There are 2 Key Traits You Need to Battle the Slowdown. FM Logistic Know How to Enable Them
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FM Logistic provides its international customers with complete logistics solutions that cover everything from warehousing and handling, to transport and distribution, co-packing and co-manufacturing, and supply chain optimization. Operating in 14 countries including France, Russia, Poland, India, Vietnam, Brazil, and China, FM Logistic supports its clients by offering specialized services across a number of markets including consumer goods, retail, cosmetics, and health.
“We are in the process of transforming all our tools and integrating new solutions to be able to work faster and more efficiently. Two main IT priorities are mobility and openness, in terms of working from anywhere and being able to interact with other information systems.”
– Communication Manager, FM Logistic
As a business that has existed since 1967 and in 2018 achieved a turnover of €1.178 billion with 9.5% annual growth, FM Logistic is always looking to help secure the company’s position within an evolving sector. To better serve its customers, FM Logistic decided to launch an innovation project in 2017 to transform the internal digital tools of the company and replace its intranet, email, and productivity software. The company looked for an integrated solution that would enable more collaborative ways of working and bring its international operations closer together. The company found that implementing G Suite and LumApps social intranet was the perfect combination.
“We are in the process of transforming all our tools and integrating new solutions to be able to work faster and more efficiently,” says the Communication Manager at FM Logistic. “Two main IT priorities are mobility and openness, in terms of working from anywhere and being able to interact with other information systems.”
A global transformation
For large international companies with global operations, implementing a single integrated solution to enable collaboration across regions while respecting regional variations can be a real challenge.
“We have 26,000 employees spread across a broad geography, with a variation in cultures and technological maturity. There was an aspiration to work in collaboration, but the necessary tools were not in place,” says FM Logistic’s Communication Manager. FM Logistic looked for a solution to enable new, more collaborative work practices, which were flexible in terms of usage and that, most importantly, would work as part of an integrated solution.
To do that, FM Logistic worked with Google Partner Devoteam G Cloud to implement G Suite alongside the LumApps intranet portal. It took six months to complete the migration of 7,000 accounts, with employees accessing the Business or Basic G Suite edition according to their needs. “Before, with our physical infrastructure we found ourselves buying additional hard drives as we ran out of storage,” says the Technical Project Manager at FM Logistic. “That’s no longer a problem, and we can tailor access depending on whether or not employees need unlimited storage .”
“It’s a real advantage to be able to access your account from any device and work from anywhere. Hangouts Meet has been really transformational in that respect: it has reduced the need for travel significantly, even in the few months we’ve been using it so far.”
– Communication Manager, FM Logistic
“We followed Google’s G Suite migration advice and had early adopter ambassadors in every country. By the time we got around to the final country, very little input was needed as they were ready to go!” says the Technical Project Manager. “Many of them were already familiar with the product, so it was very intuitive. Our feedback surveys gave a satisfaction rating of almost 4.5 out of 5 in relation to the transition. We also had significant executive support, with two members of the executive committee on the steering board. That really helped the project to move quickly.”
As a result, employees were quick to understand the benefits of the new system. “It’s a real advantage to be able to access your account from any device and work from anywhere. That was clear from the start,” says the Communication Manager. “Hangouts Meet has been really transformational in that respect: it has reduced the need for travel significantly, even in the few months we’ve been using it so far.”
LumApps: a fully-integrated enterprise hub
One of FM Logistic’s main reasons for choosing G Suite was the seamless integration with LumApps’ intranet portal. LumApps adds value to G Suite, thereby boosting and sustaining employee adoption. FM Logistic chose LumApps to create a hub for its enterprise, where everything is centralized from internal communications to business apps.
“We didn’t want a patchwork of solutions, we needed a platform that worked as an integral whole,” says the Technical Project Manager. “With LumApps, employees access the intranet using their Google authentication and can access all their G Suite tools through the intranet. Moreover, LumApps is Google native, so the integration is seamless and we know that it will evolve to accommodate any changes that might take place.”
