Optimizing Data Loads

In This Section:

Understanding Data Loads

Grouping Sparse Member Combinations

Making the Data Source as Small as Possible

Making Source Fields as Small as Possible

Positioning Data in the Same Order as the Outline

Loading from Essbase Server

Managing Parallel Data Load Processing

Some information in this chapter applies only to block storage databases and is not relevant to aggregate storage databases. Also see Comparison of Aggregate and Block Storage.

Understanding Data Loads

This section does not apply to aggregate storage databases.

Loading a large data source into an Essbase database can take hours. You can shorten the data loading process by minimizing the time spent on these actions:

  • Reading and parsing the data source

  • Reading and writing to the database

To optimize data load performance, think in terms of database structure. Essbase loads data block by block. For each unique combination of sparse dimension members, one data block contains the data for all the dense dimension combinations, assuming that at least one cell contains data. For faster access to block locations, Essbase uses an index. Each entry in the index corresponds to one data block. See Sparse and Dense Dimensions, Selection of Dense and Sparse Dimensions, and Dense and Sparse Selection Scenarios.

When Essbase loads a data source, Essbase processes the data in three main stages:

  • Input: Essbase reads a portion of the data source.

  • Preparation: Essbase arranges the data in preparation for putting it into blocks.

  • Write: Essbase puts the data into blocks in memory and then writes the blocks to disk, finding the correct block on the disk by using the index, which is composed of pointers based on sparse intersections.

This process is repeated until all data is loaded. By using one or more processing threads in each stage, Essbase can perform some processes in parallel. See Managing Parallel Data Load Processing.

Examples in this chapter assume that you are familiar with the following topic: Data Sources.

Grouping Sparse Member Combinations

This section does not apply to aggregate storage databases.

The most effective strategy to improve performance is to minimize the number of disk I/Os that Essbase must perform while reading or writing to the database. Because Essbase loads data block by block, organizing the source data to correspond to the physical block organization reduces the number of physical disk I/Os that Essbase must perform.

Arrange the data source so that records with the same unique combination of sparse dimensions are grouped together. This arrangement corresponds to blocks in the database.

The examples in this chapter illustrate ways that you can organize the data following this strategy. These examples use a subset of the Sample.Basic database, as shown in Table 184:

Table 184. Dimensions and Values for Examples

Sparse, Nonattribute Dimensions

Dense Dimensions

Scenario (Budget, Actual)

Measures (Sales, Margin, COG, Profit)

Product (Cola, Root Beer)

Year (Jan, Feb)

Market (Florida, Ohio)



Because you do not load data into attribute dimensions, they are not relevant to this discussion although they are sparse.

Consider the following data source. Because it is not grouped by sparse-dimension member combinations, this data has not been sorted for optimization. As Essbase reads each record, it must deal with different members of the sparse dimensions.

Actual    Cola          Ohio      Sales    25
Budget    "Root Beer"   Florida   Sales    28
Actual    "Root Beer"   Ohio      Sales    18
Budget    Cola          Florida   Sales    30

This data loads slowly because Essbase accesses four blocks instead of one.

An optimally organized data source for the same Sample.Basic database shows different records sorted by a unique combination of sparse-dimension members: Actual -> Cola -> Ohio. Essbase accesses only one block to load these records.

Actual     Cola    Ohio    Jan   Sales     25
Actual     Cola    Ohio    Jan   Margin    18
Actual     Cola    Ohio    Jan   COGS      20
Actual     Cola    Ohio    Jan   Profit     5

You can use a data source that loads many cells per record. Ensure that records are grouped together by unique sparse-dimension member combinations. Then order the records so that the dimension in the record for which you provide multiple values is a dense dimension.

The next data source example uses a header record to identify the members of the Measures dimension, which is dense. The data is sorted first by members of the dense dimension Year and grouped hierarchically by members of the other dimensions. Multiple values for the Measures dimension are provided on each record.

