kubernetes-client | Java client for Kubernetes & OpenShift | REST library
kandi X-RAY | kubernetes-client Summary
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- Build http client for mock http .
- Forward to the given port .
- Creates a cluster scoped resource .
- Set the Kubernetes configuration from the system properties
- Normalize a file name .
- Activate the Kubernetes configuration .
- Get the quantity in bytes .
- Performs a roll update .
- Initialize the role bindings .
- Handle a patch request .
kubernetes-client Key Features
kubernetes-client Examples and Code Snippets
Trending Discussions on kubernetes-client
Trending Discussions on kubernetes-client
QUESTION
Is it possible, using the docker SDK for Python, to launch a container in a remote machine?
import docker
client = docker.from_env()
client.containers.run("bfirsh/reticulate-splines", detach=True)
# I'd like to run this container ^^^ in a machine that I have ssh access to.
Going through the documentation it seems like this type of management is out of scope for said SDK, so searching online I got hints that the kubernetes client for Python could be of help, but don't know where to begin.
ANSWER
Answered 2022-Mar-21 at 11:49It's possible, simply do this:
client = docker.DockerClient(base_url=your_remote_docker_url)
Here's the document I found related to this:
https://docker-py.readthedocs.io/en/stable/client.html#client-reference
If you only has SSH access to it, there is an use_ssh_client
option
QUESTION
Python version 3.8.10 Kubernetes version 23.3.0
I'm trying to run a command into a specific pod in kubernetes using python. I've tried to reduce the code as much as I could, so I'm running this.
from kubernetes import client, config
config.load_kube_config()
v1 = client.CoreV1Api()
response = v1.connect_get_namespaced_pod_exec(pod_name , namespace, command="df -h", stderr=True, stdin=True, stdout=True, tty=True)
print(response)
But it's not working. I'm getting this response.
kubernetes.client.exceptions.ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'Audit-Id': '511c23ce-03bb-4b52-a559-3f354fc80235', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Fri, 18 Mar 2022 18:06:11 GMT', 'Content-Length': '139'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"Upgrade request required","reason":"BadRequest","code":400}
If I run typical example of list all pods . It's working fine. So, it should not be a configuration issue. I've read about this problem in the past here and here. But I assume it cannot be that, due to they are closed issues.
If I run k9s shell request, I can connect with pod with no problem. This is what I see in ps a
when I'm doing this /usr/bin/kubectl --context gke_cloudpak_europe-west2-xxxxx exec -it -n namespace_name pod_name -c rt -- sh -c command -v bash >/dev/null && exec bash || exec sh
Another update, I've found this info. At last of page there is a paragraph with says.
Why Exec/Attach calls doesn’t work
Starting from 4.0 release, we do not support directly calling exec or attach calls. you should use stream module to call them. so instead of resp = api.connect_get_namespaced_pod_exec(name, ... you should call resp = stream(api.connect_get_namespaced_pod_exec, name, ....
Using Stream will overwrite the requests protocol in core_v1_api.CoreV1Api() This will cause a failure in non-exec/attach calls. If you reuse your api client object, you will need to recreate it between api calls that use stream and other api calls.
I've tried to do it in this way, but same result :(
Any idea about what I'm doing wrong?
Thanks a lot for your help.
Regards
ANSWER
Answered 2022-Mar-21 at 21:45Yes, this official guide says that you should use resp = **stream**(api.connect_get_namespaced_pod_exec(name, ...
instead.
So you have to edit your code like this:
...
from kubernetes.stream import stream
...
v1 = client.CoreV1Api()
response = stream(v1.connect_get_namespaced_pod_exec, pod_name , namespace, command="df -h", stderr=True, stdin=True, stdout=True, tty=True)
print(response)
QUESTION
I want to create a google cloud function to create pods on my gke cluster. I use the python kubernetes client to create them (I don't know if there is a better way to achive this).
Normally I would use the command: gcloud container clusters get-credentials cluster_name --region=cluster_region
but cloud sdk is not installed in the cloud function environment.
