In This Section:

Designing for Calculation Performance

Monitoring and Tracing Calculations

Using Simulated Calculations to Estimate Calculation Time

Estimating Calculation Effects on Database Size

Hybrid Aggregation Mode in Block Storage Databases

Managing Caches to Improve Performance

Working with the Block Locking System

The information in this chapter applies only to block storage databases and is not relevant to aggregate storage databases.

Also see:

You can configure a database to optimize calculation performance.

The best configuration for the site depends on the nature and size of the database. Use the information in the following topics as guidelines only.

A data block size of 8 Kb to 100 Kb provides optimal performance in most cases.

If data blocks are much smaller than 8 KB, the index is usually very large, forcing Essbase to write to and retrieve the index from disk. This process slows calculation.

If data blocks are much larger than 100 KB, Intelligent Calculation does not work effectively. See Understanding Intelligent Calculation.

To optimize calculation performance and data storage, balance data block density and data block size by rearranging the dense and sparse dimension configuration of the database. Keep these suggestions in mind:

You can view information about a database, including the potential and actual number of data blocks and the data block size.

You may improve calculation performance by changing the order of standard (not attribute) sparse dimensions in the database outline. Order standard sparse dimensions by the number of members they contain, placing the dimension that contains the fewest members first. This arrangement provides many possible improvements, depending on the site:

The calculator cache functions more effectively, providing approximately a 10% performance improvement if you have a database outline with a large dimension (for example, one containing 1000 members).

Parallel calculation, if enabled, more likely will be used if the standard sparse dimension with the most members is the last standard sparse dimension in the outline.

Many companies load data incrementally. For example, a company may load data each month for that month.

To optimize calculation performance when you load data incrementally, make the dimension tagged as time a sparse dimension. If the time dimension is sparse, the database contains a data block for each time period. When you load data by time period, Essbase accesses fewer data blocks because fewer blocks contain the relevant time period. Thus, if you have Intelligent Calculation enabled, only the data blocks marked as dirty are recalculated. For example, if you load data for March, only the data blocks for March and the dependent parents of March are updated.

However, making the time dimension sparse when it is naturally dense may significantly increase the size of the index, creating possibly slower performance due to more physical I/O activity to accommodate the large index.

If the dimension tagged as time is dense, you still receive some benefit from Intelligent Calculation when you do a partial data load for a sparse dimension. For example, if Product is sparse and you load data for one product, Essbase recalculates only the blocks affected by the partial load, although time is dense and Intelligent Calculation is enabled.

For information on incremental loads, see Loading Data into Aggregate Storage Databases.

Calculation performance may be affected if a database outline has multiple flat dimensions. A flat dimension has very few parents, and each parent has many thousands of children; in other words, flat dimensions have many members and few levels.

You can improve performance for outlines with multiple flat dimensions by adding intermediate levels to the database outline.

You may achieve significant improvements in calculation performance by carefully grouping formulas and dimensions in a calculation script. In this way, you can ensure that Essbase cycles through the data blocks in the database as few times as possible during a calculation.

Order commands in calculation scripts to make the database calculation as simple as possible. Consider applying all formulas to the database outline and using a default calculation (CALC ALL). This method may improve calculation performance.

See Developing Calculation Scripts for Block Storage Databases and Calculation Passes.

You can display information in the application log about how Essbase is calculating the database by using the following commands in a calculation script.

You can use the SET MSG SUMMARY and SET MSG DETAIL calculation commands in a calculation script to do the following:

SET MSG DETAIL also provides an information message every time Essbase calculates a data block. SET MSG DETAIL is useful for reviewing the calculation order of data blocks and for testing intelligent recalculations.

Because SET MSG DETAIL causes a high processing overhead, use it only during test calculations. |

SET MSG SUMMARY causes a processing overhead of approximately 1% to 5%, depending on database size, and is therefore appropriate for all calculations.

You can use the SET NOTICE calculation command in a calculation script to display calculation completion notices that tell you what percentage of the database has been calculated. You can use the SET MSG SUMMARY command with the SET NOTICE command to show calculation progress between completion notices. Completion notices do not significantly reduce calculation performance, except when used with a very small database.

You can simulate a calculation using SET MSG ONLY in a calculation script. A simulated calculation produces results that help you analyze the performance of a real calculation that is based on the same data and outline.

By running a simulated calculation with a command such as SET NOTICE HIGH, you can mark the relative amount of time each sparse dimension takes to complete. Then, by performing a real calculation on one or more dimensions, you can estimate how long the full calculation will take, because the time a simulated calculation takes to run is proportional to the time that the actual calculation takes to run.

