        errors = []

        if not self.connection.account:
            errors.append("Snowflake account is not configured (connection.account)")

        if not self.connection.warehouse:
            errors.append("Snowflake warehouse is not configured (connection.warehouse)")

        if self.connection.authenticator not in ("externalbrowser", "snowflake", "oauth", "okta"):
            errors.append(f"Invalid authenticator: {self.connection.authenticator}")

        if self.cache.ttl_seconds < 0:
            errors.append("Cache TTL must be non-negative")

        if self.query.max_rows < 1:
            errors.append("max_rows must be at least 1")

        return errors

    @property
    def is_configured(self) -> bool:
        """Return True if minimum required settings are present."""
        return bool(self.connection.account)

def _parse_table_reference(data: dict) -> TableReference:
    """Parse a table reference from TOML data."""
    return TableReference(
        database=data.get("database", ""),
        schema=data.get("schema", ""),
        view=data.get("view", ""),
        table=data.get("table", ""),
        key_columns=data.get("key_columns", []),
    )

def load_snowflake_config(config_path: Optional[Path] = None) -> SnowflakeConfig:
    """
    Load Snowflake configuration from TOML file.

    Args:
        config_path: Path to the TOML config file. Defaults to config/snowflake.toml
                     relative to the project root.

    Returns:
        SnowflakeConfig dataclass with all settings.

    Raises:
        FileNotFoundError: If the config file doesn't exist.
        tomllib.TOMLDecodeError: If the TOML is invalid.
    """
    if config_path is None:
        # Default to config/snowflake.toml relative to this file's directory
        config_path = Path(__file__).parent / "snowflake.toml"

    if not config_path.exists():
        # Return default config if file doesn't exist
        return SnowflakeConfig()

    with open(config_path, "rb") as f:
        data = tomllib.load(f)

    # Parse connection settings
    conn_data = data.get("connection", {})
    connection = ConnectionConfig(
        account=conn_data.get("account", ""),
        warehouse=conn_data.get("warehouse", "ANALYST_WH"),
        database=conn_data.get("database", "DATA_HUB"),
        schema=conn_data.get("schema", "DWH"),
        authenticator=conn_data.get("authenticator", "externalbrowser"),
        user=conn_data.get("user", ""),
        role=conn_data.get("role", ""),
    )

    # Parse timeout settings
    timeout_data = data.get("timeouts", {})
    timeouts = TimeoutConfig(
        connection_timeout=timeout_data.get("connection_timeout", 30),
        query_timeout=timeout_data.get("query_timeout", 300),
        login_timeout=timeout_data.get("login_timeout", 120),
    )

    # Parse cache settings
    cache_data = data.get("cache", {})
    cache = CacheConfig(
        enabled=cache_data.get("enabled", True),
        directory=cache_data.get("directory", "data/cache"),
        ttl_seconds=cache_data.get("ttl_seconds", 86400),
        ttl_current_data_seconds=cache_data.get("ttl_current_data_seconds", 3600),
        max_size_mb=cache_data.get("max_size_mb", 500),
    )

    # Parse table references
    tables_data = data.get("tables", {})
    tables = TablesConfig(
        activity=_parse_table_reference(tables_data.get("activity", {})),
        patient=_parse_table_reference(tables_data.get("patient", {})),
        medication=_parse_table_reference(tables_data.get("medication", {})),
        organization=_parse_table_reference(tables_data.get("organization", {})),
    )

    # Parse query settings
    query_data = data.get("query", {})
    query = QueryConfig(
        quote_identifiers=query_data.get("quote_identifiers", True),
        test_limit=query_data.get("test_limit", 20),
        max_rows=query_data.get("max_rows", 100000),
        chunk_size=query_data.get("chunk_size", 10000),
    )

    return SnowflakeConfig(
        connection=connection,
        timeouts=timeouts,
        cache=cache,
        tables=tables,
        query=query,
    )

# Module-level cached config (loaded on first access)
_cached_config: Optional[SnowflakeConfig] = None

def get_snowflake_config() -> SnowflakeConfig:
    """
    Get the Snowflake configuration (cached after first load).

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    if _cached_config is None:
        _cached_config = load_snowflake_config()
    return _cached_config

def reload_snowflake_config() -> SnowflakeConfig:
    """
    Reload the Snowflake configuration from disk.

    Returns:
        SnowflakeConfig dataclass with all settings.
    """
    global _cached_config
    _cached_config = load_snowflake_config()
    return _cached_config

# Export public API
__all__ = [
    "SnowflakeConfig",
    "ConnectionConfig",
    "TimeoutConfig",
    "CacheConfig",
    "TableReference",
    "TablesConfig",
    "QueryConfig",
    "load_snowflake_config",
    "get_snowflake_config",
    "reload_snowflake_config",
]


Wait ÔÇö the `load_snowflake_config` also has a hardcoded fallback on line 185: `timeout_data.get("connection_timeout", 30)`. I should update this fallback too for consistency, but actually let me leave it. The TOML value (600) will be used since the key exists. The fallback 30 only applies if the key is missing from TOML.

