Logging, Monitoring, and Debugging

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Overview

There are a variety of ways in which you can log and debug the execution of platform application services, tools, and APIs.

Note
To learn how to use the platform's default monitoring service and pre-deployed Grafana dashboards to monitor application services, see Monitoring Platform Services.
Note
If you are integrating the platform with other logging tools (e.g. datadog), contact Iguazio Support.

For further troubleshooting assistance, visit Iguazio Support.

Logging Application Services

The platform has a default tenant-wide log-forwarder application service (log-forwarder) for forwarding application-service logs. The logs are forwarded to an instance of the Elasticsearch open-source search and analytics engine by using the open-source Filebeat log-shipper utility. The log-forwarder service is disabled by default. To enable it, on the Services dashboard page, select to edit the log-forwarder service; in the Custom Parameters tab, set the Elasticsearch URL parameter to an instance of Elasticsearch that will be used to store and index the logs; then, save and apply your changes to deploy the service.

Typically, the log-forwarder service should be configured to work with your own remote off-cluster instance of Elasticsearch.

Note
  • The default transfer protocol, which is used when the URL doesn't begin with "http://" or "https://", is HTTPS.
  • The log-forwarder service doesn't support off-cluster Elasticsearch user authentication, regardless of whether you use an HTTP or HTTPS URL.
  • The default port, which is used when the URL doesn't end with ":<port number>", is port 80 for HTTP and port 443 for HTTPS.

In cloud platform environments you can also configure the log-forwarder service to work with a tenant-wide Elasticsearch service that uses a pre-deployed on-cluster vanilla installation of Elasticsearch. To do so, you first need to create such an Elasticsearch service: from the Services dashboard page, select to create a new service of type "Elasticsearch", optionally edit the default configuration, and apply your changes to deploy the service. You can then configure the log-forwarder service to forward the application-service logs to this service by selecting your Elasticsearch service from the drop-down list of the Elasticsearch parameter.

After enabling the log-forwarder service, you can view the forwarded logs in the Logs page of the platform dashboard (including filtering options), provided you have the Service Admin management policy. The Logs column on the Services dashboard page has links to filtered log views for each service.

Note
Note that the Logs page displays all logs from the configured log-forwarder Elasticsearch instance, regardless of their origin. It's therefore recommended that you use a dedicated Elasticsearch instance for each platform cluster that enables log forwarding.

Warning
Note that you cannot delete an Elasticsearch service while it's being used by the log-forwarder service.

Checking Service Status

In the Service pages, users with both the Service Admin and the Application Read Only view policies can check the status of the pods.

  • Press View pod state to see the status. You can also download a txt file from the popup.

Kubernetes Tools

You can use the Kubernetes kubectl CLI from a platform web-shell or Jupyter Notebook application service to monitor, log, and debug your Kubernetes application cluster:

  • Use the get pods command to display information about the cluster's pods: 
    kubectl get pods
  • Use the logs command to view the logs for a specific pod; replace POD with the name of one of the pods returned by the get command: 
    kubectl logs POD
  • Use the top pod command to view pod resource metrics and monitor resource consumption; replace [POD] with the name of one of the pods returned by the get command or remove it to display logs for all pods: 
    kubectl top pod [POD]
Note

To run kubectl commands from a web-shell service, the service must be configured with an appropriate service account; for more information about the web-shell service accounts, see The Web-Shell Service.

  • The get pods and logs commands require the "Log Reader" service account or higher.
  • The top pod command requires the "Service Admin" service account.

For more information about the kubectl CLI, see the Kubernetes documentation.

IGZTOP - Performance Reporting Tool

IGZTOP is a small tool which is intended to display useful information about pods in the default-tenant namespace. It is a performance troubleshooting tool providing insights on how your MLrun jobs are consuming resources in the cluster. and enhances the standard set of provided Kubernetes tools.

Prerequisites

To use the IGZTOP feature you will need the to have a shell service configured with service admin rights. To setup a new service see Creating a New Service. To run the shell service:

  1. In the side navigation menu, select Services.
  2. On the Services page, press the Shell Service name (link) from the list of services. A shell window opens.