For example, if the calculation starts at 9:50:00 AM, and the first notice is time-stamped at 09:50:10 AM and the second is time-stamped at 09:50:20 AM, you know that each part of the calculation took 10 seconds. If you then run a real calculation on only the first portion and note that it took 30 seconds to run, you know that the other portion also will take 30 seconds. If there were two messages total, then you would know that the real calculation will take approximately 60 seconds (20 / 10 * 30 = 60 seconds).

Use the following topics to learn how to perform a simulated calculation and how to use a simulated calculation to estimate calculation time.

Before you can estimate calculation time, you must perform a simulated calculation on a data model that is based on your actual database.

To perform a simulated calculation:

Create a data model that uses all dimensions and all levels of detail about which you want information.

Load all data. This procedure calculates only data loaded in the database.

Create a calculation script with these entries:

SET MSG ONLY; SET NOTICE HIGH; CALC ALL;

If you are using dynamic calculations on dense dimensions, substitute the CALC ALL command with the specific dimensions that you need to calculate; for example, CALC DIM EAST.

Find the first sparse calculation message in the application log and note the time in the message.

Calculate the dense dimensions of the model that are not being dynamically calculated:

CALC DIM (DENSE_DIM1, DENSE_DIM2, …);

Calculate the sparse dimensions of the model:

CALC DIM (SPARSEDIM1, SPARSEDIM2, …);

Project the intervals at which notices will occur, and then verify against sparse calculation results. You can then estimate calculation time.

When you perform a simulated calculation, you record the results and use them to estimate actual calculation time.

To estimate total calculation time:

Note the times of all the intervals between application log messages generated by SET NOTICE HIGH.

See Table 175.

Use the following calculation to estimate the time for a real calculation:

Total time required for simulated calculation, divided by the first simulated calculation notice interval, multiplied by the first real calculation time interval.

In this example, 43 / 7 * 45 = 276.4 seconds, so the real calculation should take 276.4 seconds.

The simulated calculation should return a time accurate to about 5%, excluding the following issues:

When these factors are present, this estimating technique more closely predicts calculation time when Essbase reaches 30%–40% of the simulated calculations (30%–40% of the messages generated by SET NOTICE HIGH). See the Oracle Essbase Technical Reference.

Using SET MSG ONLY as a calculation-time estimating technique should be validated against later CALCNOTICE intervals. The results of this estimating technique vary because of the following chain of influences:

After you have estimated and analyzed a simulated calculation, you can make changes in the outline to improve performance.

From top to bottom in the outline, order sparse dimensions to create the fewest percentage increases in upper blocks:

Upper level blocks after consolidating only sparse dimension 1: 1,000,000

Upper level blocks after consolidating only sparse dimension 2: 3,000,000

Upper level blocks after consolidating only sparse dimension 3: 10,000,000

Upper level blocks after consolidating only sparse dimension 4: 300,000

Upper level blocks after consolidating only sparse dimension 5: 5,700,000

Use the simulated calculation to generate the upper block count. These numbers may be accurate despite actual dimension sizes as noted next to the items above.

Given the current number of blocks in a database, you can estimate the number of blocks that a CALC ALL will produce.

To estimate the database size resulting from a calculation using interactive mode:

Load data and issue a CALC ALL command and note the average block size.

Start the MaxL shell, log on to Essbase, and start an application and database. For example:

essmsh login username password; alter system load application appname; alter application appname load database dbname;

Providing the application and database name, enter the following MaxL statement and note the value that is returned for the number of blocks:

query database appname.dbname get estimated size;

Multiply the number of blocks by the average size of the blocks in the database.

Be aware of the following conditions when you query Essbase for an estimate of the full size of a database:

You must perform this query after a CALC ALL. Any other calculation will not produce accurate results.

You can obtain accurate results with formulas only if they are on sparse dimensions.

You cannot obtain accurate results with top-down calculations on any member in combination with a lock on data (committed access).

If you need to estimate partitions, you must query Essbase for a database size estimate on every partition and add the results. If you query for the size of only the source database, the estimate includes only the data on the source database server.

You may achieve significant improvements in calculation performance by carefully using formulas in the database outline. For example, you may achieve improved calculation performance by placing formulas on members in the database outline instead of placing the formulas in a calculation script. See Developing Formulas for Block Storage Databases.

The following sections discuss how to handle formula issues that affect performance.

Using the database outline to roll up values is more efficient than using a formula to calculate values. For example, the consolidation of members 100-10, 100-20, and 100-30 into member 100, as shown in Figure 141, Consolidation Example, is more efficient than applying the following formula to member 100:

100-10 + 100-20 + 100-30

If you use a simple formula, and block size is not unusually large, you can place the formula on a member of either a sparse or a dense dimension without significantly affecting calculation performance. The bigger the block size, the more impact simple formulas have on calculation performance. For a discussion of the relationship between block size and calculation performance, see Block Size and Block Density.