“The main advantages we see are communication and knowledge sharing,” says the Communication Manager. “With LumApps, all 26,000 of our employees are now able to access the portal in their own language and access local news.”
For the next step, FM Logistic is considering integrating social communities so that employees can express themselves and be more engaged in the corporate culture.
Supporting digital maturity
Thanks to Drive, employees can now work from wherever they are, and are able to work more efficiently and more collaboratively. “From an administrative point of view, users can benefit from automatic updates, so there is less pressure on the IT department,” says the Technical Project Manager. “And we’re seeing many innovative uses of the tools to work in more efficient ways: many services have gone paperless with Forms, so less time is wasted; commercial agents are using Drive to work together on tenders simultaneously; and with Sheets, tasks are automatically sent from the team manager’s file to employee’s Calendar.”
“G Suite and LumApps are the first step of our digital transformation, in terms of collaboration and moving into the cloud. We have real confidence in our partners LumApps and Google, and we’re investing in those relationships for the long-term benefit of the company.”
– Communication Manager, FM Logistic
Now, FM Logistic wants to further support its employees in exploring all the tools G Suite has to offer. “We are embarking on a second phase to give our employees the skills they need for advanced uses of Docs, Sheets, and Forms,” says the Communication Manager. “It’s a learning curve, but it’s key to our goal of transforming our everyday processes.”
“G Suite and LumApps are the first step of our digital transformation, in terms of collaboration and moving into the cloud. We have real confidence in our partners LumApps and Google, and we’re investing in those relationships for the long-term benefit of the company.”
Google Extends Support for Windows Server Containers on Anthos for Faster App Modernization and Consistent Dev Experience
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Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.
Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.
Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.
With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE.
“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.”
Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem.
Windows Server running on-prem with Anthos
The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.
Get started today
When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.
This Chart, from Home Depot, Dramatically Demonstrates the Power of a Cloud Data Warehouse
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The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.
However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.
The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.
Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.
In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.
Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.
BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.
THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.
While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.
As for performance, look at this chart:
With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.
Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.
With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.
FAQs: Everything Your Need to Know About Cloud Computing
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There are a number of terms and concepts in cloud computing, and not everyone is familiar with all of them. To help, we’ve put together a list of common questions, and the meanings of a few of those acronyms. You can find all these, and many more, in our learning resources.
What are containers?
Containers are packages of software that contain all of the necessary elements to run in any environment. In this way, containers virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer’s personal laptop. Containerization allows development teams to move fast, deploy software efficiently, and operate at an unprecedented scale. Read more.
Containers vs. VMs: What’s the difference?
You might already be familiar with VMs: a guest operating system such as Linux or Windows runs on top of a host operating system with access to the underlying hardware. Containers are often compared to virtual machines (VMs). Like virtual machines, containers allow you to package your application together with libraries and other dependencies, providing isolated environments for running your software services. However, the similarities end here as containers offer a far more lightweight unit for developers and IT Ops teams to work with, carrying a myriad of benefits. Containers are much more lightweight than VMs, virtualize at the OS level while VMs virtualize at the hardware level, and share the OS kernel and use a fraction of the memory VMs require. Read more.
What is Kubernetes?
With the widespread adoption of containers among organizations, Kubernetes, the container-centric management software, has become the de facto standard to deploy and operate containerized applications. Google Cloud is the birthplace of Kubernetes—originally developed at Google and released as open source in 2014. Kubernetes builds on 15 years of running Google’s containerized workloads and the valuable contributions from the open source community. Inspired by Google’s internal cluster management system, Borg, Kubernetes makes everything associated with deploying and managing your application easier. Providing automated container orchestration, Kubernetes improves your reliability and reduces the time and resources attributed to daily operations. Read more.
What is microservices architecture?
Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can call on many internal microservices to compose its response. Containers are a well-suited microservices architecture example, since they let you focus on developing the services without worrying about the dependencies. Modern cloud-native applications are usually built as microservices using containers. Read more.
