Patent Publication Number: US-10783062-B2

Title: Automated diagnostic testing of databases and configurations for performance analytics visualization software

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims priority to U.S. patent application Ser. No. 15/815,458, filed Nov. 16, 2017, which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Managed networks may include various types of computer networks that can be remotely administered. This management may involve one or more computing devices disposed with a remote network management platform collecting information about the configuration and operational states of software applications executing on behalf on the managed network, and then presenting representations of this information by way of one or more user interfaces. The user interfaces may be, for instance, web-based user interfaces. 
     Some of the collected information may relate to key performance indicators (KPIs). KPIs include any sort of measurement, reading, or data that is relevant to the managed network. Thus, KPIs may reflect performance of computing devices on the network itself (e.g., memory utilization, processor utilization, transactions per second) or performance of higher-level applications executing on the remote network management platform (e.g., a number of times per day that users on the managed network have requested a particular type of technical assistance). Among other capabilities, the user interfaces may be able to display KPIs in numerous visualizations, such as charts, graphs, or tables. 
     KPI data may be collected by scripts that are integrated into or separate from the user interface definitions. Collected KPI data may be stored in various database tables. Over time, however, entries in these tables can become corrupted, can be deleted, or the tables themselves may be modified or deleted. As a consequence, the scripts may fail to provide the proper data for certain visualizations. Furthermore, some scripts may be written in such as fashion that they use excessive resources, are otherwise inefficient, or do not operate properly in other ways. 
     SUMMARY 
     In order to overcome the deficiencies of current performance analytics software applications, at least one diagnostic program may be employed. Such a program may be configured to obtain diagnostic tests from a diagnostic database within the remote network management platform. The diagnostic tests may include one or more pre-defined rules. The rules may be checked to determine whether the KPIs and the visualizations defined thereof are consistent with one another, have an expected configuration, and any collection procedures for the KPIs have been performing properly. 
     After executing the diagnostic tests, the diagnostic program may write results to a diagnostic database within the remote network management platform. These results may be later retrieved and displayed upon a graphical user interface (GUI) of a client device accessing the remote network management platform (e.g., from the managed network). This allows users to rapidly identify and correct misconfigurations related to the performance analytics software applications. 
     Accordingly, a first example embodiment may involve a system for diagnostic testing of a performance analytics software application. The system may be disposed within a computational instance of a remote network management platform that remotely manages a managed network. The system may include a performance analytics database containing performance analytics data that define KPIs associated with the managed network and that define dashboards that are configured to specify, on a performance analytics GUI within the managed network, graphical representations of the KPIs. The system may also include a diagnostic database containing representations of a plurality of tests, the tests configured to determine whether the KPIs and the dashboards comply with pre-defined consistency, configuration, and performance rules. The system may also include a computing device operational to execute a diagnostic software program, where the diagnostic software program is configured to: obtain, from the diagnostic database, a representation of a particular test of the plurality of tests, where the particular test includes a plurality of the pre-defined consistency, configuration, and performance rules; apply each of the plurality of the pre-defined consistency, configuration, and performance rules to the KPIs and the dashboards stored in the performance analytics database; and write, when applying at least one of the plurality of the pre-defined consistency, configuration, and performance rules indicates a problem, an associated severity, problem description, and solution description to the diagnostic database as output of the particular test. 
     A second example embodiment may involve diagnostic testing of a performance analytics software application, where a performance analytics database, a diagnostic database, and a computing device are disposed within a computational instance of a remote network management platform that remotely manages a managed network. The second example embodiment may also involve obtaining, by the computing device and from the diagnostic database, a representation of a particular test from a plurality of tests stored therein, where the particular test is configured to determine compliance with a plurality of pre-defined consistency, configuration, and performance rules for (i) KPIs associated with the managed network and (ii) dashboards that are configured to specify, on a performance analytics GUI within the managed network, graphical representations of the KPIs, where representations of the KPIs and dashboards are stored in the performance analytics database. The second example embodiment may also involve applying, by the computing device, each of the plurality of the pre-defined consistency, configuration, and performance rules to the KPIs and the dashboards. The second example embodiment may also involve, when applying at least one of the plurality of the pre-defined consistency, configuration, and performance rules indicates a problem, writing, by the computing device, an associated severity, problem description, and solution description to the diagnostic database as output of the particular test. 
     In a third example embodiment, an article of manufacture may include a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations in accordance with the first and/or second example embodiment. 
     In a fourth example embodiment, a system may include various means for carrying out each of the operations of the first and/or second example embodiment. 
     These as well as other embodiments, aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, this summary and other descriptions and figures provided herein are intended to illustrate embodiments by way of example only and, as such, that numerous variations are possible. For instance, structural elements and process steps can be rearranged, combined, distributed, eliminated, or otherwise changed, while remaining within the scope of the embodiments as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a schematic drawing of a computing device, in accordance with example embodiments. 
         FIG. 2  illustrates a schematic drawing of a server device cluster, in accordance with example embodiments. 
         FIG. 3  depicts a remote network management architecture, in accordance with example embodiments. 
         FIG. 4  depicts a communication environment involving a remote network management architecture, in accordance with example embodiments. 
         FIG. 5A  depicts another communication environment involving a remote network management architecture, in accordance with example embodiments. 
         FIG. 5B  is a flow chart, in accordance with example embodiments. 
         FIG. 6A  depicts a performance analytics dashboard in the form of a graphical user interface, in accordance with example embodiments. 
         FIG. 6B  depicts a performance analytics dashboard in the form of a graphical user interface, in accordance with example embodiments. 
         FIG. 7  depicts a performance analytics diagnostic architecture, in accordance with example embodiments. 
         FIG. 8A  depicts performance analytics diagnostic output in the form of a graphical user interface, in accordance with example embodiments. 
         FIG. 8B  depicts a performance analytics diagnostic graphical user interface, in accordance with example embodiments. 
         FIG. 8C  depicts a performance analytics diagnostic graphical user interface, in accordance with example embodiments. 
         FIG. 8D  depicts a performance analytics diagnostic graphical user interface, in accordance with example embodiments. 
         FIG. 8E  depicts a performance analytics diagnostic graphical user interface, in accordance with example embodiments. 
         FIG. 9  is a flow chart, in accordance with example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, devices, and systems are described herein. It should be understood that the words “example” and “exemplary” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or feature described herein as being an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features unless stated as such. Thus, other embodiments can be utilized and other changes can be made without departing from the scope of the subject matter presented herein. 