How to Run IGZTOP

Use the following command in the shell service window. Use the flags (options) to customize your results.

  igztop [--sort=<KEY>] [--limit=<KEY>] [--wide] [--all] [--kill-job=<JOB>] [--no-borders] [--check-permissions] [--update]

Options Description

-h --help

 Displays the command help

-v --version

 Displays the current version

-s --sort=<KEY>

 Sort by key (for example --sort memory)

-l --limit=<KEY>

 filter using a key:value pair (for example 'node=k8s-node1', 'name=trino')

-w --wide

 Show additional fields including “requests” and “limits”

-a --all

 Include pods which are not currently running

-b --no-borders

 Print the table without borders

-k --kill-job=<JOB>

 Kill one or more MLRun job pods (use * to kill multiple matching pods

-c --check-permissions

 Check the permissions of the user running this Service

-u --update

 Fetch the latest version of IGZTOP

Examples

The following is an example of the default output.

$ igztop
+--------------------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| NAME                                             | CPU(m) | MEMORY(Mi) | NODE      | STATUS  | GPUs | GPU Util. | MLRun Proj. | MLRun Owner |
+--------------------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| authenticator-fb7dc6df7-vg9tl                    | 1      | 6          | k8s-node2 | Running |      |           |             |             |
| docker-registry-676df9d6f6-x788h                 | 1      | 6          | k8s-node2 | Running |      |           |             |             |
| framesd-8b4c489f-sx9rw                           | 1      | 17         | k8s-node1 | Running |      |           |             |             |
| grafana-5b456dc59-vjszb                          | 1      | 31         | k8s-node1 | Running |      |           |             |             |
| jupyter-58c5bf598f-z86qm                         | 9      | 641        | k8s-node1 | Running | 1/1  | 0.00%     |             |             |
| metadata-envoy-deployment-786b65df7d-lldp2       | 3      | 11         | k8s-node2 | Running |      |           |             |             |
| metadata-grpc-deployment-7cfc8d9b8-prdr8         | 1      | 1          | k8s-node2 | Running |      |           |             |             |
| metadata-writer-7bfd6bf6dd-x8fj9                 | 1      | 140        | k8s-node2 | Running |      |           |             |             |
| metrics-server-exporter-75b49dd6b8-k9r5q         | 5      | 14         | k8s-node2 | Running |      |           |             |             |
| ml-pipeline-565cb8497d-wkx2b                     | 5      | 16         | k8s-node2 | Running |      |           |             |             |
| ml-pipeline-persistenceagent-746949f667-822ch    | 1      | 9          | k8s-node1 | Running |      |           |             |             |
| ml-pipeline-scheduledworkflow-6fd754dfcf-slrtk   | 1      | 9          | k8s-node1 | Running |      |           |             |             |
| ml-pipeline-ui-7694dc85ff-4cjc5                  | 4      | 52         | k8s-node1 | Running |      |           |             |             |
| ml-pipeline-viewer-crd-6fbf9f8fbf-9cwvq          | 2      | 10         | k8s-node2 | Running |      |           |             |             |
| ml-pipeline-visualizationserver-66ccfd8f98-l6g86 | 5      | 80         | k8s-node1 | Running |      |           |             |             |
| mlrun-api-68cfd974db-cgph4                       | 4      | 317        | k8s-node2 | Running |      |           |             |             |
| mlrun-ui-88cbcffc4-8s76d                         | 0      | 11         | k8s-node1 | Running |      |           |             |             |
| monitoring-prometheus-server-78d5bbc9d4-vgrhm    | 5      | 42         | k8s-node2 | Running |      |           |             |             |
| mpi-operator-7589fbff58-87xr7                    | 1      | 13         | k8s-node1 | Running |      |           |             |             |
| mysql-kf-67b6cb589d-dvh94                        | 4      | 460        | k8s-node1 | Running |      |           |             |             |
| nuclio-controller-5bcbd8bcf6-6hqvv               | 1      | 10         | k8s-node2 | Running |      |           |             |             |
| nuclio-dashboard-84ddf6f9bd-c4scb                | 1      | 43         | k8s-node1 | Running |      |           |             |             |
| nuclio-dlx-86cbc4cdb9-wcds2                      | 1      | 5          | k8s-node2 | Running |      |           |             |             |
| nuclio-scaler-7b96584d57-l9cwj                   | 1      | 10         | k8s-node1 | Running |      |           |             |             |
| oauth2-proxy-5bd48c96d8-ghvqq                    | 1      | 4          | k8s-node2 | Running |      |           |             |             |
| trino-coordinator-65d6d64b5f-vc7gh              | 17     | 1319       | k8s-node1 | Running |      |           |             |             |
| trino-worker-76d774b948-dxxjr                   | 8      | 1229       | k8s-node2 | Running |      |           |             |             |
| trino-worker-76d774b948-vlxcn                   | 13     | 1236       | k8s-node1 | Running |      |           |             |             |
| provazio-controller-controller-c9bff698c-xk6r9   | 3      | 50         | k8s-node2 | Running |      |           |             |             |
| shell-7b47994578-z7s6k                           | 50     | 108        | k8s-node2 | Running |      |           |             |             |
| spark-operator-658bdfb6f5-bl2lb                  | 2      | 10         | k8s-node1 | Running |      |           |             |             |
| v3io-webapi-b9b9f                                | 8      | 1147       | k8s-node1 | Running |      |           |             |             |
| v3io-webapi-p4kbq                                | 8      | 1431       | k8s-node2 | Running |      |           |             |             |
| v3iod-fztqm                                      | 13     | 2541       | k8s-node2 | Running |      |           |             |             |
| v3iod-jg68d                                      | 14     | 2536       | k8s-node1 | Running |      |           |             |             |
| v3iod-locator-599b9d5c9f-76vrr                   | 0      | 5          | k8s-node1 | Running |      |           |             |             |
| workflow-controller-7d59d94444-5n7r7             | 1      | 12         | k8s-node2 | Running |      |           |             |             |
+--------------------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| SUM                                              | 197m   | 13582Mi    |           |         |      |           |             |             |
+--------------------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+