What is ETL?
ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake. ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. Organizations have been using ETL for decades. But what’s new is that both the sources of data, as well as the target databases, are now moving to the cloud. Additionally, we’re seeing the emergence of streaming ETL pipelines, which are now unified alongside batch pipelines—that is, pipelines handling continuous streams of data in real time versus data handled in aggregate batches. Some enterprises run continuous streaming processes with batch backfill or reprocessing pipelines woven into the mix. Read more.
What is a data lake?
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Read more.
What is a data warehouse?
Data-driven companies require robust solutions for managing and analyzing large quantities of data across their organizations. These systems must be scalable, reliable, and secure enough for regulated industries, as well as flexible enough to support a wide variety of data types and use cases. The requirements go way beyond the capabilities of any traditional database. That’s where the data warehouse comes in. A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting and can store both current and historical data in one place. It is designed to give a long-range view of data over time, making it a primary component of business intelligence. Read more.
What is streaming analytics?
Streaming analytics is the processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated. Read more.
What is machine learning (ML)?
Today’s enterprises are bombarded with data. To drive better business decisions, they have to make sense of it. But the sheer volume coupled with complexity makes data difficult to analyze using traditional tools. Building, testing, iterating, and deploying analytical models for identifying patterns and insights in data eats up employees’ time. Then after being deployed, such models also have to be monitored and continually adjusted as the market situation or the data itself changes. Machine learning is the solution. Machine learning allows businesses to enable the data to teach the system how to solve the problem at hand with machine learning algorithms—and how to get better over time. Read more.
What is natural language processing (NLP)?
Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations. Read more.
Learn more
This is just a sampling of frequently asked questions about cloud computing. To learn more, visit our resources page at cloud.google.com/learn.
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Value Realization with Google Cloud for Retail SAP Data
Retail engagements have changed drastically over the last few years, and by the virtue of COVID-19 pandemic, retail data and customers’ expectations plummeted. To drive value and transformation across the entire value-chain retailers can make most of Google Cloud’s secure, reliable IaaS by migrating their SAP systems and taking advantage of integrations, insights and innovations. In times of change retail companies can gain maximum visibility of SAP data unlocking Google Cloud’s infrastructure modernization and Big Data and analytics capabilities. Watch the video to understand how Google Cloud and SAP partnership is a golden handshake for retail businesses’ future.
Pacemaker’s Automated Alerts and Alert Reporting: No More Outages for SAP Systems on Google Cloud!
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When critical services fail, businesses risk losing revenue, productivity, and trust. That’s why Google Cloud customers running SAP applications choose to deploy high availability (HA) systems on Google Cloud.
In these deployments Linux operating system clustering provides application and guest awareness for the application state and automates recovery actions in case of failure — including cluster node, resource or node failover or failed action.
Pacemaker is the most popular software Linux administrators use to manage their HA clusters, which includes automating notifications about events — including failover fencing and node, attribute, and resource events — and reporting on events. With automated alerts and reports, Linux administrators can not only learn about events as they happen, but they can also make sure other stakeholders are alerted to take action when critical events occur. They can even discover past events to assess the overall health of their HA systems.
Here, we break down the steps to setting up automated alerts for HA cluster events and alert reporting.
How to Deploy the Alert Script
To set up event-based alerts, you’ll need to take the following steps to execute the script.
