Logging, Monitoring, and Troubleshooting Guide

Red Hat OpenStack Platform 15-Beta

An In-Depth Guide to OpenStack Logging, Monitoring, and Troubleshooting

OpenStack Documentation Team

Abstract

This guide provides a detailed overview on logging and monitoring a Red Hat OpenStack Platform environment, and how to solve problems.

Chapter 1. About This Guide

Warning

Red Hat is currently reviewing the information and procedures provided in this guide for this release.

This document is based on the Red Hat OpenStack Platform 12 document, available at https://access.redhat.com/documentation/en-us/red_hat_openstack_platform/?version=12.

If you require assistance for the current Red Hat OpenStack Platform release, please contact Red Hat support.

This document provides an overview of the logging and monitoring capabilities that are available in a Red Hat OpenStack Platform environment, and how to troubleshoot possible issues.

Chapter 2. Logging

Red Hat OpenStack Platform writes informational messages to specific log files; you can use these messages for troubleshooting and monitoring system events.

Note

You need not attach the individual log files to your support cases manually. All the required information will be gathered automatically by the sosreport utility, which is described in Chapter 5, Troubleshooting.

2.1. Log Files for OpenStack Services

Each OpenStack component has a separate logging directory containing files specific to a running service.

2.1.1. Bare Metal Provisioning (ironic) Log Files

ServiceService NameLog Path

OpenStack Ironic API

openstack-ironic-api.service

/var/log/containers/ironic/ironic-api.log

OpenStack Ironic Conductor

openstack-ironic-conductor.service

/var/log/containers/ironic/ironic-conductor.log

2.1.2. Block Storage (cinder) Log Files

ServiceService NameLog Path

Block Storage API

openstack-cinder-api.service

/var/log/containers/cinder/api.log

Block Storage Backup

openstack-cinder-backup.service

/var/log/containers/cinder/backup.log

Informational messages

The cinder-manage command

/var/log/containers/cinder/cinder-manage.log

Block Storage Scheduler

openstack-cinder-scheduler.service

/var/log/containers/cinder/scheduler.log

Block Storage Volume

openstack-cinder-volume.service

/var/log/containers/cinder/volume.log

2.1.3. Compute (nova) Log Files

ServiceService NameLog Path

OpenStack Compute API service

openstack-nova-api.service

/var/log/containers/nova/nova-api.log

OpenStack Compute certificate server

openstack-nova-cert.service

/var/log/containers/nova/nova-cert.log

OpenStack Compute service

openstack-nova-compute.service

/var/log/containers/nova/nova-compute.log

OpenStack Compute Conductor service

openstack-nova-conductor.service

/var/log/containers/nova/nova-conductor.log

OpenStack Compute VNC console authentication server

openstack-nova-consoleauth.service

/var/log/containers/nova/nova-consoleauth.log

Informational messages

nova-manage command

/var/log/containers/nova/nova-manage.log

OpenStack Compute NoVNC Proxy service

openstack-nova-novncproxy.service

/var/log/containers/nova/nova-novncproxy.log

OpenStack Compute Scheduler service

openstack-nova-scheduler.service

/var/log/containers/nova/nova-scheduler.log

2.1.4. Dashboard (horizon) Log Files

ServiceService NameLog Path

Log of certain user interactions

Dashboard interface

/var/log/containers/horizon/horizon.log

The Apache HTTP server uses several additional log files for the Dashboard web interface, which can be accessed using a web browser or command-line clients (keystone, nova). The following log files can be helpful in tracking the usage of the Dashboard and diagnosing faults:

PurposeLog Path

All processed HTTP requests

/var/log/containers/httpd/horizon_access.log

HTTP errors

/var/log/containers/httpd/horizon_error.log

Admin-role API requests

/var/log/containers/httpd/keystone_wsgi_admin_access.log

Admin-role API errors

/var/log/containers/httpd/keystone_wsgi_admin_error.log

Member-role API requests

/var/log/containers/httpd/keystone_wsgi_main_access.log

Member-role API errors

/var/log/containers/httpd/keystone_wsgi_main_error.log

Note

There is also /var/log/containers/httpd/default_error.log, which stores errors reported by other web services running on the same host.

2.1.5. Data Processing (sahara) Log Files

ServiceService NameLog Path

Sahara API Server

openstack-sahara-all.service
openstack-sahara-api.service

/var/log/containers/sahara/sahara-all.log
/var/log/containers/messages

Sahara Engine Server

openstack-sahara-engine.service

/var/log/containers/messages

2.1.6. Database as a Service (trove) Log Files

ServiceService NameLog Path

OpenStack Trove API Service

openstack-trove-api.service

/var/log/containers/trove/trove-api.log

OpenStack Trove Conductor Service

openstack-trove-conductor.service

/var/log/containers/trove/trove-conductor.log

OpenStack Trove guestagent Service

openstack-trove-guestagent.service

/var/log/containers/trove/logfile.txt

OpenStack Trove taskmanager Service

openstack-trove-taskmanager.service

/var/log/containers/trove/trove-taskmanager.log

2.1.7. Identity Service (keystone) Log Files

ServiceService NameLog Path

OpenStack Identity Service

openstack-keystone.service

/var/log/containers/keystone/keystone.log

2.1.8. Image Service (glance) Log Files

ServiceService NameLog Path

OpenStack Image Service API server

openstack-glance-api.service

/var/log/containers/glance/api.log

OpenStack Image Service Registry server

openstack-glance-registry.service

/var/log/containers/glance/registry.log

2.1.9. Networking (neutron) Log Files

ServiceService NameLog Path

OpenStack Neutron DHCP Agent

neutron-dhcp-agent.service

/var/log/containers/neutron/dhcp-agent.log

OpenStack Networking Layer 3 Agent

neutron-l3-agent.service

/var/log/containers/neutron/l3-agent.log

Metadata agent service

neutron-metadata-agent.service

/var/log/containers/neutron/metadata-agent.log

Metadata namespace proxy

n/a

/var/log/containers/neutron/neutron-ns-metadata-proxy-UUID.log

Open vSwitch agent

neutron-openvswitch-agent.service

/var/log/containers/neutron/openvswitch-agent.log

OpenStack Networking service

neutron-server.service

/var/log/containers/neutron/server.log

2.1.10. Object Storage (swift) Log Files

OpenStack Object Storage sends logs to the system logging facility only.