The following examples compares Trino memory usage across all cluster nodes. 

$ igztop --sort memory --limit name=trino
+-------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| NAME                                | CPU(m) | MEMORY(Mi) | NODE      | STATUS  | GPUs | GPU Util. | MLRun Proj. | MLRun Owner |
+-------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| trino-coordinator-65d6d64b5f-vc7gh  | 17     | 1320       | k8s-node1 | Running |      |           |             |             |
| trino-worker-76d774b948-vlxcn       | 12     | 1237       | k8s-node1 | Running |      |           |             |             |
| trino-worker-76d774b948-dxxjr       | 12     | 1230       | k8s-node2 | Running |      |           |             |             |
+-------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| SUM                                 | 41m    | 3787Mi     |           |         |      |           |             |             |
+-------------------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+

The following example displays which pods are using GPUs.

$ igztop --sort memory --limit gpu
+--------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| NAME                     | CPU(m) | MEMORY(Mi) | NODE      | STATUS  | GPUs | GPU Util. | MLRun Proj. | MLRun Owner |
+--------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| jupyter-58c5bf598f-z86qm | 10     | 644        | k8s-node1 | Running | 1/1  | 0.00%     |             |             |
+--------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+
| SUM                      | 10m    | 644Mi      |           |         |      |           |             |             |
+--------------------------+--------+------------+-----------+---------+------+-----------+-------------+-------------+

The following example displays the --all flag and includes pods which are not running.