A simple formula is, for example, a ratio or a percentage and meets the following requirements:

Does not reference values from a different dimension (sparse or dense). For example, a simple formula cannot reference Product -> Jan.

Does not use range functions. For example, a simple formula cannot use @AVGRANGE, @MAXRANGE, @MINRANGE, or @SUMRANGE.

Does not use relationship or financial functions. For example, a simple formula cannot use @ANCESTVAL, @NEXT, @PARENTVAL, @SHIFT, @ACCUM, or @GROWTH. For a complete list of relationship and financial functions, see the Oracle Essbase Technical Reference.

For information on how formulas affect calculation performance, see Bottom-Up and Top-Down Calculation.

If you use a complex formula, you can improve performance by applying the following guidelines:

If possible, apply the formula to a member in a dense dimension.

Use the FIX command in a calculation script to calculate only required data blocks. See Using the FIX Command.

Increase the density of the database (ratio of existing data blocks to possible data blocks).

A complex formula is one that meets any of the following requirements:

References a member or members in a different dimension (sparse or dense); for example, Product -> Jan.

Uses one or more range functions, for example, @AVGRANGE, @MAXRANGE, @MINRANGE, or @SUMRANGE.

Uses relationship or financial functions; for example, @ANCESTVAL, @NEXT, @PARENTVAL, @SHIFT, @ACCUM, or @GROWTH. For a complete list of relationship and financial functions, see the Oracle Essbase Technical Reference.

When applied to sparse dimension members, complex formulas create more calculation overhead and therefore slow performance. This problem occurs because the presence of complex formulas requires Essbase to perform calculations on all possible as well as all existing data blocks related to the member with the complex formula. The presence of a relationship or financial function on a sparse dimension member causes Essbase to perform calculations on all blocks, possible as well as existing, increasing the overhead even more.

Thus, a complex formula that includes a relationship or financial function creates a greater overhead increase than does a complex formula that does not include a relationship or financial function.

For a discussion about how complex formulas affect calculation performance, see Bottom-Up and Top-Down Calculation.

Two examples illustrate complex formula overhead:

If a database has 90 existing data blocks and 100 potential data blocks, the overhead for complex formulas is not large, not more than 10 extra blocks to read and possibly write values to.

If a database has 10 existing data blocks and 100 potential data blocks, the overhead is as much as ten times what it would be without the complex formula (depending on the outline structure and other factors), as many as 90 extra blocks to read and possibly write to.

In all cases, the lower the ratio of existing data blocks to possible data blocks, the higher the calculation performance overhead and the slower the performance.

You can use the SET FRMLBOTTOMUP calculation command to optimize the calculation of formulas in sparse dimensions in large database outlines. With this command, you can force a bottom-up calculation on sparse member formulas that otherwise would be calculated top-down. See Forcing a Bottom-Up Calculation.

Forcing a bottom-up calculation on a top-down formula enables efficient use of the CALC ALL and CALC DIM commands. Review the discussions of the SET FRMLBOTTOMUP calculation command and the CALCOPTFRMLBOTTOMUP configuration setting in the Oracle Essbase Technical Reference.

If you assign a constant to a member in a sparse dimension, Essbase automatically creates a data block for every combination of sparse dimension members that contains the member.

For example, assume that a member or a calculation script formula contains the following expression:

California = 120;

In this formula, California is a member in a sparse dimension and 120 is a constant value. Essbase automatically creates all possible data blocks for California and assigns the value 120 to all data cells. Many thousands of data blocks may be created. To improve performance, create a formula that does not create unnecessary values.

To assign constants in a sparse dimension to only those intersections that require a value, use FIX in a manner similar to the following example:

FIX(Colas,Misc,Actual) California = 120; ENDFIX

In this example, Colas is a member of the sparse dimension, Product; Actual is a member of the dense dimension, Scenario; and Misc is a member of the dense dimension, Measures. The value 120 is assigned to any intersection of California (in the Market dimension), Actual (in the Scenario dimension), Misc (in the Measures dimension), Colas (in the Product dimension), and any member in the Year dimension, because a specific member of Year is not specified in the script.

Because Sample.Basic includes only two sparse dimensions, this example affects only one block. If more sparse dimensions existed, Essbase would ensure data blocks for all combinations of the sparse dimensions with California and Colas, creating blocks if necessary. Within the new blocks, Essbase sets Measures and Scenario values (other than those assigned the value 120) to #MISSING.