1. Download the script file ‘gcp_crm_alert.sh’ from
https://github.com/GoogleCloudPlatform/pacemaker-alerts-cloud-logging
2. Under root user, add exec flag for the script and execute deployment with:
chmod +x ./gcp_crm_alert.sh./gcp_crm_alert.sh -d
3. Confirm that the deployment runs successfully. If it does, you will see the following INFO log messages:
In the Red Hat Enterprise Linux (RHEL) system:
gcp_crm_alert.sh:2022-01-24T23:48:30+0000:INFO:'pcs alert recipient add gcp_cluster_alert value=gcp_cluster_alerts id=gcp_cluster_alert_recepient options value=/var/log/crm_alerts_log' rc=0
In the SUSE Linux Enterprise Server (SLES):
gcp_crm_alert.sh:2022-01-25T00:13:27+00:00:INFO:'crm configure alert gcp_cluster_alert /usr/share/pacemaker/alerts/gcp_crm_alert.sh meta timeout=10s timestamp-format=%Y-%m-%dT%H:%M:%S.%06NZ to { /var/log/crm_alerts_log attributes gcloud_timeout=5 gcloud_cmd=/usr/bin/gcloud }' rc=0Now, in the event of a cluster node, resource, node failover, or failed action, Pacemaker will start the alert mechanism. For further details on the alerting agent, check out the Pacemaker Explained documentation.
How to Use Cloud Logging for Alert Reporting
Alerted events are published in Cloud Logging. Below is an example of the log record payload, where the cluster alert key-value pairs get recorded in the jsonPayload node.
{
"insertId": "ktildwg1o3fbim", "jsonPayload": { "CRM_alert_recipient": "/var/log/crm_alerts_log", "CRM_alert_attribute_name": "", "CRM_alert_kind": "resource", "CRM_alert_status": "0", "CRM_alert_rsc": "STONITH-sapecc-scs", "CRM_alert_rc": "0", "CRM_alert_timestamp_usec": "", "CRM_alert_interval": "0", "CRM_alert_node_sequence": "21", "CRM_alert_task": "start", "CRM_alert_nodeid": "", "CRM_alert_timestamp": "2022-01-25T00:17:06.515313Z", "CRM_alert_timestamp_epoch": "", "CRM_alert_desc": "ok", "CRM_alert_target_rc": "0", "CRM_alert_version": "1.1.15", "CRM_alert_attribute_value": "", "CRM_alert_node": "sapecc-ers", "CRM_alert_exec_time": "" }, "resource": { "type": "global", "labels": { "project_id": "gcp-tse-sap-on-gcp-lab" } }, "timestamp": "2022-01-25T00:17:09.662557309Z", "severity": "INFO", "logName": "projects/gcp-tse-sap-on-gcp-lab/logs/sapecc-ers%2F%2Fvar%2Flog%2Fcrm_alerts_log", "receiveTimestamp": "2022-01-25T00:17:09.662557309Z" }
To get notified of a resource event — for example, when the HANA topology resource monitor fails — you can use the following filter for the alerting definition:
jsonPayload.CRM_alert_node=("hana-venus" OR "hana-mercury") -jsonPayload.CRM_alert_status="0" jsonPayload.CRM_alert_rsc="rsc_SAPHanaTopology_SBX_HDB00" jsonPayload.CRM_alert_task="monitor" To define an alert for a fencing event, your can apply this filter: jsonPayload.CRM_alert_node=("hana-venus" OR "hana-mercury") jsonPayload.CRM_alert_kind="fencing" The fencing log entry gets recorded with warning severity to give you deeper insight, and this additional information is also helpful for more specific filtering criteria: { "insertId": "1plznskfjsxt82", "jsonPayload": { "CRM_alert_attribute_value": "", "CRM_alert_recipient": "/var/log/crm_alerts_log", "CRM_alert_rsc": "", "CRM_alert_rc": "0", "CRM_alert_timestamp_usec": "529261", "CRM_alert_desc": "Operation reboot of hana-mercury by hana-venus for crmd.2361@hana-venus: OK (ref=2a9bf814-9adf-4247-af3f-94ac254fc3ca)", "CRM_alert_target_rc": "", "CRM_alert_nodeid": "", "CRM_alert_kind": "fencing", "CRM_alert_node_sequence": "33", "CRM_alert_task": "st_notify_fence", "CRM_alert_status": "", "CRM_alert_exec_time": "", "CRM_alert_attribute_name": "", "CRM_alert_timestamp_epoch": "1643072786", "CRM_alert_version": "1.1.19", "CRM_alert_timestamp": "2022-01-25T01:06:26.529261Z", "CRM_alert_interval": "", "CRM_alert_node": "hana-mercury" }, "resource": { "type": "global", "labels": { "project_id": "gcp-tse-sap-on-gcp-lab" } }, "timestamp": "2022-01-25T01:06:27.267017052Z", "severity": "WARNING", "logName": "projects/gcp-tse-sap-on-gcp-lab/logs/hana-venus%2F%2Fvar%2Flog%2Fcrm_alerts_log", "receiveTimestamp": "2022-01-25T01:06:27.267017052Z"
