Home » SAP

Category Archives: SAP

Python Code to get EBS stats for SAP Systems

In this article, we have developed a Python script to simplify the process of retrieving AWS CloudWatch metrics for Elastic Block Store (EBS) volumes. The script takes input from a CSV file, containing metrics such as metric-name, VolumeId, START_TIME, and END_TIME, and uses the boto3 library to interact with AWS services.

By using this script, users can avoid manually executing individual AWS CLI commands for each metric and volume, making the process more efficient and less error-prone. The script iterates through the CSV file, calls AWS CloudWatch using boto3, and collects the required metric statistics, such as the Average value, for each metric and volume within the specified time range.

The output is then written back to a CSV file with the columns metric-name, VolumeId, Timestamp, and Average. This organized output allows users to easily analyze and further process the data for their specific use cases.

Users can customize the input CSV file with desired metrics and volumes, making it adaptable to various AWS environments and monitoring requirements.

SAMPLE - input.csv
CODE - sap_get_metric_statistics.py
import csv
import boto3

# Function to get CloudWatch metric statistics
def get_metric_statistics(metric_name, volume_id, start_time, end_time):
    cloudwatch = boto3.client('cloudwatch')
    response = cloudwatch.get_metric_statistics(
                'Name': 'VolumeId',
                'Value': volume_id
    return response['Datapoints']

# Main function
def main():
    input_file = 'input.csv'
    output_file = 'output.csv'

    with open(input_file, 'r') as csvfile:
        csvreader = csv.DictReader(csvfile)
        next(csvreader)  # Skip the header row
        data = list(csvreader)

    with open(output_file, 'w', newline='') as file:
        csvwriter = csv.writer(file)
        csvwriter.writerow(['metric-name', 'VolumeId', 'Timestamp', 'Average'])

        for entry in data:
            metric_name = entry['metric-name']
            volume_id = entry['VolumeId']
            start_time = entry['START_TIME']
            end_time = entry['END_TIME']

            datapoints = get_metric_statistics(metric_name, volume_id, start_time, end_time)
            for datapoint in datapoints:
                csvwriter.writerow([metric_name, volume_id, datapoint['Timestamp'], datapoint['Average']])

if __name__ == "__main__":

SAMPLE - output.csv

VolumeReadOps,volume-1,2023-07-20 10:00:00,120.0
VolumeReadOps,volume-1,2023-07-20 10:05:00,130.0
VolumeReadOps,volume-1,2023-07-20 10:10:00,115.0
VolumeWriteOps,volume-1,2023-07-20 10:00:00,50.0
VolumeWriteOps,volume-1,2023-07-20 10:05:00,60.0
VolumeWriteOps,volume-1,2023-07-20 10:10:00,55.0
BurstBalance,volume-1,2023-07-20 10:00:00,75.0
BurstBalance,volume-1,2023-07-20 10:05:00,80.0
BurstBalance,volume-1,2023-07-20 10:10:00,70.0
VolumeBytesRead,volume-1,2023-07-20 10:00:00,2000.0
VolumeBytesRead,volume-1,2023-07-20 10:05:00,2200.0
VolumeBytesRead,volume-1,2023-07-20 10:10:00,1900.0
VolumeBytesWrite,volume-1,2023-07-20 10:00:00,1500.0
VolumeBytesWrite,volume-1,2023-07-20 10:05:00,1700.0
VolumeBytesWrite,volume-1,2023-07-20 10:10:00,1400.0


The SAP HANA (High-Performance Analytic Appliance) in-memory columnar database management system was created by SAP SE. It is intended to handle large amounts of data in real time while also providing quick analytics and data processing capabilities. Here’s an in-depth explanation of SAP HANA, complete with examples:

