Data Warehousing - Tuning

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Data Warehousing - Tuning

Introduction

The data warehouse evolves throughout the period of time and the it is unpredictable that what query the user is going to be produced in future. Therefore it becomes more difficult to tune data warehouse system. In this chapter we will discuss about how to tune the different aspects of data warehouse such as performance, data load, queries ect.

Difficulties in Data Warehouse Tuning

Here is the list of difficulties that can occur while tuning the data warehouse.

  • The data warehouse never remain constant throughout the period of time.

  • It is very difficult to predict that what query the user is going to produce in future.

  • The need of the business also changes with time.

  • The users and their profile never remains the same with time.

  • The user can switch from one group to another.

  • the data load on the warehouse also changes with time.

Note: It is very important to have the complete knowledge of data warehouse.

Performance Assessment

Here is the list of objective measures of performance.

  • Average query response time

  • Scan rates.

  • Time used per day query.

  • Memory usage per process.

  • I/O throughput rates

Following are the points to be remembered.

  • It is necessary to specify the measures in service level agreement(SLA).

  • It is of no use to trying to tune response time if they are already better than those required.

  • It is essential to have realistic expectations while performance assessment.

  • It is also essential that the users have the feasible expectations.

  • To hide the complexity of the system from the user the aggregations and views should be used.

  • It is also possible that the user can write a query you had not tuned for.

Data Load Tuning

  • Data Load is very critical part of overnight processing.

  • Nothing else can run until data load is complete.

  • This is the entry point into the system.

Note: If there is delay in transferring the data or in arrival of data then the entire system is effected badly. Therefore it is very important to tune the data load first.

There are various approaches of tuning data load that are discussed below:

  • The very common approach is to insert data using the SQL Layer. In this approach the normal checks and constraints need to be performed. When the data is inserted into the table the code will run to check is there enough space available to insert the data. if the sufficient space is not available then more space may have to be allocated to these tables. These checks take time to perform and are costly to CPU. But pack the data tightly by making maximal use of space.

  • The second approach is to bypass all these checks and constraints and place the data directly into preformatted blocks. These blocks are later written to the database. It is faster than the first approach but it can work only with the whole blocks of data. This can lead to some space wastage.

  • The third approach is that while loading the data into the table that already contains the table, we can either maintain the indexes.

  • The fourth approach says that to load the data in tables that already contains the data, drop the indexes & recreate them when the data load is complete. Out of third and fourth, which approach is better depends on how much data is already loaded and how many indexes need to be rebuilt.

Integrity Checks

The integrity checking highly affects the performance of the load

Following are the points to be remembered.

  • The integrity checks need to be limited because processing required can be heavy.

  • The integrity checks should be applied on the source system to avoid performance degrade of data load.

Tuning Queries

We have two kinds of queries in data warehouse:

  • Fixed Queries

  • Ad hoc Queries

Fixed Queries

The fixed queries are well defined. The following are the examples of fixed queries.

  • regular reports

  • Canned queries

  • Common aggregations

Tuning the fixed queries in data warehouses is same as in relational database systems. the only difference is that the amount of data to be queries may be different. It is good to store the most successful execution plan while testing the fixed queries. Storing these executing plan will allow us to spot changing data size and data skew as this will cause the execution plan to change.

Note: We cannot do more on fact table but while dealing with the dimension table or the aggregations, the usual collection of SQL tweaking, storage mechanism and access methods can be used to tune these queries.

Ad hoc Queries

To know the ad hoc queries it is important to know the ad hoc users of the data warehouse. Here is the list of points that need to understand about the users of the data warehouse:

  • The number of users in the group.

  • Whether they use ad hoc queries at regular interval of time.

  • Whether they use ad hoc queries frequently.

  • whether they use ad hoc queries occasionally at unknown intervals.

  • The maximum size of query they tend to run

  • The average size of query they tend to run.

  • Whether they require drill-down access to the base data.

  • The elapsed login time per day

  • The peak time of daily usage

  • The number of queries they run per peak hour.

Following are the points to be remembered.

  • It is important to track the users profiles and identify the queries that are run on regular basis.

  • It is also important to identify tuning performed does not affect the performance.

  • Identify the similar and ad hoc queries that are frequently run.

  • If these queries are identified then the database will change and new indexes can be added for those queries.

  • If these queries are identified then new aggregations can be created specifically for those queries that would result in their efficient execution.




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