     Accordingly, the example embodiments described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. For example, the separation of features into “client” and “server” components may occur in a number of ways. 
     Further, unless context suggests otherwise, the features illustrated in each of the figures may be used in combination with one another. Thus, the figures should be generally viewed as component aspects of one or more overall embodiments, with the understanding that not all illustrated features are necessary for each embodiment. 
     Additionally, any enumeration of elements, blocks, or steps in this specification or the claims is for purposes of clarity. Thus, such enumeration should not be interpreted to require or imply that these elements, blocks, or steps adhere to a particular arrangement or are carried out in a particular order. 
     I. INTRODUCTION 
     A large enterprise is a complex entity with many interrelated operations. Some of these are found across the enterprise, such as human resources (HR), supply chain, information technology (IT), and finance. However, each enterprise also has its own unique operations that provide essential capabilities and/or create competitive advantages. 
     To support widely-implemented operations, enterprises typically use off-the-shelf software applications, such as customer relationship management (CRM) and human capital management (HCM) packages. However, they may also need custom software applications to meet their own unique requirements. A large enterprise often has dozens or hundreds of these custom software applications. Nonetheless, the advantages provided by the embodiments herein are not limited to large enterprises and may be applicable to an enterprise, or any other type of organization, of any size. 
     Many such software applications are developed by individual departments within the enterprise. These range from simple spreadsheets to custom-built software tools and databases. But the proliferation of siloed custom software applications has numerous disadvantages. It negatively impacts an enterprise&#39;s ability to run and grow its business, innovate, and meet regulatory requirements. The enterprise may find it difficult to integrate, streamline and enhance its operations due to lack of a single system that unifies its subsystems and data. 
     To efficiently create custom applications, enterprises would benefit from a remotely-hosted application platform that eliminates unnecessary development complexity. The goal of such a platform would be to reduce time-consuming, repetitive application development tasks so that software engineers and individuals in other roles can focus on developing unique, high-value features. 
     In order to achieve this goal, the concept of Application Platform as a Service (aPaaS) is introduced, to intelligently automate workflows throughout the enterprise. An aPaaS system is hosted remotely from the enterprise, but may access data, applications, and services within the enterprise by way of secure connections. Such an aPaaS system may have a number of advantageous capabilities and characteristics. These advantages and characteristics may be able to improve the enterprise&#39;s operations and workflow for IT, HR, CRM, customer service, application development, and security. 
     The aPaaS system may support development and execution of model-view-controller (MVC) applications. MVC applications divide their functionality into three interconnected parts (model, view, and controller) in order to isolate representations of information from the manner in which the information is presented to the user, thereby allowing for efficient code reuse and parallel development. These applications may be web-based, and offer create, read, update, delete (CRUD) capabilities. This allows new applications to be built on a common application infrastructure. 
     The aPaaS system may support standardized application components, such as a standardized set of widgets for graphical user interface (GUI) development. In this way, applications built using the aPaaS system have a common look and feel. Other software components and modules may be standardized as well. In some cases, this look and feel can be branded or skinned with an enterprise&#39;s custom logos and/or color schemes. 
     The aPaaS system may support the ability to configure the behavior of applications using metadata. This allows application behaviors to be rapidly adapted to meet specific needs. Such an approach reduces development time and increases flexibility. Further, the aPaaS system may support GUI tools that facilitate metadata creation and management, thus reducing errors in the metadata. 
     The aPaaS system may support clearly-defined interfaces between applications, so that software developers can avoid unwanted inter-application dependencies. Thus, the aPaaS system may implement a service layer in which persistent state information and other data is stored. 
     The aPaaS system may support a rich set of integration features so that the applications thereon can interact with legacy applications and third-party applications. For instance, the aPaaS system may support a custom employee-onboarding system that integrates with legacy HR, IT, and accounting systems. 
     The aPaaS system may support enterprise-grade security. Furthermore, since the aPaaS system may be remotely hosted, it should also utilize security procedures when it interacts with systems in the enterprise or third-party networks and services hosted outside of the enterprise. For example, the aPaaS system may be configured to share data amongst the enterprise and other parties to detect and identify common security threats. 
     Other features, functionality, and advantages of an aPaaS system may exist. This description is for purpose of example and is not intended to be limiting. 
     As an example of the aPaaS development process, a software developer may be tasked to create a new application using the aPaaS system. First, the developer may define the data model, which specifies the types of data that the application uses and the relationships therebetween. Then, via a GUI of the aPaaS system, the developer enters (e.g., uploads) the data model. The aPaaS system automatically creates all of the corresponding database tables, fields, and relationships, which can then be accessed via an object-oriented services layer. 
     In addition, the aPaaS system can also build a fully-functional MVC application with client-side interfaces and server-side CRUD logic. This generated application may serve as the basis of further development for the user. Advantageously, the developer does not have to spend a large amount of time on basic application functionality. Further, since the application may be web-based, it can be accessed from any Internet-enabled client device. Alternatively or additionally, a local copy of the application may be able to be accessed, for instance, when Internet service is not available. 
     The aPaaS system may also support a rich set of pre-defined functionality that can be added to applications. These features include support for searching, email, templating, workflow design, reporting, analytics, social media, scripting, mobile-friendly output, and customized GUIs. 
     The following embodiments describe architectural and functional aspects of example aPaaS systems, as well as the features and advantages thereof. 
     II. EXAMPLE COMPUTING DEVICES AND CLOUD-BASED COMPUTING ENVIRONMENTS 
       FIG. 1  is a simplified block diagram exemplifying a computing device  100 , illustrating some of the components that could be included in a computing device arranged to operate in accordance with the embodiments herein. Computing device  100  could be a client device (e.g., a device actively operated by a user), a server device (e.g., a device that provides computational services to client devices), or some other type of computational platform. Some server devices may operate as client devices from time to time in order to perform particular operations, and some client devices may incorporate server features. 
     In this example, computing device  100  includes processor  102 , memory  104 , network interface  106 , and an input/output unit  108 , all of which may be coupled by a system bus  110  or a similar mechanism. In some embodiments, computing device  100  may include other components and/or peripheral devices (e.g., detachable storage, printers, and so on). 
     Processor  102  may be one or more of any type of computer processing element, such as a central processing unit (CPU), a co-processor (e.g., a mathematics, graphics, or encryption co-processor), a digital signal processor (DSP), a network processor, and/or a form of integrated circuit or controller that performs processor operations. In some cases, processor  102  may be one or more single-core processors. In other cases, processor  102  may be one or more multi-core processors with multiple independent processing units. Processor  102  may also include register memory for temporarily storing instructions being executed and related data, as well as cache memory for temporarily storing recently-used instructions and data. 