  1. SAP HANA uses in-memory computing, which means it stores and processes data in the server’s main memory (RAM) rather than on traditional disk storage. This allows for faster data access and processing, leading to significant performance gains. Complex analytical queries, for example, that used to take hours can now be completed in seconds with SAP HANA.
  2. Columnar Data Storage: SAP HANA employs a columnar data storage format, in which data is stored column by column rather than row by row. This method improves data compression, speeds up data retrieval, and allows for more efficient data analysis. For example, if you need to calculate total sales across multiple products, SAP HANA can access and aggregate only the relevant columns, resulting in faster results.
  3. SAP HANA supports real-time analytics by processing and analyzing data as it enters the system. Traditional databases frequently necessitate separate data extraction, transformation, and loading (ETL) processes before data can be analyzed. SAP HANA allows you to perform complex analytical operations on real-time data streams. A retail company, for example, can track sales in real-time, allowing for immediate decision-making based on up-to-date information.
  4. SAP HANA offers advanced analytical capabilities such as predictive analytics, text analytics, and geospatial analysis. It supports machine learning and statistical analysis through built-in algorithms and libraries. For example, a telecommunications company can use SAP HANA to analyze customer call records and predict customer churn based on variables such as call duration, network quality, and customer demographics.
  5. Data Integration and Virtualization: SAP HANA enables seamless integration with a wide range of structured and unstructured data sources. It can replicate, extract, and transform data from a variety of systems, including SAP applications, external databases, and big data platforms. SAP HANA can also create virtual data models, which provide a unified view of data from multiple sources. For example, to gain comprehensive insights into customer satisfaction, you can combine sales data from a SAP ERP system with customer feedback from social media.
  6. SAP HANA is used in a variety of industries for a wide range of applications. It is the engine that drives SAP’s business suite, including SAP S/4HANA, which offers integrated enterprise resource planning (ERP) functionality. SAP HANA is also used for real-time analytics, supply chain optimization, fraud detection, customer experience management, Internet of Things data processing, and other applications. A logistics company, for example, can use SAP HANA to optimize delivery routes based on real-time traffic data, resulting in increased efficiency.

New Directory Structure in SAP NetWeaver 7.5 ABAP Installation

In the latest SAP NetWeaver 7.5 release, there have been significant changes to the directory structure for ABAP instances. This article discusses the modifications and how they impact the Primary Application Server (PAS) and Additional Application Server (AAS) instances. Users noticed that the ABAP Primary Application Server (PAS) instance directory, previously named DVBEMGS<Instance_Number>, is no longer present in the directory /usr/sap/<SID>. Instead, a structure named D<Instance_Number> (e.g., D00) similar to an Additional Application Server (AAS) is now found. With the introduction of the ABAP SAP Central Services (ASCS) instance, the distinction between the ABAP PAS and AAS has been removed. This modification resulted in the adoption of the D<Instance_Number> format for all application server instances, regardless of whether they are PAS or AAS.

Example of a new ABAP SAP System based on NW 7.5:

– PAS: D10
– AAS: D13
– AAS: D15

Important Points to Note:
1. The instance directory name for both PAS and AAS is now D<Instance_Number>.
2. Reverting to the old directory naming structure is not feasible.
3. This new directory structure applies only to fresh installations of SAP NetWeaver 7.5 and not to upgraded systems.
4. The changes do not apply to Java or Dual Stack Systems.

Overview of Configuring SAP HANA System Replication

Configuring SAP HANA System Replication between a primary and secondary site involves several steps. Here is an overview of the process:

  1. Prerequisites:
    • Ensure that you have a fully installed and configured SAP HANA system on both the primary and secondary sites.
    • Make sure the network connectivity is established between the primary and secondary sites, including the necessary ports for HANA communication.
  2. Enable System Replication:
    • On the primary site, open the SAP HANA Cockpit or SAP HANA Studio.
    • Connect to the primary HANA instance as a user with administrative privileges.
    • Navigate to the “System Replication” section and enable the system replication feature.
  3. Configure the Primary Site:
    • Set the replication mode to “sync” or “async” based on your requirements.
    • Define the secondary site and specify the connection details (IP address, port, etc.) of the secondary HANA instance.
    • Configure the replication parameters like the replication mode, log retention, etc.
    • Save the configuration and start the replication process on the primary site.
  4. Prepare the Secondary Site:
    • Install and configure a new SAP HANA system on the secondary site if it’s not already done.
    • Ensure that the secondary site has the same hardware resources and HANA version as the primary site.
    • Configure the network settings and ensure that the secondary site can communicate with the primary site.
  5. Establish the Initial Data Copy:
    • Initiate the initial data replication from the primary site to the secondary site.
    • This process involves copying the data from the primary database to the secondary database to synchronize them.
    • Monitor the data copy process and ensure it completes successfully.
  6. Test the Replication:
    • Once the initial data copy is complete, verify that the data is consistent between the primary and secondary sites.
    • Perform tests and checks to ensure that the replication is working as expected.
    • Validate that the secondary site is in a synchronized state with the primary site.
  7. Monitor and Maintain:
    • Set up monitoring tools to track the replication status and performance.
    • Regularly monitor the replication processes, log files, and system alerts.
    • Perform periodic checks to ensure the replication is functioning correctly.