Note

By default, all Object Storage log files to /var/log/containers/swift/swift.log, using the local0, local1, and local2 syslog facilities.

The log messages of Object Storage are classified into two broad categories: those by REST API services and those by background daemons. The API service messages contain one line per API request, in a manner similar to popular HTTP servers; both the frontend (Proxy) and backend (Account, Container, Object) services post such messages. The daemon messages are less structured and typically contain human-readable information about daemons performing their periodic tasks. However, regardless of which part of Object Storage produces the message, the source identity is always at the beginning of the line.

An example of a proxy message:

Apr 20 15:20:34 rhev-a24c-01 proxy-server: 127.0.0.1 127.0.0.1 20/Apr/2015/19/20/34 GET /v1/AUTH_zaitcev%3Fformat%3Djson%26marker%3Dtestcont HTTP/1.0 200 - python-swiftclient-2.1.0 AUTH_tk737d6... - 2 - txc454fa8ea4844d909820a-0055355182 - 0.0162 - - 1429557634.806570053 1429557634.822791100

An example of ad-hoc messages from background daemons:

Apr 27 17:08:15 rhev-a24c-02 object-auditor: Object audit (ZBF). Since Mon Apr 27 21:08:15 2015: Locally: 1 passed, 0 quarantined, 0 errors files/sec: 4.34 , bytes/sec: 0.00, Total time: 0.23, Auditing time: 0.00, Rate: 0.00
Apr 27 17:08:16 rhev-a24c-02 object-auditor: Object audit (ZBF) "forever" mode completed: 0.56s. Total quarantined: 0, Total errors: 0, Total files/sec: 14.31, Total bytes/sec: 0.00, Auditing time: 0.02, Rate: 0.04
Apr 27 17:08:16 rhev-a24c-02 account-replicator: Beginning replication run
Apr 27 17:08:16 rhev-a24c-02 account-replicator: Replication run OVER
Apr 27 17:08:16 rhev-a24c-02 account-replicator: Attempted to replicate 5 dbs in 0.12589 seconds (39.71876/s)
Apr 27 17:08:16 rhev-a24c-02 account-replicator: Removed 0 dbs
Apr 27 17:08:16 rhev-a24c-02 account-replicator: 10 successes, 0 failures

2.1.11. Orchestration (heat) Log Files

ServiceService NameLog Path

OpenStack Heat API Service

openstack-heat-api.service

/var/log/containers/heat/heat-api.log

OpenStack Heat Engine Service

openstack-heat-engine.service

/var/log/containers/heat/heat-engine.log

Orchestration service events

n/a

/var/log/containers/heat/heat-manage.log

2.1.12. Shared Filesystem Service (manila) Log Files

ServiceService NameLog Path

OpenStack Manila API Server

openstack-manila-api.service

/var/log/containers/manila/api.log

OpenStack Manila Scheduler

openstack-manila-scheduler.service

/var/log/containers/manila/scheduler.log

OpenStack Manila Share Service

openstack-manila-share.service

/var/log/containers/manila/share.log

Note

Some information from the Manila Python library can also be logged in /var/log/containers/manila/manila-manage.log.

2.1.13. Telemetry (ceilometer) Log Files

ServiceService NameLog Path

OpenStack ceilometer notification agent

openstack-ceilometer-notification.service

/var/log/containers/ceilometer/agent-notification.log

OpenStack ceilometer alarm evaluation

openstack-ceilometer-alarm-evaluator.service

/var/log/containers/ceilometer/alarm-evaluator.log

OpenStack ceilometer alarm notification

openstack-ceilometer-alarm-notifier.service

/var/log/containers/ceilometer/alarm-notifier.log

OpenStack ceilometer API

httpd.service

/var/log/containers/ceilometer/api.log

Informational messages

MongoDB integration

/var/log/containers/ceilometer/ceilometer-dbsync.log

OpenStack ceilometer central agent

openstack-ceilometer-central.service

/var/log/containers/ceilometer/central.log

OpenStack ceilometer collection

openstack-ceilometer-collector.service

/var/log/containers/ceilometer/collector.log

OpenStack ceilometer compute agent

openstack-ceilometer-compute.service

/var/log/containers/ceilometer/compute.log

2.1.14. Log Files for Supporting Services

The following services are used by the core OpenStack components and have their own log directories and files.

ServiceService NameLog Path

Message broker (RabbitMQ)

rabbitmq-server.service

/var/log/rabbitmq/rabbit@short_hostname.log
/var/log/rabbitmq/rabbit@short_hostname-sasl.log (for Simple Authentication and Security Layer related log messages)

Database server (MariaDB)

mariadb.service

/var/log/mariadb/mariadb.log

Document-oriented database (MongoDB)

mongod.service

/var/log/mongodb/mongodb.log

Virtual network switch (Open vSwitch)

openvswitch-nonetwork.service

/var/log/openvswitch/ovsdb-server.log
/var/log/openvswitch/ovs-vswitchd.log

2.2. Configure Logging Options

Each component maintains its own separate logging configuration in its respective configuration file. For example, in Compute, these options are set in /etc/nova/nova.conf:

  • Increase the level of informational logging by enabling debugging. This option greatly increases the amount of information captured, so you may want to consider using it only temporarily, or first reviewing your log rotation settings.

    debug=True
  • Enable verbose logging:

    verbose=True
  • Change the log file path:

    log_dir=/var/log/containers/nova
  • Send your logs to a central syslog server:

    use_syslog=True
    syslog_log_facility=LOG_USER
Note

Options are also available for timestamp configuration and log formatting, among others. Review the component’s configuration file for additional logging options.

2.3. Remote Logging Installation and Configuration

All OpenStack services generate and update log files. These log files record actions, errors, warnings, and other events. In a distributed environment like OpenStack, collecting these logs in a central location simplifies debugging and administration.

For more information about centralized logging, see the Monitoring Tools Configuration guide.

Chapter 3. Configuring the Time Series Database (Gnocchi) for Telemetry

Time series database (Gnocchi) is a multi-tenant, metrics and resource database. It is designed to store metrics at a very large scale while providing access to metrics and resources information to operators and users.

