The motorcycle industry has long been an integral part of the global market, capturing the hearts and minds of enthusiasts around the world. With its undeniable impact, this industry has carved a niche for itself, catering to the desires and aspirations of millions. As the world becomes increasingly interconnected, the significance of the motorcycle industry cannot be understated. Motorcycles serve as more than just a means of transportation; they embody a sense of freedom, adventure, and individuality, particularly through the appeal of sports bikes. 

From iconic brands to innovative designs, motorcycles have captivated the imaginations of riders and enthusiasts alike. By examining current market trends and consumer preferences, including the growing popularity of sports bikes, we can explain the factors that influence the choices made by riders. Furthermore, we can utilize this analysis to provide insightful future projections for the industry, offering valuable insights for businesses and enthusiasts alike. 

By understanding these factors, businesses can adapt their strategies to meet evolving demands, while consumers can make informed decisions based on their preferences and aspirations.

I. Overview of the Motorcycle Industry:

The global motorcycle industry refers to the production, sales, and distribution of motorcycles worldwide. It includes various types of motorcycles and is primarily driven by consumer demand for personal transportation, leisure activities, and sport.

In terms of size, the global motorcycle industry is significant. According to a report by Grand View Research, the global motorcycle market size was valued at 108.0 billion USD in 2022.

Some of the major companies in the industry are Honda, Suzuki, Kawasaki, BMW Motorrad, Harley-Davidson, Ducati, KTM, and Triumph.

The biggest markets for motorcycles are in Asia-Pacific, Europe, North America, and Latin America. These places have lots of people who want affordable transportation and enjoy riding motorcycles.

The motorcycle industry helps the economy in many ways. It creates jobs in manufacturing, sales, and tourism. Motorcycle tourism brings in money from people visiting popular riding destinations. There’s also a whole industry around selling parts and accessories for motorcycles. The industry also provides employment opportunities for different kinds of workers.

The motorcycle industry also helps the government by bringing in tax money from sales and import fees.

II. Market Trends in the Motorcycle Industry:

Recent Market Trends in the Motorcycle Industry:

a) Electric Motorcycles: The rising demand for eco-friendly transportation options has led to an increased interest in electric motorcycles. Advancements in battery technology have improved their range and performance, making them a viable alternative to traditional gasoline-powered motorcycles.

b) Adventure Motorcycles: Adventure touring has gained popularity among motorcycle enthusiasts, leading to a surge in demand for adventure motorcycles. These bikes offer versatility, comfort, and the ability to handle both on-road and off-road terrains.

c) Customization and Personalization: Consumers are increasingly seeking motorcycles that reflect their individuality. This trend has given rise to customization options offered by manufacturers and aftermarket companies, allowing riders to personalize their bikes according to their preferences.

d) Connectivity and Smart Features: Motorcycles are becoming more technologically advanced, with features such as Bluetooth connectivity, GPS navigation systems, and smartphone integration. These smart features enhance convenience, safety, and entertainment for riders.

Factors Influencing Market Growth:

a) Technological Advancements: Continuous innovation in motorcycle technology, including improved engines, lightweight materials, and advanced safety features, is driving market growth. These advancements enhance performance, fuel efficiency, and rider safety.

b) Environmental Concerns: Growing environmental awareness has led to an increased demand for greener transportation options. Electric motorcycles and advancements in fuel efficiency are addressing these concerns and attracting environmentally conscious consumers.

c) Changing Consumer Behaviors: Shifting consumer preferences towards experiences rather than material possessions have influenced the motorcycle industry. Motorcycles are seen as a means of adventure, freedom, and self-expression, appealing to a wide range of demographics.

The COVID-19

The COVID-19 pandemic has had a significant impact on the motorcycle industry, both in terms of production and sales. The following points highlight its positive effects:

1. Increased demand for personal transportation: With public transportation systems being limited or restricted in many areas, people have turned to motorcycles as a safer and more convenient mode of personal transportation. 

2. Rise in recreational activities: As people have been seeking outdoor activities to maintain social distancing, recreational motorcycle riding has gained popularity. 

