I am building a service which creates on demand node red instance on Kubernetes. This service needs to have custom authentication, and some other service specific data in a JSON file.
Every instance of node red will have a Persistent Volume associated with it, so one way I though of doing this was to attach the PVC with a pod and copy the files into the PV, and then start the node red deployment over the modified PVC.
I use following script to accomplish this
def paste_file_into_pod(self, src_path, dest_path):
dir_name= path.dirname(src_path)
bname = path.basename(src_path)
exec_command = ['/bin/sh', '-c', 'cd {src}; tar cf - {base}'.format(src=dir_name, base=bname)]
with tempfile.TemporaryFile() as tar_buffer:
resp = stream(self.k8_client.connect_get_namespaced_pod_exec, self.kube_methods.component_name, self.kube_methods.namespace,
command=exec_command,
stderr=True, stdin=True,
stdout=True, tty=False,
_preload_content=False)
print(resp)
while resp.is_open():
resp.update(timeout=1)
if resp.peek_stdout():
out = resp.read_stdout()
tar_buffer.write(out.encode('utf-8'))
if resp.peek_stderr():
print('STDERR: {0}'.format(resp.read_stderr()))
resp.close()
tar_buffer.flush()
tar_buffer.seek(0)
with tarfile.open(fileobj=tar_buffer, mode='r:') as tar:
subdir_and_files = [tarinfo for tarinfo in tar.getmembers()]
tar.extractall(path=dest_path, members=subdir_and_files)
This seems like a very messy way to do this. Can someone suggest a quick and easy way to start node red in Kubernetes with custom settings.js and some additional files for config?
The better approach is not to use a PV for flow storage, but to use a Storage Plugin to save flows in a central database. There are several already in existence using DBs like MongoDB
You can extend the existing Node-RED container to include a modified settings.js in /data that includes the details for the storage and authentication plugins and uses environment variables to set the instance specific at start up.
Examples here: https://www.hardill.me.uk/wordpress/tag/multi-tenant/
Related
I would like to use an Azure Machine Learning Compute Cluster as a compute target but do not want it to containerize my project. Is there a way to deactivate this "feature" ?
The main reasons behind this request is that :
I already set up a docker-compose file that is used to specify 3 containers for Apache Airflow and want to avoid a Docker-in-Docker situation. Especially that I already tried to do so but failed so far (here's the link my other related SO question).
I prefer not to use a Compute Instance as it is tied to an Azure account which is not ideal for automation purposes.
Thanks in advance !
Use the provisioning_configuration method of the AmlCompute class to specify configuration parameters.
In the following example, a persistent compute target provisioned by AmlCompute is created. The provisioning_configuration parameter in this example is of type AmlComputeProvisioningConfiguration, which is a child class of ComputeTargetProvisioningConfiguration.
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your CPU cluster
cpu_cluster_name = "cpu-cluster"
# Verify that cluster does not exist already
try:
cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
print('Found existing cluster, use it.')
except ComputeTargetException:
compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
max_nodes=4)
cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
cpu_cluster.wait_for_completion(show_output=True)
Refer - https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)?view=azure-ml-py
I have a python script which takes video and converts it to a series of small panoramas. Now, theres an S3 bucket where a video will be uploaded (mp4). I need this file to be sent to the ec2 instance whenever it is uploaded.
This is the flow:
Upload video file to S3.
This should trigger EC2 instance to start.
Once it is running, I want the file to be copied to a particular directory inside the instance.
After this, I want the py file (panorama.py) to start running and read the video file from the directory and process it and then generate output images.
These output images need to be uploaded to a new bucket or the same bucket which was initially used.
Instance should terminate after this.
What I have done so far is, I have created a lambda function that is triggered whenever an object is added to that bucket. It stores the name of the file and the path. I had read that I now need to use an SQS queue and pass this name and path metadata to the queue and use the SQS to trigger the instance. And then, I need to run a script in the instance which pulls the metadata from the SQS queue and then use that to copy the file(mp4) from bucket to the instance.
How do i do this?
I am new to AWS and hence do not know much about SQS or how to transfer metadata and automatically trigger instance, etc.
Your wording is a bit confusing. It says that you want to "start" an instance (which suggests that the instance already exists), but then it says that it wants to "terminate" an instance (which would permanently remove it). I am going to assume that you actually intend to "stop" the instance so that it can be used again.
You can put a shell script in the /var/lib/cloud/scripts/per-boot/ directory. This script will then be executed every time the instance starts.
When the instance has finished processing, it can call sudo shutdown now -h to turn off the instance. (Alternatively, it can tell EC2 to stop the instance, but using shutdown is easier.)
For details, see: Auto-Stop EC2 instances when they finish a task - DEV Community
I tried to answer in the most minimalist way, there are many points below that can be further improved. I think below is still quite some as you mentioned you are new to AWS.
Using AWS Lambda with Amazon S3
Amazon S3 can send an event to a Lambda function when an object is created or deleted. You configure notification settings on a bucket, and grant Amazon S3 permission to invoke a function on the function's resource-based permissions policy.