3.1. Understanding the Time Series Database

This section defines the commonly used terms for the Time series database (Gnocchi)features.

Aggregation method
A function used to aggregate multiple measures into an aggregate. For example, the min aggregation method aggregates the values of different measures to the minimum value of all the measures in the time range.
Aggregate
A data point tuple generated from several measures according to the archive policy. An aggregate is composed of a time stamp and a value.
Archive policy
An aggregate storage policy attached to a metric. An archive policy determines how long aggregates are kept in a metric and how aggregates are aggregated (the aggregation method).
Granularity
The time between two aggregates in an aggregated time series of a metric.
Measure
An incoming data point tuple sent to the Time series database by the API. A measure is composed of a time stamp and a value.
Metric
An entity storing aggregates identified by an UUID. A metric can be attached to a resource using a name. How a metric stores its aggregates is defined by the archive policy that the metric is associated to.
Resource
An entity representing anything in your infrastructure that you associate a metric with. A resource is identified by a unique ID and can contain attributes.
Time series
A list of aggregates ordered by time.
Timespan
The time period for which a metric keeps its aggregates. It is used in the context of archive policy.

3.2. Metrics

The Time series database (Gnocchi) stores metrics from Telemetry that designate anything that can be measured, for example, the CPU usage of a server, the temperature of a room or the number of bytes sent by a network interface.

A metric has the following properties:

  • UUID to identify the metric
  • Metric name
  • Archive policy used to store and aggregate the measures

The Time series database stores the following metrics by default, as defined in the etc/ceilometer/polling.yaml file:

[root@controller-0 ~]# podman exec -ti ceilometer_agent_central cat /etc/ceilometer/polling.yaml
---
sources:
    - name: some_pollsters
      interval: 300
      meters:
        - cpu
        - memory.usage
        - network.incoming.bytes
        - network.incoming.packets
        - network.outgoing.bytes
        - network.outgoing.packets
        - disk.read.bytes
        - disk.read.requests
        - disk.write.bytes
        - disk.write.requests
        - hardware.cpu.util
        - hardware.memory.used
        - hardware.memory.total
        - hardware.memory.buffer
        - hardware.memory.cached
        - hardware.memory.swap.avail
        - hardware.memory.swap.total
        - hardware.system_stats.io.outgoing.blocks
        - hardware.system_stats.io.incoming.blocks
        - hardware.network.ip.incoming.datagrams
        - hardware.network.ip.outgoing.datagrams

The polling.yaml file also specifies the default polling interval of 300 seconds (5 minutes).

3.3. Time Series Database Components

Currently, Gnocchi uses the Identity service for authentication and Redis for incoming measure storage. To store the aggregated measures, Gnocchi relies on either Swift or Ceph (Object Storage). Gnocchi also leverages MySQL to store the index of resources and metrics.

The Time series database provides the statsd deamon (gnocchi-statsd) that is compatible with the statsd protocol and can listen to the metrics sent over the network. In order to enable statsd support in Gnocchi, you need to configure the [statsd] option in the configuration file. The resource ID parameter is used as the main generic resource where all the metrics are attached, a user and project ID that are associated with the resource and metrics, and an archive policy name that is used to create the metrics.

All the metrics are created dynamically as the metrics are sent to gnocchi-statsd, and attached with the provided name to the resource ID you configured.

3.4. Running the Time Series Database

Run the Time series database by running the HTTP server and metric daemon:

# gnocchi-api
# gnocchi-metricd

3.5. Running As A WSGI Application

You can run Gnocchi through a WSGI service such as mod_wsgi or any other WSGI application. The file gnocchi/rest/app.wsgi provided with Gnocchi allows you to enable Gnocchi as a WSGI application.

The Gnocchi API tier runs using WSGI. This means it can be run using Apache httpd and mod_wsgi, or another HTTP daemon such as uwsgi. You should configure the number of processes and threads according to the number of CPUs you have, usually around 1.5 × number of CPUs. If one server is not enough, you can spawn any number of new API servers to scale Gnocchi out, even on different machines.

3.6. metricd Workers

By default, the gnocchi-metricd daemon spans all your CPU power in order to maximize CPU utilization when computing metric aggregation. You can use the gnocchi status command to query the HTTP API and get the cluster status for metric processing. This command displays the number of metrics to process, known as the processing backlog for the gnocchi-metricd. As long as this backlog is not continuously increasing, that means that gnocchi-metricd is able to cope with the amount of metric that are being sent. If the number of measure to process is continuously increasing, you need to (maybe temporarily) increase the number of the gnocchi-metricd daemons. You can run any number of metricd daemons on any number of servers.

For director-based deployments, you can adjust certain metric processing parameters in your environment file:

  • MetricProcessingDelay - Adjusts the delay period between iterations of metric processing.
  • GnocchiMetricdWorkers - Configure the number of metricd workers.

3.7. Monitoring the Time Series Database

The /v1/status endpoint of the HTTP API returns various information, such as the number of measures to process (measures backlog), which you can easily monitor. Making sure that the HTTP server and the gnocchi-metricd daemon are running and are not writing anything alarming in their logs is a sign of good health of the overall system.

3.8. Backing up and Restoring the Time Series Database

In order to be able to recover from an unfortunate event, you need to backup both the index and the storage. That means creating a database dump (PostgreSQL or MySQL) and doing snapshots or copies of your data storage (Ceph, Swift or your file system). The procedure to restore is: restore your index and storage backups, re-install Gnocchi if necessary, and restart it.

Chapter 4. Capacity Metering using the Telemetry Service

The OpenStack Telemetry service provides usage metrics that can be leveraged for billing, charge-back, and show-back purposes. Such metrics data can also be used by third-party applications to plan for capacity on the cluster and can also be leveraged for auto-scaling virtual instances using OpenStack Heat. For more information, see Auto Scaling for Instances.

The combination of ceilometer and gnocchi can be used for monitoring and alarms. This is supported on small-size clusters and with known limitations. For real-time monitoring, Red Hat OpenStack Platform ships with agents that provide metrics data, and can be consumed by separate monitoring infrastructure and applications. For more information, see Monitoring Tools Configuration.

