Incremental_Data_Transfer

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Snippet Name: Incremental_Data_Transfer

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Created Date: 2025-06-16 13:12:50
Last Modified Date: 2025-05-31 10:32:00

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Title: Incremental Data Transfer
Author: user
In SQLServer2019, i have a million row table with varbinary max column in it, so each row is rather large. i need to move it from one server to another, so i created a linked server. these two versions of the same command  seem to take a very long time. the first is clearly spooling the whole table into tempdb,  the second i expected to start putting data into teh table faster. how can i move the data more incrementally?  SET IDENTITY_INSERT [ICIMS].[Resume] ON
 INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
	                  select [ResumeID],[FileName],[Content],[DateResume],[PersonID]  
					  from [LinkedServerName].ANWorkForce.ICIMS.[Resume]
 SET IDENTITY_INSERT [ICIMS].[Resume] OFF

  SET IDENTITY_INSERT [ICIMS].[Resume] ON
 INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
	                  EXECUTE('select [ResumeID],[FileName],[Content],[DateResume],[PersonID]  
					  from ANWorkForce.ICIMS.[Resume]') AT [LinkedServerName]
 SET IDENTITY_INSERT [ICIMS].[Resume] OFF

Author: ChatGPT
Moving a large table containing `VARBINARY(MAX)` columns via a linked server can indeed cause performance bottlenecks. The approaches you tried are transferring the entire dataset in one go, which leads to significant overhead on `tempdb`, the network, and server resources. To address this, you can process the data incrementally (in batches), which helps reduce these bottlenecks.

### Key Recommendations for Incremental Data Movement:

1. **Batching with TOP and OFFSET:** Process rows in smaller batches to avoid overwhelming the system. For example:
    ```sql
    DECLARE @BatchSize INT = 1000;
    DECLARE @RowOffset INT = 0;
    DECLARE @TotalRows INT = (SELECT COUNT(*) FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume]);

    WHILE @RowOffset < @TotalRows
    BEGIN
        INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
        SELECT TOP (@BatchSize) [ResumeID],[FileName],[Content],[DateResume],[PersonID]
        FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume]
        WHERE [ResumeID] NOT IN (
            SELECT TOP (@RowOffset) [ResumeID]
            FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume]
            ORDER BY [ResumeID]
        )
        ORDER BY [ResumeID];

        SET @RowOffset += @BatchSize;
    END;
    ```

2. **OpenQuery for Linked Server:** Use `OPENQUERY` to push the workload to the remote server. This avoids bringing the entire dataset to the local server at once:
    ```sql
    DECLARE @BatchSize INT = 1000;
    DECLARE @RowOffset INT = 0;

    WHILE EXISTS (SELECT 1 FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume] OFFSET @RowOffset ROWS FETCH NEXT @BatchSize ROWS ONLY)
    BEGIN
        INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
        SELECT [ResumeID],[FileName],[Content],[DateResume],[PersonID]
        FROM OPENQUERY([LinkedServerName], 
            'SELECT [ResumeID],[FileName],[Content],[DateResume],[PersonID]
             FROM ANWorkForce.ICIMS.[Resume]
             ORDER BY [ResumeID]
             OFFSET ' + CAST(@RowOffset AS NVARCHAR(MAX)) + ' ROWS FETCH NEXT ' + CAST(@BatchSize AS NVARCHAR(MAX)) + ' ROWS ONLY');

        SET @RowOffset += @BatchSize;
    END;
    ```

3. **SQL Server Integration Services (SSIS):** If the linked server method continues to perform poorly, SSIS can provide better performance for large data migrations by managing the `VARBINARY(MAX)` data type more efficiently and providing batch-level control.

4. **BCP Utility with Files:** Export data from the source server to a file using the `bcp` utility and then import it into the destination server:
    - Export:
      ```bash
      bcp "SELECT [ResumeID],[FileName],[Content],[DateResume],[PersonID] FROM ANWorkForce.ICIMS.[Resume]" queryout ResumeData.dat -S SourceServer -T -n
      ```
    - Import:
      ```bash
      bcp [ICIMS].[Resume] in ResumeData.dat -S DestinationServer -T -n
      ```

5. **Reduce SELECT Scope (Vertical Filtering):** If the entire table doesn???t need to be transferred (e.g., specific columns or rows), filter the `SELECT` statement to reduce data size.