Actually, I should also update the fallback to match the new intent. Let me do that for consistency:


-        connection_timeout=timeout_data.get("connection_timeout", 30),

+        connection_timeout=timeout_data.get("connection_timeout", 600),


Now let me test the query again with the updated timeout. The test will create a fresh connector since each `python -c` call starts fresh.


Bash

cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()
print(f'Network timeout: {connector.config.timeouts.connection_timeout}s')

# Get 5 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Test GROUP BY + COUNT(*) query
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'GROUP BY query: {elapsed:.1f}s, {len(results)} rows')
for r in results[:20]:
    print(f'  {str(r.get(\"PatientPseudonym\",\"\"))[:8]}... | {r.get(\"Search_Term\")} | freq={r.get(\"code_frequency\")}')
" 2>&1

Test GROUP BY query with 600s network timeout


Network timeout: 600s
Test patients: 5
GROUP BY query: 39.6s, 71 rows
  25C9481D... | drug misuse | freq=1
  25C9481D... | hiv | freq=2
  AFB426FF... | retinal vein occlusion | freq=1
  AFB426FF... | atrial fibrillation | freq=1
  7E0ABB65... | renal cell carcinoma | freq=2
  7E0ABB65... | melanoma | freq=2
  AFB426FF... | uveitis | freq=1
  7E0ABB65... | squamous cell carcinoma | freq=2
  708EC81F... | influenza | freq=16
  708EC81F... | drug misuse | freq=1
  708EC81F... | irritable bowel syndrome | freq=1
  0F7C0582... | rheumatoid arthritis | freq=1
  AFB426FF... | lung cancer | freq=1
  7E0ABB65... | malignant melanoma | freq=2
  AFB426FF... | prostate cancer | freq=1
  0F7C0582... | drug misuse | freq=10
  7E0ABB65... | colon cancer | freq=2
  AFB426FF... | macular degeneration | freq=1
  708EC81F... | migraine | freq=1
  0F7C0582... | diabetes | freq=33


The query now works with the 600s timeout. It took 39.6s for 5 patients ÔÇö significantly slower than the old QUALIFY query (19.7s). Let me now test with the 500-patient batch size to estimate total time.

Actually, let me first consider: at 39.6s per batch with batch_size=5, that's ~8s per patient. For 36K patients, that would be absurd. But the first batch is always the slowest (cold cache). Let me test with 500 patients to see if Snowflake's caching kicks in.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 500 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 500
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Test GROUP BY + COUNT(*) query with 500 patients
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'500-patient query: {elapsed:.1f}s, {len(results)} rows')

# Summary stats
from collections import Counter
patient_count = len(set(r.get('PatientPseudonym') for r in results))
search_terms = Counter(r.get('Search_Term') for r in results)
print(f'Unique patients in results: {patient_count}')
print(f'Unique Search_Terms: {len(search_terms)}')
print(f'Avg indications per patient: {len(results)/patient_count:.1f}')
print(f'Top 5 Search_Terms: {search_terms.most_common(5)}')
" 2>&1

Test GROUP BY query with 500 patients to estimate performance


Test patients: 500
500-patient query: 38.4s, 5517 rows
Unique patients in results: 488
Unique Search_Terms: 115
Avg indications per patient: 11.3
Top 5 Search_Terms: [('sepsis', 470), ('drug misuse', 445), ('influenza', 438), ('cardiovascular disease', 250), ('arthritis', 210)]


Excellent. The 500-patient batch takes 38.4s ÔÇö very similar to the 5-patient batch (39.6s). This confirms the bottleneck is query compilation/planning, not the data volume. Snowflake's query plan for the CTE+JOIN is the expensive part, and it's roughly constant regardless of batch size.

Key data:
- 500 patients ÔåÆ 38.4s, 5517 rows, 488 matched (97.6% match rate)
- Avg 11.3 indications per patient (this includes non-drug-related diagnoses like "sepsis", "influenza")
- 115 unique Search_Terms found

For 36K patients at 500/batch = 72 batches ├ù ~38s = ~46 minutes. That's significantly longer than the old approach (9 minutes). But subsequent batches might be faster due to Snowflake query caching.

Let me test a second batch to see if caching helps.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get patients 501-1000
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 500 OFFSET 500
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients (batch 2): {len(patients)}')

placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'Batch 2 (500 patients): {elapsed:.1f}s, {len(results)} rows')
" 2>&1

Test second batch of 500 patients for caching effect


Test patients (batch 2): 500
Batch 2 (500 patients): 39.6s, 6332 rows


No caching benefit ÔÇö still ~40s per batch. This means 72 batches ├ù 40s = ~48 minutes just for the GP lookup. That's too slow. Let me think about optimization.

The core problem: each batch re-executes the massive CTE (148 cluster mappings ÔåÆ SNOMED codes expansion) and joins with the huge `PrimaryCareClinicalCoding` table. The CTE is identical across all batches ÔÇö only the patient list changes.

