Release Notes 4.7

What’s New

Simplifying service.monitor setup: script based equipment of Elasticsearch Stack

The initial configuration and setup of service.monitor has been significantly simplified. By using Python scripts, the service.monitor-related configurations of Kibana and Elasticsearch can now be carried out almost automatically. This applies to the Kibana Space, the Index Templates, the Index Lifecycle Templates, the Elastic Ingest Pipelines, Dashboards & Visualizations and the Kibana Rules.

More complete FME Flow support

The capabilities of service.monitor for integrating FME Flow from Safe have been expanded and improved. A new addition is the transfer of log data generated by the server processes. The transfer of job and job log data has been migrated from Logstash to Python. This enables faster recording and greater efficiency. Various diagrams have been added to the FME Flow dashboards.

Basic Kibana rules to check your GeoIT infrastructure

service.monitor now offers a number of rules that help to identify incidents and problem situations when monitoring events.

Directly send monitoring events to Elasticsearch

Monitoring can now be configured so that information about monitoring events can be sent directly to Elasticsearch and not via Logstash.

Update Notes

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Discontinued Features

  • INSPIRE Capabilities of Monitoring.

Deprecated Features

  • Apache Tomcat 9

  • Elasticsearch 7.x

  • In the course of adapting the field names to the Elastic Common Scheme, the following fields will be discontinued and removed in version 5.0:

    • Field code of ct-log data (will be replaced by log.syslog.severity.code)

    • Field ags.source of ct-arcgis data (will be replaced by log.logger)

    • Field level of ct-arcgis data (will be replaced by log.syslog.severity.code)

    • Field log_entry.* of ct-fme-log data (will be replaced by an ECS compatible representation)

  • Logstash ct-fme-*-pipelines

  • Logstash ct-monitoring-pipeline

Known Limitations

The following list contains known issues of the version 4.7 at the time of release:

Issue Komponente Beschreibung

SRVMON-800

FME Python API

API responds with an error when there are no objects at FME Flow jobroutes endpoint

SRVMON-802

FME Python API

Requesting FME Flow configured with a self signed SSL certificate fails via SSL

Changelog

4.7.0

New Features

SRVMON-749

Docker Images / Docker compose files for running Elasticsearch, Kibana and Logstash with the most recent service.monitor release

SRVMON-752

Enable script based / automatic configuration of service.monitor within Elasticsearch Stack

SRVMON-754

Extend FME Server Dashboards with diagrams developed in customer project

SRVMON-755

Replace FME Logstash pipelines with Python based job & log harvesting

SRVMON-756

Harvest/aggregate FME Server log files that are only available via file system (using Elastic Ingest Pipelines)

SRVMON-759

[Python scripting/automation] Add support for Kibana Alerts&Rules

SRVMON-765

Support Token Authentication in FmeScheduleJobExecution as additional authentication method

SRVMON-768

[python data setup] Provide script that imports all saved objects for 'service.monitor for FME' only

SRVMON-770

Sync log data models of service.monitor data sources and assure compatibility w/ ECS

SRVMON-784

Replace logstash FME job route pipeline with python implementation

SRVMON-789

For FME Python scripting/Logstash: ensure full compatibility with Elastic Common Schema

SRVMON-767

Provide http json notification templates for german language

SRVMON-790

Verify using labels when "tagging" events

Fixed Issues

SRVMON-751

[Analytics] Ingest pipeline definition with invalid JSON

SRVMON-761

[monitoring] even if no auth method is selected credential reference will be used on "Test Request" action

SRVMON-766

http json notification fails in error state if no timeout value has been set

SRVMON-771

Equipping elastic with saved objects fails if there is already a single pre-existing object

SRVMON-787

FME Log Logstash pipeline sets wrong fields