3. Shift towards individual mobility: COVID-19 has accelerated the trend towards individual mobility, as people prefer traveling alone or with their close contacts to minimize exposure. Motorcycles offer a suitable option for such individual mobility needs, leading to increased sales.

4. Growth in e-commerce and online sales: The pandemic has forced businesses to adopt digital platforms, and the motorcycle industry is no exception. Many motorcycle manufacturers, dealerships, and accessory sellers have expanded their online presence, enabling customers to research, purchase, and get motorcycles delivered to their doorstep. 

5. Increased focus on health and well-being: With health concerns being paramount during the pandemic, some individuals have turned to motorcycling as a means to reduce stress, enjoy outdoor activities, and improve mental well-being. 

The negative aspects of COVID-19 pandemic were:

1. Supply Chain Disruptions: Lockdowns and restrictions on manufacturing have disrupted the global supply chain, causing delays in production and delivery of motorcycles.

2. Reduced Consumer Spending: Economic uncertainties and reduced disposable incomes have led to a decline in motorcycle sales. 

3. Shift in Commuting Patterns: Remote working arrangements and reduced travel have resulted in a decline in daily commuting. This change has affected motorcycle sales, particularly in urban areas.

III. Consumer Preferences in the Motorcycle Industry:

The motorcycle industry is a dynamic and competitive market, driven by the ever-changing preferences of consumers. 

To understand consumer preferences, it is essential to analyze their buying patterns. Market research indicates that consumers consider several factors before making a motorcycle purchase. These include price range, brand reputation, performance, style, and additional features.

Price Range: Consumers often have specific budget constraints when purchasing a motorcycle. They compare prices across different brands and models to find the best value for their money.

Brand Reputation: Established brands with a strong reputation for quality, reliability, and customer service tend to attract loyal customers. Consumers often prefer brands with a proven track record in the motorcycle industry.

Performance: Performance is a significant consideration for many motorcycle enthusiasts. Consumers look for factors such as engine power, speed, acceleration, handling, and fuel efficiency when evaluating different models.

Style: Motorcycle style plays a vital role in consumer preferences. Whether it’s a cruiser, sportbike, adventure bike, or a classic vintage design, consumers often choose motorcycles that align with their personal style and image.

Additional Features: Consumers also consider additional features such as safety features, technological advancements, comfort, storage capacity, and customization options when making purchasing decisions.

2. Analyze factors influencing consumer decisions:

Several factors influence consumer decisions in the motorcycle industry:

Economic Factors: Economic conditions impact consumer preferences. During economic downturns, consumers may prioritize affordability and fuel efficiency over performance or luxury.

Environmental Concerns: Increasing environmental awareness has led to a rise in demand for electric motorcycles or those with better fuel efficiency. Consumers are increasingly considering the environmental impact of their purchases.

Social Influences: Peer recommendations, online reviews, and social media play a significant role in shaping consumer decisions. Positive word-of-mouth and brand advocacy can greatly influence consumer preferences.

Marketing and Advertising: Effective marketing campaigns that highlight a motorcycle’s unique features, performance, and style can sway consumer decisions. Promotions, discounts, and financing options also impact consumer choices.

3. Explore emerging consumer segments and their impact on market demand:

The motorcycle industry is witnessing the emergence of new consumer segments that are impacting market demand:

Millennials: Younger generations have shown a growing interest in motorcycles, seeking adventure, freedom, and a unique mode of transportation. Manufacturers are adapting by introducing more affordable, lightweight, and eco-friendly models that cater to their preferences.

Female Riders: The number of female riders is steadily increasing, leading to a shift in motorcycle design, marketing, and accessories. Manufacturers are focusing on creating motorcycles with lower seat heights, improved ergonomics, and designs that appeal to female riders.

Adventure Touring: Adventure touring motorcycles have gained popularity among consumers who seek long-distance travel, both on and off-road. Manufacturers are introducing models with enhanced durability, comfort, and advanced technology to cater to this segment.

