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crazylion lee

GitHub - etsy/statsd: Daemon for easy but powerful stats aggregation - 0 views

github.com/statsd

node.js aggregation stats daemon sysadmin

shared by crazylion lee on 21 Apr 17 - No Cached
  • crazylion lee on 21 Apr 17
     
    "Daemon for easy but powerful stats aggregation"
張 旭

Custom Resources | Kubernetes - 0 views

kubernetes.io/...custom-resources

kubernetes

shared by 張 旭 on 11 May 21 - No Cached
  • Custom resources are extensions of the Kubernetes API
  • A resource is an endpoint in the Kubernetes API that stores a collection of API objects of a certain kind
  • Custom resources can appear and disappear in a running cluster through dynamic registration
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  • Once a custom resource is installed, users can create and access its objects using kubectl
  • When you combine a custom resource with a custom controller, custom resources provide a true declarative API.
  • A declarative API allows you to declare or specify the desired state of your resource and tries to keep the current state of Kubernetes objects in sync with the desired state.
  • Custom controllers can work with any kind of resource, but they are especially effective when combined with custom resources.
  • The Operator pattern combines custom resources and custom controllers.
  • the API represents a desired state, not an exact state.
  • define configuration of applications or infrastructure.
  • The main operations on the objects are CRUD-y (creating, reading, updating and deleting).
  • The client says "do this", and then gets an operation ID back, and has to check a separate Operation object to determine completion of the request.
  • The natural operations on the objects are not CRUD-y.
  • High bandwidth access (10s of requests per second sustained) needed.
  • Use a ConfigMap if any of the following apply
  • You want to put the entire config file into one key of a configMap.
  • You want to perform rolling updates via Deployment, etc., when the file is updated.
  • Use a secret for sensitive data, which is similar to a configMap but more secure.
  • You want to build new automation that watches for updates on the new object, and then CRUD other objects, or vice versa.
  • You want the object to be an abstraction over a collection of controlled resources, or a summarization of other resources.
  • CRDs are simple and can be created without any programming.
  • Aggregated APIs are subordinate API servers that sit behind the primary API server
  • CRDs allow users to create new types of resources without adding another API server
  • Defining a CRD object creates a new custom resource with a name and schema that you specify.
  • The name of a CRD object must be a valid DNS subdomain name
  • each resource in the Kubernetes API requires code that handles REST requests and manages persistent storage of objects.
  • The main API server delegates requests to you for the custom resources that you handle, making them available to all of its clients.
  • The new endpoints support CRUD basic operations via HTTP and kubectl
  • Custom resources consume storage space in the same way that ConfigMaps do.
  • Aggregated API servers may use the same storage as the main API server
  • CRDs always use the same authentication, authorization, and audit logging as the built-in resources of your API server.
  • most RBAC roles will not grant access to the new resources (except the cluster-admin role or any role created with wildcard rules).
  • CRDs and Aggregated APIs often come bundled with new role definitions for the types they add.
crazylion lee

Riemann - A network monitoring system - 0 views

riemann.io/index.html

monitoring dashboard sysadmin

shared by crazylion lee on 17 Apr 16 - No Cached
  • crazylion lee on 17 Apr 16
     
    "Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. Combine statistics from every Riak node in your cluster and forward to Graphite. Track user activity from second to second."
張 旭

Best practices for building Kubernetes Operators and stateful apps | Google Cloud Blog - 0 views

cloud.google.com/...es-operators-and-stateful-apps

kubernetes system operator

shared by 張 旭 on 17 May 21 - No Cached
  • use the StatefulSet workload controller to maintain identity for each of the pods, and to use Persistent Volumes to persist data so it can survive a service restart.
  • a way to extend Kubernetes functionality with application specific logic using custom resources and custom controllers.
  • An Operator can automate various features of an application, but it should be specific to a single application
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  • Kubebuilder is a comprehensive development kit for building and publishing Kubernetes APIs and Controllers using CRDs
  • Design declarative APIs for operators, not imperative APIs. This aligns well with Kubernetes APIs that are declarative in nature.
  • With declarative APIs, users only need to express their desired cluster state, while letting the operator perform all necessary steps to achieve it.
  • scaling, backup, restore, and monitoring. An operator should be made up of multiple controllers that specifically handle each of the those features.
  • the operator can have a main controller to spawn and manage application instances, a backup controller to handle backup operations, and a restore controller to handle restore operations.
  • each controller should correspond to a specific CRD so that the domain of each controller's responsibility is clear.
  • If you keep a log for every container, you will likely end up with unmanageable amount of logs.
  • integrate application-specific details to the log messages such as adding a prefix for the application name.
  • you may have to use external logging tools such as Google Stackdriver, Elasticsearch, Fluentd, or Kibana to perform the aggregations.
  • adding labels to metrics to facilitate aggregation and analysis by monitoring systems.
  • a more viable option is for application pods to expose a metrics HTTP endpoint for monitoring tools to scrape.
  • A good way to achieve this is to use open-source application-specific exporters for exposing Prometheus-style metrics.
張 旭