                                 Sales  Margin   COG  Profit
Jan Actual  Cola         Ohio       25      18    20       5
Jan Actual  Cola         Florida    30      19    20      10
Jan Actual  "Root Beer"  Ohio       18      12    10       8
Jan Actual  "Root Beer"  Florida    28      18    20       8

Notice that the heading and first data line that requires two lines in this example; the previous example needs four lines for the same data.

For information about arranging data in source files before loading, see Data Sources that Do Not Need a Rules File.

Making the Data Source as Small as Possible

Make the data source as small as possible. The fewer fields that Essbase reads in the data source, the less time is needed to read and load the data.

Group the data into ranges. Eliminating redundancy in the data source reduces the number of fields that Essbase must read before loading data values.

The following example data source is not organized in ranges. It includes unneeded repetition of fields. All values are Profit values. Profit must be included only at the beginning of the group of data applicable to it. This example contains 33 fields that Essbase must read to load the data values properly.

Jan     "New York"   Cola          4
Jan     "New York"   "Diet Cola"   3
Jan     Ohio         Cola          8
Jan     Ohio         "Diet Cola"   7
Feb     "New York"   Cola          6
Feb     "New York"   "Diet Cola"   8
Feb     Ohio         Cola          7
Feb     Ohio         "Diet Cola"   9

The next example provides the same data optimized by grouping members in ranges. By eliminating redundancy, this example contains only 23 fields that Essbase must read in order to load the data values properly.

Jan   "New York"   Cola          4
                   "Diet Cola"   3
      Ohio         Cola          8
                   "Diet Cola"   7
Feb   "New York"   Cola          6
                   "Diet Cola"   8
      Ohio         Cola          7
                   "Diet Cola"   9

Essbase assigns the first value, 4, to Jan->New York->Cola; it assigns the next value, 3, to Jan->New York->Diet Cola and so on.

Although sorted efficiently, the data source sorted and grouped by dense dimensions shows a lot of repetition that can slow down the load process. You can further optimize this data by grouping the data into ranges. The optimized data source below eliminates the redundant fields, reducing processing time.

                                Sales  Margin   COG  Profit
Jan Actual  Cola         Ohio      25      18    20       5
                         Florida   30      19    20      10
            "Root Beer"  Ohio      18      12    10       8
                         Florida   28      18    20       8

See Formatting Ranges of Member Fields.

Making Source Fields as Small as Possible

Making fields in a data source smaller enables Essbase to read and load faster.

Make the fields in the data source as small as possible by performing the following tasks:

  • Remove excess white space in the data source. For example, use tabs instead of blank spaces.

  • Round computer-generated numbers to the precision you need. For example, if the data value has nine decimal points and you care about two, round the number to two decimal points.

  • Use #MI instead of #MISSING.

Positioning Data in the Same Order as the Outline

This section does not apply to aggregate storage databases.

The index is organized in the same order as the sparse dimensions in the outline. To further optimize the data source, with the sparse data combinations in the data source grouped together, arrange the data so that sparse dimensions are in the same order as the outline.

Essbase pages portions of the index in and out of memory as requested by the data load or other operations. Arranging the source data to match the order of entries in the index speeds the data load because it requires less paging of the index. Less paging results in fewer I/O operations.

Essbase uses the index cache size to determine how much of the index can be paged into memory. Adjusting the size of the index cache may also improve data load performance.


If the index cache size is large enough to hold the entire index in memory, positioning data in the same order as the outline does not affect the speed of data loads.

See Sizing the Index Cache.

Loading from Essbase Server

Loading the data source from Essbase Server is faster than loading from a client computer. To load a data source from the server, move the data source to the server and start the load.

Loading data from the server improves performance because the data need not be transported over the network from the client computer to the server computer.

Managing Parallel Data Load Processing

The methods described earlier in this chapter give you the most substantial data load performance enhancements. If you have not done so, carefully evaluate your processor speed and memory requirements and upgrade your computers to meet them.