4.1. View Measures

To list all the measures for a particular resource:

# gnocchi measures show --resource-id UUID METER_NAME

To list only measures for a particular resource, within a range of timestamps:

# gnocchi measures show --aggregation mean --start START_TIME --end STOP_TIME --resource-id UUID METER_NAME

Where START_TIME and END_TIME are in the form iso-dateThh:mm:ss.

4.2. Create New Measures

You can use measures to send data to the Telemetry service, and they do not need to correspond to a previously-defined meter. For example:

# gnocchi measures add -m 2015-01-12T17:56:23@42 --resource-id UUID METER_NAME

4.3. Example: View Cloud Usage Measures

This example shows the average memory usage of all instances for each project.

gnocchi measures aggregation --resource-type instance --groupby project_id -m memory

4.4. Example: View L3 Cache Usage

If your Intel hardware and libvirt version supports Cache Monitoring Technology (CMT), you can use the cpu_l3_cache meter to monitor the amount of L3 cache used by an instance.

Monitoring the L3 cache requires the following:

  • cmt in the LibvirtEnabledPerfEvents parameter.
  • cpu_l3_cache in the gnocchi_resources.yaml file.
  • cpu_l3_cache in the Ceilometer polling.yaml file.

Enable L3 Cache Monitoring

To enable L3 cache monitoring:

  1. Create a YAML file for telemetry (for example, ceilometer-environment.yaml) and add cmt to the LibvirtEnabledPerfEvents parameter.

    parameter_defaults:
        LibvirtEnabledPerfEvents: cmt
  2. Launch the overcloud with this YAML file.

      #!/bin/bash
    
      openstack overcloud deploy \
      --templates \
     <additional templates> \
     -e  /home/stack/ceilometer-environment.yaml
  3. Verify that cpu_l3_cache is enabled in gnocchi on the Compute node.

    $ sudo -i
    # podman exec -ti ceilometer_agent_compute cat /etc/ceilometer/gnocchi_resources.yaml | grep cpu_l3_cache
  4. Verify that cpu_l3_cache is enabled for Telemetry polling.

    # podman exec -ti ceilometer_agent_compute cat /etc/ceilometer/polling.yaml  | grep cpu_l3_cache
  5. If cpu_l3_cache is not enabled for Telemetry, enable it and restart the service.

    # podman exec -ti ceilometer_agent_compute echo "        - cpu_l3_cache" >> /etc/ceilometer/polling.yaml
    
    # podman exec -ti ceilometer_agent_compute pkill -HUP -f "ceilometer.*master process"
    Note

    This podman change will not persist over a reboot.

After you have launched a guest instance on this compute node, you can use the gnocchi measures show command to monitor the CMT metrics.

(overcloud) [stack@undercloud-0 ~]$ gnocchi measures show --resource-id a6491d92-b2c8-4f6d-94ba-edc9dfde23ac cpu_l3_cache
+---------------------------+-------------+-----------+
| timestamp                 | granularity |     value |
+---------------------------+-------------+-----------+
| 2017-10-25T09:40:00+00:00 |       300.0 | 1966080.0 |
| 2017-10-25T09:45:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T09:50:00+00:00 |       300.0 | 2129920.0 |
| 2017-10-25T09:55:00+00:00 |       300.0 | 1966080.0 |
| 2017-10-25T10:00:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:05:00+00:00 |       300.0 | 2195456.0 |
| 2017-10-25T10:10:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:15:00+00:00 |       300.0 | 1998848.0 |
| 2017-10-25T10:20:00+00:00 |       300.0 | 2097152.0 |
| 2017-10-25T10:25:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:30:00+00:00 |       300.0 | 1966080.0 |
| 2017-10-25T10:35:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:40:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:45:00+00:00 |       300.0 | 1933312.0 |
| 2017-10-25T10:50:00+00:00 |       300.0 | 2850816.0 |
| 2017-10-25T10:55:00+00:00 |       300.0 | 2359296.0 |
| 2017-10-25T11:00:00+00:00 |       300.0 | 2293760.0 |
+---------------------------+-------------+-----------+

4.5. View Existing Alarms

To list the existing Telemetry alarms, use the aodh command. For example:

# aodh alarm list
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+
| alarm_id                             | type                                       | name                       | state             | severity | enabled |
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+
| 922f899c-27c8-4c7d-a2cf-107be51ca90a | gnocchi_aggregation_by_resources_threshold | iops-monitor-read-requests | insufficient data | low      | True    |
+--------------------------------------+--------------------------------------------+----------------------------+-------------------+----------+---------+

To list the meters assigned to a resource, specify the UUID of the resource (an instance, image, or volume, among others). For example:

# gnocchi resource show 5e3fcbe2-7aab-475d-b42c-a440aa42e5ad

4.6. Create an Alarm

You can use aodh to create an alarm that activates when a threshold value is reached. In this example, the alarm activates and adds a log entry when the average CPU utilization for an individual instance exceeds 80%. A query is used to isolate the specific instance’s id (94619081-abf5-4f1f-81c7-9cedaa872403) for monitoring purposes:

# aodh alarm create --type gnocchi_aggregation_by_resources_threshold --name cpu_usage_high --metric cpu_util --threshold 80 --aggregation-method sum --resource-type instance --query '{"=": {"id": "94619081-abf5-4f1f-81c7-9cedaa872403"}}' --alarm-action 'log://'
+---------------------------+-------------------------------------------------------+
| Field                     | Value                                                 |
+---------------------------+-------------------------------------------------------+
| aggregation_method        | sum                                                   |
| alarm_actions             | [u'log://']                                           |
| alarm_id                  | b794adc7-ed4f-4edb-ace4-88cbe4674a94                  |
| comparison_operator       | eq                                                    |
| description               | gnocchi_aggregation_by_resources_threshold alarm rule |
| enabled                   | True                                                  |
| evaluation_periods        | 1                                                     |
| granularity               | 60                                                    |
| insufficient_data_actions | []                                                    |
| metric                    | cpu_util                                              |
| name                      | cpu_usage_high                                        |
| ok_actions                | []                                                    |
| project_id                | 13c52c41e0e543d9841a3e761f981c20                      |
| query                     | {"=": {"id": "94619081-abf5-4f1f-81c7-9cedaa872403"}} |
| repeat_actions            | False                                                 |
| resource_type             | instance                                              |
| severity                  | low                                                   |
| state                     | insufficient data                                     |
| state_timestamp           | 2016-12-09T05:18:53.326000                            |
| threshold                 | 80.0                                                  |
| time_constraints          | []                                                    |
| timestamp                 | 2016-12-09T05:18:53.326000                            |
| type                      | gnocchi_aggregation_by_resources_threshold            |
| user_id                   | 32d3f2c9a234423cb52fb69d3741dbbc                      |
+---------------------------+-------------------------------------------------------+

