Available metrics: InfluxDB

InfluxDB

Metrics retrieved from the Sense servers can be stored in an InfluxDB database. You don’t have to be an InfluxDB expert to use Butler SOS, but understanding some basic concepts are helpful.

Storing metrics in InfluxDB is not mandatory, but some kind of metrics storage - either in InfluxDB, New Relic or Prometheus - is needed to take full benefit of Butler SOS’ features.

• InfluxDB is a time series database. This means it is super good at storing values that have a timestamp associated with them - and pretty bad at everything else. In many respects time series databases are the opposite of traditional SQL databases (who are usually pretty bad at handling time series data).
• Because of it’s focus on time series data, InfluxDB v1 has its own query language, InfluxQL. It is somewhat similar to SQL, but also has many unique commands and features.
  • Influx DB v2 has a new query language called Flux. There are compatibility layers in InfluxDB v2 that allow you to use InfluxQL, meaning that existing Grafana dashboards and can be kept as they are, even if you upgrade to InfluxDB v2.
  • Flux is a more powerful query language than InfluxQL, but it also has a steeper learning curve. By learning Flux you will be able to do more advanced things with your data, for example in Grafana dashboards.
• It’s worth browsing through the InfluxDB documentation to get a feel for what InfluxDB is and how it works.
  • InfluxDB v1 documentation.
  • InfluxDB v2 documentation.

Metrics structure

The metrics are grouped based on what kind of Qlik Sense data they represent. InfluxDB is a very capable database, so we will only touch on the basics here.

InfluxDB v1 vs v2

That are some differences between InfluxDB v1 and v2 when it comes to terminology and concepts.

For example in InfluxDB v1, the main concepts are databases, measurements, field keys and tag keys.
In InfluxDB v2 the main concepts are buckets, measurements, fields and tags.

The concepts are very similar, but the names are different.

The metrics below are the same for both InfluxDB v1 and v2.

Overview

Measurements are just what it sounds like: snapshots of some value(s), taken at a specific point in time. A measurement can contain several field keys, which for practical purposes can be viewed as the individual metrics.

For example, the list of measurements look like this (using the InfluxDB command line client to explore the database structure):

> use senseops
Using database senseops
> show measurements
name: measurements
name
----
apps
butlersos_memory_usage
cache
cpu
log_event
log_event_logdb
mem
saturated
sense_server
session
user_events
user_session_details
user_session_list
user_session_summary
users
>

Let’s take a look at what field keys the apps measurement contains:

> show field keys from apps
name: apps
fieldKey                     fieldType
--------                     ---------
active_docs                  string
active_docs_count            integer
active_docs_names            string
active_session_docs_names    string
calls                        integer
in_memory_docs               string
in_memory_docs_count         integer
in_memory_docs_names         string
in_memory_session_docs_names string
loaded_docs                  string
loaded_docs_count            integer
loaded_docs_names            string
loaded_session_docs_names    string
selections                   integer
>

Ok, so the field keys are the actual metrics for which we gather data. Collectively those metrics (again: field keys in InfluxDB lingo) above are grouped into a measurement called apps.

There is one more concept you need to understand: tag keys

It’s pretty simple: Tag keys are used to categorise (or simply “tag”) measurements.
Let’s say you use Butler SOS to collect data from ten Sense servers. That’s great, but how will you later distinguish between server 3 and server 8? You need some way of telling your Grafana dashboard to show the data for server 3 (if that’s what you want).

Tags solve this. In the Butler SOS YAML config file you can define any number of tags that will be used to tag data coming in from Qlik Sense.

The beauty of tags is that they play very nicely with Grafana - without them the Grafana dashboards would not be nearly as flexible as they are.

To see what tag keys a certain measurement has you use a query similar to the one above/for fields:

> show tag keys from apps
name: apps
tagKey
------
host
serverBrand
serverLocation
server_description
server_group
server_name
server_type

Note that this list of tags consists of two parts:

1. Tags always present. These are inserted by Butler SOS and are present for all measurements. These are host, server_description and server_name.
2. Tags configured in Butler SOS’ config fil. In the example above these are serverBrand, serverLocation, server_group andserver_type.

