Upload And Download ~UPD~ Agent Job Description

Insights Hub needs a data source configuration for interpreting the data it receives from an agent. Without this configuration Insights Hub cannot understand the data. The data source configuration contains data sources and data points. Data sources are logical groups, e.g. a sensor or a machine, which contain one or more measurable data points, e.g. temperature or pressure.

Insights Hub needs a data point mapping for storing the data it receives from an agent. This maps the data points from the data source configuration to properties of the digital entity, that represents the agent. When Insights Hub receives data from an agent, it looks up which property the data point is mapped to and stores the data there.

Upload And Download Agent Job Description

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at the end of a request represents a mandatory new line (Carriage Return and Line Feed). This representation is used for emphasis in the following samples.

Replace these characters with \r\n when uploading data to Insights Hub.

The multipart message starts with the --f0Ve5iPP2ySppIcDSR6Bak identifier. It closes with the --f0Ve5iPP2ySppIcDSR6Bak-- identifier, which is equal to the starting identifier appended by a double dash. After accepting the structure of a multipart request, Insights Hub returns an HTTP response 200 OK with an empty body. Insights Hub validates and stores data asynchronously. The agent can continue uploading data as long as its access token is valid.

Agents can upload files into Insights Hub, where they are stored in the respective asset. Within Insights Hub metadata and payload couples are referred as tuples. Each tuple may contain different type of data (for example one tuple may contain a specific timestamp data, whereas the other may contain octet/stream data). The example below contains an exchange payload for an octet/stream mime type:

Agents can upload events into Insights Hub, where they are stored in the respective asset. The events must be of an event type derived from type AgentBaseEvent. The following rules apply for uploading events:

Agents can upload to compress data using .zip format. Sending large volumes of data proves to be costly for the end users from the time and cost perspective. An option to compress the data using .zip format, will reduce the consumption of data/bandwidth.

If Insights Hub cannot process data after it has been uploaded via MindConnect API, MindConnect's Recovery Service stores the unprocessed data for 15 calendar days. For a list of the stored unprocessed data, use the recoverableRecords endpoint. It responds with a page of recoverable records as shown below. The response can be filtered by the agentId, correlationId, requestTime and dropReason fields using a JSON filter.

Data uploaded via MindConnect API can be diagnosed with the Diagnostic Service feature. In order to activate an agent for the Diagnostic Service, the diagnosticActivations endpoint of the MindConnect API is provided, which needs to be used with following parameters:

The Diagnostic Service can be activated for up to 5 agents per tenant. Diagnostic activation statuses are set to inactive after 1 hour. Activations can be deleted by the diagnosticActivations/id endpoint.

A list of diagnostic activations is provided by the diagnosticActivations endpoint. It responds with a page of diagnostic activations as shown below. The response can be filtered by the agentId and status fields using a JSON filter.

Diagnostic data is listed by the diagnosticInformation endpoint. Its response is a page as shown below, which can be filtered by the agentId, correlationId,message,source,timestamp and severity fields using a JSON filter.

The output indicates the agent has not been bootstrapped with any beacon details for uploading files. This is typically done through settings in the mgssetup.ini file on Windows, or the mgsft_rollout_response file on UNIX-like operating systems.

The "FlexNet Manager Platform cannot access the directories describing your FlexNet Manager Platform applications" log message suggests the configuration of the agent on this machine is somewhat suspect.

As per the thread that @durgeshsingh referred to, uninstalling the agent, deleting /var/opt/managesoft and re-installing the agent may be the most pragmatic and safest path forward here to get to a clean/known state.

A few things to check--i noticed you're using SSL. This could be as simple as the cert chain isn't trusted, and therefore won't make the secure connection. I've run into a lot of that with my customers that use internally generated (non-commercial) SSL certs with 3rd part root CAs. Also, if the revocation isn't available to look up, it won't work either. When installing the Unix agent, copy the bootstrap file "mgsft_rollout_response" into the same directory you run the install from. The file should look like this:

The following sections explain the structure and fields of this JSON file. You can also view the schema definition for this configuration file. The schema definition is located at installation-directory/doc/amazon-cloudwatch-agent-schema.json on Linux servers, and at installation-directory/amazon-cloudwatch-agent-schema.json on servers running Windows Server.

