Enable logging for package execution in SSIS

-
Logging can be enabled and used at design time and runtime. To begin logging, in the Visual Studio menu select SSIS > Logging to open the Configure SSIS Logs window. In the Containers section, select the Control Flow objects for logging—the overall package itself and/or each of the individual tasks within the package. Then in the Providers and Logs tab, under the Add A New Log > Provider Type, select where you want the logging to be saved to, and click the Add button:












After adding each of the Provider Types selected you want to log to, go to the Select the Logs to Use for the Container section to set up the Connection in the Configuration column. In the case of a Text file, select a file to write to. For SQL Server DB as the target, simply select the target database. You are not limited to selecting only one target location for the logging, multiple destinations can be added and they will all be written to concurrently.












For every item checked in the Containers pane, you also need to individually select the check box under the section Select The Logs To Use For The Container, for each of the target SSIS Log Provider. If you don’t check both sides, it won’t create all the container logs you would expect to generate.

Next, go to the Details tab to choose all the events that will be logged for this container. All of these events are logged to all locations specified in the Providers And Logs tab.











In the Details tab, click the Advanced button to select which additional Event logging attributes to include or exclude from the logging.













When logging to SQL Server, the log records are written to a system table, dbo.sysssislog. It automatically creates this default system table the first time it starts logging records to SQL Server. The table is located in the System Tables folder, not with user tables.









The dbo.sysssislog table can be easily queried and filtered by specific types of events, start and end times, as well as other details in the log table.

Any package logging sent to the file system (flat file, XML), can be viewed directly to see the package execution results. Searching his file is limited to string searches, you won’t able to query and sort the data, but could be consumed by other applications for further analysis or zipped and archived for compliance requirements.

Comments

Post a Comment

Popular Posts

Non-blocking, semi-blocking, and blocking transforms in SSIS

-
Not all Data Flow transforms are created equal. In fact, some are much faster and require a  lot less memory (non-blocking transforms). Others are slower while consuming much more  memory (blocking transforms), and some are in between (semi-blocking transforms). We don’t  always have a choice if we need to use a certain transform to do a certain specified task, but  it is good to know the differences between these types of transforms so that we can better  understand performance and resource utilization issues. Synchronous and asynchronous There is another related aspect about Data Flow transforms, which is their ability to quickly  process a row as they are coming into the transform, independently of any other rows that  came before or after (synchronous transforms). The other type of transform needs to be  dependent on some or all of the rows that come before and after (asynchronous transforms). On the whole, non-blocking transforms are also synchronous, thus are fast and
Read more

Implement additive, semi-additive, and non-additive measures

-
Additive Additive measures are numeric values that can be summed up by all the dimensions in the  fact table, for example the Sales Amount. These are the most common measures and they are  the easiest to understand and implement. Semi-additive Semi-additive measures are numeric values that can be summed up by some dimensions  in the fact table, but not all. In most cases, they can be aggregated by any other dimension  except time.  An example of a semi-additive measure is Account Balance. You can determine  the ending balance of an account at the end of a month, but adding up these monthly account  balances do not make sense. Semi-additive measures are usually found in fact tables that store  periodic snapshots of data. Non-additive Non-additive measures, as the name implies, are measures that cannot be summed up.  These measures include percentages such as Discount Percent, ratios such as Profit Margin, and  averages such as Average Unit Cost. Exam 70-767
Read more

Design an appropriate storage solution, including hardware, disk, and file layout

-
Image
The data warehouse storage’s performance can be measured by  analyzing the time it takes for read and write operations to complete, known as latency , and by  analyzing how much data volume it can read or write in each time frame, known as throughput .  High latency and low throughput can be an indication of an improper selection for the  storage infrastructure or incorrect hardware and software configurations. Performance of a storage subsystem can  be estimated based on several criteria: Volume of data Rate of change and growth of the data Data retention policy Number of concurrent users Typical workloads expected Two important metrics associated with storage are throughput and Input/Output Per  Second (IOPS) . Throughput is a measure of the amount of data that can be transferred in a  period. This is typically measured in megabytes or gigabytes per second. IOPS is the number of  I/O read or write operations that can complete in a second.  It is important
Read more