What is code pushdown technique ?

Code Pushdown is similar to the Code-To-Data Paradigm

“Code pushdown” refers to a technique used in the context of databases, particularly in systems like SAP HANA. In the past, when dealing with complex tasks that involved calculations on data, the common approach was to transfer the data from the database to a separate presentation server where the calculations would take place. This process often led to inefficiencies and slower outcomes.

However, with the advent of technologies like SAP HANA, the approach has shifted. In a “code pushdown” strategy, the calculations are moved or “pushed down” into the database itself. Instead of moving large amounts of data to a different server for processing, the database now handles the calculations directly where the data resides.

Code Push Down offers several advantages:

  1. Efficiency: Databases like HANA are optimized for data processing. Performing calculations directly within the database leverages its inherent efficiency, as it’s designed to handle data manipulation tasks efficiently.
  2. Memory Utilization: HANA, for instance, is known for its efficient use of memory. By performing calculations in the database, unnecessary data transfer is avoided, leading to better memory management and utilization.
  3. Faster Results: Since calculations occur in the same location as the data, there’s no need to transfer large datasets back and forth between servers. This results in quicker outcomes, as there’s less latency introduced by data movement.
  4. Reduced Network Load: Pushing down calculations reduces the load on the network, as only the required data and results need to be transferred between the application and the database.

In essence, “code pushdown” aligns with the architecture and strengths of modern databases like HANA. It optimizes the processing of data by minimizing unnecessary data movement and leveraging the database’s built-in capabilities. This approach results in faster, more efficient, and streamlined operations for applications that rely on data-intensive calculations.

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