I've read the python api documentation and I found that it is possible to pass the path to the kubeconfig file, but I didn't found how to create that file
ANSWER
Answered 2022-Feb-09 at 16:11The get credential does nothing special:
- Check if the cluster exists
- Check if you have the permission on the cluster
- Create the Kube config file with your access token.
That's all.
Now, when you use the kubectl command, the access token is used and put in the Authorization: Bearer
header and perform an API call to Kubernetes control plane.
Therefore, if you want to reach directly the control plane from your Cloud Functions with an API call, simply the Cloud Functions access token in the security header and that's all!
QUESTION
what is python kubernetes client equivalent for
kubectl get deploy -o yaml
i referred this example for getting python deployment but there is no read deployment option
ANSWER
Answered 2022-Feb-02 at 08:21read_namespaced_deployment()
does the thing:
from kubernetes import client, config
config.load_kube_config()
api = client.AppsV1Api()
deployment = api.read_namespaced_deployment(name='foo', namespace='bar')
QUESTION
With kubectl, I know i can run below command if I want to see specific resources YAML file
kubectl -n get -o yaml
How would I get this same data using python's kubernetes-client ? Everything I've found so far only talks about creating a resource from a given yaml file.
In looking at docs, I noticed that each resource type generally has a get_api_resources() function which returns a V1ApiResourceList, where each item is a V1ApiResource. I was hoping there would be a way to get the resource's yaml-output by using a V1ApiResource object, but doesnt appear like that's the way to go about it.
Do you all have any suggestions ? Is this possible with kubernetes-client API ?
ANSWER
Answered 2022-Feb-01 at 21:38If you take a look at the methods available on an object, e.g.:
>>> import kubernetes.config
>>> client = kubernetes.config.new_client_from_config()
>>> core = kubernetes.client.CoreV1Api(client)
>>> res = core.read_namespace('kube-system')
>>> dir(res)
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_api_version', '_kind', '_metadata', '_spec', '_status', 'api_version', 'attribute_map', 'discriminator', 'kind', 'local_vars_configuration', 'metadata', 'openapi_types', 'spec', 'status', 'to_dict', 'to_str']
...you'll see there is a to_dict
method. That returns the object as a dictionary, which you can then serialize to YAML or JSON or whatever:
>>> import yaml
>>> print(yaml.safe_dump(res.to_dict()))
api_version: v1
kind: Namespace
metadata:
[...]
QUESTION
THE PLOT:
I am working on a kubernetes environment where we have PROD and ITG setup. The ITG setup has multi-cluster environment whereas PROD setup is a single-cluster environment. I am trying to automate some process using Python where I have to deal with kubeconfig file and I am using the kubernetes library for it.
THE PROBLEM:
The kubeconfig file for PROD has "current-context" key available but the same is missing from the kubeconfig file for ITG.
prdconfig:
apiVersion: v1
clusters:
- cluster:
insecure-skip-tls-verify: true
server: https://cluster3.url.com:3600
name: cluster-ABC
contexts:
- context:
cluster: cluster-LMN
user: cluster-user
name: cluster-LMN-context
current-context: cluster-LMN-context
kind: Config
preferences: {}
users:
- name: cluster-user
user:
exec:
command: kubectl
apiVersion:
args:
- kubectl-custom-plugin
- authenticate
- https://cluster.url.com:8080
- --user=user
- --token=/api/v2/session/xxxx
- --token-expiry=1000000000
- --force-reauth=false
- --insecure-skip-tls-verify=true
itgconfig:
apiVersion: v1
clusters:
- cluster:
insecure-skip-tls-verify: true
server: https://cluster1.url.com:3600
name: cluster-ABC
- cluster:
insecure-skip-tls-verify: true
server: https://cluster2.url.com:3601
name: cluster-XYZ
contexts:
- context:
cluster: cluster-ABC
user: cluster-user
name: cluster-ABC-context
- context:
cluster: cluster-XYZ
user: cluster-user
name: cluster-XYZ-context
kind: Config
preferences: {}
users:
- name: cluster-user
user:
exec:
command: kubectl
apiVersion:
args:
- kubectl-custom-plugin
- authenticate
- https://cluster.url.com:8080
- --user=user
- --token=/api/v2/session/xxxx
- --token-expiry=1000000000
- --force-reauth=false
- --insecure-skip-tls-verify=true
When I try loading the kubeconfig file for PROD using config.load_kube_config(os.path.expanduser('~/.kube/prdconfig'))
it works.
