The Application Checklist is intended to be a best practices checklist for a single Qlik Sense application. It requires a number of inputs from the user, e.g. disk size, number of tables, number of rows, etc. If all of the input fields have been filled and the completed boxes have been checked, a report may be generated. This report is a pdf that will simply log all of the data that you entered with some scoring. The scoring is a sum of the boxes checked in total and per category – if all boxes are checked, the maximum score will be received. At this point in time there is no additional intelligence derived by any of the field inputs. The intent of the report is that it can be shared either internally or externally (with Qlik, for example) to help others know what has been done with the application, and know the high level metadata around it. This report could be used to help diagnose an issue, or simply just used as a best practices checklist. It could also be used internally as part of a workflow process to ensure an application has gone through the necessary optimization steps before entering production.
Application Metadata Analyzer
About the Endpoint
As of the Qlik Sense June 2018 release, a new application level metadata endpoint is available via the Qlik Sense Proxy (exposed via the Engine). Data is populated for this endpoint post-reload in a June 2018+ environment. From this data, a Qlik application has been built that ingests it and provides lightweight dashboard to analyze your applications. The URL to access this data is from a single application is
where server is a server that the application can be accessed over and GUID is the application ID. Note that the application does not need to be lifted into RAM for the metadata to be accessed.
Data from this endpoint includes:
- server metadata including number of cores, total RAM
- reload time
- app RAM base footprint
- field metadata including cardinality, tags, total count
- table metadata including fields, rows, key fields
Example output from that endpoint can be seen here.
Application Metadata Analyzer App
The Application Metadata Analyzer is a Qlik Sense application that collates the metadata from every application on your server to give you a single app view of your application metadata along with additional data from the QRS API. Adding this additional data allows us to visualize who owns what applications, are they published or private, what nodes are they available on, etc. That application can be found here, and is shipped with Qlik Sense Enterprise on Windows as of the September 2019 release: https://community.qlik.com/t5/Qlik-Monitoring-Administration/Sense-App-Metadata-Analyzer/gpm-p/1592163.
Data Connection Analyzer
The Qlik Sense Data Connection Analyzer is a Qlik application that parses script log files and queries the QRS API, allowing analysis of data connection usage, patterns, age, and cleanliness across a Qlik Sense site. In short, this app will help to run a leaner, more performant, and more easily and holistically governed Qlik Sense site.
The application is supported by Qlik's Americas Enterprise Architecture team, and uses only native connections and capabilities of the Qlik platform—making it plug-and-play.
The application can be found on GitHub here: https://github.com/eapowertools/qs-data-connection-analyzer
Common Questions & Associated Benefits of the App
- Which connections are no longer used? (A data connection is found in a script log and exists in the Qlik Sense site, however no associated applications currently exist that at one point had used it).
- Removing unused connections will increase performance across a site, as less connections will have to be evaluated in security rule evaluations. If a site has hundreds or thousands of connections, this calculation time can build up.
- Removing unused connections makes general administration easier, as there is less to manage.
- What connections have never been used? (A data connection exists in the Qlik Sense site, but no reference exists to it in any script log.)
- It is common that users will create data connections to test connectivity, but then never actually use them. By leveraging this app, one can identify connections that have never been used and have existed for x amount of time (say 90+ days), so that action can be taken to remove them. The benefits from both simpler management and performance are listed in the bullet above. What connections have been deleted that used to be used? (A data connection that is found in script logs, but no longer exists in the Qlik Sense site and no app is using it.)
- By parsing the script logs, one can visualize old data connection names/paths that can help to serve as an audit trail.
- Depending on how this application is deployed, whether it be strictly administrative or potentially visible to developers, this metric is important both administratively and socially throughout the organization.
- By analyzing the connection strings, one can tackle duplicate connections to the same source data. This eases administration overhead and will ensure that there is reusability/consistency across the platform.
- This is valuable from an access/security perspective to ensure governance of data sources.
- While "User A" might own "Data Connection A", "User B" might also have read access to that data connection. This can of course be visualized through the audit capability of the QMC, however this application will physically reveal who is executing any reloads of those data connections, giving greater visibility and allowing a deeper level of auditing and governance.
- Let's say a data source is being transformed and will be moved from one database to location. The first question one might ask is, "What applications are using that connection, so we can re-route it to the new db and make adjustments to the load scripts?". This historically has not been easy to answer. This application allows one to select that connection and visualize apps that are using it.
- The application visualizes what connections are being run as tasks, manually, or in ODAG (or other API) requests. This is crucial in understanding user behavior.
Telemetry Dashboard for Qlik Sense
With the February 2018 of Qlik Sense, it is possible to capture granular usage metrics from the QIX in-memory engine based on configurable thresholds. This provides the ability to capture CPU and RAM utilization of individual chart objects, CPU and RAM utilization of reload tasks, and more.
- Qlik Sense Enterprise February 2018 or later
- Enable Telemetry Logging, instructions can be found on the wiki
- All information (including installing, logging and uninstall) can be found the wiki page. The sidebar is used for navigation.
Download Latest Here
Qlik Sense Scalability Tools
This package (referred to as Qlik Sense Scalability Tool) contains a complete set of tools for easy creation, execution and analysis of load/performance tests.
Note: Using windows built-in unzip functionality will not allow the .exe to launch unless unblocking zipped packagefirst (properties -> unblock).
Included parts are:
- Standalone application for creating and executing a simulation script
- Documentation on how to use the package
- Regression analyzer
- Benchmarking package
- App evaluator package
Qlik Sense documents to help analyze result and log files (previously included in this package) can be found here: https://community.qlik.com/t5/Qlik-Scalability/Scalability-Results-Analyzer/gpm-p/1493648
Download and other information:
Please refer to the Qlik Community page on Scalability Tools for the most recent download and ChangeLogs.
Your use of Qlik Sense Scalability Tool will be subject to the same license agreement between you and Qlik for your Qlik Sense License. Qlik does not provide maintenance and support services for the Qlik Sense Scalability Tool, however please check QlikCommunity for additional information on use of these products.
Scalability Tools with Qlik Sense YouTube Series
By Barry Harmsen
By Matt Fryer
By Ralf Becher
By Dmitry Gudkov
By Aaron Couron
By Chandraish Sinha
By Dalton Ruer
By Trey Bayne
By Steve Dark
By Oleg Troyansky
By Stefan Walther
By Bill Lay
By Karl Pover
By Qlik Staff
By Qlik Staff
By Richard Pearce
By Rob Wunderlich
By George Beaton
By Stephen Redmond
By Juan Vitantonio
By Julian Villafuerte
By Adam Cooke
By Nick Webster