     Memory  104  may be any form of computer-usable memory, including but not limited to random access memory (RAM), read-only memory (ROM), and non-volatile memory (e.g., flash memory, hard disk drives, solid state drives, compact discs (CDs), digital video discs (DVDs), and/or tape storage). Thus, memory  104  represents both main memory units, as well as long-term storage. Other types of memory may include biological memory. 
     Memory  104  may store program instructions and/or data on which program instructions may operate. By way of example, memory  104  may store these program instructions on a non-transitory, computer-readable medium, such that the instructions are executable by processor  102  to carry out any of the methods, processes, or operations disclosed in this specification or the accompanying drawings. 
     As shown in  FIG. 1 , memory  104  may include firmware  104 A, kernel  104 B, and/or applications  104 C. Firmware  104 A may be program code used to boot or otherwise initiate some or all of computing device  100 . Kernel  104 B may be an operating system, including modules for memory management, scheduling and management of processes, input/output, and communication. Kernel  104 B may also include device drivers that allow the operating system to communicate with the hardware modules (e.g., memory units, networking interfaces, ports, and busses), of computing device  100 . Applications  104 C may be one or more user-space software programs, such as web browsers or email clients, as well as any software libraries used by these programs. Memory  104  may also store data used by these and other programs and applications. 
     Network interface  106  may take the form of one or more wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Network interface  106  may also support communication over one or more non-Ethernet media, such as coaxial cables or power lines, or over wide-area media, such as Synchronous Optical Networking (SONET) or digital subscriber line (DSL) technologies. Network interface  106  may additionally take the form of one or more wireless interfaces, such as IEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or a wide-area wireless interface. However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over network interface  106 . Furthermore, network interface  106  may comprise multiple physical interfaces. For instance, some embodiments of computing device  100  may include Ethernet, BLUETOOTH®, and Wifi interfaces. 
     Input/output unit  108  may facilitate user and peripheral device interaction with example computing device  100 . Input/output unit  108  may include one or more types of input devices, such as a keyboard, a mouse, a touch screen, and so on. Similarly, input/output unit  108  may include one or more types of output devices, such as a screen, monitor, printer, and/or one or more light emitting diodes (LEDs). Additionally or alternatively, computing device  100  may communicate with other devices using a universal serial bus (USB) or high-definition multimedia interface (HDMI) port interface, for example. 
     In some embodiments, one or more instances of computing device  100  may be deployed to support an aPaaS architecture. The exact physical location, connectivity, and configuration of these computing devices may be unknown and/or unimportant to client devices. Accordingly, the computing devices may be referred to as “cloud-based” devices that may be housed at various remote data center locations. 
       FIG. 2  depicts a cloud-based server cluster  200  in accordance with example embodiments. In  FIG. 2 , operations of a computing device (e.g., computing device  100 ) may be distributed between server devices  202 , data storage  204 , and routers  206 , all of which may be connected by local cluster network  208 . The number of server devices  202 , data storages  204 , and routers  206  in server cluster  200  may depend on the computing task(s) and/or applications assigned to server cluster  200 . 
     For example, server devices  202  can be configured to perform various computing tasks of computing device  100 . Thus, computing tasks can be distributed among one or more of server devices  202 . To the extent that these computing tasks can be performed in parallel, such a distribution of tasks may reduce the total time to complete these tasks and return a result. For purpose of simplicity, both server cluster  200  and individual server devices  202  may be referred to as a “server device.” This nomenclature should be understood to imply that one or more distinct server devices, data storage devices, and cluster routers may be involved in server device operations. 
     Data storage  204  may be data storage arrays that include drive array controllers configured to manage read and write access to groups of hard disk drives and/or solid state drives. The drive array controllers, alone or in conjunction with server devices  202 , may also be configured to manage backup or redundant copies of the data stored in data storage  204  to protect against drive failures or other types of failures that prevent one or more of server devices  202  from accessing units of cluster data storage  204 . Other types of memory aside from drives may be used. 
     Routers  206  may include networking equipment configured to provide internal and external communications for server cluster  200 . For example, routers  206  may include one or more packet-switching and/or routing devices (including switches and/or gateways) configured to provide (i) network communications between server devices  202  and data storage  204  via cluster network  208 , and/or (ii) network communications between the server cluster  200  and other devices via communication link  210  to network  212 . 
     Additionally, the configuration of cluster routers  206  can be based at least in part on the data communication requirements of server devices  202  and data storage  204 , the latency and throughput of the local cluster network  208 , the latency, throughput, and cost of communication link  210 , and/or other factors that may contribute to the cost, speed, fault-tolerance, resiliency, efficiency and/or other design goals of the system architecture. 
     As a possible example, data storage  204  may include any form of database, such as a structured query language (SQL) database. Various types of data structures may store the information in such a database, including but not limited to tables, arrays, lists, trees, and tuples. Furthermore, any databases in data storage  204  may be monolithic or distributed across multiple physical devices. 
     Server devices  202  may be configured to transmit data to and receive data from cluster data storage  204 . This transmission and retrieval may take the form of SQL queries or other types of database queries, and the output of such queries, respectively. Additional text, images, video, and/or audio may be included as well. Furthermore, server devices  202  may organize the received data into web page representations. Such a representation may take the form of a markup language, such as the hypertext markup language (HTML), the extensible markup language (XML), or some other standardized or proprietary format. Moreover, server devices  202  may have the capability of executing various types of computerized scripting languages, such as but not limited to Perl, Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP), JavaScript, and so on. Computer program code written in these languages may facilitate the providing of web pages to client devices, as well as client device interaction with the web pages. 
     III. EXAMPLE REMOTE NETWORK MANAGEMENT ARCHITECTURE 
       FIG. 3  depicts a remote network management architecture, in accordance with example embodiments. This architecture includes three main components, managed network  300 , remote network management platform  320 , and third-party networks  340 , all connected by way of Internet  350 . 
     Managed network  300  may be, for example, an enterprise network used by a business for computing and communications tasks, as well as storage of data. Thus, managed network  300  may include various client devices  302 , server devices  304 , routers  306 , virtual machines  308 , firewall  310 , and/or proxy servers  312 . Client devices  302  may be embodied by computing device  100 , server devices  304  may be embodied by computing device  100  or server cluster  200 , and routers  306  may be any type of router, switch, or gateway. 