Commands to configure SAP HANA HSR

To configure HANA system replication between a primary and secondary site, you’ll need to perform several steps. Here’s an overview of the commands involved in the configuration process:

  1. Connect to the primary site’s HANA database using the HANA Studio or HANA Cockpit, or by using the hdbsql command line tool.
  2. Check the current replication status and configuration:
This command will show you the current replication status and settings.
  1. If replication is not yet enabled, you’ll need to enable it. Use the following command:
ALTER SYSTEM SET CONFIGURATION ('systemReplication', 'enabled', 'true') WITH RECONFIGURE

This command enables system replication and triggers a reconfiguration.
  1. Create the secondary site configuration. Connect to the secondary site’s HANA database and execute the following command:
CREATE SYSTEM REPLICATION CONFIGURATION '<configuration_name>' SITE 'secondary_site' HOST '<secondary_host>' PORT <secondary_port> USER '<replication_user>' PASSWORD '<replication_password>'

Replace <configuration_name>, <secondary_site>, <secondary_host>, <secondary_port>, <replication_user>, and <replication_password> with the appropriate values.
  1. Configure the replication mode and other parameters. Execute the following command at the primary site:
ALTER SYSTEM ALTER CONFIGURATION ('systemReplication') SET ('mode', '<sync_mode>') WHERE TARGET = 'secondary_site'

Replace <sync_mode> with the desired synchronization mode. Common options are 'sync', 'async', or 'near_sync'.
  1. Start the replication process:

Replace <configuration_name> with the name specified in step 4.
  1. Validate the replication setup. Check the replication status using:

This command will show you the current replication status.

Difference between SAP HANA 1.0 and 2.0

SAP HANA is an in-memory database and application platform developed by SAP. It provides real-time data processing and analytics capabilities, enabling organizations to make faster and more informed decisions. HANA has gone through several major releases, with HANA 1.0 and HANA 2.0 being two significant versions.

  1. Architecture:
    • HANA 1.0: In HANA 1.0, the architecture was based on a single-engine approach, known as the row-store. It stored data in a row-based format, which optimized transactional processing.
    • HANA 2.0: HANA 2.0 introduced a new architecture called the multiple-engine approach. It incorporates both the row-store and column-store engines, allowing for efficient processing of both transactional and analytical workloads.
  2. Hybrid Data Tiering:
    • HANA 1.0: In HANA 1.0, all data had to reside in memory for processing. While this ensured high performance, it could be expensive as memory is generally more costly than other storage options.
    • HANA 2.0: HANA 2.0 introduced the concept of hybrid data tiering. It allows organizations to have a combination of in-memory and disk-based data storage. Frequently accessed data can be kept in memory, while less frequently accessed data can be moved to disk-based storage. This approach reduces memory costs and allows for larger data sets to be stored.
  3. Dynamic Tiering:
    • HANA 1.0: HANA 1.0 did not have a built-in capability for managing cold or rarely accessed data. All data had to be stored in memory, which limited the size of the data sets that could be handled.
    • HANA 2.0: HANA 2.0 introduced the Dynamic Tiering feature, which allows the system to automatically move data between in-memory and disk-based storage based on its usage patterns. This feature enables efficient management of large data volumes and improves overall performance.
  4. Enhanced Analytical Capabilities:
    • HANA 1.0: HANA 1.0 provided robust analytical capabilities with its column-store engine, enabling high-speed analytical processing. However, some advanced analytical features were not available.
    • HANA 2.0: HANA 2.0 expanded the analytical capabilities by introducing new features such as graph processing, spatial processing, and text analytics. These additions allow organizations to perform more sophisticated analytics on their data.
  5. Enhanced Development Tools:
    • HANA 1.0: HANA 1.0 had a set of development tools for creating applications and models. However, there were limitations in terms of ease of use and functionality.
    • HANA 2.0: HANA 2.0 introduced improved development tools, including the Web IDE (Integrated Development Environment) and the Business Application Studio. These tools provide a more intuitive and feature-rich development environment, enabling developers to build applications more efficiently.

