OLED | Python code and automated script

Use scan to split the strings then select based on regex logical values:

A way to process the data could be to select all items with discounts:

^{See also: This follow-up question.}

That your cast's operand is a COM object (as evidenced by System.__ComObject being reported as the object type in the error message) may be the source of the problem, because I don't think PowerShell can cast COM objects to other types.

However, given that PowerShell can dynamically discover members on objects, in many cases where C# requires casts, PowerShell doesn't (and casts to interfaces are no-ops in PowerShell, except when guiding method overload resolution). Similarly, there's no (strict) need to type variables.^[1]

Thus, as you've confirmed, simply omitting the cast of $thMainPipe.InnerObject to type [Microsoft.SqlServer.Dts.Pipeline.Wrapper.MainPipe] worked:

I discovered that the right encoding format is iso-8859-1:

I will try to give some tips to help you improve your package performance. You should start troubleshooting your package systematically to find the performance bottleneck.

Some provided links are related to SQL Server. No worries! The same rules are applied in all database management systems.

First, you should ensure that you have sufficient resources to load the data from the source server into the destination server.

Ensure that the available memory on the source, ETL, and destination servers can handle the amount of data you are trying to load. Besides, make sure that your network connection bandwidth is not decreasing the data transfer performance.

• How Does SSIS Manage Connection Traffic?

Make sure that the following hardware issues are not occurring in any of the servers:

1. Drive out of storage
2. Server is out of memory

Make sure that your machine is not running out of memory. You can simply use the Task Manager to identify the amount of available memory.

After checking the available resources, you should ensure that your data source is not a heap. At least it would be best if you had a primary key created on that table.

• Clustered Index vs. Heap in SQL Server
• SQL Server clustered tables vs. Heap tables

If your SQL Command contains any filtering, ordering, or Joins, you should create the indexes needed by those operations.

• Use WHERE, JOIN, ORDER BY, SELECT Column Order When Creating Indexes
• Is the WHERE-JOIN-ORDER-(SELECT) rule for index column order wrong?

Instead of using OLE DB source to connect to Oracle, try using the Microsoft Connector for Oracle (Previously known as Attunity connectors). Microsoft previously mentioned that it should provide faster performance than traditional OLE DB providers.

Use the Oracle connection manager rather than OLE DB connection manager.

To be honest, I am not sure if this component can make the data load faster since I didn't test it before

The last thing to try is to remove the ODBC destination and add any dummy task. For example, use a row count component.

Run the package; if the data is loaded faster, then loading data from Oracle is not decreasing the performance.

Now, let us start troubleshooting the SSIS package.

First, try to execute the package in 64-bit mode. You can change this from the package configuration. Make sure that the Run64bitRuntime property is set to True.

Using SSIS, data is loaded in memory while being transferred from source to destination. There are two properties in the data flow task that specifies how much data is transferred in memory buffers used by the SSIS pipelines.

Based on the following Integration Services performance tuning white paper:

DefaultMaxBufferRows – DefaultMaxBufferRows is a configurable setting of the SSIS Data Flow task that is automatically set at 10,000 records. SSIS multiplies the Estimated Row Size by the DefaultMaxBufferRows to get a rough sense of your dataset size per 10,000 records. You should not configure this setting without understanding how it relates to DefaultMaxBufferSize.

DefaultMaxBufferSize – DefaultMaxBufferSize is another configurable setting of the SSIS Data Flow task. The DefaultMaxBufferSize is automatically set to 10 MB by default. As you configure this setting, keep in mind that its upper bound is constrained by an internal SSIS parameter called MaxBufferSize which is set to 100 MB and can not be changed.

You should try to change those values and test your package performance each time you change them until the package performance increases.

You should make sure that the destination table does not have any constraints or triggers since they significantly decrease the data load performance; each batch inserted should be validated or preprocessed before storing it.

• Are SQL Server database triggers evil?
• The benefits, costs, and documentation of database constraints

Besides, the more you have indexes the lower is the data load performance.

ODBC destination has several custom properties that can affect the data load performance such as BatchSize (Rows per batch), TransactionSize (TransactionSize is only available in the advanced editor).

• ODBC connection is very slow

Found a solution: instead of creating a dict or storing the individual elements on the page I decided to call the Python function directly from within the HTML file. In the corresponding app.py (where the render template is defined), according to the Flask documentation, the resendVerification function (posted above) needs to be declared in a wrapper, like so:

The reason is it was used the connection string to the listener. When the 'listener' was deleted from the connection string the data transfer became work fastly and without any issues.

1- make sure you typed the name of module correctly

2- make sure you use the correct python version

python3:

The OLE DB source task is a non-blocking object does not retain data either partially (semi-blocking component) or totally (blocking component). They are therefore more efficient in terms of execution time.

Database snapshots was introduced at page level if you are using SQL Server. It might be that before the SQL query started insertion it used to take snapshot of the entire source query and then start insertion into destination database. Every update/insert transaction on the source server potentially bears the overhead of the page copy operation to maintain the snapshot particularly in SSMS 2017.

Bad habits : adding NOLOCK to the source.

You can check this blog : https://blog.sqlauthority.com/2015/07/03/sql-server-difference-between-read-committed-snapshot-and-snapshot-isolation-level/