And when I try loading the kubeconfig file for ITG using config.load_kube_config(os.path.expanduser('~/.kube/itgconfig'))
, I get the following error:
ConfigException: Invalid kube-config file. Expected key current-context in C:\Users/.kube/itgconfig
Although it is very clear from the error message that it is considering the kubeconfig file as invalid, as it does not have "current-context" key in it.
THE SUB-PLOT:
When working with kubectl, the missing "current-context" does not make any difference as we can always specify context along with the command. But the 'load_kube_config()' function makes it mandatory to have "current-context" available.
THE QUESTION:
So, is "current-context" a mandatory key in kubeconfig file?
THE DISCLAIMER:
I am very new to kubernetes and have very little experience working with it.
ANSWER
Answered 2021-Aug-25 at 09:01As described in the comments: If we want to use kubeconfig
file to work out of the box by default, with specific cluster using kubectl or python script we can mark one of the contexts in our kubeconfig
file as the default by specifying current-context
.
Note about Context:
A
context
element in a kubeconfig fileis used to group access parameters
under a convenient name. Each context has three parameters: cluster, namespace, and user.By default, the kubectl command-line tool uses parameters from the current context to communicate with the cluster
.
In order to mark one of our contexts (f.e. dev-fronted) in our kubeconfig file as the default one please run:
kubectl config use-context dev-fronted
Now whenever you run a kubectl command, the action will apply to the cluster, and namespace listed in the dev-frontend context. And the command will use the credentials of the user listed in the dev-frontend context
Please take a look at:
determine the context to use based on the first hit in this chain:
Use the --context command-line flag if it exists. Use the current-context from the merged kubeconfig files.
An empty context is allowed at this point.
determine the cluster and user. At this point, there might or might not be a context. Determine the cluster and user based on the first hit in this chain, which is run twice: once for user and once for cluster:
Use a command-line flag if it exists: --user or --cluster. If the context is non-empty, take the user or cluster from the context.
The user and cluster can be empty at this point.
Whenever we run kubectl
commands without specified current-context
we should provide additional configuration parameters to tell kubectl which configuration to use, in your example it could be f.e.:
kubectl --kubeconfig=/your_directory/itgconfig get pods --context cluster-ABC-context
As described earlier - to simplify this task we can use configure current-context
in kubeconfig
file configuration:
kubectl config --kubeconfig=c/your_directory/itgconfig use-context cluster-ABC-context
Going further into errors generated by your script we should notice errors from config/kube_config.py:
config/kube_config.py", line 257, in set_active_context context_name = self._config['current-context']
kubernetes.config.config_exception.ConfigException:: Invalid kube-config file. Expected key current-context in ...
Here is an example with additional context="cluster-ABC-context"
parameter:
from kubernetes import client, config
config.load_kube_config(config_file='/example/data/merged/itgconfig', context="cluster-ABC-context")
v1 = client.CoreV1Api()
print("Listing pods with their IPs:")
ret = v1.list_pod_for_all_namespaces(watch=False)
for i in ret.items:
print("%s\t%s\t%s" % (i.status.pod_ip, i.metadata.namespace, i.metadata.name))
...
Listing pods with their IPs:
10.200.xxx.xxx kube-system coredns-558bd4d5db-qpzb8
192.168.xxx.xxx kube-system etcd-debian-test
...
Additional information
QUESTION
I have a service running in Kubernetes and currently, there are two ways of making GET requests to the REST API.