Measurements and fields

The measurements are grouped based on what part of Sense they are retrieved from. The groups are

1. General health metrics.
2. Messages from the log database.
3. Detailed metrics about what users are connected to (i.e. have sessions open with) which virtual proxies.
4. Messages from the log database.
5. Log events: Warning, error and fatal messages from QSEoW logs.
6. User events: Session and connection related messages from QSEoW logs.
7. Metric relating to Butler SOS itself (i.e. not retrieved from Sense).

General health metrics

A shared set of tag keys are available for all general health metrics:

Tag key	Description
host	Host name, taken from config file’s Butler-SOS.serversToMonitor.servers[].host property. Usually a fully qualified host name, or in some cases an IP address.
server_name	Human readible/friendly server name, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverName property.
server_description	Description of the server, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverDescription property.

In addition to the above, all tags defined in the YAML config file for the servers will be included as tag keys.

Measurement: apps

Source: Health check API

Field key	Type	Description
active_docs	string	An array of GUIDs of active apps. Empty if no apps are active. An app is active when a user is currently performing some action on it.
active_docs_count	integer	Number of currently active apps
active_docs_names	string	Names of currently active (non-session) apps
active_session_docs_names	string	Names of currently active session apps
in_memory_docs	string	An array ofthe GUIDs of all apps currently loaded into the memory, even if they do not have any open sessions or connections to it. The apps disappear from the list when the engine has purged them out from memory.
in_memory_docs_count	integer	Numer of apps currently in memory
in_memory_docs_names	string	Names of (non-session) apps currently in memory
in_memory_session_docs_names	string	Names of session apps currently in memory
loaded_docs	string	An array of the GUIDs of apps currently loaded into memory and that have open sessions or connections. Empty if no apps are loaded.
loaded_docs_count	integer	Number of currently loaded apps
loaded_docs_names	string	Names of currently loaded (non-session) apps
loaded_session_docs_names	string	Names of currently loaded session apps
calls	integer	Number of calls to the Qlik associative engine since it started
selections	integer	Numer of selections made in Qlik associative engine since it started

Measurement: cache

Source: Health check API

Field key	Type	Description
added	integer	Number of cache objects added to the cache
bytes_added	integer	Number of bytes added to the cache
hits	integer	Number of cache hits in engine
lookups	integer	Number of lookups in egnine
replaced	integer	Number of cache objects replaced

Measurement: cpu

Source: Health check API

Field key	Type	Description
total	integer	Percentage of the CPU used by the engine, averaged over a time period of 30 seconds.

Measurement: mem

Source: Health check API

Field key	Type	Description
allocated	integer	The total amount of allocated memory (committed + reserved) from the operating system in MB.
committed	integer	The total amount of committed memory for the engine process in MB.
free	integer	The total amount of free memory (minimum of free virtual and physical memory) in MB.

Measurement: saturated

Source: Health check API

Field key	Type	Description
saturated	boolean	When the value is true, the engine is running with high resource usage; otherwise the value is false. See link above for details.

Measurement: sense_server

Source: Health check API

Field key	Type	Description
started	string	ISO timestamp when the engine service was started.
uptime	string	Time since engine service was started (human readable).
version	string	Engine version.

Measurement: session

Source: Health check API

Field key	Type	Description
active	integer	Number of active engine sessions. A session is active when a user is currently performing some action on an app, for example, making selections or creating content.
total	integer	Total number of engine sessions.

Measurement: users

Source: Health check API

Field key	Type	Description
active	integer	Number of users currently doing something in some app.
total	integer	Number of users with established sessions to the Sense server.

User session details

User session metrics have slightly different tag keys depending on the granularity level of the metric - those metrics are therefore listed under each heading below.

Measurement: user_session_summary

Source: Session module API

Field key	Type	Description
session_count	float	Total number of sessions, per server and virtual proxy.
session_user_id_list	string	List of user IDs with sessions, per server and virtual proxy. NOTE: A single user may have more than one session open to a particular server/virtual proxy.

Tag keys:

Tag key	Description
host	Host name, taken from config file’s Butler-SOS.serversToMonitor.servers[].host property. Usually a fully qualified host name, or in some cases an IP address.
server_name	Human readible/friednly server name, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverName property.
server_description	Description of the server, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverDescription property.
user_session_host	Host name the session metrics are associated with.
user_session_virtual_proxy	Virtual proxy name the session metrics are associated with.