If you assign nonconstant values to members of a sparse dimension, blocks are created based on the Create Blocks on Equations setting. The Create Blocks on Equations setting is defined at the database level, as a database property. (See Nonconstant Values.)

Within calculation scripts, you can temporarily override the Create Blocks on Equations setting. Consider the effects of the following calculation when West does not have a value and Create Blocks on Equations is enabled:

West = California + 120;

Unneeded blocks may be created for all sparse-member intersections with West, even if the corresponding block value is #MISSING for all of the children of West. Especially in a large database, creation and processing of unneeded blocks requires additional processing time.

To control creation of blocks when you assign nonconstant values to members of a sparse dimension, use the SET CREATEBLOCKONEQ ON | OFF calculation command, as shown in the following script:

FIX (Colas); SET CREATEBLOCKONEQ OFF West = California + 120; SET CREATEBLOCKONEQ ON East = “New York” + 100; ENDFIX

Because the Create Block on Equation setting is disabled at the beginning of the script, West blocks are created only when values exist for the children of West. Later, because the Create Block on Equation setting is enabled, all blocks for East are created.

Use caution when using a cross-dimensional operator ( -> ) in the following situations:

For faster calculation script performance, use FIX in the calculation script to qualify the use of a formula rather than a formula that includes a cross-dimensional operator on the left of an equation.

For example, assume that you want to increase the Jan -> Sales values in Sample.Basic by 5%. To improve performance by calculating only the relevant combinations of members, use the FIX command:

FIX(Jan) Sales = Sales * .05; ENDFIX

With the FIX command, Essbase calculates the formula only for specified member combinations, in this example, for combinations that include Jan.

Compare this technique to using the slower cross-dimensional operator approach. For the previous example, you place the following formula on the Sales member in the database outline:

Sales(Sales -> Jan = Sales -> Jan * .05;)

As Essbase cycles through the database, it calculates the formula for every member combination that includes a member from the dimension tagged as time (Jan, Feb, Mar, and so on), although only January combinations need to be calculated.

See Using the FIX Command and the Oracle Essbase Technical Reference.

When you use a cross-dimensional operator in an equation in a dense dimension, Essbase does not automatically create the required blocks if both of these conditions apply:

You can use the following techniques to create the blocks and avoid the performance issue.

Ensure that the results members are from a sparse dimension, not from a dense dimension. In this example, the results member Budget is from a sparse dimension:

FIX(Sales) Budget = Actual * 1.1; ENDFIX FIX(Expenses) Budget = Actual * .95; ENDFIX

Use the DATACOPY calculation command to create and then calculate the required blocks. See Using DATACOPY to Copy Existing Blocks.

Use a member formula that contains the dense member equations:

FIX(Sales, Expenses) Budget (Sales = Sales -> Actual * 1.1; Expenses = Expenses -> Actual * .95;) ENDFIX

Formulas in a block storage outline can have dependencies on one another such that they cause a nested execution of formulas within one or more blocks. Such formulas are called recursive formulas. Sometimes recursive formulas result in large or unending loops that result in abnormal termination of the server.

To avoid abnormal termination, you can use the CALCLIMITFORMULARECURSION configuration setting to stop a formula execution that reaches beyond a default number of execution levels. See the Oracle Essbase Technical Reference.

A top-down calculation is less efficient than a bottom-up calculation, because more blocks are calculated than is necessary. Although a top-down calculation is less efficient than a bottom-up calculation, top-down calculations are necessary in some cases to ensure that calculation results are correct.

The following topics describe which calculation to use in different situations:

Essbase uses one of two calculation methods to do a full calculation of a database outline—bottom-up calculation or top-down calculation. By default, Essbase does a bottom-up calculation.

For a bottom-up calculation, Essbase determines which data blocks must be calculated before it calculates the database. Essbase then calculates only the blocks that must be calculated. The calculation begins with the existing block with the lowest block number and works up through each block in number order until the existing block with the highest block number is reached. See Block Calculation Order.

If the database outline contains a complex member formula, Essbase performs a top-down calculation for the relevant member.

Use the following information to learn more about simple and complex formula interactions with bottom-up and top-down calculation:

For simple formulas, Essbase does a bottom-up calculation to determine which blocks must be calculated before running the full calculation. For example, for a simple formula on a member (such as A = B + C), A is calculated only if B or C exists in the database. That is, the dependency of the formula on B and C is known before the calculation is started.

Before starting a calculation, Essbase searches the database outline and marks complex formulas that require top-down calculation; for example, a member formula that contains a cross-dimensional reference. When Essbase reaches a member with a top-down formula, it does a top-down calculation for the member.

When a formula on a member is complex, all possible blocks for the member must be examined to see if an existing block must be changed or a new block created; it is difficult to determine the dependency that blocks have on other blocks before the start of the calculation. The top-down method slows calculation performance because Essbase must search for appropriate blocks to calculate to execute the formula.