Another way to speed data loads is to work with the Essbase parallel data load feature to optimize processor resources. The parallel data load feature recognizes opportunities to process data load tasks simultaneously. Although some opportunities present themselves on single-processor computers, many more opportunities are available on multiple-processor computers.

To fine-tune processor use for specific application and database situations, Essbase provides these essbase.cfg settings: DLTHREADSPREPARE, DLTHREADSWRITE, and DLSINGLETHREADPERSTAGE.

Understanding Parallel Data Load Processing

When Essbase loads a data source, it works with a portion of data at a time. Essbase looks at each stage as a task and uses separate processing threads in memory to perform each task.

One form of parallel processing occurs when one thread takes advantage of processor resources that are left idle during the wait time of another thread. For example, while a thread performs I/O processing, it must wait for the slower hardware to perform its task. While this thread waits, another thread can use the idle processor resource. Processing staged tasks in parallel can improve processor efficiency by minimizing idle time.

When computers have multiple processors, Essbase can perform an additional form of parallel processing. When a data load stage completes its work on a portion of data, it can pass the work to the next stage and start work immediately on another portion of data. Processing threads perform their tasks simultaneously on the different processors, providing even faster throughput.

Optimizing Parallel Data Load Processing

Although Essbase uses parallel processing to optimize processor resources across the data load stages, processor resources are idle at times. To take advantage of these times, Essbase can further divide record processing in the preparation and write stages. To tailor parallel processing to your situation, you can use the DLTHREADSPREPARE and DLTHREADSWRITE essbase.cfg settings to tell Essbase to use additional threads during these stages.

Setting Parallel Data Load Settings

As shown in Table 185, Essbase provides three essbase.cfg settings that enable you to manage parallel data load processing.

You can specify setting values that apply to all applications on a given Essbase Server or specify settings multiple times with different values for different applications and databases.

Table 185. Parallel Data Load essbase.cfg Settings




Specifies how many threads Essbase may use during the data load stage that codifies and organizes the data in preparation to being written to blocks in memory.


Specifies how many threads Essbase may use during the data load stage that writes data to the disk. High values may require allocation of additional cache. See Implications in Sizing the Data Cache.


For aggregate storage databases, Essbase Server uses one thread with aggregate storage cache. The DLTHREADSWRITE setting is ignored.


Specifies that Essbase use a single thread per stage, ignoring the values in the DLTHREADSPREPARE and DLTHREADSWRITE settings.

Only when the DLSINGLETHREADPERSTAGE setting is set to FALSE for the specific application and database being loaded does the data load process use the thread values specified in the DLTHREADSPREPARE and DLTHREADSWRITE settings.

See the Oracle Essbase Technical Reference.

Implications in Sizing the Data Cache

For block storage databases, Essbase Server allocates the data cache memory area to hold uncompressed data blocks. Each thread specified by the DLTHREADSWRITE setting uses an area in the data cache equal to the size of an expanded block.

Depending on the size of the block, the number of threads, and how much data cache is used by other concurrent operations during a data load, it may be possible to need more data cache than is available. In such circumstances, decrease the number of threads or increase the size of the data cache.

See Changing the Data Cache Size.

Testing Different Thread Values

While processing data loads, you can view processor activity. Different operating systems provide different tools for viewing processor activity. For example, the Task Manager in Windows enables you to view processor and memory usage and processes. Among the tools available on UNIX are top and vmstat. You can also use third-party tools to view and analyze system use.

  To assess system use during data load processing;

  1. Start with the default parallel data load processing thread, in which Essbase uses a single thread per stage.

  2. Perform and time the data load.

  3. Monitor the entire process, identifying the stages during which the processor may be idle.

  4. Alter the essbase.cfg settings described in Setting Parallel Data Load Settings.

  5. Repeat the last three steps until you find values that provide the best performance.