The first is
kubectl port-forward --namespace test service/test-svc 9090
and then running
curl http://localhost:9090/sub/path \
-d param1=abcd \
-d param2=efgh \
-G
For the second one, we do a kubctl proxy
kubectl proxy --port=8080
followed by
curl -lk 'http://127.0.0.1:8080/api/v1/namespaces/test/services/test-svc:9090/proxy/sub/path?param1=abcd¶m2=efgh'
Both work nicely. However, my question is: How do we repeat one of these with the Python Kubernetes client (https://github.com/kubernetes-client/python)?
Many thanks for your support in advance!
Progress
I found a solution that brings us closer to the desired result:
from kubernetes import client, config
config.load_kube_config("~/.kube/config", context="my-context")
api_instance = client.CoreV1Api()
name = 'test-svc' # str | name of the ServiceProxyOptions
namespace = 'test' # str | object name and auth scope, such as for teams and projects
api_response = api_instance.api_client.call_api(
'/api/v1/namespaces/{namespace}/services/{name}/proxy/ping'.format(namespace=namespace, name=name), 'GET',
auth_settings = ['BearerToken'], response_type='json', _preload_content=False
)
print(api_response)
yet the result is
(, 200, HTTPHeaderDict({'Audit-Id': '1ad9861c-f796-4e87-a16d-8328790c50c3', 'Cache-Control': 'no-cache, private', 'Content-Length': '16', 'Content-Type': 'application/json', 'Date': 'Thu, 27 Jan 2022 15:05:10 GMT', 'Server': 'uvicorn'}))
Whereas the desired output was
{
"ping": "pong!"
}
Do you know how to extract it form here?
ANSWER
Answered 2022-Jan-28 at 11:21This should be something which uses:
from kubernetes.stream import portforward
To find which command maps to an API call in Python, you can used
kubectl -v 10 ...
For example:
k -v 10 port-forward --namespace znc service/znc 1666
It spits a lot of output, the most important out put is the curl commands:
POST https://myk8s:16443/api/v1/namespaces/znc/pods/znc-57647bb8d8-dcq6b/portforward 101 Switching Protocols in 123 milliseconds
This allows you to search the code of the python client. For example there is:
core_v1.connect_get_namespaced_pod_portforward
However, using it is not so straight forward. Luckily, the maintainers include a great example on how to use portforward method:
# Copyright 2020 The Kubernetes Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Shows the functionality of portforward streaming using an nginx container.
"""
import select
import socket
import time
import six.moves.urllib.request as urllib_request
from kubernetes import config
from kubernetes.client import Configuration
from kubernetes.client.api import core_v1_api
from kubernetes.client.rest import ApiException
from kubernetes.stream import portforward
##############################################################################
# Kubernetes pod port forwarding works by directly providing a socket which
# the python application uses to send and receive data on. This is in contrast
# to the go client, which opens a local port that the go application then has
# to open to get a socket to transmit data.
#
# This simplifies the python application, there is not a local port to worry
# about if that port number is available. Nor does the python application have
# to then deal with opening this local port. The socket used to transmit data
# is immediately provided to the python application.
#
# Below also is an example of monkey patching the socket.create_connection
# function so that DNS names of the following formats will access kubernetes
# ports:
#
# ..kubernetes
# .pod..kubernetes
# .svc..kubernetes
# .service..kubernetes
#
# These DNS name can be used to interact with pod ports using python libraries,
# such as urllib.request and http.client. For example:
#
# response = urllib.request.urlopen(
# 'https://metrics-server.service.kube-system.kubernetes/'
# )
#
##############################################################################
def portforward_commands(api_instance):
name = 'portforward-example'
resp = None
try:
resp = api_instance.read_namespaced_pod(name=name,
namespace='default')
except ApiException as e:
if e.status != 404:
print("Unknown error: %s" % e)
exit(1)
if not resp:
print("Pod %s does not exist. Creating it..." % name)
pod_manifest = {
'apiVersion': 'v1',
'kind': 'Pod',
'metadata': {
'name': name
},
'spec': {
'containers': [{
'image': 'nginx',
'name': 'nginx',
}]
}
}
api_instance.create_namespaced_pod(body=pod_manifest,
namespace='default')
while True:
resp = api_instance.read_namespaced_pod(name=name,
namespace='default')
if resp.status.phase != 'Pending':
break
time.sleep(1)
print("Done.")