To edit an existing threshold alarm, use the aodh alarm update command. For example, to increase the alarm threshold to 75%:

# aodh alarm update --name cpu_usage_high --threshold 75

4.7. Disable or Delete an Alarm

To disable an alarm:

# aodh alarm update --name cpu_usage_high --enabled=false

To delete an alarm:

# aodh alarm delete --name cpu_usage_high

4.8. Example: Monitor the disk activity of instances

The following example demonstrates how to use an Aodh alarm to monitor the cumulative disk activity for all the instances contained within a particular project.

1. Review the existing projects, and select the appropriate UUID of the project you need to monitor. This example uses the admin tenant:

$ openstack project list
+----------------------------------+----------+
| ID                               | Name     |
+----------------------------------+----------+
| 745d33000ac74d30a77539f8920555e7 | admin    |
| 983739bb834a42ddb48124a38def8538 | services |
| be9e767afd4c4b7ead1417c6dfedde2b | demo     |
+----------------------------------+----------+

2. Use the project’s UUID to create an alarm that analyses the sum() of all read requests generated by the instances in the admin tenant (the query can be further restrained with the --query parameter).

# aodh alarm create --type gnocchi_aggregation_by_resources_threshold --name iops-monitor-read-requests --metric disk.read.requests.rate --threshold 42000 --aggregation-method sum --resource-type instance --query '{"=": {"project_id": "745d33000ac74d30a77539f8920555e7"}}'
+---------------------------+-----------------------------------------------------------+
| Field                     | Value                                                     |
+---------------------------+-----------------------------------------------------------+
| aggregation_method        | sum                                                       |
| alarm_actions             | []                                                        |
| alarm_id                  | 192aba27-d823-4ede-a404-7f6b3cc12469                      |
| comparison_operator       | eq                                                        |
| description               | gnocchi_aggregation_by_resources_threshold alarm rule     |
| enabled                   | True                                                      |
| evaluation_periods        | 1                                                         |
| granularity               | 60                                                        |
| insufficient_data_actions | []                                                        |
| metric                    | disk.read.requests.rate                                   |
| name                      | iops-monitor-read-requests                                |
| ok_actions                | []                                                        |
| project_id                | 745d33000ac74d30a77539f8920555e7                          |
| query                     | {"=": {"project_id": "745d33000ac74d30a77539f8920555e7"}} |
| repeat_actions            | False                                                     |
| resource_type             | instance                                                  |
| severity                  | low                                                       |
| state                     | insufficient data                                         |
| state_timestamp           | 2016-11-08T23:41:22.919000                                |
| threshold                 | 42000.0                                                   |
| time_constraints          | []                                                        |
| timestamp                 | 2016-11-08T23:41:22.919000                                |
| type                      | gnocchi_aggregation_by_resources_threshold                |
| user_id                   | 8c4aea738d774967b4ef388eb41fef5e                          |
+---------------------------+-----------------------------------------------------------+

4.9. Example: Monitor CPU usage

If you want to monitor an instance’s performance, you would start by examining the gnocchi database to identify which metrics you can monitor, such as memory or CPU usage. For example, run gnocchi resource show against an instance to identify which metrics can be monitored:

  1. Query the available metrics for a particular instance UUID:

    $ gnocchi resource show --type instance d71cdf9a-51dc-4bba-8170-9cd95edd3f66
    +-----------------------+---------------------------------------------------------------------+
    | Field                 | Value                                                               |
    +-----------------------+---------------------------------------------------------------------+
    | created_by_project_id | 44adccdc32614688ae765ed4e484f389                                    |
    | created_by_user_id    | c24fa60e46d14f8d847fca90531b43db                                    |
    | creator               | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389   |
    | display_name          | test-instance                                                       |
    | ended_at              | None                                                                |
    | flavor_id             | 14c7c918-df24-481c-b498-0d3ec57d2e51                                |
    | flavor_name           | m1.tiny                                                             |
    | host                  | overcloud-compute-0                                                 |
    | id                    | d71cdf9a-51dc-4bba-8170-9cd95edd3f66                                |
    | image_ref             | e75dff7b-3408-45c2-9a02-61fbfbf054d7                                |
    | metrics               | compute.instance.booting.time: c739a70d-2d1e-45c1-8c1b-4d28ff2403ac |
    |                       | cpu.delta: 700ceb7c-4cff-4d92-be2f-6526321548d6                     |
    |                       | cpu: 716d6128-1ea6-430d-aa9c-ceaff2a6bf32                           |
    |                       | cpu_l3_cache: 3410955e-c724-48a5-ab77-c3050b8cbe6e                  |
    |                       | cpu_util: b148c392-37d6-4c8f-8609-e15fc15a4728                      |
    |                       | disk.allocation: 9dd464a3-acf8-40fe-bd7e-3cb5fb12d7cc               |
    |                       | disk.capacity: c183d0da-e5eb-4223-a42e-855675dd1ec6                 |
    |                       | disk.ephemeral.size: 15d1d828-fbb4-4448-b0f2-2392dcfed5b6           |
    |                       | disk.iops: b8009e70-daee-403f-94ed-73853359a087                     |
    |                       | disk.latency: 1c648176-18a6-4198-ac7f-33ee628b82a9                  |
    |                       | disk.read.bytes.rate: eb35828f-312f-41ce-b0bc-cb6505e14ab7          |
    |                       | disk.read.bytes: de463be7-769b-433d-9f22-f3265e146ec8               |
    |                       | disk.read.requests.rate: 588ca440-bd73-4fa9-a00c-8af67262f4fd       |
    |                       | disk.read.requests: 53e5d599-6cad-47de-b814-5cb23e8aaf24            |
    |                       | disk.root.size: cee9d8b1-181e-4974-9427-aa7adb3b96d9                |
    |                       | disk.usage: 4d724c99-7947-4c6d-9816-abbbc166f6f3                    |
    |                       | disk.write.bytes.rate: 45b8da6e-0c89-4a6c-9cce-c95d49d9cc8b         |
    |                       | disk.write.bytes: c7734f1b-b43a-48ee-8fe4-8a31b641b565              |
    |                       | disk.write.requests.rate: 96ba2f22-8dd6-4b89-b313-1e0882c4d0d6      |
    |                       | disk.write.requests: 553b7254-be2d-481b-9d31-b04c93dbb168           |
    |                       | memory.bandwidth.local: 187f29d4-7c70-4ae2-86d1-191d11490aad        |
    |                       | memory.bandwidth.total: eb09a4fc-c202-4bc3-8c94-aa2076df7e39        |
    |                       | memory.resident: 97cfb849-2316-45a6-9545-21b1d48b0052               |
    |                       | memory.swap.in: f0378d8f-6927-4b76-8d34-a5931799a301                |
    |                       | memory.swap.out: c5fba193-1a1b-44c8-82e3-9fdc9ef21f69               |
    |                       | memory.usage: 7958d06d-7894-4ca1-8c7e-72ba572c1260                  |
    |                       | memory: a35c7eab-f714-4582-aa6f-48c92d4b79cd                        |
    |                       | perf.cache.misses: da69636d-d210-4b7b-bea5-18d4959e95c1             |
    |                       | perf.cache.references: e1955a37-d7e4-4b12-8a2a-51de4ec59efd         |
    |                       | perf.cpu.cycles: 5d325d44-b297-407a-b7db-cc9105549193               |
    |                       | perf.instructions: 973d6c6b-bbeb-4a13-96c2-390a63596bfc             |
    |                       | vcpus: 646b53d0-0168-4851-b297-05d96cc03ab2                         |
    | original_resource_id  | d71cdf9a-51dc-4bba-8170-9cd95edd3f66                                |
    | project_id            | 3cee262b907b4040b26b678d7180566b                                    |
    | revision_end          | None                                                                |
    | revision_start        | 2017-11-16T04:00:27.081865+00:00                                    |
    | server_group          | None                                                                |
    | started_at            | 2017-11-16T01:09:20.668344+00:00                                    |
    | type                  | instance                                                            |
    | user_id               | 1dbf5787b2ee46cf9fa6a1dfea9c9996                                    |
    +-----------------------+---------------------------------------------------------------------+