When the object uploaded it will trigger the lambda function. Which creates the instance with ec2 user data Run commands on your Linux instance at launch.
For the ec2 instance make you provide the necessary permissions via Using instance profiles for download and uploading the objects.
user data has a script that does the rest of the work which you need for your workflow
Download the s3 object, you can pass the name and s3 bucket name in the same script
Once #1 finished, start the panorama.py which processes the videos.
In the next step you can start uploading the objects to the S3 bucket.
Eventually terminating the instance will be a bit tricky which you can achieve Change the instance initiated shutdown behavior
OR
you can use below method for terminating the instnace, but in that case your ec2 instance profile must have access to terminate the instance.
ec2-terminate-instances $(curl -s http://169.254.169.254/latest/meta-data/instance-id)
You can wrap the above steps into a shell script inside the userdata.
Lambda ec2 start instance:
def launch_instance(EC2, config, user_data):
ec2_response = EC2.run_instances(
ImageId=config['ami'], # ami-0123b531fc646552f
InstanceType=config['instance_type'],
KeyName=config['ssh_key_name'],
MinCount=1,
MaxCount=1,
SecurityGroupIds=config['security_group_ids'],
TagSpecifications=tag_specs,
# UserData=base64.b64encode(user_data).decode("ascii")
UserData=user_data
)
new_instance_resp = ec2_response['Instances'][0]
instance_id = new_instance_resp['InstanceId']
print(f"[DEBUG] Full ec2 instance response data for '{instance_id}': {new_instance_resp}")
return (instance_id, new_instance_resp)
Upload file to S3 -> Launch EC2 instance
I deployed gcp-spark operator on k8s. Its working perfectly fine. Able to run scala and python jobs with no issues.
But, I am unable to create volume mounts on my pods. Unable to use local fs. Looks like spark-operator should be enabled with webhooks for it to work. Going by here.
There was an spark-operator with webhooks yaml here, but the name is different to the deployment coming through the operator hub. I updated the names to the best of my knowledge and tried to apply the deployment. But ran into the below issue.
kubectl apply -f spark-operator-with-webhook.yaml
Warning: kubectl apply should be used on resource created by either kubectl create --save-config or kubectl apply
deployment.apps/spark-operator configured
service/spark-webhook unchanged
The Job "spark-operator-init" is invalid: spec.template: Invalid value: core.PodTemplateSpec{ObjectMeta:v1.ObjectMeta{Name:"", GenerateName:"", Namespace:"", SelfLink:"", UID:"", ResourceVers......int(nil)}}: field is immutable
Is there an easy way of enabling webhooks on spark-operator? I want to be able to mount local fs on the sparkapplication. Please assist.
I purged the init object and redeployed. The manifest was successfully applied.
I decided to post this question because, I have ran out of debugging ideas, just ideas are golden since I know it can be difficult to help debugging a virtual instance through here (debugging code is hard enough jaja). Anyway, I have created a virtual machine in Compute engine , I created a logs file that I populate, for example, with this command in a python script, let's call it logging.py:
import logging
logging.basicConfig(filename= 'app.log' , level = logging.INFO , format = ' %(asctime)s - %(name) - %(levelname)s - %(message)s')
logging.info('Some message ' + str(type(variable)))
everytime I use python3 logging.py , the app.log is effectively populated. ( Logging.py and app.log are in the same directory the /home/username/ folder )
I want stackdriver to show this log in the logging viewer everytime it's written, so , I installed the stackdriver agent as follows, in the virtual machine command line:
$ curl -sSO https://dl.google.com/cloudagents/install-logging-agent.sh
$ sudo bash install-logging-agent.sh
No errors that I see are delivered here, in fact, you can see here the messages obtained
Messags on the stackdriver viewer:
After this, I proceed to create a .conf file that I create in /etc/google-fluentd/config.d/app.conf
with this parameters
<source>
type tail
format none
path /home/username/app.log
pos_file /var/lib/google-fluentd/pos/app.pos
read_from_head true
tag whatever-tag
</source>
After that is created, I launch sudo service google-fluentd restart.
Aftert I execute, python3 logging.py , no logs are added to stack drivers logging viewer.
So, where might Have I gone wrong?
Things I have tried/checked:
-Have more than 13 gygabytes of RAM available
-If I run logger "some message" on the command line, I effectively add a log with "some message" to the log viewer
-If I run
ps ax | grep fluentd
I obtain :
3033 ? Sl 0:09 /opt/google-fluentd/embedded/bin/ruby /usr/sbin/google-fluentd --log /var/log/google-fluentd/google-fluentd.log --no-supervisor
3309 pts/0 S+ 0:00 grep --color=auto fluentd
-Both my user, and the service account I use, have logger admin permission in IAM roles.
-This is the documentation I have based myself on:
https://cloud.google.com/logging/docs/agent/troubleshooting?hl=es-419
https://cloud.google.com/logging/docs/reference/v2/rest/v2/entries/list?hl=es-419
https://cloud.google.com/logging/docs/agent/configuration?hl=es-419
https://medium.com/google-cloud/how-to-log-your-application-on-google-compute-engine-6600d81e70e3
https://cloud.google.com/logging/docs/agent/installation
-If I run sudo service google-fluentd status , the agent appears active.