$ igztop -a
+---------------------------------------------------------------+--------+------------+-----------+-----------+--------------------------------+-------------+
| NAME                                                          | CPU(m) | MEMORY(Mi) | NODE      | STATUS    | MLRun Proj.                    | MLRun Owner |
+---------------------------------------------------------------+--------+------------+-----------+-----------+--------------------------------+-------------+
| v3io-webapi-dcknw                                             | 19     | 5069       | k8s-node1 | Running   |                                |             |
| trino-coordinator-68df99bdf9-8swbp                            | 20     | 1855       | k8s-node1 | Running   |                                |             |
| ml-pipeline-viewer-crd-656775dc5c-n4tzl                       | 2      | 11         | k8s-node1 | Running   |                                |             |
| prep-data-5vpsb                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| nuclio-dlx-555d6c4cc5-64mns                                   | 1      | 5          | k8s-node1 | Running   |                                |             |
| workflow-controller-64fdf54f56-8wq2v                          | 1      | 11         | k8s-node1 | Running   |                                |             |
| shell-service-admin-6f4bb85cc8-5d9v7                          | 56     | 89         | k8s-node1 | Running   |                                |             |
| nuclio-dashboard-5bcff7dc4c-b7r4j                             | 7      | 43         | k8s-node1 | Running   |                                |             |
| prep-data-fblfn                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| v3iod-x5fmt                                                   | 16     | 5080       | k8s-node1 | Running   |                                |             |
| prep-data-77k4m                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| spark-m9zrumpebc-rdxs6-worker-8647d4466f-rjk9h                | 9      | 302        | k8s-node1 | Running   |                                |             |
| metadata-grpc-deployment-6475f89f9b-t2gns                     | 1      | 1          | k8s-node1 | Running   |                                |             |
| metadata-writer-85ddb586d-wzz8v                               | 1      | 140        | k8s-node1 | Running   |                                |             |
| spark-m9zrumpebc-rdxs6-master-5fd5d6964b-bk527                | 8      | 361        | k8s-node1 | Running   |                                |             |
| nuclio-controller-dd6fcbc74-4pt82                             | 1      | 13         | k8s-node1 | Running   |                                |             |
| spark-operator-7f698b78dc-8x2sh                               | 2      | 10         | k8s-node1 | Running   |                                |             |
| prep-data-g6dps                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| mpi-operator-68dbcc88cc-z8nls                                 | 1      | 14         | k8s-node1 | Running   |                                |             |
| spark-operator-init-zxp8r                                     |        |            | k8s-node1 | Completed |                                |             |
| mlrun-ui-657cb7f889-rpn7t                                     | 0      | 11         | k8s-node1 | Running   |                                |             |
| prep-data-bgwg2                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| metadata-envoy-deployment-5f6c59895d-59qk9                    | 4      | 11         | k8s-node1 | Running   |                                |             |
| mysql-kf-77b69d9d9-4wwkc                                      | 3      | 463        | k8s-node1 | Running   |                                |             |
| framesd-7b89dc9cfb-t4p7x                                      | 1      | 17         | k8s-node1 | Running   |                                |             |
| ml-pipeline-86dc8d885-z6zpm                                   | 5      | 16         | k8s-node1 | Running   |                                |             |
| spark-master-7cf99697df-9dprz                                 | 7      | 408        | k8s-node1 | Running   |                                |             |
| ml-pipeline-visualizationserver-78ccd99c4b-zqwzb              | 4      | 76         | k8s-node1 | Running   |                                |             |
| ml-pipeline-scheduledworkflow-868c7467f5-6jxst                | 1      | 8          | k8s-node1 | Running   |                                |             |
| metrics-server-exporter-594f9c9c97-hvnsd                      | 8      | 12         | k8s-node1 | Running   |                                |             |
| mlrun-api-5dbb8899cb-5zwh5                                    | 28     | 403        | k8s-node1 | Running   |                                |             |
| shell-none-6d64c8675c-tndv4                                   | 5      | 66         | k8s-node1 | Running   |                                |             |
| provazio-controller-controller-77866b6b98-n7psm               | 4      | 50         | k8s-node1 | Running   |                                |             |
| monitoring-prometheus-server-5c959fc895-hfqg2                 | 6      | 43         | k8s-node1 | Running   |                                |             |
| v3iod-locator-597b4c7c87-jn2gx                                | 0      | 5          | k8s-node1 | Running   |                                |             |
| spark-worker-664878f4b6-mwq2r                                 | 8      | 404        | k8s-node1 | Running   |                                |             |
| prep-data-pq8jm                                               |        |            | k8s-node1 | Completed | getting-started-tutorial-admin | admin       |
| docker-registry-6d8b657f8d-h5297                              | 1      | 45         | k8s-node1 | Running   |                                |             |
| hive-5bfbc84589-5l9rg                                         | 7      | 284        | k8s-node1 | Running   |                                |             |
| ml-pipeline-persistenceagent-56f77bdb5f-vn9ms                 | 1      | 9          | k8s-node1 | Running   |                                |             |
| shell-app-admin-57474fc864-dkzct                              | 5      | 95         | k8s-node1 | Running   |                                |             |
| trino-worker-758dcccf6f-hst2f                                 | 12     | 1809       | k8s-node1 | Running   |                                |             |
| jupyter-77bfdd798d-vbt6r                                      | 9      | 585        | k8s-node1 | Running   |                                |             |
| mariadb-74d5c8cb74-zjq82                                      | 4      | 76         | k8s-node1 | Running   |                                |             |
| shell-log-reader-7d67d7f567-vbldj                             | 5      | 52         | k8s-node1 | Running   |                                |             |
| ml-pipeline-ui-7db966bb54-znch6                               | 4      | 46         | k8s-node1 | Running   |                                |             |
| oauth2-proxy-85b8c78fb7-8ktzx                                 | 1      | 3          | k8s-node1 | Running   |                                |             |
| nuclio-getting-started-tutorial-admin-serving-6d8fbdc9d-r4x2w | 1      | 165        | k8s-node1 | Running   |                                |             |
| authenticator-6647dd8467-r2g9n                                | 1      | 7          | k8s-node1 | Running   |                                |             |
| nuclio-scaler-78cc778b6c-kr7m9                                | 1      | 10         | k8s-node1 | Running   |                                |             |
+---------------------------------------------------------------+--------+------------+-----------+-----------+--------------------------------+-------------+
| SUM                                                           | 339m   | 18868Mi    |           |           |                                |             |
+---------------------------------------------------------------+--------+------------+-----------+-----------+--------------------------------+-------------+