IV. Future Projections for the Motorcycle Industry:

The motorcycle industry has witnessed significant growth and transformation over the years, driven by evolving consumer preferences and technological advancements. Let’s highlight insights into Future Projections:

1.1 Shifting Consumer Preferences:

Current trends indicate a growing interest in motorcycles as a mode of transportation due to their affordability, fuel efficiency, and maneuverability in urban areas. This preference is expected to continue, particularly among younger generations seeking sustainable and cost-effective mobility options.

1.2 Rise of Electric Motorcycles:

With increasing environmental concerns and advancements in battery technology, the motorcycle industry is witnessing a surge in electric motorcycles. These vehicles offer zero emissions, reduced maintenance costs, and improved performance. Future projections suggest a significant market share for electric motorcycles, especially in urban areas with strict emission regulations.

1.3 Connectivity Features:

As technology continues to advance, motorcycles are becoming more connected. Future projections indicate that connected motorcycles will enhance rider safety and provide a more immersive riding experience.

2. Potential Challenges and Opportunities:

2.1 Government Regulations:

The motorcycle industry may face challenges due to increasing government regulations pertaining to emissions and safety standards. However, these regulations also present opportunities for manufacturers to innovate and develop more environmentally friendly and safer motorcycles.

2.2 Economic Factors:

Fluctuating economies and income disparities can impact the motorcycle industry. While economic downturns may lead to reduced sales, emerging markets with a growing middle class present opportunities for expansion. Manufacturers must adapt their strategies to cater to diverse economic conditions.

2.3 Changing Mobility Landscape:

The rise of ride-sharing services and autonomous vehicles poses both challenges and opportunities for the motorcycle industry. Manufacturers may need to adapt their products to cater to shared mobility platforms, while autonomous technology can enhance rider safety and create new markets for motorcycle manufacturers.

3. Key Areas of Growth:

3.1 Electric Motorcycles:

As mentioned earlier, electric motorcycles are poised for significant growth. Advancements in battery technology, increased charging infrastructure, and improved performance will drive the adoption of electric motorcycles in the coming years.

3.2 Connectivity Features:

The integration of connectivity features in motorcycles will continue to grow. This includes features such as smartphone integration, real-time data display, and advanced safety systems. Manufacturers that invest in developing and implementing these features are likely to gain a competitive edge.

3.3 Emerging Markets:

Emerging markets, particularly in Asia and Africa, present substantial growth opportunities for the motorcycle industry. Rising disposable incomes, urbanization, and insufficient public transportation systems contribute to increased demand for motorcycles as a primary mode of transportation.

V. Conclusion:

In conclusion, the motorcycle industry is poised for an exciting future. By embracing innovation and staying attuned to changing consumer demands, businesses can position themselves for success in this dynamic market. As technology continues to evolve, we can expect motorcycles to become more sustainable, versatile, and connected than ever before. Looking ahead, we predict that the motorcycle industry will continue to evolve at a rapid pace. With the ongoing advancements in technology, we anticipate a significant increase in the adoption of electric motorcycles, as they become more affordable and offer improved performance. Furthermore, the integration of artificial intelligence and connectivity features will revolutionize the riding experience, providing enhanced safety and convenience.

The post The Business of Motorcycles: Analyzing market trends, consumer preferences, and future projections for the industry. appeared first on Productivity Land.

I met a research paper referred MergeSet from VictoriaMetrics.

It says

MergeSet is a simplified one-level LSM-tree implemented by VictoriaMetrics [6], which concatenates a tag and a posting ID together in a key, and the posting list is naturally formed because of the sorted order maintained by the LSM-tree.

And I have preliminarily read the source code from VM, and learned that MergeSet is designed to store a bundle of key-value pairs.

My questions are as follows:

1. Does MergeSet concatenate each tag key-value pair with related TSID and store it as the key of a tuple in the MergeSet.table?
2. If the tag key-value pair and TSID are both embedded in keys, what does the value of MergeSet.table represent?