     Virtual machines  308  may be embodied by one or more of computing device  100  or server cluster  200 . In general, a virtual machine is an emulation of a computing system, and mimics the functionality (e.g., processor, memory, and communication resources) of a physical computer. One physical computing system, such as server cluster  200 , may support up to thousands of individual virtual machines. In some embodiments, virtual machines  308  may be managed by a centralized server device or application that facilitates allocation of physical computing resources to individual virtual machines, as well as performance and error reporting. Enterprises often employ virtual machines in order to allocate computing resources in an efficient, as needed fashion. Providers of virtualized computing systems include VMWARE® and MICROSOFT®. 
     Firewall  310  may be one or more specialized routers or server devices that protect managed network  300  from unauthorized attempts to access the devices, applications, and services therein, while allowing authorized communication that is initiated from managed network  300 . Firewall  310  may also provide intrusion detection, web filtering, virus scanning, application-layer gateways, and other applications or services. In some embodiments not shown in  FIG. 3 , managed network  300  may include one or more virtual private network (VPN) gateways with which it communicates with remote network management platform  320  (see below). 
     Managed network  300  may also include one or more proxy servers  312 . An embodiment of proxy servers  312  may be a server device that facilitates communication and movement of data between managed network  300 , remote network management platform  320 , and third-party networks  340 . In particular, proxy servers  312  may be able to establish and maintain secure communication sessions with one or more computational instances of remote network management platform  320 . By way of such a session, remote network management platform  320  may be able to discover and manage aspects of the architecture and configuration of managed network  300  and its components. Possibly with the assistance of proxy servers  312 , remote network management platform  320  may also be able to discover and manage aspects of third-party networks  340  that are used by managed network  300 . 
     Firewalls, such as firewall  310 , typically deny all communication sessions that are incoming by way of Internet  350 , unless such a session was ultimately initiated from behind the firewall (i.e., from a device on managed network  300 ) or the firewall has been explicitly configured to support the session. By placing proxy servers  312  behind firewall  310  (e.g., within managed network  300  and protected by firewall  310 ), proxy servers  312  may be able to initiate these communication sessions through firewall  310 . Thus, firewall  310  might not have to be specifically configured to support incoming sessions from remote network management platform  320 , thereby avoiding potential security risks to managed network  300 . 
     In some cases, managed network  300  may consist of a few devices and a small number of networks. In other deployments, managed network  300  may span multiple physical locations and include hundreds of networks and hundreds of thousands of devices. Thus, the architecture depicted in  FIG. 3  is capable of scaling up or down by orders of magnitude. 
     Furthermore, depending on the size, architecture, and connectivity of managed network  300 , a varying number of proxy servers  312  may be deployed therein. For example, each one of proxy servers  312  may be responsible for communicating with remote network management platform  320  regarding a portion of managed network  300 . Alternatively or additionally, sets of two or more proxy servers may be assigned to such a portion of managed network  300  for purposes of load balancing, redundancy, and/or high availability. 
     Remote network management platform  320  is a hosted environment that provides aPaaS services to users, particularly to the operators of managed network  300 . These services may take the form of web-based portals, for instance. Thus, a user can securely access remote network management platform  320  from, for instance, client devices  302 , or potentially from a client device outside of managed network  300 . By way of the web-based portals, users may design, test, and deploy applications, generate reports, view analytics, and perform other tasks. 
     As shown in  FIG. 3 , remote network management platform  320  includes four computational instances  322 ,  324 ,  326 , and  328 . Each of these instances may represent a set of web portals, services, and applications (e.g., a wholly-functioning aPaaS system) available to a particular customer. In some cases, a single customer may use multiple computational instances. For example, managed network  300  may be an enterprise customer of remote network management platform  320 , and may use computational instances  322 ,  324 , and  326 . The reason for providing multiple instances to one customer is that the customer may wish to independently develop, test, and deploy its applications and services. Thus, computational instance  322  may be dedicated to application development related to managed network  300 , computational instance  324  may be dedicated to testing these applications, and computational instance  326  may be dedicated to the live operation of tested applications and services. A computational instance may also be referred to as a hosted instance, a remote instance, a customer instance, or by some other designation. 
     The multi-instance architecture of remote network management platform  320  is in contrast to conventional multi-tenant architectures, over which multi-instance architectures have several advantages. In multi-tenant architectures, data from different customers (e.g., enterprises) are comingled in a single database. While these customers&#39; data are separate from one another, the separation is enforced by the software that operates the single database. As a consequence, a security breach in this system may impact all customers&#39; data, creating additional risk, especially for entities subject to governmental, healthcare, and/or financial regulation. Furthermore, any database operations that impact one customer will likely impact all customers sharing that database. Thus, if there is an outage due to hardware or software errors, this outage affects all such customers. Likewise, if the database is to be upgraded to meet the needs of one customer, it will be unavailable to all customers during the upgrade process. Often, such maintenance windows will be long, due to the size of the shared database. 
     In contrast, the multi-instance architecture provides each customer with its own database in a dedicated computing instance. This prevents comingling of customer data, and allows each instance to be independently managed. For example, when one customer&#39;s instance experiences an outage due to errors or an upgrade, other computational instances are not impacted. Maintenance down time is limited because the database only contains one customer&#39;s data. Further, the simpler design of the multi-instance architecture allows redundant copies of each customer database and instance to be deployed in a geographically diverse fashion. This facilitates high availability, where the live version of the customer&#39;s instance can be moved when faults are detected or maintenance is being performed. 
     In order to support multiple computational instances in an efficient fashion, remote network management platform  320  may implement a plurality of these instances on a single hardware platform. For example, when the aPaaS system is implemented on a server cluster such as server cluster  200 , it may operate a virtual machine that dedicates varying amounts of computational, storage, and communication resources to instances. But full virtualization of server cluster  200  might not be necessary, and other mechanisms may be used to separate instances. In some examples, each instance may have a dedicated account and one or more dedicated databases on server cluster  200 . Alternatively, computational instance  322  may span multiple physical devices. 
     In some cases, a single server cluster of remote network management platform  320  may support multiple independent enterprises. Furthermore, as described below, remote network management platform  320  may include multiple server clusters deployed in geographically diverse data centers in order to facilitate load balancing, redundancy, and/or high availability. 
     Third-party networks  340  may be remote server devices (e.g., a plurality of server clusters such as server cluster  200 ) that can be used for outsourced computational, data storage, communication, and service hosting operations. These servers may be virtualized (i.e., the servers may be virtual machines). Examples of third-party networks  340  may include AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote network management platform  320 , multiple server clusters supporting third-party networks  340  may be deployed at geographically diverse locations for purposes of load balancing, redundancy, and/or high availability. 