When a formula is compiled, if the formula is to be calculated top-down, Essbase logs a message in the application log file.

Consider the following complex formula:

A = B -> D + C -> D

To calculate the formula, Essbase must examine every combination of A to see whether B -> D or C -> D exists.

If it is appropriate for the site, you can force a bottom-up calculation on a top-down formula.

Forcing a bottom-up calculation on a formula usually increases performance time. If the formula contains complex functions (for example, range functions) or if the formula's dependencies are not straightforward, a bottom-up calculation may produce results different from those of a top-down calculation.

Before changing the setting CALCOPTFRMLBOTTOMUP or using the calculation script command SET FRMLBOTTOMUP in a production environment, check the validity of calculation results by comparing, relative to the same data, the results of a bottom-up calculation and the results of a top-down calculation. |

Hybrid aggregation mode is available for block storage databases. Hybrid aggregation for block storage databases means that wherever possible, block storage data calculation executes with efficiency similar to that of aggregate storage databases. See Using Hybrid Aggregation.

See also the ASODYNAMICAGGINBSO configuration setting topic in the Oracle Essbase Technical Reference.

The following section describes the caches that are used with block storage databases. For information about the aggregate storage cache, see Managing the Aggregate Storage Cache.

When calculating a database, Essbase uses approximately 30 bytes of memory per member in the database outline. So if the database has 5,000 members, Essbase needs approximately 150 KB of memory to calculate the database.

You can avoid excess memory use by combining calculation scripts. You can obtain good performance by using parallel calculation with a single calculation script. See Using Parallel Calculation.. |

Essbase uses memory to optimize calculation performance, especially for large calculations. The amount of memory used is not controllable, except by altering the size of the database outline. However, you can ensure that the memory cache sizes enable Essbase to optimize the calculation.

Essbase uses memory caches to coordinate memory usage:

Calculator cache. Ensure that the calculator cache is large enough to optimize calculation performance.

Index cache. If the database is large, the default index cache is not large enough to provide optimum calculation performance.

Application cache. If hybrid aggregation mode is used in block storage databases, the application cache can help you manage memory usage for retrievals. The application cache is similar to the aggregate storage cache; for more information, see Managing the Aggregate Storage Cache.

When you first calculate a database, the size of the calculator cache is significant for calculation performance. If possible, ensure that the calculator cache is large enough for Essbase to use the optimal calculator cache option. |

See Sizing Caches. Read the entire topic before making changes.

When a block is calculated, Essbase locks the block and all blocks that contain the children of the block. Essbase calculates the block and then releases the block and the blocks containing the children.

By default, Essbase locks up to 100 blocks concurrently when calculating a block. This number of block locks is sufficient for most database calculations. If you are calculating a formula in a sparse dimension, Essbase works most efficiently if it can lock all required child blocks concurrently. Therefore, when calculating a formula in a sparse dimension, you may want to set a lock number higher than 100 if you are consolidating very large numbers of children (for example, more than 100). By increasing the number, you ensure that Essbase can lock all required blocks, and performance is not impaired.

Essbase locking behavior depends on the isolation level setting. See Locking Under Committed Access and Locking Under Uncommitted Access.

For consolidations in a sparse dimension, block locking is not a consideration, because Essbase does not need to lock all blocks containing children concurrently. |

You can use the SET LOCKBLOCK command in a calculation script along with the CALCLOCKBLOCK setting in the essbase.cfg file to specify the maximum number of blocks that Essbase can lock concurrently when calculating a block. If you do not modify the default setting, and the default 100 blocks is not sufficient during calculation, the calculation may require more time than expected.

Essbase uses the block locking system to manage concurrent access to users. This system ensures that only one user at a time can update or calculate a particular data block. How Essbase handles locking blocks and committing data depends on the isolation level setting.

When Essbase calculates a data block, it creates an exclusive lock; other users cannot update or calculate it, but they can have read-only access. When Essbase finishes the calculation, it releases the block. Other users can then update the block if they have the appropriate security access.

When a user is updating a data block, the block is locked. If a database calculation requires a data block that is being updated by another user, the calculation waits for one of the following conditions:

Essbase does not provide a message to say that the calculation is waiting for the data block to be released.

You can prevent calculation delays caused by waiting for locked blocks by using Essbase security options to do either of the following:

For information on how Essbase handles locks and transactions, see Understanding How Essbase Handles Transactions and Data Locks.

You can improve performance significantly by tagging an accounts dimension member as two-pass in the database outline, if it is appropriate for the application. The combination of data and calculation needs may require the use of a calculation script to calculate a formula twice, instead of two-pass tagging, to preserve accuracy.