    In this result, the metrics value lists the components you can monitor using Aodh alarms, for example cpu_util.

  2. To monitor CPU usage, you will need the cpu_util metric. To see more information on this metric:

    $ gnocchi metric show --resource d71cdf9a-51dc-4bba-8170-9cd95edd3f66 cpu_util
    +------------------------------------+-------------------------------------------------------------------+
    | Field                              | Value                                                             |
    +------------------------------------+-------------------------------------------------------------------+
    | archive_policy/aggregation_methods | std, count, min, max, sum, mean                                   |
    | archive_policy/back_window         | 0                                                                 |
    | archive_policy/definition          | - points: 8640, granularity: 0:05:00, timespan: 30 days, 0:00:00  |
    | archive_policy/name                | low                                                               |
    | created_by_project_id              | 44adccdc32614688ae765ed4e484f389                                  |
    | created_by_user_id                 | c24fa60e46d14f8d847fca90531b43db                                  |
    | creator                            | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389 |
    | id                                 | b148c392-37d6-4c8f-8609-e15fc15a4728                              |
    | name                               | cpu_util                                                          |
    | resource/created_by_project_id     | 44adccdc32614688ae765ed4e484f389                                  |
    | resource/created_by_user_id        | c24fa60e46d14f8d847fca90531b43db                                  |
    | resource/creator                   | c24fa60e46d14f8d847fca90531b43db:44adccdc32614688ae765ed4e484f389 |
    | resource/ended_at                  | None                                                              |
    | resource/id                        | d71cdf9a-51dc-4bba-8170-9cd95edd3f66                              |
    | resource/original_resource_id      | d71cdf9a-51dc-4bba-8170-9cd95edd3f66                              |
    | resource/project_id                | 3cee262b907b4040b26b678d7180566b                                  |
    | resource/revision_end              | None                                                              |
    | resource/revision_start            | 2017-11-17T00:05:27.516421+00:00                                  |
    | resource/started_at                | 2017-11-16T01:09:20.668344+00:00                                  |
    | resource/type                      | instance                                                          |
    | resource/user_id                   | 1dbf5787b2ee46cf9fa6a1dfea9c9996                                  |
    | unit                               | None                                                              |
    +------------------------------------+-------------------------------------------------------------------+
    • archive_policy - Defines the aggregation interval for calculating the std, count, min, max, sum, mean values.
  3. Use Aodh to create a monitoring task that queries cpu_util. This task will trigger events based on the settings you specify. For example, to raise a log entry when an instance’s CPU spikes over 80% for an extended duration:

    aodh alarm create \
      --project-id 3cee262b907b4040b26b678d7180566b \
      --name high-cpu \
      --type gnocchi_resources_threshold \
      --description 'High CPU usage' \
      --metric cpu_util \
      --threshold 80.0 \
      --comparison-operator ge \
      --aggregation-method mean \
      --granularity 300 \
      --evaluation-periods 1 \
      --alarm-action 'log://' \
      --ok-action 'log://' \
      --resource-type instance \
      --resource-id d71cdf9a-51dc-4bba-8170-9cd95edd3f66
    +---------------------------+--------------------------------------+
    | Field                     | Value                                |
    +---------------------------+--------------------------------------+
    | aggregation_method        | mean                                 |
    | alarm_actions             | [u'log://']                          |
    | alarm_id                  | 1625015c-49b8-4e3f-9427-3c312a8615dd |
    | comparison_operator       | ge                                   |
    | description               | High CPU usage                       |
    | enabled                   | True                                 |
    | evaluation_periods        | 1                                    |
    | granularity               | 300                                  |
    | insufficient_data_actions | []                                   |
    | metric                    | cpu_util                             |
    | name                      | high-cpu                             |
    | ok_actions                | [u'log://']                          |
    | project_id                | 3cee262b907b4040b26b678d7180566b     |
    | repeat_actions            | False                                |
    | resource_id               | d71cdf9a-51dc-4bba-8170-9cd95edd3f66 |
    | resource_type             | instance                             |
    | severity                  | low                                  |
    | state                     | insufficient data                    |
    | state_reason              | Not evaluated yet                    |
    | state_timestamp           | 2017-11-16T05:20:48.891365           |
    | threshold                 | 80.0                                 |
    | time_constraints          | []                                   |
    | timestamp                 | 2017-11-16T05:20:48.891365           |
    | type                      | gnocchi_resources_threshold          |
    | user_id                   | 1dbf5787b2ee46cf9fa6a1dfea9c9996     |
    +---------------------------+--------------------------------------+
    • comparison-operator - The ge operator defines that the alarm will trigger if the CPU usage is greater than (or equal to) 80%.
    • granularity - Metrics have an archive policy associated with them; the policy can have various granularities (for example, 5 minutes aggregation for 1 hour + 1 hour aggregation over a month). The granularity value must match the duration described in the archive policy.
    • evaluation-periods - Number of granularity periods that need to pass before the alarm will trigger. For example, setting this value to 2 will mean that the CPU usage will need to be over 80% for two polling periods before the alarm will trigger.
    • [u’log://'] - This value will log events to your Aodh log file.