pf = portforward(
api_instance.connect_get_namespaced_pod_portforward,
name, 'default',
ports='80',
)
http = pf.socket(80)
http.setblocking(True)
http.sendall(b'GET / HTTP/1.1\r\n')
http.sendall(b'Host: 127.0.0.1\r\n')
http.sendall(b'Connection: close\r\n')
http.sendall(b'Accept: */*\r\n')
http.sendall(b'\r\n')
response = b''
while True:
select.select([http], [], [])
data = http.recv(1024)
if not data:
break
response += data
http.close()
print(response.decode('utf-8'))
error = pf.error(80)
if error is None:
print("No port forward errors on port 80.")
else:
print("Port 80 has the following error: %s" % error)
# Monkey patch socket.create_connection which is used by http.client and
# urllib.request. The same can be done with urllib3.util.connection.create_connection
# if the "requests" package is used.
socket_create_connection = socket.create_connection
def kubernetes_create_connection(address, *args, **kwargs):
dns_name = address[0]
if isinstance(dns_name, bytes):
dns_name = dns_name.decode()
dns_name = dns_name.split(".")
if dns_name[-1] != 'kubernetes':
return socket_create_connection(address, *args, **kwargs)
if len(dns_name) not in (3, 4):
raise RuntimeError("Unexpected kubernetes DNS name.")
namespace = dns_name[-2]
name = dns_name[0]
port = address[1]
if len(dns_name) == 4:
if dns_name[1] in ('svc', 'service'):
service = api_instance.read_namespaced_service(name, namespace)
for service_port in service.spec.ports:
if service_port.port == port:
port = service_port.target_port
break
else:
raise RuntimeError(
"Unable to find service port: %s" % port)
label_selector = []
for key, value in service.spec.selector.items():
label_selector.append("%s=%s" % (key, value))
pods = api_instance.list_namespaced_pod(
namespace, label_selector=",".join(label_selector)
)
if not pods.items:
raise RuntimeError("Unable to find service pods.")
name = pods.items[0].metadata.name
if isinstance(port, str):
for container in pods.items[0].spec.containers:
for container_port in container.ports:
if container_port.name == port:
port = container_port.container_port
break
else:
continue
break
else:
raise RuntimeError(
"Unable to find service port name: %s" % port)
elif dns_name[1] != 'pod':
raise RuntimeError(
"Unsupported resource type: %s" %
dns_name[1])
pf = portforward(api_instance.connect_get_namespaced_pod_portforward,
name, namespace, ports=str(port))
return pf.socket(port)
socket.create_connection = kubernetes_create_connection
# Access the nginx http server using the
# ".pod..kubernetes" dns name.
response = urllib_request.urlopen(
'http://%s.pod.default.kubernetes' % name)
html = response.read().decode('utf-8')
response.close()
print('Status Code: %s' % response.code)
print(html)
def main():
config.load_kube_config()
c = Configuration.get_default_copy()
c.assert_hostname = False
Configuration.set_default(c)
core_v1 = core_v1_api.CoreV1Api()
portforward_commands(core_v1)
if __name__ == '__main__':
main()
QUESTION
I've developed a python script, using python kubernetes-client to harvest Pods' internal IPs.
But when I try to make an http request to these IPs, from another pod, I get Connection refused
error.
I spin up a temporary curl
container:
kubectl run curl --image=radial/busyboxplus:curl -it --rm
And having the internal IP of one of the pods, I try to make a GET
request:
curl http://10.133.0.2/stats
and the response is:
curl: (7) Failed to connect to 10.133.0.2 port 80: Connection refused
Both pods are in the same default
namespace and use the same default ServiceAccount
.
I know that I can call the Pods thru the ClusterIP
service by which they're load-balanced, but this way I will only access a single Pod at random (depending which one the service forwards the call to), when I have multiple replicas of the same Deployment.
