BCP_CSV_Quoted_Import

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Snippet Name: BCP_CSV_Quoted_Import

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Created Date: 2025-06-16 13:12:48
Last Modified Date: 2025-05-31 10:32:16

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Title: BCP CSV Quoted Import
Author: user
without a format file, how can i bcp in or bulk insert into sql server a quoted delimiter csv  found at C:\_Code\LinkedInData\USAEmails.csv

Author: ChatGPT
To import a **quoted, delimited CSV file** (for example, fields enclosed in `"..."` and separated by commas) into SQL Server **using `bcp` or `BULK INSERT` without a format file**, you can use the following approach ??? but there???s an important caveat:  

SQL Server???s `BULK INSERT` and `bcp` **do not natively handle quoted delimiters or escaped quotes** ??? they treat the delimiter literally, even inside quotes.  

That means if your CSV has:

```
"FirstName","LastName","Email"
"John","Doe","john.doe@example.com"
"Jane","Smith","jane.smith@example.com"
```

and you run `BULK INSERT` or `bcp` **without preprocessing**, it will break at the commas **inside the quotes**, resulting in data misalignment.

### ??? Option 1: Preprocess the file (recommended)

Use PowerShell, Python, or a tool like `csvkit` to preprocess the file, **removing quotes or replacing commas inside quotes with another character**, then run `BULK INSERT`.

Example PowerShell one-liner to strip quotes:

```powershell
Get-Content C:\_Code\LinkedInData\USAEmails.csv | ForEach-Object { $_ -replace '"', '' } | Set-Content C:\_Code\LinkedInData\USAEmails_clean.csv
```

Then run:

```sql
BULK INSERT [dbo].[YourTargetTable]
FROM 'C:\_Code\LinkedInData\USAEmails_clean.csv'
WITH (
    FIELDTERMINATOR = ',',
    ROWTERMINATOR = '\n',
    FIRSTROW = 2 -- skip header row
);
```

---

### ??? Option 2: Use SSIS or OPENROWSET (if BULK INSERT fails)

- **SSIS (SQL Server Integration Services)**: Built-in CSV handling with text qualifiers (`"`)  
- **`OPENROWSET` with OLEDB / ACE drivers**: Can read CSV with quoted delimiters  

Example:

```sql
SELECT *
FROM OPENROWSET('Microsoft.ACE.OLEDB.12.0',
    'Text;Database=C:\_Code\LinkedInData\;HDR=YES;FMT=Delimited',
    'SELECT * FROM USAEmails.csv');
```

Make sure you have the **Access Database Engine** installed for `ACE.OLEDB.12.0`.

---

### ??? Option 3: Use a staging table with all columns as `NVARCHAR`

1. Create a **staging table** with all columns as `NVARCHAR(MAX)` or `VARCHAR(MAX)`:

```sql
CREATE TABLE dbo.USAEmails_Staging (
    Col1 NVARCHAR(MAX),
    Col2 NVARCHAR(MAX),
    Col3 NVARCHAR(MAX)
);
```

2. Import **raw CSV data**:

```sql
BULK INSERT dbo.USAEmails_Staging
FROM 'C:\_Code\LinkedInData\USAEmails.csv'
WITH (
    FIELDTERMINATOR = ',',
    ROWTERMINATOR = '\n',
    FIRSTROW = 2
);
```

3. Clean the data **inside SQL Server**:

```sql
UPDATE dbo.USAEmails_Staging
SET Col1 = REPLACE(Col1, '"', ''),
    Col2 = REPLACE(Col2, '"', ''),
    Col3 = REPLACE(Col3, '"', '');
```

---

### ??? Summary

- `BULK INSERT` / `bcp` do **not** handle quoted delimiters correctly.
- Best approach:
  - Preprocess the CSV (strip quotes)
  - Use `BULK INSERT` after cleaning
- Alternative: Use `SSIS` or `OPENROWSET`
- As a fallback: Import to staging, clean with `UPDATE`

---

If you want, I can help you write:
??? a PowerShell script  
??? a Python script  
??? a full SQL workflow for the staging table

Would you like me to prepare one? Let me know! ????

Author: user
can i use python or powershell to import that file quickly intead?

Author: ChatGPT
Yes ??? you can **absolutely** use Python or PowerShell to import the quoted CSV file into SQL Server **quickly**, and they will handle the quoted delimiters better than `bcp` or `BULK INSERT`.  

