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If possible, presort the data before it goes into the pipeline. If you must sort data, try your best to sort only small data sets in the pipeline.
As a general rule, any and all set-based operations will perform faster in Transact-SQL because the problem can be transformed into a relational domain and tuple algebra formulation that SQL Server is optimized to resolve.
Also, the SQL Server optimizer will automatically apply high parallelism and memory management to the set-based operation — an operation you may have to perform yourself if you are using Integration Services.
These are typically also calculated faster using Transact-SQL instead of in-memory calculations by a pipeline. Delta detection is the technique where you change existing rows in the target table instead of reloading the table.
If such functionality is not available, you need to do the delta detection by comparing the source input with the target table.
This can be a very costly operation requiring the maintenance of special indexes and checksums just for this purpose. Often, it is fastest to just reload the target table. Partition the problem. One of the main tenets of scalable computing is to partition problems into smaller, more manageable chunks.
This allows you to more easily handle the size of the problem and make use of running parallel processes in order to solve the problem faster. For ETL designs, you will want to partition your source data into smaller chunks of equal size. This latter point is important because if you have chunks of different sizes, you will end up waiting for one process to complete its task.
For example, looking at the graph below, you will notice that for the four processes executed on partitions of equal size, the four processes will finish processing January at the same time and then together continue to process February But for the partitions of different sizes, the first three processes will finish processing but wait for the fourth process, which is taking a much longer time.
The total run time will be dominated by the largest chunk.
If you do not have any good partition columns, create a hash of the value of the rows and partition based on the hash value. For more information on hashing and partitioning, refer to the Analysis Services Distinct Count Optimization white paper; while the paper is about distinct count within Analysis Services, the technique of hash partitioning is treated in depth too.
Some other partitioning tips: Use partitioning on your target table. This way you will be able to run multiple versions of the same package, in parallel, that insert data into different partitions of the same table. It not only increases parallel load speeds, but also allows you to efficiently transfer data.
As implied above, you should design your package to take a parameter specifying which partition it should work on. This way, you can have multiple executions of the same package, all with different parameter and partition values, so you can take advantage of parallelism to complete the task faster.
A quick code example of running multiple robocopy statements in parallel can be found within the Sample Robocopy Script to custom synchronize Analysis Services databases technical note. Minimize logged operations. When you insert data into your target SQL Server database, use minimally logged operations if possible. When data is inserted into the database in fully logged mode, the log will grow quickly because each row entering the table also goes into the log.
Therefore, when designing Integration Services packages, consider the following: Try to perform your data flows in bulk mode instead of row by row.
By doing this in bulk mode, you will minimize the number of entries that are added to the log file. The latter will place an entry for each row deleted into the log. If partitions need to be moved around, you can use the SWITCH statement to switch in a new partition or switch out the oldest partition , which is a minimally logged statement. After your problem has been chunked into manageable sizes, you must consider where and when these chunks should be executed.
The goal is to avoid one long running task dominating the total time of the ETL flow. Metadata Collection Patterns Chapter 2.
Execution Patterns Chapter 4. Data Cleansing Chapter 6.
DB2 Source Patterns Chapter 7. Flat File Source Patterns Chapter 8. XML Patterns Chapter Data Warehouse Patterns Leonard, Andy et al. Logging Patterns Leonard, Andy et al. Slowly Changing Dimensions Leonard, Andy et al. Loading the Cloud Leonard, Andy et al. Logging and Reporting Patterns Leonard, Andy et al. Parent-Child Patterns Leonard, Andy et al.
Configuration Leonard, Andy et al. Deployment Leonard, Andy et al.