Use these sections to understand more about two-pass calculation. Decide whether you can tag an accounts dimension member as two-pass to improve performance, or whether you must use a calculation script to calculate a formula twice. This section also provides information about how to enable two-pass calculation or create a calculation script for two-pass calculation.

For information about the interaction of two-pass calculation and attribute members, see Table 17, Differences Between Attribute and Standard Dimensions.

You can use a two-pass calculation on member formulas that must be calculated twice to produce the correct value.

Whenever possible, Essbase calculates two-pass formulas at the data block level, calculating the two-pass formulas simultaneously as the main calculation. Thus, Essbase need not do an extra calculation pass through the database. However, in some situations, Essbase needs an extra calculation pass through the database.

How Essbase calculates the two-pass formulas depends on whether there is a dimension tagged as time as well as a dimension tagged as accounts and on the dense-sparse configuration of the time and account dimensions.

Consider this calculation required for Profit%:

Profit % = Profit % Sales

Assume that Table 176 shows a subset of a data block with Measures and Year as dense dimensions. Measures is tagged as accounts, and Year is tagged as time. The AGGMISSG configuration setting is turned off (the default).

Data values have been loaded into the input cells. Essbase calculates the cells in which the numbers 1 through 7 appear, in that order. For example, Profit % -> Jan is calculated first; Profit% -> Qtr1 has multiple consolidation paths.

For information on how cell calculation order depends on database configuration, see Cell Calculation Order. |

Essbase uses this calculation order:

Essbase calculates the formula Profit % Sales for Profit % -> Jan, Profit % -> Feb, Profit % -> Mar, and Profit % -> Qtr1 (1, 2, 3, 4 above).

Essbase calculates Profit -> Qtr1 and Sales -> Qtr1 by adding the values for Jan, Feb, and Mar (5, 6 above).

Essbase calculates Profit % -> Qtr1 by adding the values for Profit % -> Jan, Profit % -> Feb, and Profit % -> Mar (7 above). This addition of percentages produces the value 125%, which is not the correct result.

If you tag Profit% as two-pass in the database outline, Essbase uses the Profit % Sales formula to recalculate the Profit% values and produce the correct results.

For information about multiple calculation passes, see Calculation Passes.

Two scenarios are described in detail in the following sections. If you are using Intelligent Calculation, use the scenario that matches the configuration of the database; each scenario tells you how to ensure that Essbase accurately calculates two-pass formulas.

These scenarios require that you understand the concepts of Intelligent Calculation. See Understanding Intelligent Calculation.

In this scenario, you place formulas in the outline and, as appropriate, tag specific formulas as two-pass for best performance.

Because Essbase calculates the two-pass formulas while it is calculating the data block, Essbase need not do an extra calculation pass through the database.

After the calculation, all data blocks are marked as clean for the purposes of Intelligent Calculation.

When you tag a member formula as two-pass in the outline, Essbase does the two-pass calculation while each data block is being calculated. However, when you repeat a formula in a calculation script, Essbase must read the data blocks and write them to memory to recalculate the formula.

In this scenario, you create a calculation script to perform the formula calculation for best performance.

Essbase calculates the database and then does an extra calculation pass to calculate the two-pass formulas. Even though all data blocks are marked as clean after the first database calculation, Essbase ignores the clean status on the blocks that are relevant to the two-pass formula and recalculates these blocks.

After the first calculation, Essbase has marked all data blocks as clean for the purposes of Intelligent Calculation. In a second calculation pass through the database, Essbase recalculates the required data blocks for the two-pass formulas. However, because the second calculation is a partial calculation of the database, Essbase does not mark the recalculated blocks as clean. When you recalculate the database with Intelligent Calculation turned on, these data blocks may be recalculated unnecessarily.

If the database configuration allows Essbase to use Scenario B, consider using a calculation script to perform two-pass formula calculations. If you use a calculation script, Essbase still does an extra calculation pass through the database; however, you can ensure that Essbase has marked all the data blocks as clean after the calculation. See Creating Calculation Scripts for Two-Pass and Intelligent Calculation.

Although tagging an accounts member as two-pass may bring performance benefits, some applications cannot use this method. Check these qualifications to see whether you should apply a two-pass tag or create a calculation script that performs a calculation twice for best performance and accuracy:

You can tag a member as two-pass if it is in a dimension tagged as accounts. When you perform a default calculation on the database, Essbase automatically recalculates any formulas tagged as two-pass if they are in the dimension tagged as accounts in the database outline.

You can tag a member as two-pass if it is a Dynamic Calc or Dynamic Calc and Store member of any dimension. See Dynamically Calculating Data Values.