**Optimization ideas:**
1. **Larger batch size** ÔÇö If 500 patients takes 40s and 5 patients also takes 40s, then the batch size doesn't matter much. Try 2000 or 5000 patients per batch to reduce the number of CTE compilations.
2. **Skip code_frequency** ÔÇö Use DISTINCT instead of GROUP BY + COUNT(*), compute frequency client-side later if needed at all
3. **Pre-create temp table** for the CTE results, then query against that

Let me try option 1 first ÔÇö increase batch size to 5000.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get 5000 test patients
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
    LIMIT 5000
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Test patients: {len(patients)}')

# Test GROUP BY + COUNT(*) query with 5000 patients
placeholders = ', '.join(['%s'] * len(patients))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(patients) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'5000-patient query: {elapsed:.1f}s, {len(results)} rows')
print(f'Per-patient time: {elapsed/5000*1000:.1f}ms')
print(f'Projected for 36K: {elapsed/5000*36000/60:.1f} min ({elapsed/5000*36000:.0f}s)')
" 2>&1

Test with 5000-patient batch to check if larger batches are more efficient


Test patients: 5000
5000-patient query: 41.3s, 72797 rows
Per-patient time: 8.3ms
Projected for 36K: 5.0 min (298s)


Huge improvement. 5000 patients takes 41.3s ÔÇö almost the same as 500 patients. The overhead is in the CTE compilation, not the data volume. With 5000-patient batches:
- 36K patients / 5000 per batch = ~8 batches
- 8 ├ù 41s = ~5.5 minutes for GP lookups

That's reasonable and much better than the 48 minutes with 500-patient batches. Let me try an even larger batch.


cd "C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign" && python -c "
import time
from data_processing.snowflake_connector import get_connector
from data_processing.diagnosis_lookup import CLUSTER_MAPPING_SQL

connector = get_connector()

# Get all ~36K patients at once
result = connector.execute_dict('''
    SELECT DISTINCT \"PseudoNHSNoLinked\"
    FROM DATA_HUB.CDM.\"Acute__Conmon__PatientLevelDrugs\"
    WHERE \"PseudoNHSNoLinked\" IS NOT NULL
''')
patients = [r['PseudoNHSNoLinked'] for r in result]
print(f'Total patients: {len(patients)}')

# Try with 10000-patient batch
batch = patients[:10000]
placeholders = ', '.join(['%s'] * len(batch))
query = f'''
{CLUSTER_MAPPING_SQL}
SELECT
    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",
    aic.Search_Term AS \"Search_Term\",
    COUNT(*) AS \"code_frequency\"
FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc
INNER JOIN AllIndicationCodes aic
    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode
WHERE pc.\"PatientPseudonym\" IN ({placeholders})
    AND pc.\"EventDateTime\" >= %s
GROUP BY pc.\"PatientPseudonym\", aic.Search_Term
'''

params = tuple(batch) + ('2019-04-01',)
start = time.time()
results = connector.execute_dict(query, params)
elapsed = time.time() - start
print(f'10000-patient query: {elapsed:.1f}s, {len(results)} rows')
print(f'Projected for {len(patients)}K: {elapsed/10000*len(patients)/60:.1f} min')
" 2>&1

Test with 10000-patient batch for optimal sizing


Total patients: 41969
10000-patient query: 42.8s, 150220 rows
Projected for 41969K: 3.0 min


10K patients takes ~43s. The time is nearly constant regardless of batch size (40-43s), confirming the overhead is CTE compilation. With 10K-patient batches:
- ~42K patients / 10000 = ~5 batches ├ù 43s = ~3.5 minutes

That's excellent. Let me now update the batch size in `get_patient_indication_groups()` from 500 to 5000. I'll use 5000 rather than 10000 to stay conservative (10K placeholders in an IN clause is pushing it).

Actually wait ÔÇö the caller in `refresh_pathways.py` passes `batch_size=500`. Let me check.


410:                        batch_size=500,


I need to change the batch_size in the caller. Let me update both the default in the function and the call site.


<tool_use_error>Found 2 matches of the string to replace, but replace_all is false. To replace all occurrences, set replace_all to true. To replace only one occurrence, please provide more context to uniquely identify the instance.
String:     batch_size: int = 500,</tool_use_error>

Error: Found 2 matches of the string to replace, but replace_all is false. To replace all occurrences, set replace_all to true. To replace only one occurrence, please provide more context to uniquely identify the instance.
String:     batch_size: int = 500,


There are two functions with `batch_size: int = 500`. Let me update the specific one.


Edit

def get_patient_indication_groups(
    patient_pseudonyms: list[str],
    connector: Optional[SnowflakeConnector] = None,
    batch_size: int = 500,
    earliest_hcd_date: Optional[str] = None,

def get_patient_indication_groups(
    patient_pseudonyms: list[str],
    connector: Optional[SnowflakeConnector] = None,
    batch_size: int = 5000,
    earliest_hcd_date: Optional[str] = None,