What are some common reasons why BI queries take too long to run?

3 major reasons for BI queries to take that long are: a. Unwanted data fields Management and stakeholders have tendency to ask for 1000s of cuts (most of the times not even needed or they can save time & effort in running queries), therefore, it's critical to discuss must have, good to have sort of variable list before executing the queries b. Table Joins These unwanted fields can lead to many joins within data tables and thus causing load on servers to execute, so discuss if a few fields (and thus table joins) can be excluded c. Historic data How much data should be processed? Is it for current month, year or do we need data for say last 3 years. More the data, higher the query time Thanks

In SAP BW/HANA, BI queries may take longer to run due to various reasons: Large Data Volumes: Extensive datasets, especially with complex calculations, slow down queries. Inefficient Query Design: Non-optimized calculations, excessive key figures, and complex joins can lead to performance issues. Database Performance: Constraints like insufficient memory or CPU limitations in HANA can impact speed. Complex Data Models: Intricate models with multiple layers or joins reduce efficiency. High System Load: Heavy simultaneous operations can degrade performance. Optimizing query design, data modeling, system monitoring, and database health maintenance are key. Utilizing HANA's in-memory and columnar storage features can enhance performance.

The?adding?of?indexes?to?tables?is?one?thing?that?I?have?found?to?be?helpful. Moreover. fetching?only?the?fields?that?are?necessary,?not?the?ones?that?are?not.

-In my experience, reading and writing data across a network is the single biggest driver of performance issues. The biggest factor in query optimization is reducing the amount of data that needs to be read. -For example, if you query a remote database with a join from a million record table1 to a thousand record table2, and it is joined on a field with no index or partition or sorting order, then in order to do that join, the database engine has to read all 1,000,000 records from table1 and all 1000 records from table2 to accomplish the join. If instead the joined fields have a sort order, the database engine can logically find the much smaller subset of records in the table1 to read in order to accomplish the join.

It's important to conduct regular performance audits and optimizations. Keeping the BI architecture aligned with the business needs while ensuring scalability and efficiency is crucial for maintaining optimal performance.

This is an area where training comes into play. If the decision is made to open up the database layer to direct querying, SQL training is a must. If you have users running a SELECT * on a giant table simultaneously, you're going to bring the system to a halt.

Again, the amount of data that has to be read or scanned to accomplish a task is the biggest driver of performance. This matters much much more than the amount of data available. There are many strategies in BI, even outside query optimization, to reduce the amount of data that actually needs to be read for a query to execute, regardless of how large the actual dataset is. For example, pre-aggregating common metrics would reduce the number of rows needing to be read to answer a particular query. Storage patterns that partition data together for common queries is another way to reduce the records read over a network.

Some of the things to consider when writing your queries/views/stored procs are Be aware of the output Start with making sure the tables you're using are indexed. The order of your joins can have a huge imapct on performance, start with large tables first and use join conditions when you can. Don't left join when you can inner join. Your where clause gets evaluated first, add any necessary filters to only retrieve the data you need and remember to do that in your CTEs and sub queries. Avoid table scans, don't hardcode values. Avoid select * at any cost, select only data you need in your output.