You may need to use a calculation script to calculate a two-pass formula to obtain accurate results, even if the two-pass tag would provide performance benefits. See Creating Calculation Scripts for Two-Pass and Intelligent Calculation.

Use a calculation script instead of the two-pass tag to ensure efficient use of Intelligent Calculation. See Understanding the Interaction of Two-Pass Calculation and Intelligent Calculation.

You must use a calculation script to calculate a formula twice if the database configuration means that Essbase uses Scenario A, as described in Scenario A, and if the formula references values from another data block.

You may want to use a calculation script to calculate two-pass formulas if the database configuration means that Essbase uses Scenario B, as described in Scenario B.

A database setting enables two-pass calculation in default calculations. When you perform a default calculation on a database with two-pass calculation enabled (the default setting), Essbase automatically attempts to calculate formulas tagged as two-pass in the dimension tagged as accounts in the database outline. This is true even if you have customized the default calculation script.

Use these methods to create calculation scripts to perform two-pass calculations with Intelligent Calculation, so that the calculation is accurate and as fast as possible:

Before the calculation script command that recalculates a two-pass formula, add the SET UPDATECALC OFF command to disable Intelligent Calculation. If you have Intelligent Calculation enabled (the default), Essbase calculates only the data blocks that are not marked as clean, but when you perform a default calculation of the database with Intelligent Calculation enabled, all data blocks are marked as clean, so Essbase does not perform the two-pass formula recalculation.

When you use a calculation script, Essbase does not automatically recalculate two-pass formulas. Use the CALC TWOPASS command.

If you have changed the default calculation of CALC ALL, and Intelligent Calculation is enabled, the data blocks may not be marked as clean after the first calculation. See Understanding Intelligent Calculation.

To obtain the performance benefits of Intelligent Calculation when performing the first, full calculation of the database, use one of these methods, depending on the calculation needs and outline structure:

These three options use the following example situation:

The outline has a dimension tagged as accounts, and it is a dense dimension. You want to calculate sales for each product as a percentage of sales for all products. Assume this formula should calculate the dimension:

Sales % Sales -> Product

When Essbase calculates the data block for each product, it has not yet calculated the value Sales -> Product, so the results for the sales of each product as a percentage of total sales are incorrect.

If the index is large, and you want to use Intelligent Calculation, you can use any of the following options for the best performance. All three options perform the same tasks.

Calculate the full database and marks the data blocks as clean.

Mark the recalculated blocks as clean, even though this calculation is a partial calculation of the database. If you do not use the command SET CLEARUPDATESTATUS AFTER, Essbase marks data blocks as clean only after a full calculation of the database.

Essbase cycles through the database, calculating only the formula for the relevant member (Share of Sales in our example), or calculating all formulas tagged as two-pass in the database outline.

Use this model to create a calculation script that performs a full calculation of the database with Intelligent Calculation enabled:

SET UPDATECALC ON; CALC ALL; SET UPDATECALC OFF; SET CLEARUPDATESTATUS AFTER; "Share of Sales" = Sales % Sales -> Product;

To tag a member as two-pass, and use a calculation script to calculate first the full database, then the two-pass member:

Place a formula in the database outline and tag it as two-pass.

Place the formula on the appropriate member in the dimension tagged as accounts, in our example, Share of Sales.

Create a calculation script that performs a full database calculation and then a two-pass calculation:

SET UPDATECALC ON; CALC ALL; SET UPDATECALC OFF; SET CLEARUPDATESTATUS AFTER; CALC TWOPASS;

To perform a default calculation from a client and then use a calculation script to perform the formula calculation:

Enable Intelligent Calculation, if this default has been changed.

Perform a full calculation, using any of the tools listed in Table 179, Methods for Performing a Full Calculation .

Use a calculation script similar to this example to disable Intelligent Calculation and calculate the formula:

SET UPDATECALC OFF; SET CLEARUPDATESTATUS AFTER; "Share of Sales" = Sales % Sales -> Product;

SET UPDATECALC OFF; SET CLEARUPDATESTATUS AFTER; CALC TWOPASS;

See Understanding Intelligent Calculation, Developing Formulas for Block Storage Databases, and Developing Calculation Scripts for Block Storage Databases.

To use Intelligent Calculation when the index is small:

Create a calculation script to calculate the database, but tell Essbase not to mark the calculated data blocks as clean.

Mark all data blocks as clean and do not recalculate the data blocks.

SET CLEARUPDATESTATUS OFF; CALC ALL; CALC TWOPASS; SET CLEARUPDATESTATUS ONLY; CALC ALL;

The SET CLEARUPDATESTATUS OFF command tells Essbase not to mark the calculated data blocks as clean.