Here???s a solid starting point for both:

---

### ??? Python (with `pandas` + `pyodbc`)

This is one of the fastest, easiest ways:

```python
import pandas as pd
import pyodbc

# 1. Read CSV with pandas (handles quoted fields)
csv_file = r'C:\_Code\LinkedInData\USAEmails.csv'
df = pd.read_csv(csv_file)

# 2. Connect to SQL Server
conn_str = (
    "DRIVER={ODBC Driver 17 for SQL Server};"
    "SERVER=your_server_name;"
    "DATABASE=your_database;"
    "UID=your_username;"
    "PWD=your_password"
)
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()

# 3. Insert rows into target table
for index, row in df.iterrows():
    cursor.execute("""
        INSERT INTO dbo.YourTargetTable (FirstName, LastName, Email)
        VALUES (?, ?, ?)
    """, row['FirstName'], row['LastName'], row['Email'])

# 4. Commit and close
conn.commit()
cursor.close()
conn.close()

print("Import completed!")
```

??? Handles quoted CSV  
??? No preprocessing needed  
??? Easy to modify and extend

---

### ??? PowerShell (with `Invoke-Sqlcmd` or `System.Data.SqlClient`)

Here???s a **PowerShell** version:

```powershell
# 1. Read the CSV file
$csvPath = "C:\_Code\LinkedInData\USAEmails.csv"
$data = Import-Csv -Path $csvPath

# 2. Set SQL connection details
$server = "YourSqlServer"
$database = "YourDatabase"
$table = "YourTargetTable"

# 3. Insert each row
foreach ($row in $data) {
    $query = @"
INSERT INTO dbo.$table (FirstName, LastName, Email)
VALUES (N'$($row.FirstName)', N'$($row.LastName)', N'$($row.Email)')
"@

    Invoke-Sqlcmd -ServerInstance $server -Database $database -Query $query
}

Write-Host "Import completed!"
```

??? Handles quoted CSV  
??? Requires `SqlServer` PowerShell module (`Install-Module SqlServer`)  
??? Good for small/medium files (~ <100,000 rows); for large files, batching is better

---

### ??? Tips for fast performance

- **Python**
  - Use `fast_executemany = True` on the cursor (`pyodbc` setting) to massively speed up inserts
  - Use `SQLAlchemy` + `df.to_sql()` for bulk operations

- **PowerShell**
  - Instead of row-by-row inserts, **batch into `DataTable`** and use `SqlBulkCopy` (super fast)
  - Example: use `[System.Data.SqlClient.SqlBulkCopy]` to load all rows at once

---

### ???? Want me to write you:
- A **Python script with bulk mode**?
- A **PowerShell script using `SqlBulkCopy`?**

Let me know which direction you prefer, and I???ll prepare a full working example! ????

Author: user
reading a large file into a dataframe will use a lot of resources, right?

Author: ChatGPT
??? Great point ??? yes, you are **absolutely right** to be cautious!  

Reading a **large CSV** (especially with millions of rows or many columns) into a `pandas` DataFrame will **consume significant memory**, because:

- `pandas.read_csv()` loads the **entire file into memory**.  
- It **infers datatypes** for every column, which adds some upfront cost.  
- It stores the data in an **in-memory object**, which grows ~30???50% larger than raw CSV size due to Python object overhead.

For example:
| CSV File Size | Typical RAM Used by `pandas` |
|---------------|-----------------------------|
| 1 GB CSV     | ~2???4 GB RAM in DataFrame    |
| 5 GB CSV     | ~10???20 GB RAM in DataFrame  |

So yes ??? on a typical laptop or VM, you **could run out of RAM** or get into heavy swap.