} Alerts can be delivered through multiple channels, including text and email. Below is an example of an email notification for our earlier example, when we defined an alert for a HANA topology resource monitor failure:
You can write and apply filters to your log-based alerts to isolate certain types of incidents and analyze events over time. For example, the following script will surface a resource event occurring within a two-hour window on a specific date:
timestamp>="2022-01-25T00:00:00Z" timestamp<="2022-01-25T02:00:00Z"
jsonPayload.CRM_alert_kind="resource"
With the ability to analyze these logged alerts over time, determine whether event patterns warrant any action.
[SIDEBAR]
The alert script prints details in the standard output and in the log file /var/log/crm_alerts_log, and this can grow over time. We recommend that the log file is set with the Linux logrotate service in order to limit the file system space. Use the following command to create the necessary logrotate setting for the alerting log file:
cat > /etc/logrotate.d/crm_alerts_log << END-OF-FILE /var/log/crm_alerts_log { create 0660 root root rotate 7 size 10M missingok compress delaycompress copytruncate dateext dateformat -%Y%m%d-%s notifempty } END-OF-FILE [END SIDEBAR]
Tips for Troubleshooting When you first deploy your alert script, how can you tell for certain that you’ve done it correctly? Use the following commands to test it out:
In RHEL:
pcs alert show
In SLES:
sudo crm config show | grep -A3 gcp_cluster_alert
You should see the following if the script is correct:
In RHEL:
Alerts: Alert: gcp_cluster_alert (path=/usr/share/pacemaker/alerts/gcp_crm_alert.sh) Description: "Cluster alerting for hana-node-X" Options: gcloud_cmd=/usr/bin/gcloud gcloud_timeout=5 Meta options: timeout=10s timestamp-format=%Y-%m-%dT%H:%M:%S.%06NZ Recipients: Recipient: gcp_cluster_alert_recepient (value=gcp_cluster_alerts) Options: value=/var/log/crm_alerts_log In SLES:
alert gcp_cluster_alert "/usr/share/pacemaker/alerts/gcp_crm_alert.sh" \ meta timeout=10s timestamp-format="%Y-%m-%dT%H:%M:%S.%06NZ" \ to "/var/log/crm_alerts_log" attributes gcloud_timeout=5 gcloud_cmd="/usr/bin/gcloud" If the commands do not display the alerts properly, re-deploy the script.
In case there is an issue with the script, or if the Cloud Logging records are not presenting as expected, examine the script log file /var/log/crm_alerts_log. The errors and warning can be filtered with:
egrep '(ERROR|WARN)' /var/log/crm_alerts_log
Any Pacemaker alert failures will be recorded in the messages and/or Pacemaker log. To examine recent alert failures, use the following command:
egrep '(gcp_crm_alert.sh|gcp_cluster_alert)' \ /var/log/messages /var/log/pacemaker.log
Keep in mind, though, that the Pacemaker log location may be different in your system from the one in the example above.
From reactive to proactive
Your SAP applications are too critical to risk outages. The most effective way to manage high availability clusters for your SAP systems on Google Cloud is to take full advantage of Pacemaker’s alerting capabilities, so you can be proactive in ensuring your systems are healthy and available.
Learn more about running SAP on Google Cloud.
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