To delete MLRun use igztop with the -k --kill-job option followed by a job name. Use '*' as a wildcard at the start or end of a job name to kill multiple job pods.

$ igztop -k '*data*'
pod "prep-data-5vpsb" deleted
pod "prep-data-77k4m" deleted
pod "prep-data-bgwg2" deleted
pod "prep-data-fblfn" deleted
pod "prep-data-g6dps" deleted
pod "prep-data-pq8jm" deleted

Event Logs

The Events page of the dashboard displays different platform event logs:

  • The Event Log tab displays system event logs.
  • The Alerts tab displays system alerts.
  • The Audit tab displays a subset of the system events for audit purposes — security events (such as a failed login) and user actions (such as creation and deletion of a container).

The Events page is visible to users with the IT Admin management policy — who can view all event logs — or to users with the Security Admin management policy — who can view only the Audit tab.

Cluster Support Logs

Users with the IT Admin management policy can collect and download support logs for the platform clusters from the dashboard. Log collection is triggered for a data cluster, but the logs are collected from both the data and application cluster nodes.

You can trigger collection of cluster support-logs from the dashboard in one of two ways; (note that you cannot run multiple collection jobs concurrently):

  • On the Clusters page, open the action menu () for a data cluster in the clusters table (Type = "Data"); then, select the Collect logs menu option.
  • On the Clusters page, select to display the Support Logs tab for a specific data cluster — either by selecting the Support logs option from the cluster's action menu () or by selecting the data cluster and then selecting the Support Logs tab; then, select Collect Logs from the action toolbar.

You can view the status of all collection jobs and download archive files of the collected logs from the data-cluster's Support Logs dashboard tab.

API Error Information

The platform APIs return error codes and error and warning messages to help you debug problems with your application. See, for example, the Error Information documentation in the Data-Service Web-API General Structure reference documentation.

See Also