Runtime Callback Execution in In-Memory OLAP Engines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing OLAP systems face challenges in efficiently executing runtime callback functions within in-memory computing engines, particularly in transforming multidimensional analytical metadata into executable forms and determining process callbacks to facilitate analytical processing.
Innovation Solution
The system transforms multidimensional analytical metadata into in-memory executable metadata, analyzes the metadata to determine process callbacks, and executes runtime callback functions by processing selection and transformation callbacks at nodes and part providers within the in-memory computing engine, enabling efficient analytical processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multidimensional analytical metadata is transformed into in-memory executable metadata to enable runtime callback execution, then analytical processing capability is improved, but system complexity increases
Solution Approach 1:
The system segments the metadata transformation process into distinct phases: compilation phase where multidimensional analytical metadata is converted to in-memory executable metadata, and runtime phase where callback functions are executed. This segmentation allows complex transformations to be pre-processed and stored, reducing runtime complexity while maintaining analytical processing capability.
Solution Approach 2:
The compilation phase performs preliminary transformation of multidimensional analytical metadata into in-memory executable metadata before runtime execution. This preliminary action prepares the metadata structure, defines calculation scenarios, and establishes callback function associations in advance, enabling efficient runtime processing without performing complex transformations during execution.
2Productivity
If runtime callback functions are executed through selection and transformation callbacks at nodes and part providers, then data transformation efficiency is improved, but processing time increases
Solution Approach 1:
The system establishes continuous callback execution flows where selection callbacks and transformation callbacks are chained together at calculation nodes and part providers. Once a callback is triggered, the system continuously processes through the callback chain without interruption, maintaining useful action throughout the data transformation process and minimizing idle processing time.
Solution Approach 2:
Calculation nodes and part providers serve as intermediaries that facilitate callback execution. These intermediaries manage the coordination between selection callbacks (which determine data filtering) and transformation callbacks (which perform data conversion), enabling efficient data flow management while reducing direct processing overhead between data sources and final outputs.
3Measurement precision
If in-memory executable calculation scenarios are analyzed to determine process callbacks, then callback execution accuracy is improved, but analysis complexity increases
Solution Approach 1:
The in-memory executable calculation scenarios contain self-descriptive metadata that enables automatic identification of required process callbacks. The calculation scenario structure includes embedded information about which selection and transformation callbacks should be executed at each node, allowing the system to self-determine callback requirements without complex external analysis, thereby improving execution accuracy while reducing analysis complexity.
Data Source
AI summary
In an embodiment, a runtime callback function is a part of a code that is invoked upon execution of an associated function. To execute the runtime callback function associated with an in-memory computing engine, multidimensional analytical metadata associated with an application server is received and transformed into an in-memory executable metadata, to generate an instance of an in-memory executable calculation scenario. The instance of the in-memory executable calculation scenario is analyzed to determine process callbacks associated with nodes of the in-memory executable calculation scenario. Based upon the determined process callbacks, the runtime callback function is executed by executing a selection callback at the nodes and a transformation callback at part providers associated with the in-memory executable calculation scenario.