Measurement: user_session_list

Source: Session module API

Field key	Type	Description
session_user_id_list	string	List of user IDs with sessions, per server and virtual proxy. NOTE: A single user may have more than one session open to a particular server/virtual proxy.

Tag keys:

Tag key	Description
host	Host name, taken from config file’s Butler-SOS.serversToMonitor.servers[].host property. Usually a fully qualified host name, or in some cases an IP address.
server_name	Human readible/friednly server name, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverName property.
server_description	Description of the server, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverDescription property.
user_session_host	Host name the session metrics are associated with.
user_session_virtual_proxy	Virtual proxy name the session metrics are associated with.

Measurement: user_session_details

Source: Session module API

Field key	Type	Description
session_id	string	Session GUID, uniquely identifying the session in the entire Sense cluster.
user_directory	string	Session user’s user directory.
user_id	string	Session user ID

Tag keys:

Tag key	Description
host	Host name, taken from config file’s Butler-SOS.serversToMonitor.servers[].host property. Usually a fully qualified host name, or in some cases an IP address.
server_name	Human readible/friednly server name, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverName property.
server_description	Description of the server, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverDescription property.
user_session_host	Host name the session metrics are associated with.
user_session_virtual_proxy	Virtual proxy name the session metrics are associated with.
user_session_id	Session GUID
user_session_user_directory	User’s user directory
user_session_user_id	User ID

User events

User events capture real-time events in Qlik Sense as they happen.
They originate from Sense’s log4net logging framework and are forwarded from Sense to Butler SOS by means of XML log appenders in Sense.
These events are also forwarded as MQTT messages, allowing other systems to act when warnings/errors/fatals occur in Qlik Sense.

Setup instructions here.

The following user events are handled by Butler SOS:

• Session start
• Session stop
• Connection open
• Connection close.

Measurement: user_events

Tag keys present for all user_events records:

Tag key	Description
event_action	Indicates what the event is about. Examples: Start session, Stop session, Open connection, Close connection.
host	Host name as reported in Qlik Sense’s proxy log files.
origin	Textual description of what caused the event. Can for example be AppAccess, which means a user opened or closed a browser tab with a Sense app in it.
userDirectory	Sense user directory of the user causing the event.
userId	Sense user ID for the user causing the event.
userFull	The combination of userDirectory and userId.

If the user event includes browser user agent information, the following tags will be present:

Tag key	Description
uaBrowserName	Name of connecting user’s browser.
uaBrowserMajorVersion	Connecting user’s browser version.
uaOsName	Connecting user’s operating system.
uaOsVersion	Connecting user’s operating system version.

In addition to the above tags defined in the Butler SOS config file will be added.
More info here.

Fields:

Field key	Description
appId	Id of app that is opened/closed.
appName	Name of app that is opened/closed.
userFull	Same as the userFull tag.
userId	Same as the userId tag.

Log events

Log events are used to capture warning, error and fatal messages in Sense. Once in Butler SOS these events are stored in InfluxDB (enabling Grafana dashboards).
These events are also forwarded as MQTT messages, allowing other systems to act when warnings/errors/fatals occur in Qlik Sense.

Setup instructions here.

Note: If log event categorisation is enabled in the main YAML config file, the categories defined in the config file will be added as tags to the log event data points written to InfluxDB.

Source: Proxy service

Events such as failed login attempts will be sent from the proxy service.

Proxy log events have these tags:

Tag key	Description
host	Host name as reported in Qlik Sense’s log files.
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files.
result_code	Result code as reported by the Sense soure system that caused the event. Its meaning will differ depending on where the event originated.
source	Source system within Sense that caused the event. Examples: qseow-scheduler, qseow-proxy, qseow-repository
subsystem	Subsystem where the event originated. More granular than source. Example: System.Scheduler.Scheduler.Master.Task.TaskSession
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_id	Sense user ID for the user causing the event. Example: joe
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe

Fields in proxy log events:

Field key	Description
command	Description of what caused the event, as found in the Sense logs. Example: Login:TryLogin
context	In what context (if one exists) the event occured. If no context is available Not available will be used.
exception_message	If a serious problem/exception occurs the associated message is available here.
message	Description of what the event is about. Example: Login failed for user 'LAB\\goran' wrong credentials?
origin	Example: qseow-repository.
raw_event	The raw event message as received from QSEoW. Described here.
result_code	Example: 500