     Managed network  300  may use one or more of third-party networks  340  to deploy applications and services to its clients and customers. For instance, if managed network  300  provides online music streaming services, third-party networks  340  may store the music files and provide web interface and streaming capabilities. In this way, the enterprise of managed network  300  does not have to build and maintain its own servers for these operations. 
     Remote network management platform  320  may include modules that integrate with third-party networks  340  to expose virtual machines and managed services therein to managed network  300 . The modules may allow users to request virtual resources and provide flexible reporting for third-party networks  340 . In order to establish this functionality, a user from managed network  300  might first establish an account with third-party networks  340 , and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform  320 . These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing. 
     Internet  350  may represent a portion of the global Internet. However, Internet  350  may alternatively represent a different type of network, such as a private wide-area or local-area packet-switched network. 
       FIG. 4  further illustrates the communication environment between managed network  300  and computational instance  322 , and introduces additional features and alternative embodiments. In  FIG. 4 , computational instance  322  is replicated across data centers  400 A and  400 B. These data centers may be geographically distant from one another, perhaps in different cities or different countries. Each data center includes support equipment that facilitates communication with managed network  300 , as well as remote users. 
     In data center  400 A, network traffic to and from external devices flows either through VPN gateway  402 A or firewall  404 A. VPN gateway  402 A may be peered with VPN gateway  412  of managed network  300  by way of a security protocol such as Internet Protocol Security (IPSEC) or Transport Layer Security (TLS). Firewall  404 A may be configured to allow access from authorized users, such as user  414  and remote user  416 , and to deny access to unauthorized users. By way of firewall  404 A, these users may access computational instance  322 , and possibly other computational instances. Load balancer  406 A may be used to distribute traffic amongst one or more physical or virtual server devices that host computational instance  322 . Load balancer  406 A may simplify user access by hiding the internal configuration of data center  400 A, (e.g., computational instance  322 ) from client devices. For instance, if computational instance  322  includes multiple physical or virtual computing devices that share access to multiple databases, load balancer  406 A may distribute network traffic and processing tasks across these computing devices and databases so that no one computing device or database is significantly busier than the others. In some embodiments, computational instance  322  may include VPN gateway  402 A, firewall  404 A, and load balancer  406 A. 
     Data center  400 B may include its own versions of the components in data center  400 A. Thus, VPN gateway  402 B, firewall  404 B, and load balancer  406 B may perform the same or similar operations as VPN gateway  402 A, firewall  404 A, and load balancer  406 A, respectively. Further, by way of real-time or near-real-time database replication and/or other operations, computational instance  322  may exist simultaneously in data centers  400 A and  400 B. 
     Data centers  400 A and  400 B as shown in  FIG. 4  may facilitate redundancy and high availability. In the configuration of  FIG. 4 , data center  400 A is active and data center  400 B is passive. Thus, data center  400 A is serving all traffic to and from managed network  300 , while the version of computational instance  322  in data center  400 B is being updated in near-real-time. Other configurations, such as one in which both data centers are active, may be supported. 
     Should data center  400 A fail in some fashion or otherwise become unavailable to users, data center  400 B can take over as the active data center. For example, domain name system (DNS) servers that associate a domain name of computational instance  322  with one or more Internet Protocol (IP) addresses of data center  400 A may re-associate the domain name with one or more IP addresses of data center  400 B. After this re-association completes (which may take less than one second or several seconds), users may access computational instance  322  by way of data center  400 B. 
       FIG. 4  also illustrates a possible configuration of managed network  300 . As noted above, proxy servers  312  and user  414  may access computational instance  322  through firewall  310 . Proxy servers  312  may also access configuration items  410 . In  FIG. 4 , configuration items  410  may refer to any or all of client devices  302 , server devices  304 , routers  306 , and virtual machines  308 , any applications or services executing thereon, as well as relationships between devices, applications, and services. Thus, the term “configuration items” may be shorthand for any physical or virtual device, or any application or service remotely discoverable or managed by computational instance  322 , or relationships between discovered devices, applications, and services. Configuration items may be represented in a configuration management database (CMDB) of computational instance  322 . 
     As noted above, VPN gateway  412  may provide a dedicated VPN to VPN gateway  402 A. Such a VPN may be helpful when there is a significant amount of traffic between managed network  300  and computational instance  322 , or security policies otherwise suggest or require use of a VPN between these sites. In some embodiments, any device in managed network  300  and/or computational instance  322  that directly communicates via the VPN is assigned a public IP address. Other devices in managed network  300  and/or computational instance  322  may be assigned private IP addresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255 or 192.168.0.0-192.168.255.255 ranges, represented in shorthand as subnets 10.0.0.0/8 and 192.168.0.0/16, respectively). 
     IV. EXAMPLE DEVICE, APPLICATION, AND SERVICE DISCOVERY 
     In order for remote network management platform  320  to administer the devices, applications, and services of managed network  300 , remote network management platform  320  may first determine what devices are present in managed network  300 , the configurations and operational statuses of these devices, and the applications and services provided by the devices, and well as the relationships between discovered devices, applications, and services. As noted above, each device, application, service, and relationship may be referred to as a configuration item. The process of defining configuration items within managed network  300  is referred to as discovery, and may be facilitated at least in part by proxy servers  312 . 
     For purpose of the embodiments herein, an “application” may refer to one or more processes, threads, programs, client modules, server modules, or any other software that executes on a device or group of devices. A “service” may refer to a high-level capability provided by multiple applications executing on one or more devices working in conjunction with one another. For example, a high-level web service may involve multiple web application server threads executing on one device and accessing information from a database application that executes on another device. 
       FIG. 5A  provides a logical depiction of how configuration items can be discovered, as well as how information related to discovered configuration items can be stored. For sake of simplicity, remote network management platform  320 , third-party networks  340 , and Internet  350  are not shown. 
     In  FIG. 5A , CMDB  500  and task list  502  are stored within computational instance  322 . Computational instance  322  may transmit discovery commands to proxy servers  312 . In response, proxy servers  312  may transmit probes to various devices, applications, and services in managed network  300 . These devices, applications, and services may transmit responses to proxy servers  312 , and proxy servers  312  may then provide information regarding discovered configuration items to CMDB  500  for storage therein. Configuration items stored in CMDB  500  represent the environment of managed network  300 . 