      Note

      You can define different actions to run when an alarm is triggered (alarm_actions), and when it returns to a normal state (ok_actions), such as a webhook URL.

  4. To check if your alarm has been triggered, query the alarm’s history:

    aodh alarm-history show 1625015c-49b8-4e3f-9427-3c312a8615dd --fit-width
    +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+
    | timestamp                  | type             | detail                                                                                                                                            | event_id                             |
    +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+
    | 2017-11-16T05:21:47.850094 | state transition | {"transition_reason": "Transition to ok due to 1 samples inside threshold, most recent: 0.0366665763", "state": "ok"}                             | 3b51f09d-ded1-4807-b6bb-65fdc87669e4 |
    +----------------------------+------------------+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+

4.10. Manage Resource Types

Telemetry resource types that were previously hardcoded can now be managed by the gnocchi client. You can use the gnocchi client to create, view, and delete resource types, and you can use the gnocchi API to update or delete attributes.

1. Create a new resource-type:

$ gnocchi resource-type create testResource01 -a bla:string:True:min_length=123
+----------------+------------------------------------------------------------+
| Field          | Value                                                      |
+----------------+------------------------------------------------------------+
| attributes/bla | max_length=255, min_length=123, required=True, type=string |
| name           | testResource01                                             |
| state          | active                                                     |
+----------------+------------------------------------------------------------+

2. Review the configuration of the resource-type:

$ gnocchi resource-type show testResource01
+----------------+------------------------------------------------------------+
| Field          | Value                                                      |
+----------------+------------------------------------------------------------+
| attributes/bla | max_length=255, min_length=123, required=True, type=string |
| name           | testResource01                                             |
| state          | active                                                     |
+----------------+------------------------------------------------------------+

3. Delete the resource-type:

$ gnocchi resource-type delete testResource01
Note

You cannot delete a resource type if a resource is using it.

Chapter 5. Troubleshooting

This chapter contains logging and support information to assist with troubleshooting your Red Hat OpenStack Platform deployment.

5.1. Support

If client commands fail or you run into other issues, contact Red Hat Technical Support with a description of what happened, the full console output, all log files referenced in the console output, and an sosreport from the node that is (or might be) in trouble. For example, if you encounter a problem on the compute level, run sosreport on the Nova node, or if it is a networking issue, run the utility on the Neutron node. For general deployment issues, it is best to run sosreport on the cloud controller.

For information about the sosreport command (sos package), refer to What is a sosreport and how to create one in Red Hat Enterprise Linux 4.6 and later.

Check also the /var/log/messages file for any hints.

5.2. Troubleshoot Identity Client (keystone) Connectivity Problems

When the Identity client (keystone) is unable to contact the Identity service it returns an error:

Unable to communicate with identity service: [Errno 113] No route to host. (HTTP 400)

To debug the issue check for these common causes:

Identity service is down

Identity Service now runs within httpd.service. On the system hosting the Identity service, check the service status:

# systemctl status httpd.service

If the service is not active then log in as the root user and start it.

# systemctl start httpd.service
Firewall is not configured properly
The firewall might not be configured to allow TCP traffic on ports 5000 and 35357. If so, see Managing the Overcloud Firewall in the Advanced Overcloud Customization guide for instructions on checking your firewall settings and defining custom rules.
Service Endpoints not defined correctly

On the system hosting the Identity service check that the endpoints are defined correctly.

  1. Obtain the administration token:

    # grep admin_token /etc/keystone/keystone.conf
    admin_token = 91f0866234a64fc299db8f26f8729488
  2. Determine the correct administration endpoint for the Identity service:

    http://IP:35357/VERSION

    Replace IP with the IP address or host name of the system hosting the Identity service. Replace VERSION with the API version (v2.0, or v3) that is in use.

  3. Unset any pre-defined Identity service related environment variables:

    # unset OS_USERNAME OS_TENANT_NAME OS_PASSWORD OS_AUTH_URL
  4. Use the administration token and endpoint to authenticate with the Identity service. Confirm that the Identity service endpoint is correct. For example:

    # openstack endpoint list --os-token=91f0556234a64fc299db8f26f8729488 --os-url=https://osp.lab.local:35357/v3/  --os-identity-api-version 3

    Verify that the listed publicurl, internalurl, and adminurl for the Identity service are correct. In particular ensure that the IP addresses and port numbers listed within each endpoint are correct and reachable over the network.

    If these values are incorrect, add the correct endpoint and remove any incorrect endpoints using the endpoint delete action of the openstack command. For example:

    # openstack endpoint delete 2d32fa6feecc49aab5de538bdf7aa018  --os-token=91f0866234a64fc299db8f26f8729488 --os-url=https://osp.lab.local:35357/v3/ --os-identity-api-version 3

    Replace TOKEN and ENDPOINT with the values identified previously. Replace ID with the identity of the endpoint to remove as listed by the endpoint-list action.

5.3. Troubleshoot OpenStack Networking Issues

This section discusses the different commands you can use and procedures you can follow to troubleshoot the OpenStack Networking service issues.