The raw_event is the actual log event message sent from QSEoW to Butler SOS.
It has the following components:

Part of message	Description
command	Description of what caused the event, as found in the Sense logs. Example: Login:TryLogin
context	In what context (if one exists) the event occured. If no context is available Not available will be used.
exception_message	If a serious problem/exception occurs the associated message is available here.
host	Host name as reported in Qlik Sense’s log files.
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files.
message	Description of what the event is about. Example: Login failed for user 'LAB\\goran' wrong credentials?
origin	Party of the proxy service the event originated from. Rarely used by Sense.
result_code	Result code as reported by the Sense soure system that caused the event. Its meaning will differ depending on where the event originated. Example: 500
source	Source system within Sense that caused the event. Examples: qseow-scheduler, qseow-proxy, qseow-repository
subsystem	Subsystem where the event originated. More granular than source. Example: System.Scheduler.Scheduler.Master.Task.TaskSession
tags	User defined tags. Set in the main YAML config file. Example: {"env":"DEV","foo":"bar"}
ts_iso	Timestamp (ISO format) when the event occured, according to QSEoW. Example: 20211126T214006.122+0100
ts_local	Event timestamp (time format of Sense server). Example: 2021-11-26 21:40:06,122
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe
user_id	Sense user ID for the user causing the event. Example: joe
windows_user	Windows account used to run the proxy QSEoW Windows service. Example: LAB\\qlikservice

Source: Scheduler service

Events such as failed reload tasks will be sent from the scheduler service.

Scheduler log events have these tags:

Tag key	Description
host	Host name as reported in Qlik Sense’s log files.
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files.
source	Source system within Sense that caused the event. Examples: qseow-scheduler, qseow-proxy, qseow-repository
subsystem	Subsystem where the event originated. More granular than source. Example: System.Scheduler.Scheduler.Master.Task.TaskSession
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_id	Sense user ID for the user causing the event. Example: joe
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe
task_id	Tasik ID (if a task is involved in the event, for example task failing). Example: 58dd8322-e39c-4b71-b74e-13c47a2f6dd4
task_name	Task name (if a task is involved in the event). Example: Reload task of Meetup.com

Fields in scheduler log events:

Field key	Description
app_id	Application ID (if an app is involved in the event). Example: deba4bcf-47e4-472e-97b2-4fe8d6498e11
app_name	Application name (if an app is involved in the event). Example: Meetup.com
exception_message	If a serious problem/exception occurs the associated message is available here.
execution_id	ID identifying a particular task execution. Example: 67a56c3b-2e20-4df8-ad1b-e48de28e1bfa
message	Description of what the event is about. Example: Login failed for user 'LAB\\goran' wrong credentials?
raw_event	The raw event message as received from QSEoW. Described here.

The raw_event is the actual log event message sent from QSEoW to Butler SOS.
It has the following components:

Part of message	Description
app_id	Application ID (if an app is involved in the event). Example: deba4bcf-47e4-472e-97b2-4fe8d6498e11
app_name	Application name (if an app is involved in the event). Example: Meetup.com
exception_message	If a serious problem/exception occurs the associated message is available here.
execution_id	ID identifying a particular task execution. Example: 67a56c3b-2e20-4df8-ad1b-e48de28e1bfa
host	Host name as reported in Qlik Sense’s log files.
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files.
message	Description of what the event is about. Example: Login failed for user 'LAB\\goran' wrong credentials?
source	Source system within Sense that caused the event. Example: qseow-scheduler
subsystem	Subsystem where the event originated. More granular than source. Example: System.Scheduler.Scheduler.Slave.Tasks.ReloadTask
tags	User defined tags. Set in the main YAML config file. Example: {"env":"DEV","foo":"bar"}
task_id	Tasik ID (if a task is involved in the event, for example task failing). Example: 58dd8322-e39c-4b71-b74e-13c47a2f6dd4
task_name	Task name (if a task is involved in the event). Example: Reload task of Meetup.com
ts_iso	Timestamp (ISO format) when the event occured, according to QSEoW. Example: 20211126T214006.122+0100
ts_local	Event timestamp (time format of Sense server). Example: 2021-11-26 21:40:06,122
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe
user_id	Sense user ID for the user causing the event. Example: joe
windows_user	Windows account used to run the proxy QSEoW Windows service. Example: LAB\\qlikservice

Source: Repository service

The repository service is the hub around which the rest of Qlik Sense revolves.
As such it emit events in many different situations. One example can be when a Sense node is offline (thais example is used in the field description below).