     Task list  502  represents a list of activities that proxy servers  312  are to perform on behalf of computational instance  322 . As discovery takes place, task list  502  is populated. Proxy servers  312  repeatedly query task list  502 , obtain the next task therein, and perform this task until task list  502  is empty or another stopping condition has been reached. 
     To facilitate discovery, proxy servers  312  may be configured with information regarding one or more subnets in managed network  300  that are reachable by way of proxy servers  312 . For instance, proxy servers  312  may be given the IP address range 192.168.0/24 as a subnet. Then, computational instance  322  may store this information in CMDB  500  and place tasks in task list  502  for discovery of devices at each of these addresses. 
       FIG. 5A  also depicts devices, applications, and services in managed network  300  as configuration items  504 ,  506 ,  508 ,  510 , and  512 . As noted above, these configuration items represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), applications executing thereon (e.g., web servers, email servers, databases, or storage arrays), relationships therebetween, as well as services that involve multiple individual configuration items. 
     Placing the tasks in task list  502  may trigger or otherwise cause proxy servers  312  to begin discovery. Alternatively or additionally, discovery may be manually triggered or automatically triggered based on triggering events (e.g., discovery may automatically begin once per day at a particular time). 
     In general, discovery may proceed in four logical phases: scanning, classification, identification, and exploration. Each phase of discovery involves various types of probe messages being transmitted by proxy servers  312  to one or more devices in managed network  300 . The responses to these probes may be received and processed by proxy servers  312 , and representations thereof may be transmitted to CMDB  500 . Thus, each phase can result in more configuration items being discovered and stored in CMDB  500 . 
     In the scanning phase, proxy servers  312  may probe each IP address in the specified range of IP addresses for open Transmission Control Protocol (TCP) and/or User Datagram Protocol (UDP) ports to determine the general type of device. The presence of such open ports at an IP address may indicate that a particular application is operating on the device that is assigned the IP address, which in turn may identify the operating system used by the device. For example, if TCP port  135  is open, then the device is likely executing a WINDOWS® operating system. Similarly, if TCP port  22  is open, then the device is likely executing a UNIX® operating system, such as LINUX®. If UDP port  161  is open, then the device may be able to be further identified through the Simple Network Management Protocol (SNMP). Other possibilities exist. Once the presence of a device at a particular IP address and its open ports have been discovered, these configuration items are saved in CMDB  500 . 
     In the classification phase, proxy servers  312  may further probe each discovered device to determine the version of its operating system. The probes used for a particular device are based on information gathered about the devices during the scanning phase. For example, if a device is found with TCP port  22  open, a set of UNIX®-specific probes may be used. Likewise, if a device is found with TCP port  135  open, a set of WINDOWS®-specific probes may be used. For either case, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  logging on, or otherwise accessing information from the particular device. For instance, if TCP port  22  is open, proxy servers  312  may be instructed to initiate a Secure Shell (SSH) connection to the particular device and obtain information about the operating system thereon from particular locations in the file system. Based on this information, the operating system may be determined. As an example, a UNIX® device with TCP port  22  open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. This classification information may be stored as one or more configuration items in CMDB  500 . 
     In the identification phase, proxy servers  312  may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUX®-specific probes may be used. Likewise if a device was classified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probes may be used. As was the case for the classification phase, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB  500 . 
     In the exploration phase, proxy servers  312  may determine further details about the operational state of a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase and/or the identification phase. Again, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  reading additional information from the particular device, such as processor information, memory information, lists of running processes (applications), and so on. Once more, the discovered information may be stored as one or more configuration items in CMDB  500 . 
     Running discovery on a network device, such as a router, may utilize SNMP. Instead of or in addition to determining a list of running processes or other application-related information, discovery may determine additional subnets known to the router and the operational state of the router&#39;s network interfaces (e.g., active, inactive, queue length, number of packets dropped, etc.). The IP addresses of the additional subnets may be candidates for further discovery procedures. Thus, discovery may progress iteratively or recursively. 
     Once discovery completes, a snapshot representation of each discovered device, application, and service is available in CMDB  500 . For example, after discovery, operating system version, hardware configuration and network configuration details for client devices, server devices, and routers in managed network  300 , as well as applications executing thereon, may be stored. This collected information may be presented to a user in various ways to allow the user to view the hardware composition and operational status of devices, as well as the characteristics of services that span multiple devices and applications. 
     Furthermore, CMDB  500  may include entries regarding dependencies and relationships between configuration items. More specifically, an application that is executing on a particular server device, as well as the services that rely on this application, may be represented as such in CMDB  500 . For instance, suppose that a database application is executing on a server device, and that this database application is used by a new employee onboarding service as well as a payroll service. Thus, if the server device is taken out of operation for maintenance, it is clear that the employee onboarding service and payroll service will be impacted. Likewise, the dependencies and relationships between configuration items may be able to represent the services impacted when a particular router fails. 
     In general, dependencies and relationships between configuration items be displayed on a web-based interface and represented in a hierarchical fashion. Thus, adding, changing, or removing such dependencies and relationships may be accomplished by way of this interface. 
     Furthermore, users from managed network  300  may develop workflows that allow certain coordinated activities to take place across multiple discovered devices. For instance, an IT workflow might allow the user to change the common administrator password to all discovered LINUX® devices in single operation. 
     In order for discovery to take place in the manner described above, proxy servers  312 , CMDB  500 , and/or one or more credential stores may be configured with credentials for one or more of the devices to be discovered. Credentials may include any type of information needed in order to access the devices. These may include userid/password pairs, certificates, and so on. In some embodiments, these credentials may be stored in encrypted fields of CMDB  500 . Proxy servers  312  may contain the decryption key for the credentials so that proxy servers  312  can use these credentials to log on to or otherwise access devices being discovered. 
     The discovery process is depicted as a flow chart in  FIG. 5B . At block  520 , the task list in the computational instance is populated, for instance, with a range of IP addresses. At block  522 , the scanning phase takes place. Thus, the proxy servers probe the IP addresses for devices using these IP addresses, and attempt to determine the operating systems that are executing on these devices. At block  524 , the classification phase takes place. The proxy servers attempt to determine the operating system version of the discovered devices. At block  526 , the identification phase takes place. The proxy servers attempt to determine the hardware and/or software configuration of the discovered devices. At block  528 , the exploration phase takes place. The proxy servers attempt to determine the operational state and applications executing on the discovered devices. At block  530 , further editing of the configuration items representing the discovered devices and applications may take place. This editing may be automated and/or manual in nature. 
