Introducing Performance Optimization

Objective

After completing this lesson, you will be able to understand performance optimization.

Improving the Performance of the Replication Process

SAP LT Replication Server improves replication performance by transferring data from the logging table to the target system in parts. You can boost performance by dividing the logging table into ranges, allowing multiple jobs to transfer data in parallel. There are two ways to do this: a generic method that splits changes into ranges during insertion (only available for some databases), and a manual method where you define the ranges yourself.

To find more information, access the Performance Optimization Guide.

How to Load Initial Mass Data

In SAP SLT Replication Server, the initial load involves bulk transferring historical data from a source system to a target (such as SAP HANA). To optimize this process, administrators can configure key settings in the SAP LTRS (LT Replication Server) transaction, such as the number of data transfer jobs, read processes, and maximum number of tables per job.

Enabling parallelization using mass transfer IDs and table partitioning (via LTRS) significantly improves load performance by allowing concurrent data extraction and transfer. In the next demo, we show how to optimize the initial load of mass data using optimization options settings in LTRS.

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How to Replicate Mass Data

For the real-time replication of large tables, SLT uses database triggers and logging tables to capture changes. Performance tuning through LTRS includes adjusting parameters like "Read Mode" (e.g., synchronous vs. asynchronous) and "Data Transfer Jobs per Mass Transfer ID". In this demo, we show how activating advanced replication settings in LTRS can improve the throughput by parallelizing the replication tables.

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