I was trying to fetch the data of evaluation metrics like groundedness, coherence etc from Azure Ai studio. Since the Microsoft documentation itself has given Python code, but there is no code in c#. I just want to know if there are any libraries in c# that supports the same. I have tried Metrics library for c# , but it is not giving what I expected. Do anyone have any knowledge on this? Please help

I have tried Metrics library for c# , but it is not giving what I expected

• What I am trying to achieve:

With the metric "disk_free" from springboot pet clinic application, I would like to create a visual where the unit is more representative of a disk, such as in MB or GB.

The visual of something I would like to achieve looks like this:

• The expected result and actual result:

I want to create a visual similar to the one above, where units are in MB/GB. But instead, it is in "Tri"

• What I tried:

I applied the query "disk_free" and got some value results back. Unfortunately, the unit seems to be quite unclear.

• Question:

How to change the unit to something more representative of a disk (such as MB, GB, etc) without hardcoding it?

I try to query the metric for DataProtection/BackupVault resource which has two metrics namely BackupHealthEvents and ResourceHealthEvents using the azure rest api(a sample is given below): GET https://management.azure.com/subscriptions/b324c52b-4073-4807-93af-e07d289c093e/resourceGroups/test/providers/Microsoft.DataProtection/BackupVaults/{vaultName}/backupJobs/{jobName}/providers/Microsoft.Insights/metrics?api-version=2018-01-01 It returns an output, but the metric BackupHealthEvents does not return any values even though I have created Backup jobs in the Backup vault

I had seen that not all resources might give metrics by default and we need to manually go and enable it, I saw the 'Diagnostic Setting' option and added it to DataProtection/BackupVault resource where there was an option to add the metric 'Health' for backup vaults as shown in this link. I added the destination as my LA workspace and i still do not understand why I don't get any values for the metric, it returns an empty list. I have probably checked most of the links if any additional configurations are required to get those metrics.

in my company we are using Jenkins pipelines and we would like to collect metrics about the time needed for each step of the pipeline in order to measure performance improvements introduced by changes.

We have many different pipelines, each pipeline has several steps (from 5 to 15 steps), we would like to know for each step of each pipeline

1. Name of the step and the pipeline
2. How much time the step required for each run
3. The parameters used in the specific pipeline run (e.g. in some pipelines we can enable only a subset of feature and this may affect some of the stages, like the setup)

I found several APIs which can provide these information e.g.

1. https://jenkins.example.com/api/json?tree=jobs[name]
2. https://jenkins.example.com/job/<job_name>/<run_number>/api/json
3. https://jenkins.example.com/job/<job_name>/wfapi/runs

but this means to iterate over #1 to retrieve all pipelines, iterate over #2 to retrieve all runs (with params) or each pilines for each run and finally iterate over #3 to retrieve all stages with time.

Does it exist and API which may provide all runs with stages and params for each run of each pipeline? Or even a plugin which may provides these information may be fine, provided that I can still add some additional data before storing in a DB.

We have a VM cluster running on multiple nodes, each node has two local file systems

Ideally the data should be written to "/server/victoria-metrics/data" FS according to the configuration

-storageDataPath /server/victoria-metrics/data

However after a restart of one of the nodes, the data is written to a different file system "/home/victoria-metrics" FS

I'm still trying to figure out why this's happening, but any quick advise or tips will be appreciated before the FS gets full

The question is, do we have any cli to verify the actual storage path VM is writing to ? and how to enforce the write operation to a different storage while trying not to lose the previous data

Thank you

Checked all the configuration but nothing is pointing to the new file system

:). Can anybody help me to understand the behavior of metrics (RMSE namely) when testing model. I have NN with 1 hidden layer for regression task. RMSE equal 0.07 for external test dataset. But if I break this dataset into parts that are logically and physically necessary for me (for example, part of the dataset is responsible for one large object for which "y" are predicted), then RMSE increases for parts of the test dataset. For example, I divided the external test dataset into 3 parts and separately for these parts the RMSE is equal to 0.3 0.2 0.2.

What does it mean, and why do such changes occur? what metric value really reflects what is happening in this case? The y pairs in the rmse formula do not change when moving from individual parts of a set of dates to the general one.

