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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.

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 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.

What is Heterogeneous System Copy in SAP?

In SAP, a heterogeneous system copy refers to the process of copying an SAP system from one platform or database to another. It involves migrating an SAP system from its existing environment to a different hardware platform or database system. The term “heterogeneous” indicates that the source and target systems have different characteristics, such as different operating systems or database management systems.

A heterogeneous system copy is typically performed when there is a need to move the SAP system from one infrastructure to another or when there is a requirement to upgrade the underlying technology stack. For example, it may involve moving an SAP system from an older version of the operating system to a newer version, or from one database management system to another.

The process of performing a heterogeneous system copy involves several steps and considerations. Here is a high-level overview of the key stages:

  1. Preparing for the System Copy:
    • Analyzing the existing system landscape and identifying the target platform and database system.
    • Reviewing hardware and software requirements for the target system.
    • Ensuring compatibility between the source and target systems.
  2. Exporting the Source System:
    • Performing a system backup of the source system.
    • Exporting the database contents and system-specific files from the source system.
  3. Preparing the Target System:
    • Setting up the target system infrastructure, including the installation of the operating system, database system, and other prerequisites.
    • Configuring the target system to match the specifications of the source system.
  4. Importing the Source System to the Target:
    • Importing the exported data and system files into the target system.
    • Configuring the target system to ensure compatibility with the imported data.
  5. Post-Copy Activities:
    • Performing system checks and validations to ensure the integrity and consistency of the copied system.
    • Adjusting system-specific settings and configurations.
    • Testing the system to ensure its functionality and performance.

It’s important to note that a heterogeneous system copy is a complex process that requires careful planning and execution. It involves technical expertise in both the source and target platforms, including knowledge of operating systems, database systems, and SAP system administration. Additionally, it’s crucial to consider factors such as system downtime, data consistency, and potential impact on other integrated systems during the copy process.

SAP provides documentation and tools to assist with the heterogeneous system copy process, such as the Software Provisioning Manager (SWPM) and guides specific to the source and target systems involved. It is recommended to consult these resources and engage with experienced SAP consultants or administrators to ensure a successful system copy while minimizing potential risks or disruptions to the SAP landscape.

What is SAP Homogeneous System Copy?

In SAP, a homogeneous system copy refers to the process of duplicating an entire SAP system, including its software components, configurations, and data, from one environment to another with the same characteristics. It involves copying an SAP system to a target system, ensuring that both systems are identical and compatible.

The homogeneous system copy is typically performed for various reasons, such as system upgrades, system refreshes, disaster recovery preparations, or creating sandbox/test systems. It allows organizations to replicate an existing system setup without making any fundamental changes to the system architecture or database type.

Here are the main steps involved in performing a homogeneous system copy in SAP:

  1. Preparing the source system: Before starting the system copy, it is essential to ensure that the source system is in a stable and consistent state. This includes performing necessary system checks, database backups, and ensuring the source system is free from errors or inconsistencies.
  2. Preparing the target system: The target system must be prepared to receive the copied system. This involves installing the required operating system, database software, and SAP software components. The target system should be configured with the same characteristics as the source system, such as the same SAP release, support packages, and kernel level.
  3. Exporting the source system: In this step, the source system’s data and configurations are exported to a storage medium, such as a network share or external hard drive. This process typically involves using SAP tools like Software Provisioning Manager (SWPM) or database-specific tools like BR*Tools or SQL Server Management Studio.
  4. Importing the data to the target system: Once the export process is complete, the data is transferred from the storage medium to the target system. This step involves importing the exported files into the target system’s database. It may require executing database-specific import commands or using tools provided by SAP.
  5. Post-copy activities: After the data import, several post-copy activities are performed to ensure the copied system functions correctly. This includes updating system-specific parameters, configuring network settings, adjusting database parameters, and performing system consistency checks.
  6. System verification and testing: Once the post-copy activities are completed, the target system is thoroughly tested to ensure it functions properly. This involves validating critical business processes, performing system checks, and verifying that all customizations, authorizations, and user-specific settings have been correctly copied.
  7. System cleanup and finalization: After successful testing, any temporary files or logs generated during the system copy process are removed. The source system is brought back to its original state, and any temporary modifications made during the copy process are reverted.

It’s important to note that performing a homogeneous system copy requires careful planning, thorough understanding of the SAP system landscape, and expertise in SAP administration and database management. It is recommended to follow SAP’s official documentation, guidelines, and best practices while performing a system copy to ensure a successful outcome and minimize disruptions to the production environment.