I need to be able to call each Pod of a multi-replica Deployment separately. That's why I'm going for the internal IPs.
ANSWER
Answered 2022-Jan-26 at 04:54I guess you missed the port number here
It should be like this
curl POD_IP:PORT/stats
QUESTION
I'm using Micronaut 3.2.3 with the Kubernetes integration to inject configuration values from config maps and secrets.
Dependencies:
implementation("io.micronaut.kubernetes:micronaut-kubernetes-client")
implementation("io.micronaut.kubernetes:micronaut-kubernetes-discovery-client")
bootstrap.yml
micronaut:
application:
name: ingestor
config-client:
enabled: true
kubernetes:
client:
config-maps:
enabled: true
includes:
- application
- ingestor
secrets:
enabled: true
includes:
- application
- ingestor
As you can see the application config map includes the kafka.brokers value:
kubectl get configmaps application -o yaml
apiVersion: v1
data:
application.yml: |
kafka.brokers: xxx
kubectl get configmaps ingestor -o yaml
apiVersion: v1
data:
application.yml: |
prop1: value1
I've added a simple singleton class the check whether the projerty can be injected:
@Singleton
class SomeConfiguration(@Value("\${kafka.brokers}") private val kafkaBrokers: String) {
init {
println(kafkaBrokers)
}
}
Log traces seem to indicate that those config maps are correctly accessed:
15:59:24.206 [OkHttp https://10.222.0.1/...] - - DEBUG i.m.k.c.KubernetesConfigurationClient - Adding config map with name application
15:59:24.218 [OkHttp https://10.222.0.1/...] - - DEBUG i.m.k.c.KubernetesConfigurationClient - Adding config map with name ingestor
However the application crashes because it couln'd find it:
fun main(args: Array) {
build()
.args(*args)
.eagerInitSingletons(true)
15:59:25.484 [main] - - ERROR io.micronaut.runtime.Micronaut - Error starting Micronaut server: Bean definition [xxx.SomeConfiguration] could not be loaded: Failed to inject value for parameter [kafkaBrokers] of class: xxx.configuration.SomeConfiguration
Message: Failed to inject value for parameter [kafkaBrokers] of class: xxx.configuration.SomeConfiguration
Message: Error resolving property value [${kafka.brokers}]. Property doesn't exist
ANSWER
Answered 2022-Jan-04 at 11:21This problem was kind of tricky. The key was the name of the yml file used in the config maps, both application and ingestor config maps defined it as application.yml:
apiVersion: v1
data:
application.yml: |
They can't overlap, so I changed the ingestor configmap to be:
kubectl get configmaps ingestor -o yaml
apiVersion: v1
data:
ingestor.yml: |
prop1: value1
And now the kafka.brokers
value is found and injected.
QUESTION
I recently got started with building a Kubernetes operator. I'm using the Fabric8 Java Kubernetes Client but I think my question is more general and also applies to other programming languages and libraries.
When reading through blog posts, documentation or textbooks explaining the operator pattern, I found there seem to be two options to design an operator:
- Using an infinite reconcile loop, in which all corresponding Kubernetes objects are retrieved from the API and then some action is performed.
- Using informers, which are called whenever an observed Kubernetes resource changes.
However, I don't find any source discussion which option should be used in which case. Are there any best practices?
ANSWER
Answered 2022-Jan-03 at 15:36You should use both.
When using informers, it's possible that the handler gets the events out of order or even not at all. The former means the handler needs to define and reconcile state - this approach is referred to as level-based, as opposed to edge-based. The latter means reconciliation needs to be triggered on a regular interval to account for that possibility.
The way controller-runtime does things, reconciliation is triggered by cluster events (using informers behind the scenes) related to the resources watched by the controller and on a timer. Also, by design, the event is not passed to the reconciler so that it is forced to define and act on a state.
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Vulnerabilities
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Install kubernetes-client
You can use kubernetes-client like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the kubernetes-client component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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