---

### ???? Better options for large files

Here are **more scalable strategies** for Python and PowerShell:

---

#### ??? Python ??? Chunked read + batch insert

```python
import pandas as pd
import pyodbc

conn_str = "DRIVER={ODBC Driver 17 for SQL Server};SERVER=your_server;DATABASE=your_db;UID=your_user;PWD=your_pwd"
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.fast_executemany = True

chunksize = 10000  # Adjust to balance speed/memory

for chunk in pd.read_csv(r'C:\_Code\LinkedInData\USAEmails.csv', chunksize=chunksize):
    data = [tuple(row) for row in chunk.to_numpy()]
    cursor.executemany("INSERT INTO dbo.YourTargetTable (FirstName, LastName, Email) VALUES (?, ?, ?)", data)
    conn.commit()

cursor.close()
conn.close()
print("Import completed in chunks!")
```

??? Reads only 10,000 rows at a time  
??? Fast batch inserts (`executemany`)  
??? Avoids blowing up RAM

---

#### ??? PowerShell ??? Stream + SqlBulkCopy

```powershell
Add-Type -AssemblyName System.Data

$csvPath = "C:\_Code\LinkedInData\USAEmails.csv"
$server = "YourSqlServer"
$database = "YourDatabase"
$table = "YourTargetTable"

$connString = "Server=$server;Database=$database;Integrated Security=True;"
$connection = New-Object System.Data.SqlClient.SqlConnection $connString
$connection.Open()

$reader = [System.IO.File]::OpenText($csvPath)
$header = $reader.ReadLine().Split(',')

# Create DataTable
$dataTable = New-Object System.Data.DataTable
$header | ForEach-Object { $dataTable.Columns.Add($_) }

$count = 0
while (($line = $reader.ReadLine()) -ne $null) {
    $values = $line.Split(',') -replace '"', ''
    $row = $dataTable.NewRow()
    $header | ForEach-Object { $row[$_] = $values[$header.IndexOf($_)] }
    $dataTable.Rows.Add($row)
    $count++

    # Batch every 10,000 rows
    if ($count % 10000 -eq 0) {
        $bulkCopy = New-Object Data.SqlClient.SqlBulkCopy $connection
        $bulkCopy.DestinationTableName = $table
        $bulkCopy.WriteToServer($dataTable)
        $dataTable.Rows.Clear()
        Write-Host "Imported $count rows..."
    }
}

# Final batch
if ($dataTable.Rows.Count -gt 0) {
    $bulkCopy = New-Object Data.SqlClient.SqlBulkCopy $connection
    $bulkCopy.DestinationTableName = $table
    $bulkCopy.WriteToServer($dataTable)
}

$connection.Close()
$reader.Close()
Write-Host "Final import completed!"
```

??? Streams file line by line  
??? Uses **SqlBulkCopy** ??? very fast  
??? Keeps memory low

---

### ???? Summary

| Tool       | Small Files ??? | Large Files ??? | Best Approach           |
|------------|---------------|---------------|------------------------|
| Python    | `pandas.read_csv()` + loop | `pandas.read_csv(chunksize)` + batch insert | Use `chunksize` |
| PowerShell| `Import-Csv` + loop | Stream with `SqlBulkCopy` | Use `SqlBulkCopy` |

---

If you want, I can **prepare a ready-to-run script** for either Python or PowerShell ??? just tell me:
??? Which one you prefer  
??? Approximate file size (# rows or GB)  

Would you like me to write that for you? ????

Author: user
    cursor.execute("""
        INSERT INTO [dbo].[USAEmails] ([FullName],[Industry],[JobTitle],[SubRole],[Emails],[Mobile],[PhoneNumbers],[CompanyName],[CompanyIndustry],[CompanyWebsite],[CompanySize],[Location],[Skills],[FirstName],[LastName],[BirthYear],[BirthDate],[Gender],[LinkedInUrl],[FacebookUrl],[TwitterUrl],[Githu Url],[Compan LinkedinUrl],[CompanyFacebookUrl],[CompanyTwitterUrl],[CompanyLocationName],[CompanyLocationStreetAddress],[CompanyLocationAddressLine2],[CompanyLocationPostalCode],[LocationCountry],[LocationContinent],[LinkedinConnections],[InferredSalary],[YearsExperience],[Countries],[Interests])
        VALUES (?, ?, ? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,? ,?)
    """, row['FullName'],row['Industry'],row['JobTitle'],row['SubRole'],row['Emails'],row['Mobile'],row['PhoneNumbers'],row['CompanyName'],row['CompanyIndustry'],row['CompanyWebsite'],row['CompanySize'],row['Location'],row['Skills'],row['FirstName'],row['LastName'],row['BirthYear'],row['BirthDate'],row['Gender'],row['LinkedInUrl'],row['FacebookUrl'],row['TwitterUrl'],row['Githu Url'],row['Compan LinkedinUrl'],row['CompanyFacebookUrl'],row['CompanyTwitterUrl'],row['CompanyLocationName'],row['CompanyLocationStreetAddress'],row['CompanyLocationAddressLine2'],row['CompanyLocationPostalCode'],row['LocationCountry'],row['LocationContinent'],row['LinkedinConnections'],row['InferredSalary'],row['YearsExperience'],row['Countries'],row['Interests'])


Author: ChatGPT
Thanks ??? that???s a solid starting point, but I see a couple of things to improve or be careful about to **avoid errors** and **optimize performance**.

