Sign in
agent:
Auto Exec

Fix expertprompt task

Add a key-value pair

Add credentials for various integrations

What is an "Expert"? How do we create our own expert?

Process Grafana Alerts

Managing workspaces and access control

DagKnows Architecture Overview

Managing Proxies

Setting up SSO via Azure AD for Dagknows

All the experts

Enable "Auto Exec" and "Send Execution Result to LLM" in "Adjust Settings" if desired

(Optionally) Add ubuntu user to docker group and refresh group membership

Deployment of an EKS Cluster with Worker Nodes in AWS

Adding, Deleting, Listing DagKnows Proxy credentials or key-value pairs

Comprehensive AWS Security and Compliance Evaluation Workflow (SOC2 Super Runbook)

AWS EKS Version Update 1.29 to 1.30 via terraform

Instruction to allow WinRM connection

MSP Usecase: User Onboarding Azure + M365

Post a message to a Slack channel

How to debug a kafka cluster and kafka topics?

Docusign Integration Tasks

Open VPN Troubleshooting (Powershell)

Execute a simple task on the proxy

Assign the proxy role to a user

Create roles to access credentials in proxy

Install OpenVPN client on Windows laptop

Setup Kubernetes kubectl and Minikube on Ubuntu 22.04 LTS

Install Prometheus and Grafana on the minikube cluster on EC2 instance in the monitoring namespace

Sample selenium script

update the EKS versions in different clusters

AI agent session 2024-09-12T09:36:14-07:00 by Sarang Dharmapurikar

Install kubernetes on an ec2 instance ubuntu 20.04 using kubeadm and turn this instance into a master node.

Turn an ec2 instance, ubuntu 20.04 into a kubeadm worker node. Install necessary packages and have it join the cluster.

Install Docker

Parse EDN content and give a JSON out

GitHub related tasks

Check whether a user is there on Azure AD and if the user account status is enabled

Get the input parameters of a Jenkins pipeline

Get the console output of last Jenkins job build

List my Jenkins pipelines

Get last build status for a Jenkins job

Trigger a Jenkins job with param values

Give me steps to do health checks on a Linux Server

Trigger for tickets which have status new/open, group DevOps, assignee None, and public comment includes a keyword

Process Zendesk Ticket for updating comments (auto reply)

Add a public comment to a Zendesk Ticket

Identify list out IAM users list in AWS using dagknows

Restoring an AWS Redshift Cluster from a Snapshot

Notify about disk space before cleaning up

Set an AWS IAM Password Policy

Restoring an AWS Redshift Cluster from a Snapshot

There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.

In AWS Redshift, snapshots provide point-in-time backups of clusters. This runbook aims to restore Redshift clusters from a Snapshot for recovery purposes or any other purpose, users can restore these snapshots, resulting in the creation of a new cluster with data from the chosen snapshot. As the data is restored, the new cluster's status indicates the progress until it becomes 'available' for use. Importantly, this action neither alters the original cluster nor the snapshot; it only creates a new instance. In the broader AWS ecosystem, this means potential changes to resource utilization, costs, and data management dynamics.

  1. 1

    Fetch Available AWS Redshift Snapshots

    There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.

    In AWS Redshift, snapshots are backups that capture the entire system state of a cluster at a specific point in time. Users may need to fetch or list these available snapshots for various reasons, such as monitoring, auditing, or planning a recovery operation. By fetching the list of snapshots, users can view details like snapshot creation time, source cluster, and snapshot size. Retrieving this list aids in effective snapshot management and ensures informed decision-making within the AWS environment.

    import boto3 creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def list_redshift_snapshots(region=None): snapshot_identifiers = {} try: # Get list of all AWS regions ec2_client = boto3.client('ec2',aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name='us-east-1') regions_to_check = [region] if region else [region['RegionName'] for region in ec2_client.describe_regions()['Regions']] except Exception as e: print(f"Error listing AWS regions: {e}") regions_to_check = [region] if region else [] for region in regions_to_check: try: # Initialize the boto3 Redshift client for specified region redshift = boto3.client('redshift', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) # Fetch snapshots from Redshift in the current region response = redshift.describe_cluster_snapshots() # Add snapshot identifiers to the dictionary with region as the key for snapshot in response['Snapshots']: snapshot_identifiers.setdefault(region, []).append(snapshot['SnapshotIdentifier']) # Handle exceptions specific to Redshift operations except redshift.exceptions.ClusterSnapshotNotFoundFault: print(f"No Redshift snapshots found in region {region} for the specified criteria.") except Exception as e: print(f"An error occurred in region {region}: {e}") return snapshot_identifiers # Set region to None for all regions, or specify a valid AWS region string for a specific region # target_region = None # Fetch and display available snapshots snapshots = list_redshift_snapshots(target_region) if snapshots: print("Available Redshift Snapshots:") for region, snap_list in snapshots.items(): print(f"In region {region}:") for snap in snap_list: print(f" - {snap}") else: print("No Redshift snapshots found.") context.proceed = False
    copied
    1
  2. 2

    Restore an AWS Redshift Cluster from a Snapshot

    There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.