Let it be not a pre-trained model ResNet50. And let's I provide a dataset by easy way where X_train and Y_train are the ndarray with correspond shapes [N, 32, 32, 3] and [N, class_num=3].

I set batch_size=128, shuffle=False. Now I fitting my model and after the last epoch I get the metric equals 0.976, BUT If I just make model.evaluate(X_train), where X_train is the same data I use in fit I get absolutely different value - 0.456. The question is - WHY?

That's logs of my fitting:

263/263 [==============================] - 7s 27ms/step - loss: 0.2063 - auc: 0.9899 - mc_f1: 0.9326

That's after evaluate: 1052/1052 [==============================] - 11s 9ms/step - loss: 0.6186 - auc: 0.9053 - mc_f1: 0.4993

mc_f1 is a custom metric - averaged f1 calculated for each class separately for the multiclass case.

So the code:

The metric:

class MulticlassF1(tf.keras.metrics.Metric):

    def __init__(self, name='mc_f1', num_classes=None, **kwargs):
        super(MulticlassF1, self).__init__(name=name, **kwargs)
        self.__zero_support = tf.cast(1e-7, dtype=tf.float16)
        self.__cm = self.add_weight(name='fn', initializer='zeros', shape=[num_classes, num_classes])
        if num_classes is not None:
            self.__num_classes = num_classes

    def update_state(self, y_true, y_pred, sample_weight=None):
        y_pred = K.argmax(y_pred, axis=1)
        y_true = K.argmax(y_true, axis=1)
        m = tf.math.confusion_matrix(y_true, y_pred, num_classes=self.__num_classes, dtype=tf.float32)
        self.__cm.assign_add(m)

    def reset_state(self):
        self.__cm.assign(tf.zeros((self.__num_classes, self.__num_classes)))

    def result(self):
        denominator = 0
        m = self.__cm
        for i in range(m.shape[0]):
            tp = m[i, i]
            fn = K.sum(m[:, i]) - tp
            fp = K.sum(m[i, :]) - tp
            tn = K.sum(K.flatten(m)) - (tp + fn + fp)
            tp = K.cast(tp, dtype=tf.float16)
            tn = K.cast(tn, dtype=tf.float16)
            fp = K.cast(fp, dtype=tf.float16)
            fn = K.cast(fn, dtype=tf.float16)
            precision = tp / ((tp + fp) + self.__zero_support) + self.__zero_support
            recall = tp / (tf.cast(tp + fn, dtype=tf.float16) + self.__zero_support) + self.__zero_support
            denominator += (1 / precision + 1 / recall)
        f1_combined = K.cast(2 * m.shape[0] / denominator, dtype=tf.float32)
        return f1_combined

The model declaration

def get_resnet50_model(window_size, classes):
    # Model initialization
    model = tf.keras.applications.ResNet50V2(
        include_top=False,
        weights=None,
        input_tensor=None,
        input_shape=window_size,
        pooling='max',
        classes=classes,

    )

    # Change last layers
    last_layer = model.output
    output = tf.keras.layers.Dense(classes, activation="softmax")(last_layer)
    model = tf.keras.models.Model(inputs=model.inputs, outputs=output)

    return model


mcf1 = MulticlassF1(num_classes=3)

resnet50 = get_resnet50_model([32, 32, 3], 3)
resnet50.compile(
    optimizer=Adam(),
    loss=tf.keras.losses.CategoricalCrossentropy(),
    metrics=[tf.metrics.AUC(name='auc'), mcf1],
)

Fitting:

deb_metric_callback = DebugMetricCallback(resnet50, (X_train, Y_train), mcf1)
resnet50.fit(
    X_train, Y_train,
    epochs=100,
    batch_size=128,
    # verbose=0,
    shuffle=False,
    callbacks=[deb_metric_callback],
)

I tried to do some debug and make a CallBack:

class DebugMetricCallback(Callback):
    def __init__(self, test, metr):
        super().__init__()
        self.test = test
        self.metric = metr


    def on_epoch_end(self, epoch, logs=None):
        print(self.metric.result())
        x, y = self.test
        y_pred = self.model.predict(x)
        inline_measure = MulticlassF1(num_classes=3)
        inline_measure.update_state(y, y_pred)
        print(self.metric.result().numpy(), inline_measure.result().numpy())

Here I get absolutely different confusion matrices.

