performance-analyzer-for-kubernetes-on-vsphere

You might have noticed that we, at Opvizor, consistently improve the container support of Performance Analyzer. No matter if you’re running docker container on Docker hosts or you’re using Kubernetes. As we already have one of the most detailed and complete VMware monitoring stack in the industry, especially the Kubernetes monitoring part comes in very handy for many customers. The simple reason – they run Kubernetes on vSphere, so the Kubernetes host is a virtual machine running on VMware vSphere.

Performance Analyzer takes the lead when it comes to full stack monitoring and performance analysis starting with the VMkernel (NUMA imbalance, Kernel storage queue and much more), getting into the virtual machine performance metrics (memory ballooning, cpu ready aso.), into the Guest (Kubernetes node exporter – cpu, memory, numa, disk utilization) and into the Kubernetes Pod (Container context switches, packet loss …). 

If you’re running Kubernetes on vSphere in a production environment or a serious manner, you should get Performance Analyzer imported and go for the complete picture instead of the puzzle.

Let’s get into some details

After connecting Performance Analyzer to your VMware vCenter servers as well as your Kubernetes environment, you can find the performance metrics within a short amount of time in nicely structured dashboards.

The matching between the Kubernetes Nodes and the virtual machines these are running on happens automatically. A typical dashboard to start with is Starter: VMware Virtual Machines K8s that shows all vital metrics in one spot. Please contact us, if you want to test the Kubernetes on vSphere integration.

Kubernetes on vSphere

Some highlights next to the common performance metrics are the NUMA home node and NUMA remote node utilization charts. NUMA metric collection requires a simple change either in your existing Node exporter or you deploy the Opvizor Node exporter. That dashboard is a good starting point as it shows the full stack.

The Node exporter Full dashboard provides a detailed visualization of Kubernetes nodes, no matter if they are running on top of VMware vSphere or native on a hardware. All important resource metrics can be checked and alerted on – CPU, Memory, Disk, Network. 

Kubernetes Node Details

Also detailed NUMA utilization metrics or filesystem information are available.

Coming back to the Starter dashboard as it also shows the Container information including the top consumer or troublemaker. The data is collected over time, so you can easily change between the last couple of minutes or what happened over the last weeks or even months. Please be aware that more data means more IOps, when being visualized, therefore, its important to doublecheck the out-of-the-box configuration with our support if you run environments with several hundreds or thousands of Pods.

Kubernetes Container Info

Furthermore, the dashboards shows the current load, the average load over the selected timerange and the peaks.

Of course, we provide several Pod details dashboards as well. You can check all KPIs of the container and even filter by any available Kubernetes tag (app, service and much more) and not just the pod name.

Pod performance

Many available monitoring tools either stop or start here and don’t provide VMware vSphere details or detailed container details, like Pod process details. We provide a helm chart for our customers that deploys a daemonset to run a pod process exporter on the Kubernetes nodes.

That way we can not just collect the outer performance metrics, but also the inner metrics. Number of processes, memory utilization, Context switches or written bytes are just some of the information you can collect for all or chosen processes of your container (process selection can be configured).

Pod Container process performance

Now you got the full picture how much information you can collect, visualize and use for alerts when running Kubernetes and VMware vSphere. What we missed in that post is the ability to do live troubleshooting as well as relating container log files with container performance data. We dig into that in follow up blog posts.

CNIL
Metrics and Logs

(formerly, Opvizor Performance Analyzer)

VMware vSphere & Cloud
PERFORMANCE MONITORING, LOG ANALYSIS, LICENSE COMPLIANCE!

Monitor and Analyze Performance and Log files:
Performance monitoring for your systems and applications with log analysis (tamperproof using immudb) and license compliance (RedHat, Oracle, SAP and more) in one virtual appliance!

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Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance

Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace

Goal:

No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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