    Amazon Redshift allows users to create snapshots, which are point-in-time backups of their data warehouse clusters. These snapshots can be vital for disaster recovery scenarios, testing, or data replication. When a user needs to restore a cluster from a snapshot, AWS Redshift creates a new cluster and populates it with the data from the snapshot. The new cluster will inherit the configuration of the original, but users have the option to adjust certain parameters, such as the number of nodes or the node type, during the restoration process. Importantly, restoring from a snapshot does not affect or delete the original snapshot; it remains intact and can be used for future restorations or other purposes. Note: In the AWS ecosystem, this restoration process can generate costs, depending on factors like data transfer, storage, and the computational resources used.

    import boto3 import botocore.exceptions creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def restore_redshift_from_snapshot(snapshot_identifier, cluster_identifier, node_type, number_of_nodes, region, availability_zone=None, maintenance_track_name=None): """ Restore a Redshift cluster from a given snapshot. Parameters: - snapshot_identifier (str): Identifier for the snapshot to restore from. - cluster_identifier (str): Identifier for the new cluster. - node_type (str): Node type for the new cluster. - number_of_nodes (int): Number of nodes for the new cluster. - region (str): AWS region to restore the cluster. - availability_zone (str, optional): The availability zone to restore to. If not specified, a random zone is chosen. - maintenance_track_name (str, optional): Maintenance track for the new cluster. Returns: - dict: Response from the Redshift restore operation or None if the restore operation fails. """ # Initialize the Redshift client with the specified region redshift = boto3.client('redshift', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) # Define the restore parameters restore_params = { 'SnapshotIdentifier': snapshot_identifier, 'ClusterIdentifier': cluster_identifier, 'NodeType': node_type, 'NumberOfNodes': number_of_nodes } # Optionally set the availability zone if provided if availability_zone: restore_params['AvailabilityZone'] = availability_zone # Optionally set the maintenance track name if provided if maintenance_track_name: restore_params['MaintenanceTrackName'] = maintenance_track_name try: # Initiate the restore operation response = redshift.restore_from_cluster_snapshot(**restore_params) return response # Handle specific Redshift exceptions except redshift.exceptions.ClusterAlreadyExistsFault: print(f"Cluster with identifier {cluster_identifier} already exists.") except redshift.exceptions.ClusterSnapshotNotFoundFault: print(f"Snapshot {snapshot_identifier} not found.") except redshift.exceptions.InvalidClusterSnapshotStateFault: print(f"Snapshot {snapshot_identifier} is not in the correct state for restoration.") except redshift.exceptions.InvalidRestoreFault: print(f"Invalid restore parameters for snapshot {snapshot_identifier}.") except redshift.exceptions.UnauthorizedOperation: print(f"Unauthorized to restore cluster from snapshot {snapshot_identifier}. Check your AWS IAM permissions.") # Catch parameter validation errors except botocore.exceptions.ParamValidationError as e: print(f"Parameter validation error: {e}") # Handle other general exceptions except Exception as e: print(f"Error restoring Redshift cluster from snapshot: {e}") # Return None if any exception occurs return None # Example usage: #snapshot_id = "redshift-cluster-1-snapshot123" #new_cluster_id = "redshift-cluster-restored" #node_type = "dc2.large" #num_nodes = 1 #aws_region = "us-west-2" response = restore_redshift_from_snapshot(snapshot_id, new_cluster_id, node_type, num_nodes, aws_region) if response: print("Restore operation initiated successfully.") else: print("Failed to initiate restore operation.")
    copied
    2
  3. 3

    Monitoring Restoration Progress of a Redshift Cluster

    There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.

    In AWS Redshift, when restoring a cluster from a snapshot, it's essential to track the restoration progress to ensure timely data availability and system readiness. Monitoring the progress allows users to estimate when the cluster will be operational and identify any potential issues during the restoration process. Checking the restoration progress helps in maintaining transparency and ensuring efficient cluster management in the AWS ecosystem.

    import boto3 import time import botocore.exceptions creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def monitor_restore_progress(cluster_id, region): # Initialize the boto3 Redshift client with the specified region redshift = boto3.client('redshift', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) # Mark the start time start_time = time.time() # Start an infinite loop to continuously monitor the cluster's status while True: try: # Fetch the current status of the specified Redshift cluster response = redshift.describe_clusters(ClusterIdentifier=cluster_id) # Check if the 'Clusters' list is not empty if not response['Clusters']: print(f"No cluster found with identifier: {cluster_id}") break cluster_status = response['Clusters'][0]['ClusterStatus'] # Check the cluster's status and provide appropriate feedback if cluster_status in ['creating', 'restoring']: print(f"Cluster {cluster_id} status: {cluster_status}. Restoration is in progress...") elif cluster_status == 'available': elapsed_time = time.time() - start_time mins, secs = divmod(elapsed_time, 60) hours, mins = divmod(mins, 60) print(f"Cluster {cluster_id} is now available. Restoration completed successfully in {int(hours)}h {int(mins)}m {int(secs)}s.") break else: print(f"Cluster {cluster_id} status: {cluster_status}.") break # Wait for 30 seconds before checking the status again time.sleep(30) except botocore.exceptions.ClientError as e: print(f"ClientError: {e.response['Error']['Message']}") break except Exception as e: print(f"An unexpected error occurred: {e}") break # Example usage with hardcoded values. You should replace these with actual values received from the previous task. # cluster_identifier = "redshift-cluster-restored" # Replace this with actual cluster ID received from previous task # aws_region = "us-west-2" # Replace this with actual AWS region received from previous task # Begin monitoring the restoration progress of the specified cluster in the specified region if new_cluster_id: cluster_identifier = new_cluster_id monitor_restore_progress(cluster_identifier, aws_region) else: print("No Cluster Id provided for monitoring")
    copied
    3