Also I thought about BatchNormalization, but I save and compare model before and after evaluate it wasn't changed.

I have a mysql deployment with one single pod that requires storage. That is why it is bound on /var/lib/mysql to a volume of 250G capacity via a pvc. Executing into the container and checking out available disc size the values do NOT add up: On /var/lib/mysql it shows me only 9.5G available instead of the 250G.

Executing df inside the linux container:

nim@DESKTOP-FOASM6G:/home$ kubectl exec mysql-7947979f68-74plp -- df -h
Filesystem      Size  Used Avail Use% Mounted on
overlay          95G  2.5G   92G   3% /
tmpfs            64M     0   64M   0% /dev
tmpfs           2.0G     0  2.0G   0% /sys/fs/cgroup
/dev/sda1        95G  2.5G   92G   3% /etc/hosts
shm              64M     0   64M   0% /dev/shm
/dev/sdb        9.9G  307M  9.5G   4% /var/lib/mysql
tmpfs           2.4G   12K  2.4G   1% /run/secrets/kubernetes.io/serviceaccount
tmpfs           2.0G     0  2.0G   0% /proc/acpi
tmpfs           2.0G     0  2.0G   0% /proc/scsi
tmpfs           2.0G     0  2.0G   0% /sys/firmware

Mysql pod description:

nim@DESKTOP-FOASM6G:~$ kubectl describe pod mysql-7947979f68-74plp
Name:         mysql-7947979f68-74plp
Namespace:    default
Priority:     0
Node:         gke-sblice-clust-pool-1-c1994f74-ueyy/10.156.15.207
Start Time:   Wed, 19 Jul 2023 13:18:04 +0200
Labels:       app=sql
              pod-template-hash=7947979f68
Annotations:  <none>
Status:       Running
IP:           10.16.1.11
IPs:
  IP:           10.16.1.11
Controlled By:  ReplicaSet/mysql-7947979f68
Containers:
  mysql:
    Container ID:   containerd://c1cd6e60c0f20956ca4146c315dd0665f2d1bb2eee0351e719a813a2ca62d87c
    Image:          mysql:5.6
    Image ID:       docker.io/library/mysql@sha256:20575ecebe6216036d25dab5903808211f1e9ba63dc7825ac20cb975e34cfcae
    Port:           3306/TCP
    Host Port:      0/TCP
    State:          Running
      Started:      Wed, 19 Jul 2023 13:18:06 +0200
    Ready:          True
    Restart Count:  0
    Limits:
      cpu:     1
      memory:  2500M
    Requests:
      cpu:     100m
      memory:  512Mi
    Liveness:  exec [bash -c mysqladmin -uroot -p$MYSQL_ROOT_PASSWORD ping
] delay=30s timeout=5s period=10s #success=1 #failure=3
    Readiness:  exec [bash -c mysql -h127.0.0.1 -uroot -p$MYSQL_ROOT_PASSWORD -e'SELECT 1'
] delay=5s timeout=1s period=2s #success=1 #failure=3
    Environment:
      MYSQL_ROOT_PASSWORD:  <set to the key 'MYSQL_ROOT_PASSWORD' in secret 'mysql-secrets'>  Optional: false
      MYSQL_DATABASE:       wordpress
      MYSQL_USER:           <set to the key 'MYSQL_USER' of config map 'mysql-config'>   Optional: false
      MYSQL_PASSWORD:       <set to the key 'MYSQL_PASSWORD' in secret 'mysql-secrets'>  Optional: false
    Mounts:
      /var/lib/mysql from regional-storage-180723 (rw)
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-4qncz (ro)
Conditions:
  Type              Status
  Initialized       True
  Ready             True
  ContainersReady   True
  PodScheduled      True
Volumes:
  regional-storage-180723:
    Type:       PersistentVolumeClaim (a reference to a PersistentVolumeClaim in the same namespace)
    ClaimName:  regional-storage-pvc-180723
    ReadOnly:   false
  kube-api-access-4qncz:
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
QoS Class:                   Burstable
Node-Selectors:              <none>
Tolerations:                 node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                             node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:                      <none>