The first CALC ALL command causes Essbase to cycle through the database, calculating all dirty data blocks. Essbase does not mark the calculated data blocks as clean. Essbase does not automatically recalculate the formulas tagged as two-pass in the database outline.

The CALC TWOPASS command causes Essbase to cycle through the database, recalculating the formulas that are tagged as two-pass in the dimension tagged as accounts in the database outline. Essbase recalculates the formulas because the required data blocks are not marked as clean by the previous CALC ALL. Essbase does not mark the recalculated data blocks as clean.

The SET CLEARUPDATESTATUS ONLY command tells Essbase to mark the data blocks as clean but not to calculate the data blocks. This command disables calculation.

The last CALC ALL command causes Essbase to cycle through the database and mark all the data blocks as clean. Essbase searches the index and marks the data blocks as clean. It does not calculate the data blocks.

Queries and calculations that reference a member that has been tagged as Dynamic Calc or Dynamic Calc and Store may be significantly slower than queries and calculations involving the same members, if the member has formulas involving any of these functions:

If you are experiencing slow performance, consider either removing the dynamic calculation tag or removing these functions from the attached formula.

If no data value exists for a combination of dimension members, Essbase gives the combination a value of #MISSING. Essbase treats #MISSING values and zero (0) values differently.

Table 180 shows how Essbase calculates #MISSING values. In this table, X represents any number:

**Table 180. How Essbase Treats #MISSING Values**

X + #MISSING | X |

X – #MISSING #MISSING – X | X -X |

X * #MISSING | #MISSING |

X / #MISSING #MISSING / X X / 0 | #MISSING #MISSING #MISSING |

X % #MISSING #MISSING % X X % 0 | #MISSING #MISSING #MISSING |

X == #MISSING | FALSE, unless X is #MISSING |

X != #MISSING X < > #MISSING | TRUE, unless X is #MISSING TRUE, unless X is #MISSING |

X <= #MISSING | (X <= 0) |

X >= #MISSING | (X >= 0) or (X == #MISSING) |

X > #MISSING | (X > 0) |

X < #MISSING | (X < 0) |

X AND #MISSING: Y AND #MISSING, where Y represents any nonzero value 0 AND #MISSING #MISSING AND #MISSING | #MISSING 0 #MISSING |

X OR #MISSING: Y OR #MISSING, where Y represents any nonzero value 0 OR #MISSING #MISSING OR #MISSING | Y #MISSING #MISSING |

IF (#MISSING) | IF (0) |

f (#MISSING) | #MISSING for any Essbase function of one variable |

f (X) | #MISSING for any X not in the domain of f and any EssbaseEssbase function of multiple variables (except where specifically noted) |

By default, Essbase does not roll up #MISSING values. However, if you always load data at level 0 and never at parent levels, you should enable the setting for consolidating #MISSING values. This setting provides a calculation performance improvement of 1%–30%. The performance improvement varies, depending on database size and configuration.

The default, not consolidating #MISSING values, must be in effect if you load data at parent, rather than child, levels, if any child member combinations have #MISSING values. If all child member combinations have any other values, including zero (0), Essbase rolls up the child values and overwrites the parent values correctly, so you can safely change the default. |

To consolidate, enable the setting for consolidating #MISSING values by using one of the methods described above. The degree of performance improvement you achieve depends on the ratio between upper-level blocks and input blocks in the database.

If you enable the setting for consolidating #MISSING values, the cell calculation order within a data block changes. See Cell Calculation Order. |

When the setting for consolidating #MISSING values is disabled, note that the performance overhead is particularly high in the following situations:

In these situations, the performance overhead is 10%–30%. If calculation performance is critical, you may want to reconsider the database configuration or how you load data.

For a information on how Essbase calculates #MISSING values, see Consolidating #MISSING Values.

You can use the CLEARDATA command to change the value of cells in a block to #MISSING. It does not remove the data blocks. These extra blocks can slow retrieval and calculation performance.

If the #MISSING blocks are slowing performance, perform either action:

Note: | Removing empty blocks improves performance when data values already have been loaded. However, data load process time increases if new values require that blocks be created. |

The relationship between calculation and performance is also described in the following chapters:

In Dynamically Calculating Data Values, see the following topics:

In Developing Calculation Scripts for Block Storage Databases, see the following topics:

For the relationship of two-pass calculation and the SET CLEARUPDATESTATUS command, see the Oracle Essbase Technical Reference.

When you convert currencies using the CCONV command, the resulting data blocks are marked as dirty for the purposes of Intelligent Calculation. This means that Essbase recalculates all the converted blocks when you recalculate the database. See Understanding Intelligent Calculation.