The Twelve-Factor App - 0 views

12factor.net/logs

development application programming

shared by 張 旭 on 20 Feb 19 - No Cached
  • Logs are the stream of aggregated, time-ordered events collected from the output streams of all running processes and backing services.
  • Logs have no fixed beginning or end, but flow continuously as long as the app is operating.
  • each running process writes its event stream, unbuffered, to stdout.
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  • long-term archival. These archival destinations are not visible to or configurable by the app, and instead are completely managed by the execution environment.
  • Most significantly, the stream can be sent to a log indexing and analysis system such as Splunk, or a general-purpose data warehousing system such as Hadoop/Hive.
張 旭

Logging Architecture | Kubernetes - 0 views

kubernetes.io/...logging

kubernetes log

shared by 張 旭 on 27 May 21 - No Cached
  • Application logs can help you understand what is happening inside your application
  • container engines are designed to support logging.
  • The easiest and most adopted logging method for containerized applications is writing to standard output and standard error streams.
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  • In a cluster, logs should have a separate storage and lifecycle independent of nodes, pods, or containers. This concept is called cluster-level logging.
  • Cluster-level logging architectures require a separate backend to store, analyze, and query logs
  • Kubernetes does not provide a native storage solution for log data.
  • use kubectl logs --previous to retrieve logs from a previous instantiation of a container.
  • A container engine handles and redirects any output generated to a containerized application's stdout and stderr streams
  • The Docker JSON logging driver treats each line as a separate message.
  • By default, if a container restarts, the kubelet keeps one terminated container with its logs.
  • An important consideration in node-level logging is implementing log rotation, so that logs don't consume all available storage on the node
  • You can also set up a container runtime to rotate an application's logs automatically.
  • The two kubelet flags container-log-max-size and container-log-max-files can be used to configure the maximum size for each log file and the maximum number of files allowed for each container respectively.
  • The kubelet and container runtime do not run in containers.
  • On machines with systemd, the kubelet and container runtime write to journald. If systemd is not present, the kubelet and container runtime write to .log files in the /var/log directory.
  • System components inside containers always write to the /var/log directory, bypassing the default logging mechanism.
  • Kubernetes does not provide a native solution for cluster-level logging
  • Use a node-level logging agent that runs on every node.
  • implement cluster-level logging by including a node-level logging agent on each node.
  • the logging agent is a container that has access to a directory with log files from all of the application containers on that node.
  • the logging agent must run on every node, it is recommended to run the agent as a DaemonSet
  • Node-level logging creates only one agent per node and doesn't require any changes to the applications running on the node.
  • Containers write stdout and stderr, but with no agreed format. A node-level agent collects these logs and forwards them for aggregation.
  • Each sidecar container prints a log to its own stdout or stderr stream.
  • It is not recommended to write log entries with different formats to the same log stream
  • writing logs to a file and then streaming them to stdout can double disk usage.
  • If you have an application that writes to a single file, it's recommended to set /dev/stdout as the destination
  • it's recommended to use stdout and stderr directly and leave rotation and retention policies to the kubelet.
  • Using a logging agent in a sidecar container can lead to significant resource consumption. Moreover, you won't be able to access those logs using kubectl logs because they are not controlled by the kubelet.
張 旭

Considerations for large clusters | Kubernetes - 0 views

kubernetes.io/...cluster-large

kubernetes

shared by 張 旭 on 24 Mar 22 - No Cached
  • A cluster is a set of nodes (physical or virtual machines) running Kubernetes agents, managed by the control plane.
  • Kubernetes v1.23 supports clusters with up to 5000 nodes.
  • criteria: No more than 110 pods per node No more than 5000 nodes No more than 150000 total pods No more than 300000 total containers
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  • In-use IP addresses
  • run one or two control plane instances per failure zone, scaling those instances vertically first and then scaling horizontally after reaching the point of falling returns to (vertical) scale.
  • Kubernetes nodes do not automatically steer traffic towards control-plane endpoints that are in the same failure zone
  • store Event objects in a separate dedicated etcd instance.
  • start and configure additional etcd instance
  • Kubernetes resource limits help to minimize the impact of memory leaks and other ways that pods and containers can impact on other components.
  • Addons' default limits are typically based on data collected from experience running each addon on small or medium Kubernetes clusters.
  • When running on large clusters, addons often consume more of some resources than their default limits.
  • Many addons scale horizontally - you add capacity by running more pods
  • The VerticalPodAutoscaler can run in recommender mode to provide suggested figures for requests and limits.
  • Some addons run as one copy per node, controlled by a DaemonSet: for example, a node-level log aggregator.
  • VerticalPodAutoscaler is a custom resource that you can deploy into your cluster to help you manage resource requests and limits for pods.
  • The cluster autoscaler integrates with a number of cloud providers to help you run the right number of nodes for the level of resource demand in your cluster.
  • The addon resizer helps you in resizing the addons automatically as your cluster's scale changes.
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