This container is bound to a pvc with description:

nim@DESKTOP-FOASM6G:/home$ kubectl describe pvc regional-storage-pvc-180723
Name:          regional-storage-pvc-180723
Namespace:     default
StorageClass:  normal-storageclass
Status:        Bound
Volume:        pvc-e5baeec9-435d-46b8-a442-179d54c023e6
Labels:        <none>
Annotations:   pv.kubernetes.io/bind-completed: yes
               pv.kubernetes.io/bound-by-controller: yes
               volume.beta.kubernetes.io/storage-provisioner: pd.csi.storage.gke.io
               volume.kubernetes.io/selected-node: gke-sblice-clust-pool-1-c1994f74-ueyy
               volume.kubernetes.io/storage-provisioner: pd.csi.storage.gke.io
Finalizers:    [kubernetes.io/pvc-protection]
Capacity:      233Gi
Access Modes:  RWO
VolumeMode:    Filesystem
DataSource:
  APIGroup:  snapshot.storage.k8s.io
  Kind:      VolumeSnapshot
  Name:      snapshot14072023
Used By:     mysql-7947979f68-74plp
Events:      <none>

And the PV itself is described as:

nim@DESKTOP-FOASM6G:/home$ kubectl describe pv pvc-e5baeec9-435d-46b8-a442-179d54c023e6
Name:              pvc-e5baeec9-435d-46b8-a442-179d54c023e6
Labels:            <none>
Annotations:       pv.kubernetes.io/provisioned-by: pd.csi.storage.gke.io
                   volume.kubernetes.io/provisioner-deletion-secret-name:
                   volume.kubernetes.io/provisioner-deletion-secret-namespace:
Finalizers:        [kubernetes.io/pv-protection external-attacher/pd-csi-storage-gke-io]
StorageClass:      normal-storageclass
Status:            Bound
Claim:             default/regional-storage-pvc-180723
Reclaim Policy:    Retain
Access Modes:      RWO
VolumeMode:        Filesystem
Capacity:          233Gi
Node Affinity:
  Required Terms:
    Term 0:        topology.gke.io/zone in [europe-west3-b]
    Term 1:        topology.gke.io/zone in [europe-west3-c]
Message:
Source:
    Type:              CSI (a Container Storage Interface (CSI) volume source)
    Driver:            pd.csi.storage.gke.io
    FSType:            ext4
    VolumeHandle:      projects/sblice/regions/europe-west3/disks/pvc-e5baeec9-435d-46b8-a442-179d54c023e6
    ReadOnly:          false
    VolumeAttributes:      storage.kubernetes.io/csiProvisionerIdentity=1689594187853-2313-pd.csi.storage.gke.io
Events:                <none>

Logs from the container:

nim@DESKTOP-FOASM6G:/home$ kubectl logs mysql-66fc8c8d48-v7fzd
2023-08-01 15:42:15+00:00 [Note] [Entrypoint]: Entrypoint script for MySQL Server 5.6.51-1debian9 started.
2023-08-01 15:42:15+00:00 [Note] [Entrypoint]: Switching to dedicated user 'mysql'
2023-08-01 15:42:15+00:00 [Note] [Entrypoint]: Entrypoint script for MySQL Server 5.6.51-1debian9 started.
2023-08-01 15:42:16 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).
2023-08-01 15:42:16 0 [Note] mysqld (mysqld 5.6.51) starting as process 1 ...
2023-08-01 15:42:16 1 [Note] Plugin 'FEDERATED' is disabled.
2023-08-01 15:42:16 1 [Note] InnoDB: Using atomics to ref count buffer pool pages
2023-08-01 15:42:16 1 [Note] InnoDB: The InnoDB memory heap is disabled
2023-08-01 15:42:16 1 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins
2023-08-01 15:42:16 1 [Note] InnoDB: Memory barrier is not used
2023-08-01 15:42:16 1 [Note] InnoDB: Compressed tables use zlib 1.2.11
2023-08-01 15:42:16 1 [Note] InnoDB: Using Linux native AIO
2023-08-01 15:42:16 1 [Note] InnoDB: Using CPU crc32 instructions
2023-08-01 15:42:16 1 [Note] InnoDB: Initializing buffer pool, size = 128.0M
2023-08-01 15:42:16 1 [Note] InnoDB: Completed initialization of buffer pool
2023-08-01 15:42:16 1 [Note] InnoDB: Highest supported file format is Barracuda.
2023-08-01 15:42:16 1 [Note] InnoDB: 128 rollback segment(s) are active.
2023-08-01 15:42:16 1 [Note] InnoDB: Waiting for purge to start
2023-08-01 15:42:16 1 [Note] InnoDB: 5.6.51 started; log sequence number 11955998372
2023-08-01 15:42:16 1 [Note] RSA private key file not found: /var/lib/mysql//private_key.pem. Some authentication plugins will not work.
2023-08-01 15:42:16 1 [Note] RSA public key file not found: /var/lib/mysql//public_key.pem. Some authentication plugins will not work.
2023-08-01 15:42:16 1 [Note] Server hostname (bind-address): '*'; port: 3306
2023-08-01 15:42:16 1 [Note] IPv6 is available.
2023-08-01 15:42:16 1 [Note]   - '::' resolves to '::';
2023-08-01 15:42:16 1 [Note] Server socket created on IP: '::'.
2023-08-01 15:42:16 1 [Warning] Insecure configuration for --pid-file: Location '/var/run/mysqld' in the path is accessible to all OS users. Consider choosing a different directory.
2023-08-01 15:42:16 1 [Warning] 'proxies_priv' entry '@ root@mysql-5c96657748-fv26w' ignored in --skip-name-resolve mode.
2023-08-01 15:42:16 1 [Note] Event Scheduler: Loaded 0 events
2023-08-01 15:42:16 1 [Note] mysqld: ready for connections.
Version: '5.6.51'  socket: '/var/run/mysqld/mysqld.sock'  port: 3306  MySQL Community Server (GPL)

Info regarding storage class:

regional-storage-pvc-180723   Bound         pvc-e5baeec9-435d-46b8-a442-179d54c023e6   233Gi      RWO            normal-storageclass   13d
nim@DESKTOP-FOASM6G:/home$ kubectl describe storageclass normal-storageclass
Name:            normal-storageclass
IsDefaultClass:  No
Annotations:     kubectl.kubernetes.io/last-applied-configuration={"allowVolumeExpansion":true,"allowedTopologies":[{"matchLabelExpressions":[{"key":"topology.gke.io/zone","values":["europe-west3-b","europe-west3-c"]}]}],"apiVersion":"storage.k8s.io/v1","kind":"StorageClass","metadata":{"annotations":{},"name":"normal-storageclass"},"parameters":{"replication-type":"regional-pd","type":"pd-standard"},"provisioner":"pd.csi.storage.gke.io","reclaimPolicy":"Retain","volumeBindingMode":"WaitForFirstConsumer"}

Provisioner:           pd.csi.storage.gke.io
Parameters:            replication-type=regional-pd,type=pd-standard
AllowVolumeExpansion:  True
MountOptions:          <none>
ReclaimPolicy:         Retain
VolumeBindingMode:     WaitForFirstConsumer
AllowedTopologies:
  Term 0:              topology.gke.io/zone in [europe-west3-b, europe-west3-c]
Events:                <none>

Kubectl tells me that I have 250G storage mounted and within the pod (using df -h service) it is telling me that I have 9.5G. What disc space do I have available now? What do I need to do to actually get the 250G disc space mounted correctly? And if I have 250G which way do I use to check disc space usage and how can I set my limits? Because GKE console monitoring tells me also that I have much less disc space as a metric.