-My instance hass access, to all the apis. It's an n1-standard-4 (4 vCPUs, 15 GB of memory) using ubuntu linux 18:04
So, what else can I check to debug this? I'm out of ideas here , hope I'm not being an idiot here :(
Based on my understanding, I think that you looking for the following fluentd resource types:
generic_node
“A generic node identifies a machine or other computational resource for which no more specific resource type is applicable. The label values must uniquely identify the node.”
generic_task
“A generic task identifies an application process for which no more specific resource is applicable, such as a process scheduled by a custom orchestration system. The label values must uniquely identify the task.”
The source of my information has been found here
This document explain how to send logs from your application in different ways:
Cloud Logging API
Cloud Logging Agent
Generic fluentd
As you mentioned having installed fluentd, let me provide more focused documentation about Cloud Logging Agent. I also found some python Client Library documentation that you may be interested.
Finally, I found a nginx/apache use-case guide that you may use as reference.
For some reason, if I change the directory to which both the .conf file points, and the directory where the logg is to /var/logs/ , being the final path as /var/logs/app.logs, it does work correctly. Possibly there is a configuration issue, causing the logging agent to only capture logs in specific predetermined folders, or a permissions issue that stops it from working if the log is in the username directory.
I found this solution, however, by chance(random testing basically.
). Did not find anything in the main articles that are supposed to teach me how to configure the logging agent, that could point me in the right direction, being those articles this ones,
https://cloud.google.com/logging/docs/agent/troubleshooting?hl=es-419 https://cloud.google.com/logging/docs/reference/v2/rest/v2/entries/list?hl=es-419 https://cloud.google.com/logging/docs/agent/configuration?hl=es-419 https://medium.com/google-cloud/how-to-log-your-application-on-google-compute-engine-6600d81e70e3 https://cloud.google.com/logging/docs/agent/installation
If I needed it to work in my username directory, it's not clear just by checking this articles how to do it,what configuration file I would need to change or where to start, so I recommend to google to improve that aspect of the docs.
This documentation you have sent https://docs.fluentd.org/quickstart is pretty interesting, maybe I can find the explanation there, thank you for your help.
I'm trying to test restoring Neptune instances from a snapshot using python (boto3). Long story short, we want to spin up and delete the Dev instance daily using automation.
When restoring, my restore seems to only create the cluster without creating the attached instance. I have also tried creating an instance once the cluster is up and add to the cluster, but that doesn't work either. (ref: client.create_db_instance)
My code does as follows, get the most current snapshot. Use that variable to create the cluster so the most recent data is there.
import boto3
client = boto3.client('neptune')
response = client.describe_db_cluster_snapshots(
DBClusterIdentifier='neptune',
MaxRecords=100,
IncludeShared=False,
IncludePublic=False
)
snaps = response['DBClusterSnapshots']
snaps.sort(key=lambda c: c['SnapshotCreateTime'], reverse=True)
latest_snapshot = snaps[0]
snapshot_ID = latest_snapshot['DBClusterSnapshotIdentifier']
print("Latest snapshot: " + snapshot_ID)
db_response = client.restore_db_cluster_from_snapshot(
AvailabilityZones=['us-east-1c'],
DBClusterIdentifier='neptune-test',
SnapshotIdentifier=snapshot_ID,
Engine='neptune',
Port=8182,
VpcSecurityGroupIds=['sg-randomString'],
DBSubnetGroupName='default-vpc-groupID'
)
time.sleep(60)
db_instance_response = client.create_db_instance(
DBName='neptune',
DBInstanceIdentifier='brillium-neptune',
DBInstanceClass='db.r4.large',
Engine='neptune',
DBSecurityGroups=[
'sg-string',
],
AvailabilityZone='us-east-1c',
DBSubnetGroupName='default-vpc-string',
BackupRetentionPeriod=7,
Port=8182,
MultiAZ=False,
AutoMinorVersionUpgrade=True,
PubliclyAccessible=False,
DBClusterIdentifier='neptune-test',
StorageEncrypted=True
)
The documentation doesn't help much at all. It's very good at providing the variables needed for basic creation, but not the actual instance. If I attempt to create an instance using the same Cluster Name, it either errors out or creates a new cluster with the same name appended with '-1'.
If you want to programmatically do a restore from snapshot, then you need to:
Create the cluster snapshot using create-db-cluster-snapshot
Restore cluster from snapshot using restore-db-cluster-from-snapshot
Create an instance in the new cluster using create-db-instance
You mentioned that you did do a create-db-instance call in the end, but your example snippet does not have it. If that call did succeed, then you should see an instance provisioned inside that cluster.
When you do a restore from Snapshot using the Neptune Console, it does steps #2 and #3 for you.
It seems like you did the following:
Create the snapshot via CLI
Create the cluster via CLI
Create an instance in the cluster, via Console
Today, we recommend restoring the snapshot entirely via the Console or entirely using the CLI.