Debugging Networking Device
  • Use the ip a command to display all the physical and virtual devices.
  • Use the ovs-vsctl show command to display the interfaces and bridges in a virtual switch.
  • Use the ovs-dpctl show command to show datapaths on the switch.
Tracking Networking Packets
  • Use the tcpdump command to see where packets are not getting through.

    # tcpdump -n -i INTERFACE -e -w FILENAME

    Replace INTERFACE with the name of the network interface to see where the packets are not getting through. The interface name can be the name of the bridge or host Ethernet device.

    The -e flag ensures that the link-level header is dumped (in which the vlan tag will appear).

    The -w flag is optional. You can use it only if you want to write the output to a file. If not, the output is written to the standard output (stdout).

    For more information about tcpdump, refer to its manual page by running man tcpdump.

Debugging Network Namespaces
  • Use the ip netns list command to list all known network namespaces.
  • Use the ip netns exec command to show routing tables inside specific namespaces.

    # ip netns exec NAMESPACE_ID bash
    # route -n

    Start the ip netns exec command in a bash shell so that subsequent commands can be invoked without the ip netns exec command.

5.4. Troubleshoot Networks and Routes Tab Display Issues in the Dashboard

The Networks and Routers tabs only appear in the dashboard when the environment is configured to use OpenStack Networking. In particular note that by default the PackStack utility currently deploys Nova Networking and as such in environments deployed in this manner the tab will not be visible.

If OpenStack Networking is deployed in the environment but the tabs still do not appear ensure that the service endpoints are defined correctly in the Identity service, that the firewall is allowing access to the endpoints, and that the services are running.

5.5. Troubleshoot Instance Launching Errors in the Dashboard

When using the dashboard to launch instances if the operation fails, a generic ERROR message is displayed. Determining the actual cause of the failure requires the use of the command line tools.

Use the nova list command to locate the unique identifier of the instance. Then use this identifier as an argument to the nova show command. One of the items returned will be the error condition. The most common value is NoValidHost.

This error indicates that no valid host was found with enough available resources to host the instance. To work around this issue, consider choosing a smaller instance size or increasing the overcommit allowances for your environment.

Note

To host a given instance, the compute node must have not only available CPU and RAM resources but also enough disk space for the ephemeral storage associated with the instance.

5.6. Troubleshoot Keystone v3 Dashboard Authentication

django_openstack_auth is a pluggable Django authentication back end, that works with Django’s contrib.auth framework, to authenticate a user against the OpenStack Identity service API. Django_openstack_auth uses the token object to encapsulate user and Keystone related information. The dashboard uses the token object to rebuild the Django user object.

The token object currently stores:

  • Keystone token
  • User information
  • Scope
  • Roles
  • Service catalog

The dashboard uses Django’s sessions framework for handling user session data. The following is a list of numerous session back ends available, which are controlled through the SESSION_ENGINE setting in your local_settings.py file:

  • Local Memory Cache
  • Memcached
  • Database
  • Cached Database
  • Cookies

In some cases, particularly when a signed cookie session back end is used and, when having many or all services enabled all at once, the size of cookies can reach its limit and the dashboard can fail to log in. One of the reasons for the growth of cookie size is the service catalog. As more services are registered, the bigger the size of the service catalog would be.

In such scenarios, to improve the session token management, include the following configuration settings for logging in to the dashboard, especially when using Keystone v3 authentication.

  1. In /usr/share/openstack-dashboard/openstack_dashboard/settings.py add the following configuration:

    DATABASES =
    {
      'default':
      {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'horizondb',
        'USER': 'User Name',
        'PASSWORD': 'Password',
        'HOST': 'localhost',
       }
    }
  2. In the same file, change SESSION_ENGINE to:

    SESSION_ENGINE = 'django.contrib.sessions.backends.cached_db'
  3. Connect to the database service using the mysql command, replacing USER with the user name by which to connect. The USER must be a root user (or at least as a user with the correct permission: create db).

    # mysql -u USER -p
  4. Create the Horizon database.

    mysql > create database horizondb;
  5. Exit the mysql client.

    mysql > exit
  6. Change to the openstack_dashboard directory and sync the database using:

    # cd /usr/share/openstack-dashboard/openstack_dashboard
    $ ./manage.py syncdb

    You do not need to create a superuser, so answer 'n' to the question.

  7. Restart Apache http server. For Red Hat Enterprise Linux:

    # systemctl restart httpd

5.7. OpenStack Dashboard - Red Hat Access Tab

The Red Hat Access tab, which is part of the OpenStack dashboard, allows you to search for and read articles or solutions from the Red Hat Customer Portal, view logs from your instances and diagnose them, and work with your customer support cases.

Figure 5.1. Red Hat Access Tab.

Red Hat Access Tab - begin searching
Important

You must be logged in to the Red Hat Customer Portal in the browser in order to be able to use the functions provided by the Red Hat Access tab.

If you are not logged in, you can do so now:

  1. Click Log In.
  2. Enter your Red Hat login.
  3. Enter your Red Hat password.
  4. Click Sign in.

This is how the form looks:

Figure 5.2. Logging in to the Red Hat Customer Portal.

Red Hat Access Tab - log in

If you do not log in now, you will be prompted for your Red Hat login and password when you use one of the functions that require authentication.

5.7.2. Logs

Here you can read logs from your OpenStack instances:

Figure 5.4. Instance Logs on the Red Hat Access Tab.

Red Hat Access Tab - instance logs

Find the instance of your choice in the table. If you have many instances, you can filter them by name, status, image ID, or flavor ID. Click View Log in the Actions column for the instance to check.

When an instance log is displayed, you can click Red Hat Diagnose to get recommendations regarding its contents:

Figure 5.5. Instance Logs on the Red Hat Access Tab.

Red Hat Access Tab - instance log details

If none of the recommendations are useful or a genuine problem has been logged, click Open a New Support Case to report the problem to Red Hat Support.

5.7.3. Support

The last option in the Red Hat Access Tab allows you to search for your support cases at the Red Hat Customer Portal:

Figure 5.6. Search for Support Cases.

Red Hat Access Tab - support cases

You can also open a new support case by clicking the appropriate button and filling out the form on the following page:

Figure 5.7. Open a New Support Case.

Red Hat Access Tab - new support case

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