     The blocks represented in  FIG. 5B  are for purpose of example. Discovery may be a highly configurable procedure that can have more or fewer phases, and the operations of each phase may vary. In some cases, one or more phases may be customized, or may otherwise deviate from the exemplary descriptions above. 
     V. PERFORMANCE ANALYTICS VISUALIZATIONS 
     As described herein, a visualization may take various forms. Visualizations typically involve the presentation of KPIs in a graphical format. 
     KPIs may also be referred to as metrics or indicators. In general, KPIs are a type of performance measurement used to evaluate current and past conditions, as well as to forecast trends. KPIs can be used to evaluate the success of a particular activity, such as making progress toward strategic goals or the repeated achievement of some level of operational goal (for example, zero defects, a mean time to resolution of less than 24 hours for certain types of IT issues, or less than 70% processor utilization on a particular server device). 
     The act of measuring a KPI may be referred to as collection. KPIs are associated with one or more KPI sources that define one or more fields in a database table (sometimes called a facts table) that are to be collected in order to provide the KPI data. KPI sources may also specify filters to include only a subset of the information in a field. 
     A dashboard is single-screen GUI component that contains one or more tabs that logically group components that generally belong together. In some embodiments, a dashboard may be equivalent to or contained within a GUI window. Tabs may be graphical control elements that allow multiple documents or panels to be contained within a single dashboard. Tabs can be used to switch between such documents or panels. Individual GUI widgets may be present on such tabs. These widgets may display a KPI as a latest value, a time series, in a chart, in a speedometer, in a dial, in a scorecard, or in a column. Other variations are possible. 
     Breakdowns allow organization and filtering of KPI data on tabs and dashboards. In some embodiments, breakdowns may take the form of a drop down GUI widget. Regardless, the KPI data can be divided in various ways based on category. For instance, IT trouble ticket incidents can be divided by priority or by originating department. In some cases, breakdowns can use these multiple ways of dividing data in tandem, such as breaking down IT trouble ticket incidents first by priority, then by originating department. 
     A scorecard can be a dashboard, tab, or widget that displays data related to a single KPI (e.g., in a time series chart widget) and enables detailed analysis of this data. In some embodiments, each KPI may have an associated scorecard that is automatically created. The data may be viewed by breakdown and/or in aggregate (e.g., counts, sums, and maximums of the values). Scorecards may also provide ways of viewing the database fields on which the KPI values are based. 
     Any of these elements (dashboards, tabs, widgets, breakdowns, and scorecards) may be considered a visualization and can be user customized. For instance, a user can rearrange the tabs of a dashboard, add or remove widgets from a tab, and create new breakdowns. 
     Example dashboards are shown in  FIGS. 6A and 6B . Dashboard  600  of  FIG. 6A  includes multiple tabs  602 , such as an “Incident KPIs” tab, a “Tier analysis” tab, and so on. The “Incident KPIs” tab is displayed, and includes a widget in the form of a bar chart  604 , titled “Open incidents by age”. Bar chart  604  plots, for each day of an approximately three-month time period, the total number of open incidents for the age ranges of 0-1 days, 1-5 days, 6-30 days, 31-90 days, and over 90 days. These age ranges may be defined by the “Age” category of breakdown  606 . 
     These incidents may be, for example, trouble tickets or help requests opened with an IT organization. Each incident may therefore involve a particular problem that a user has experienced, such as a computer crashing, a user being unable to log on to a service, slow performance of a service, a request for new equipment, and so on. The IT organization may track its performance by measuring how long it takes to resolve the incidents. For example, bar chart  604  suggests that there were fewer open incidents near the end of the time frame than at the middle of the time frame, but that the incidents near the end of the time frame had remained open for a longer duration (i.e., there were more open incidents in the 31-90 days age range). 
     Dashboard  600  may also include section  608 , which includes three widgets for: the extent of the open incident backlog (in this case, there are 422 open incidents currently), the first call resolution rate (in this case, 83.6%), and a seven-day running average of the mean time for an incident to be resolved (in this case, 3.08 days). This latter KPI may also be referred to mean time to resolution, or MTTR. 
     Dashboard  610  of  FIG. 6B  shows different example visualizations related to open incidents. This dashboard contains the same tabs  602 , but includes charts  612  and  614  instead of bar chart  604  and section  608 . Chart  612  plots, for the same time frame of the visualization in  FIG. 6A , open incidents against the average age of these open incidents on a dual y-axis graph. Chart  614  also plots open incidents, but includes representations of the age distribution of these incidents. 
     Dashboards  600  and  610  also include various selectors, such as breakdowns in the form of drop down menus that allow the user to view these KPIs in different ways. Regardless of their exact mechanisms, these dashboards allow the user to rapidly determine the status of the organization&#39;s incident response KPIs through the use of visualizations that combine these KPIs. 
     The data displayed in bar chart  604 , section  608 , chart  610 , and chart  612  may be visualizations defined by a data model. Thus, information defining these visualizations may be stored in a database according to that data model. The information may also be identified as representing one or more KPIs, and each KPI may be represented as one or more tables in the data model. As demonstrated in  FIGS. 6A and 6B , multiple visualizations may use the same KPIs to provide different views of the represented data. 
     As such, if any of the database tables, or entries within these tables, that are relied on by the visualization software are missing, the visualization may fail. For instance, the user may see a blank visualization, a partial visualization, or an error message. Furthermore, since the elements of visualizations can be defined and/or modified by users, it is possible for the users to misconfigure the visualization. As examples, the user may define a KPI without defining how values of the KPI are to be collected, define a KPI without using the KPI in any visualization, or associate the KPI with invalid conditions. Moreover, inefficiently designed visualization tasks, such as reading a whole table to access just a few entries or trying to combine too many KPIs in a single visualization, can leads to performance degradation in not only performance analytics tasks, but also for the computational instance in general. 
     VI. PERFORMANCE ANALYTICS DIAGNOSTIC TESTING 
     In order to detect the types of problems described above, a computational instance of a remote network management platform may periodically or from time to time carry out automated diagnostic testing of its performance analytics subsystem. These diagnostic tests may be configured to be performed at a particular point in time each day or each week, may be performed in response to a user request, or may be triggered by a different event. 
     The diagnostic testing may involve a script (or another software program) on the computational instance obtaining a set of rules for each diagnostic test. Each of the rules may specify a potential performance analytics problem. The script may query a number of database tables, fields within these tables, and/or configuration information stored within one or more files on the computational instance. For each rule, the script may check one or more conditions, values, or parameters. Based on the outcome of the check(s), the script may log or inform the user of one or more detected problems found by the diagnostic test. In some cases, the script can generate a GUI (e.g., a web page) displaying the outcome of the diagnostic test. 