Overall, HANA 2.0 builds upon the foundation of HANA 1.0, enhancing its capabilities and introducing new features to improve performance, scalability, and flexibility. The multiple-engine approach, hybrid data tiering, dynamic tiering, enhanced analytical capabilities, and improved development tools make HANA 2.0 a more powerful and comprehensive platform for data processing and analytics.

What is SAP HANA System Replication

SAP HANA System Replication is a feature of SAP HANA that provides high availability and disaster recovery capabilities for SAP HANA databases. It allows you to create and maintain one or more standby systems that can take over the primary system’s operations in the event of a failure or planned downtime.

System Replication works by continuously replicating the data and log entries from the primary SAP HANA system to one or more secondary systems. The secondary systems, also known as replicas, are kept in sync with the primary system through synchronous or asynchronous replication methods.

Here are some key aspects of SAP HANA System Replication:

  1. High Availability: System Replication ensures high availability by automatically switching to a secondary system if the primary system fails. This helps minimize downtime and ensures business continuity.
  2. Disaster Recovery: System Replication serves as a disaster recovery solution by providing a standby system that can be activated in the event of a catastrophic failure or a planned downtime for maintenance.
  3. Synchronous and Asynchronous Replication: SAP HANA supports both synchronous and asynchronous replication modes. In synchronous replication, transactions are committed on the primary and secondary systems simultaneously, ensuring zero data loss but potentially impacting performance. Asynchronous replication introduces a slight delay in data replication, providing better performance but with a possibility of some data loss in case of a failure.
  4. Automatic Failover and Switchover: When a failure is detected on the primary system, System Replication automatically triggers a failover to one of the secondary systems. In planned scenarios, you can initiate a switchover to the secondary system for maintenance activities, allowing the primary system to be offline temporarily.
  5. Monitoring and Administration: SAP HANA provides tools and monitoring capabilities to manage and monitor the System Replication setup. You can monitor the replication status, perform failover or switchover operations, and configure various parameters related to replication behavior.

SAP HANA System Replication plays a crucial role in ensuring the availability and resilience of SAP HANA databases, making it a critical component for mission-critical applications running on SAP HANA.

What are the reasons for slowness in an SAP System?

There can be various reasons for slowness in a SAP (Systems, Applications, and Products in Data Processing) system. Here are some common factors that can contribute to system slowness:

  1. Hardware limitations: Insufficient server resources, such as CPU, memory, or disk space, can lead to performance issues in a SAP system.
  2. Database performance: The performance of the underlying database system, such as SAP HANA or Oracle, can significantly impact the overall system speed. Inefficient query execution, poorly optimized database tables, or inadequate indexing can result in slow response times.
  3. Network issues: Slow network connectivity or high network latency can impact the performance of a SAP system, especially when accessing data from remote locations or when multiple users are accessing the system simultaneously.
  4. Configuration settings: Inappropriate configuration settings within the SAP system can cause performance degradation. This includes parameters related to buffer sizes, thread counts, or memory allocation, which should be appropriately tuned based on the system requirements.
  5. Customizations and enhancements: Custom code modifications or enhancements made to the SAP system might introduce inefficiencies that can impact performance. Poorly written custom programs, inefficient data retrieval, or excessive use of database locks can all contribute to slowness.
  6. Batch jobs and background processing: If there are numerous long-running or resource-intensive batch jobs or background processes running in the SAP system, they can consume significant system resources and impact overall system performance.
  7. Data volume and growth: As the amount of data in the SAP system increases over time, it can lead to slower response times. Larger database sizes require more time for data retrieval and processing, affecting system performance.
  8. System monitoring and maintenance: Lack of proactive monitoring, regular system maintenance, and performance optimization activities can contribute to gradual degradation of SAP system performance.
  9. Integration issues: SAP systems often integrate with other applications or external systems. Issues with data transfers, communication delays, or errors in the integration layer can cause performance problems.
  10. User behavior and load: High user concurrency, excessive use of complex reports, or running resource-intensive transactions concurrently can overload the system and result in slower response times.

It’s worth noting that these factors can interact with each other, and multiple factors may contribute to slowness simultaneously. Identifying the root cause of performance issues requires a systematic analysis of the system, including monitoring various system components, analyzing system logs, and considering the specific context and usage patterns of the SAP system in question.