If you create or edit the agent configuration file manually, you can give it any name. For simplicity in troubleshooting, we recommend that you name it /opt/aws/amazon-cloudwatch-agent/etc/amazon-cloudwatch-agent.json on a Linux server and $Env:ProgramData\Amazon\AmazonCloudWatchAgent\amazon-cloudwatch-agent.json on servers running Windows Server. After you have created the file, you can copy it to other servers where you want to install the agent.

You can specify this field to have the agent perform logging for AWS SDK endpoints. The value for this field can include one or more of the following options. Separate multiple options with the | character.

The CloudWatch agent automatically rotates the log file that it creates. A log file is rotated out when it reaches 100 MB in size. The agent keeps the rotated log files for up to seven days, and it keeps as many as five backup log files that have been rotated out. Backup log files have a timestamp appended to their filename. The timestamp shows the date and time that the file was rotated out: for example, amazon-cloudwatch-agent-2018-06-08T21-01-50.247.log.gz.

The only supported key-value pairs for append_dimensions are shown in the following list. Any other key-value pairs are ignored. The agent supports these key-value pairs exactly as they are shown in the following list. You can't change the key values to publish different dimension names for them.

The following is an example of a metrics section for a Linux server. In this example, three CPU metrics, three netstat metrics, three process metrics, and one disk metric are collected, and the agent is set up to receive additional metrics from a collectd client.

Within the subsection for each object, you specify a measurement array of the counters to collect. The measurement array is required for each object that you specify in the configuration file. You can also specify a resources field to name the instances to collect metrics from. You can also specify * for resources to collect separate metrics for every instance. If you omit resources for counters that have instances, the data for all instances is aggregated into one set. If you omit resources for counters that don't have instances, the counters are not collected by the CloudWatch agent. To determine whether counters have instances, you can use one of the following commands.

Only the latest file is pushed to CloudWatch Logs based on file modification time. We recommend that you use wildcards to specify a series of files of the same type, such as access_log.2018-06-01-01 and access_log.2018-06-01-02, but not multiple kinds of files, such as access_log_80 and access_log_443. To specify multiple kinds of files, add another log stream entry to the agent configuration file so that each kind of log file goes to a different log stream.

The CloudWatch agent wizard uses -1 as the default value for this field when it is used to create the agent configuration file and you don't specify a value for log retention. This -1 value specifies set by the wizard specifies that the events in the log group don't expire. However, manually editing this value to -1 has no effect.

If you configure the agent to write multiple log streams to the same log group, specifying the retention_in_days in one place will set the log retention for the entire log group. If you specify retention_in_days for the same log group in multiple places, the retention is set if all of those values are equal. However, if different retention_in_days values are specified for the same log group in multiple places, the log retention will not be set and the agent will stop, returning an error.

The agent's IAM role or IAM user must have the logs:PutRetentionPolicy for it to be able to set retention policies. For more information, see Allowing the CloudWatch agent to set log retention policy.

The CloudWatch agent doesn't check the performance of any regular expression that you supply, or restrict the run time of the evaluation of the regular expressions. We recommend that you are careful not to write an expression that is expensive to evaluate. For more information about possible issues, see Regular expression Denial of Service - ReDoS

The order of the filters in the configuration file matters for performance. In the preceding example, the agent drops all the logs that match Firefox before it starts evaluating the second filter. To cause fewer log entries to be evaluated by more than one filter, put the filter that you expect to rule out more logs first in the configuration file.

This list of symbols is different than the list used by the older CloudWatch Logs agent. For a summary of these differences, see Timestamp differences between the unified CloudWatch agent and the earlier CloudWatch Logs agent ff782bc1db