6. **MaxTransferSize for Linked Server:** If the linked server is being used, you can optimize its performance by adjusting the `MaxTransferSize` parameter.

### Why Your Current Methods Take Time
1. Both methods involve the full dataset spooling to `tempdb` due to implicit linked server behavior.
2. Even with the second method using `EXECUTE` at the remote server, the engine likely pulls all rows before inserting.

### Conclusion
Batch processing is the most effective approach for large data transfers in your scenario. Using `TOP` with `ORDER BY` ensures manageable chunks of data are moved incrementally. Consider also testing SSIS or `bcp` for better control over the data transfer process.

Author: user
won't that still spool the whole table into tempdb so that it can compare the exists/not exists?

Author: ChatGPT
You're correct: even in a batched approach, if the `NOT IN` or `EXISTS` queries require the entire table to be evaluated for each batch, it can still spool the entire table into `tempdb`. This is inefficient, particularly for large datasets. To avoid this, the strategy should focus on **minimizing intermediate spooling** by avoiding such constructs and directly targeting batch-specific rows.

### Optimized Approach: Key-Based Batching

Instead of relying on `NOT IN` or `EXISTS`, use a key-based batching approach. This method assumes your table has a column (e.g., `ResumeID`) that is sequential or indexed and can be used to define batch ranges.

#### Steps for Key-Based Batching

1. **Determine the Range of Keys:**
   Identify the minimum and maximum values of the key column (`ResumeID`) to process the data incrementally.

   ```sql
   DECLARE @MinID INT, @MaxID INT, @BatchSize INT;
   SET @BatchSize = 1000;

   SELECT @MinID = MIN(ResumeID), @MaxID = MAX(ResumeID)
   FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume];
   ```

2. **Process Rows in Ranges:**
   Use a loop to process batches based on key ranges.

   ```sql
   DECLARE @CurrentMinID INT, @CurrentMaxID INT;
   SET @CurrentMinID = @MinID;

   WHILE @CurrentMinID <= @MaxID
   BEGIN
       SET @CurrentMaxID = @CurrentMinID + @BatchSize - 1;

       INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
       SELECT [ResumeID], [FileName], [Content], [DateResume], [PersonID]
       FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume]
       WHERE [ResumeID] BETWEEN @CurrentMinID AND @CurrentMaxID;

       SET @CurrentMinID = @CurrentMaxID + 1;
   END;
   ```

3. **Advantages of Key-Based Batching:**
   - No `NOT IN` or `EXISTS` queries that require comparing with the destination table.
   - Each batch operates independently, avoiding spooling the entire table into `tempdb`.
   - Better control over the size of each batch.

### Alternative: Use OpenQuery with Key-Based Batches

If you want to offload as much work as possible to the remote server:

```sql
DECLARE @CurrentMinID INT, @CurrentMaxID INT, @BatchSize INT;
SET @BatchSize = 1000;

-- Assume the Min and Max IDs are known or pre-calculated
SET @CurrentMinID = 1;
SET @CurrentMaxID = @CurrentMinID + @BatchSize - 1;

WHILE @CurrentMinID <= @MaxID
BEGIN
    INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
    SELECT *
    FROM OPENQUERY([LinkedServerName], 
        'SELECT [ResumeID], [FileName], [Content], [DateResume], [PersonID]
         FROM ANWorkForce.ICIMS.[Resume]
         WHERE ResumeID BETWEEN ' + CAST(@CurrentMinID AS NVARCHAR(20)) + 
         ' AND ' + CAST(@CurrentMaxID AS NVARCHAR(20)));

    SET @CurrentMinID = @CurrentMaxID + 1;
    SET @CurrentMaxID = @CurrentMinID + @BatchSize - 1;
END;
```

### Key Considerations
1. **Sequential Key Assumption:**
   The batching approach assumes that `ResumeID` is either sequential or has meaningful gaps that don???t impact data integrity. If this isn't the case, you can replace it with a different batching strategy, like ordering by `DateResume`.