Here???s an improved and ready-to-use Python pattern using **`executemany` + list of tuples** for batching, plus a few tips to avoid pitfalls:

---

### ??? Improved Python Pattern

```python
import pandas as pd
import pyodbc

# Connect to SQL Server
conn_str = (
    "DRIVER={ODBC Driver 17 for SQL Server};"
    "SERVER=your_server;"
    "DATABASE=your_database;"
    "UID=your_user;"
    "PWD=your_password"
)
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.fast_executemany = True  # This speeds up executemany massively

# Read CSV in chunks
chunksize = 5000  # adjust as needed
csv_file = r'C:\_Code\LinkedInData\USAEmails.csv'

for chunk in pd.read_csv(csv_file, chunksize=chunksize):
    # Build list of tuples for executemany
    data = [
        tuple(
            row[col] if col in row and pd.notnull(row[col]) else None
            for col in [
                'FullName', 'Industry', 'JobTitle', 'SubRole', 'Emails', 'Mobile',
                'PhoneNumbers', 'CompanyName', 'CompanyIndustry', 'CompanyWebsite',
                'CompanySize', 'Location', 'Skills', 'FirstName', 'LastName',
                'BirthYear', 'BirthDate', 'Gender', 'LinkedInUrl', 'FacebookUrl',
                'TwitterUrl', 'Githu Url', 'Compan LinkedinUrl', 'CompanyFacebookUrl',
                'CompanyTwitterUrl', 'CompanyLocationName', 'CompanyLocationStreetAddress',
                'CompanyLocationAddressLine2', 'CompanyLocationPostalCode', 'LocationCountry',
                'LocationContinent', 'LinkedinConnections', 'InferredSalary', 'YearsExperience',
                'Countries', 'Interests'
            ]
        )
        for index, row in chunk.iterrows()
    ]

    # Prepare INSERT statement
    sql = """
        INSERT INTO [dbo].[USAEmails] (
            [FullName],[Industry],[JobTitle],[SubRole],[Emails],[Mobile],[PhoneNumbers],[CompanyName],
            [CompanyIndustry],[CompanyWebsite],[CompanySize],[Location],[Skills],[FirstName],[LastName],
            [BirthYear],[BirthDate],[Gender],[LinkedInUrl],[FacebookUrl],[TwitterUrl],[Githu Url],
            [Compan LinkedinUrl],[CompanyFacebookUrl],[CompanyTwitterUrl],[CompanyLocationName],
            [CompanyLocationStreetAddress],[CompanyLocationAddressLine2],[CompanyLocationPostalCode],
            [LocationCountry],[LocationContinent],[LinkedinConnections],[InferredSalary],[YearsExperience],
            [Countries],[Interests]
        ) VALUES (
            ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?
        )
    """

    # Bulk insert the chunk
    cursor.executemany(sql, data)
    conn.commit()
    print(f"Inserted chunk of {len(data)} rows")

# Cleanup
cursor.close()
conn.close()
print("All data imported successfully!")
```

---

### ???? Key Improvements & Notes

??? Uses `chunksize` to avoid loading the entire CSV into memory.

??? Uses `executemany()` + `fast_executemany = True` ??? **100x faster** on large batches.

??? Handles **NULLs** properly (`pd.notnull(row[col]) else None`).

??? Avoids hardcoding positional parameters in the loop (it builds tuples dynamically).

??? Outputs progress per chunk.

---

### ???? Recommendations for large files

- Start with `chunksize=5000` and increase to `10000` if memory allows.  
- Create a **staging table** if you want to load raw data first, then clean inside SQL.  
- Add `try-except` blocks if you need robust error handling per chunk.

---

If you like, I can also send you:
??? a version with full `try-except` logging,  
??? a script to **pre-check** the table schema vs CSV header, or  
??? a PowerShell version using `SqlBulkCopy`.

Want me to prep one of those for you? Let me know! ????

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