Repository log events have these tags:

Tag key	Description
host	Host name as reported in Qlik Sense’s log files.
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files.
source	Source system within Sense that caused the event. Examples: qseow-scheduler, qseow-proxy, qseow-repository
subsystem	Subsystem where the event originated. More granular than source. Example: System.Scheduler.Scheduler.Master.Task.TaskSession
result_code	Result code as reported by the Sense soure system that caused the event. Its meaning will differ depending on where the event originated.
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_id	Sense user ID for the user causing the event. Example: joe
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe

Fields in scheduler log events:

Field key	Description
command	Description of what caused the event, as found in the Sense logs. Example: Login:TryLogin
context	In what context (if one exists) the event occured. If no context is available Not available will be used.
exception_message	If a serious problem/exception occurs the associated message is available here.
message	Description of what the event is about. Example: Login failed for user 'LAB\\goran' wrong credentials?
origin	Example: qseow-repository.
raw_event	The raw event message as received from QSEoW. Described here.
result_code	Example: 500

The raw_event is the actual log event message sent from QSEoW to Butler SOS.
It has the following components:

Part of message	Description
command	Description of what caused the event, as found in the Sense logs. Example: Check service status
context	In what context (if one exists) the event occured. If no context is available Not available will be used. Example: /qps/servicestatusworker
exception_message	If a serious problem/exception occurs the associated message is available here.
host	Host name of event source, as reported in Qlik Sense’s log files. Example: pro2-win1
level	Sense log level. Possible values are WARN, ERROR, FATAL.
log_row	Row number in Sense log file where the event can be found. Useful if you after all have to dig into the log files. Example: 7296
message	Description of what the event is about. Example: Method: 'SendRimQrsStatusRequest'. Failed to retrieve service status from 'http://pro2-win3.lab.ptarmiganlabs.net:4444/status/'. Server host 'pro2-win3.lab.ptarmiganlabs.net'. Error message: 'Unable to connect to the remote server'
origin	Party of the proxy service the event originated from. Rarely used by Sense.
result_code	Result code as reported by the Sense soure system that caused the event. Its meaning will differ depending on where the event originated. Example: 500
source	Source system within Sense that caused the event. Example: qseow-repository
subsystem	Subsystem where the event originated. More granular than source. Example: Service.Repository.Repository.Core.Status.ServiceStatusWorker
tags	User defined tags. Set in the main YAML config file. Example: {"env":"DEV","foo":"bar"}
ts_iso	Timestamp (ISO format) when the event occured, according to QSEoW. Example: 20211128T201538.508+0100
ts_local	Event timestamp (time format of Sense server). Example: 2021-11-28 20:15:38,508
user_directory	Sense user directory of the user causing the event. Example: MYCOMPANY
user_full	The combination of user_directory and user_id. Example: MYCOMPANY\joe
user_id	Sense user ID for the user causing the event. Example: joe
windows_user	Windows account used to run the proxy QSEoW Windows service. Example: LAB\\qlikservice

Messages from the log database

All log data written to InfluxDB share a common set of tag keys:

Tag key	Description
host	Host name, taken from config file’s Butler-SOS.serversToMonitor.servers[].host property. Usually a fully qualified host name, or in some cases an IP address.
server_name	Human readible/friednly server name, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverName property.
server_description	Description of the server, taken from config file’s Butler-SOS.serversToMonitor.servers[].serverDescription property.
log_level	The logging level of the log event (ERROR, WARNING, INFO etc).
source_process	Which Sense service the log event originated in.

Measurement: log_event_logdb

Source: More or less log db. A query is done to the log db in Postgres, the results are stored in InfluxDB. There is thus no Qlik API call per se.

Field key	Type	Description
message	string	Log entry as retrieved from the Sense log database (Postgres).

Butler SOS metrics

Measurement: butlersos_memory_usage

These metrics tell you how much memory Butler SOS itself uses.
More info on these metrics and what they mean is available here.

Field key	Type	Description
heap_total	float	Total size of the allocated heap.
heap_used	float	Actual memory used during the execution of Butler SOS.
process_memory	float	Total memory allocated for the execution of Butler SOS.