2. **Clustered Index on Key:**
   Ensure there???s a clustered index on `ResumeID` (or the chosen key column) for efficient retrieval of ranges.

3. **Batch Size Tuning:**
   Adjust `@BatchSize` to find the optimal size based on network bandwidth, server performance, and `VARBINARY(MAX)` data size.

### Why This Avoids Spooling
- **Direct Range Fetch:** The query fetches only the rows within the specified range, ensuring the remote server does the filtering.
- **Minimal `tempdb` Usage:** Without `NOT IN`, `EXISTS`, or similar constructs, there???s no need for SQL Server to materialize large intermediate datasets.

This approach minimizes `tempdb` usage and provides a more efficient and controlled way to move large datasets.

Author: user
the latest example uses a variable @MaxId that was not declared, and does nto seem to be assigned a value, i think

Author: ChatGPT
You're absolutely correct that in the example, `@MaxID` was not properly declared or assigned a value. Here's the corrected version:

### Full Corrected Example: Key-Based Batching

This assumes you have a column like `ResumeID` that is numeric and can be used for batching.

#### Declare Variables and Set Initial Values
```sql
DECLARE @MinID INT, @MaxID INT, @CurrentMinID INT, @CurrentMaxID INT, @BatchSize INT;

-- Define the batch size
SET @BatchSize = 1000;

-- Get the minimum and maximum ResumeID from the source table
SELECT @MinID = MIN(ResumeID), @MaxID = MAX(ResumeID)
FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume];

-- Initialize the current starting ID
SET @CurrentMinID = @MinID;
```

#### Loop Through Batches
```sql
WHILE @CurrentMinID <= @MaxID
BEGIN
    -- Calculate the end of the current batch
    SET @CurrentMaxID = @CurrentMinID + @BatchSize - 1;

    -- Insert the current batch into the destination table
    INSERT INTO [ICIMS].[Resume]([ResumeID],[FileName],[Content],[DateResume],[PersonID])
    SELECT [ResumeID], [FileName], [Content], [DateResume], [PersonID]
    FROM [LinkedServerName].ANWorkForce.ICIMS.[Resume]
    WHERE [ResumeID] BETWEEN @CurrentMinID AND @CurrentMaxID;

    -- Move to the next batch
    SET @CurrentMinID = @CurrentMaxID + 1;
END;
```

### Explanation of Changes
1. **`@MaxID` Declaration and Assignment:**
   - Declared `@MaxID` and assigned its value using `SELECT @MaxID = MAX(ResumeID)` in the initial setup.

2. **Loop Logic:**
   - The loop starts from `@MinID` and continues until `@CurrentMinID` exceeds `@MaxID`.
   - Each iteration processes a batch defined by `@CurrentMinID` and `@CurrentMaxID`.

3. **Batch Size:**
   - The `@BatchSize` defines how many rows are processed in each iteration.

4. **Efficient Range Query:**
   - The `WHERE [ResumeID] BETWEEN @CurrentMinID AND @CurrentMaxID` ensures only rows within the current batch are fetched from the source.

### Key Benefits
- **No Full-Table Spooling:** This approach avoids comparing large datasets or spooling into `tempdb`.
- **Incremental Processing:** Data is moved in smaller, manageable chunks, reducing resource contention.
- **Flexibility:** You can adjust `@BatchSize` as needed to balance between performance and resource usage.

This corrected version ensures the batch processing is complete and all variables are properly declared and assigned.

Author: user
your example is correctly grabbing rows in batches of 1000 and gives the feedback of progress i was looking for. thank you.

Author: ChatGPT
You're very welcome! ???? I'm glad the example is working for you and provides the feedback you needed. Processing large datasets efficiently and getting that sense of progress is always satisfying. If you have any more questions or need further assistance with SQL Server or anything else, feel free to ask. Happy coding! ????

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