Worrying about RAM is not always necessary and many times is not worth the effort, especially if it makes your script harder to follow. However, for large datasets, particularly in the 32bit environment, you may be forced to optimize RAM usage. Using the mem files allows you to identify the most productive candidates for tuning. The QV Reference Guide points out that an optimized load will run faster than an unoptimized load.
I think it would be useful to have brief discussion of the impact on RAM usage as well. Very informative. I had a different problem now after generating memory statistics and am actually confused.
We have a massive massive data set. Generating the report — I saw sum of bytes comes up to 7GB whole model — all tables, records, variables everything. One thing that stands out in your comment is the 7GB vs 17GB. Is is possible that the mem files or value shown in the optimizer are truncating a digit? How much RAM is consumed per application?
Environment : QlikView, all versions Qlik Sense Enterprise on Windows, all versions Resolution: An estimate of the RAM needed per app can be built on the below, but for accuracy always test by loading the app into memory and using the Qlik Scalability Tools to obtain a baseline of memory usage for each app as it is accessed by the foreseeable number of users.
Tags 3. Tags: memory use. Labels 2. Labels: Administration Configuration. Version history. It is recommended that the reader download and read the Scalability Overview Technology White Paper after reading this technical brief. Figure 1 depicts a simplified view of a standard QlikView deployment containing the location of the various QlikView products as well as both data and application locations. QlikView Developer is a Windows-based desktop tool that is used by designers and developers to create 1 a data extract and transformation model and 2 to create the graphical user interface or presentation layer.
It loads QlikView applications into memory and calculates and presents user selections in real time. These source files contain either a scripts within QVW files to extract data from the various data sources e. The main QlikView product component that resides on the Back End is the QlikView Publisher: the Publisher is responsible for data loads and distribution. Within the Back End, the Windows file system is always in charge of authorization i. QlikView is not responsible for access privileges.
The Back End depicted in figure 1 is suitable for both development, testing and deployment environments.
Front End: The Front End is where end users interact with the documents and data that they are authorized to see via the QlikView Server. It contains the QlikView user documents that have been created via the QlikView Publisher on the back end. Associative In-Memory Technology: QlikView uses an associative in-memory technology to allow users to analyze and process data very quickly.
Unique entries are only stored once in-memory: everything else are pointers to the parent data. Memory and CPU sizing is very important for QlikView, end user experience is directly connected to the hardware QlikView is running on.
The main performance factors are data model complexity, amount of unique data, UI design and concurrent users. All available cores will be used almost linearly when calculating the QlikView objects tables and graphs.
QlikView Server has a central cache function. This means that QlikView object calculations only need to be done once. Obviously the benefits are better user experience i. It is important to realize that the data stored in RAM is the unaggregated granular data.
When the user interface requires aggregates e. This requires processing power from the CPU. Under normal conditions chart recalculation takes place almost instantaneously. However with A major function of the QVS is truly massive datasets and without a corresponding increase in processing power, the time to to load QlikView applications calculate charts can become greater than 1 sec. Quite simply QlikView scales almost perfectly with the addition of more uncompressed format.
One must take into account some additional processing overhead when scaling with cores, however the effects on proportional linear scaling are minimal. A poorly designed linearly according to the number of simultaneous users making the request and the amount of application could utilize processing power available to the application. This is most commonly offset by White Paper.
0コメント