As part of the migration from Oracle ACS1 to SQL Server, it was necessary to build a tool that could quickly copy the content of ACS1 tables to converted versions of the same table on SQL Server. The Oracle and SQL Server tables have the same names, column names, and the same or at least a compatible type. There are ways to copy Oracle to Oracle and SQL Server to SQL Server, but you have to write a program to copy tables across different databases. While Talend does data copying in production, a developer needs something they can run on demand. The same program can be run in a way to compare the two tables and report differences.
While ACS1 is in production, the tool is used to copy the real data to a development SQL Server. As we migrate some functions to SQL Server, specific groups of tables will be copied back from the now authoritative SQL Server to Oracle to support anyone with an old Oracle based reporting tool. Eventually the Oracle database will go away.
Because Yale applications are written in Java, it is natural to consider writing the function in that language. Although JDBC is portable and supports both types of databases, Java is still a strongly typed language which may add a third point of conversion (Oracle to Java to SQL Server rather than just Oracle to SQL Server).
Powershell is an interpreted language built on top of the .NET framework. However, in normal processing Powershell sets up but does not even participate in the data copy. It provides each database with a connection string and authenticates with a userid having appropriate privileges to read and write data.
.NET defines some abstract classes of objects that perform generic functions. Oracle and SQL Server provide drivers that implement these template classes. When the script configures Oracle as the input connection and asks for a DataAdapter object, Oracle complied code provides it. Similarly, the SQL Server output connection provides a SQL Server BulkCopy object.
The only parameter to the DataAdapter is a SELECT statement to read data from a table. In most cases it is just “SELECT * FROM tablename”. The DataAdapter communicates with the database, reads metadata about the named table, and discovers the column names and types for you.
The script has to create one generic .NET object, and empty DataSet object. This is an empty collection that can hold a .NET DataTable object.
The script can then pass the empty .NET DataSet to the Oracle DataAdapter.Fill(DataSet) method. Oracle then reads the entire table into memory, creating one DataTable object with one DataRow object for each row in the table.
The table was read by Oracle and stored in objects of whatever type Oracle chose based on the definition of the table and its columns in its database. The Powershell script does not need to know how many columns or rows there are, nor the names or type of anything. The most it has to do is select the first DataTable in the DataSet collection and then pass it to the BulkCopy.WriteToServer(DataTable) method.
With this call, the SQL Server BulkCopy object looks at the DataTable and DataRows that Oracle created. It also can read the metadata from the SQL Server database to discover how the output table is defined. It develops a strategy for using the objects in memory to populate the output table.
As long as the Oracle and SQL Server tables have the same number of columns, with the same names and compatible types, the data can be copied as is or with whatever transformations the database driver regards as necessary. For example, an Oracle NUMERIC or DECIMAL column can be converted to a SQL Server INT or BIGINT column, but that is up to the databases and does not involve the script programming.
All the real work is done by compiled code provided by the database vendors. Essentially the script simply introduces Oracle to SQL Server, tells them to copy the data, and then they make all the decisions until the operation has completed.
BulkCopy.WriteToServer by default temporarily disables triggers and optimizes the evaluation of constrains. As the name suggests, it moves the data in bulk rather than doing one INSERT per row.
BulkCopy can append data to a table that is already filled, but when copying an entire table, the script will typically TRUNCATE the output table before refilling it with BulkCopy. If other programs may be accessing the database at the same time, the entire operation can be performed in a Transaction.
In the normal case, the script is trivial. Additional code has been added, but it is optional and used only to meet special needs. For example, the script can be run to read both the input and output tables into memory, perform a generic object compare by primary key of the corresponding rows in each table, and then either report the differences or make only the individual changes needed to synchronize the output with the input. For this to work, the data types of the two tables have to be identical so the values of the data in each row can be compared at the binary level.
Alternately, the script can be run to compare the data in each cell of each table. Running nested loops in an interpreted language like Powershell uses a lot of time, but this is just CPU on the desktop computer which costs nothing and can be run in the background. The advantage here is that Powershell as an interpreted language with weak, dynamic typing will convert compatible types as needed to determine if the values are equivalent even if they are not identical at the binary level.
The simplest and fastest thing is to TRUNCATE the output and copy all the data.
If you also want a report, you can compare and only update changed rows, and then the script will tell you the number of rows modified, added, or deleted since the last copy. If you just want the report, turn on the Read-Only flag and it will not change the “output” table.
The script can be modified to add custom conversions if the default behavior is inadequate. However, before writing a lot of code, you can write a custom SELECT that creates the input DataAdater, and in the SQL you can CAST or CONVERT the data in certain columns to a more appropriate type. This is simpler and runs much faster than Powershell loops.