     As an example, a particular dashboard may require: the existence of a particular database table, the existence of a particular field within the database table, and that the particular field contain a value within a particular range. This information may be encoded as three distinct rules of a diagnostic test. When the script executes, these rules may cause the script to check for the existence of the particular database table, the existence of the particular field within the database table, and the field to contain a value within the particular range. If all of these checks pass, the script may record an indication that the requirements of the diagnostic test are satisfied. However, if one or more of these checks fail, the script may record an indication that the requirements of the diagnostic test have not been satisfied, specify which rules have failed checks, provide a detailed description of each failed check, and/or suggest an action that can be taken in order to rectify the underlying problem. Furthermore, the indication may include a reference to the specific database table and records therein that are problematic so that the problem can be more easily remediated. 
     A. Example Diagnostic Architecture 
       FIG. 7  depicts an example architecture for performance analytics diagnostic testing. As discussed previously, managed network  300  is remotely managed by computational instance  322  of a remote network management platform. 
     Computational instance  322  includes performance analytics database  700 , storing performance analytics data  702 . Performance analytics data  702  may be information collected and/or related to various KPIs, such as values of these KPIs over time. Performance analytics data  702  may also include dashboard definitions, for example. Performance analytics data  702 , or data derived therefrom, may be displayed on performance analytics dashboards, such as those shown in  FIGS. 6A and 6B . 
     Performance analytics programs  704  may include one or more software programs that receive and process performance analytics data  702 . These programs may be standalone executable programs or scripts embedded into web pages, for example. In some cases, performance analytics programs  704  may obtain and/or generate GUI data  706  (e.g., markup files, text, and/or graphics) based at least in part on performance analytics data  702 . Performance analytics programs  704  may transmit GUI data  706  to performance analytics GUI  708  on managed network  300 . Performance analytics programs  704  may also store a representation of its settings as configuration  716 , and provide a copy of configuration  716  to other program upon request. 
     Performance analytics GUI  708  may represent computer hardware and software configured to display GUI data  706  in the form of a GUI. Examples of such a GUI are shown in  FIGS. 6A and 6B . The displayed GUI may be interactive, and thus allow a user to modify the information displayed. In some cases, this will result in performance analytics GUI  708  requesting more of performance analytics data  702  by way of performance analytics programs  704 . 
     Computational instance  322  may also include diagnostic database  706 , storing diagnostic data  712 . Diagnostic data  712  may define rules and tests that can be used to detect and/or diagnose problems with performance analytics database  700  and/or performance analytics programs  704 . 
     Diagnostic programs  714  may include one or more software programs that process diagnostic data  712 . These programs may be standalone executable programs or scripts embedded into web pages, for example. In some cases, diagnostic programs  714  may generate GUI data  718  (e.g., markup files, text, and/or graphics) based at least in part on performance analytics data  702 , diagnostic data  712 , and configuration  716 . Configuration  716  may define how certain operations of performance analytics programs  704  are to be carried out. Diagnostic programs  714  may transmit GUI data  718  to diagnostic GUI  720  on managed network  300 . 
     Diagnostics GUI  708  may represent computer hardware and software configured to display GUI data  718  in the form of a GUI. Examples of such GUIs are shown in  FIGS. 8A, 8B, 8C, 8D, and 8E . The displayed GUI may be interactive, and thus allow a user to modify the information displayed. In some cases, this will result in diagnostic GUI  720  requesting more of performance analytics data  702 , diagnostic data  712 , and/or configuration  716  by way of performance analytics programs  704 . 
     As an example, diagnostic database  710  may include a diagnostic test. This diagnostic test may define a rule that specifies that dashboards defined in performance analytics database  700 , that use database tables, require the respective database tables to exist in performance analytics database  700 . Diagnostic programs  714  may retrieve the diagnostic test from diagnostic database  710  in the form of diagnostic data  712 . Diagnostic programs  714  may parse this data to obtain the definition of the diagnostic test. 
     Then, diagnostic programs  714  may retrieve definitions of the dashboards defined in performance analytics database  700 , in the form of performance analytics data  702 . For each dashboard, diagnostic programs  714  may check for the existence of any database tables referenced therein by querying performance analytics database for these database tables. If all of the database tables referenced by the dashboard are present in performance analytics database  700 , diagnostic programs  714  may indicate that the diagnostic test passed. For any database tables referenced by the dashboard that are not present in performance analytics database  700 , diagnostic programs  714  may indicate as such. 
     B. Example Diagnostic Test Types 
     Table 1, below, depicts representations of a number of diagnostic tests that can be performed by a diagnostic program. The diagnostic tests shown are not comprehensive, and new diagnostic tests may be defined. Each diagnostic test in Table 1 is shown with a category, severity, description, and solution. 
     The category provides a general type of the diagnostic test. For example, the categories in Table 1 include “bad table or field”, “invalid user”, “bad configuration”, and “performance problem”. Other categories may exist, and it is possible for one diagnostic test to be placed in multiple categories. 
     The severity indicates a degree of seriousness associated with a failed diagnostic test. The severities shown in Table 1 include an “error” severity, indicating that an associated KPI or dashboard will likely fail to display properly until the error is addressed. Table 1 also includes a “warning” severity, indicating that an associated KPI or dashboard will likely display at least some data properly, but the KPI or dashboard are misconfigured in some fashion that may cause some data to be improperly displayed or not displayed at all. Table 1 also includes an “information” severity, indicating that the KPI or dashboard is misconfigured in a fashion that is unlikely to prevent the proper display of data. But this misconfiguration should still be corrected for purposes of correctness or efficiency. 
     The description provides a short textual explanation of each diagnostic test. This may include an explanation of a problem detected by the diagnostic test. This information can be displayed to the user, for example, by way of a GUI. 
     The solution provides a short textual explanation of what the user can do in order to address the problem detected by the diagnostic test. In some cases, this explanation may be best practice guidance, and might or might not actually solve the problem. Regardless, this information can also be displayed to the user, for example, by way of a GUI. 
     As an illustrative example, the first diagnostic test in Table 1 is of the “bad field or table” category with an “error” severity. The description indicates that the script that creates the performance analytics dashboard (e.g., one of performance analytics programs  704 ) references or otherwise uses one or more fields in a database table (e.g., within performance analytics database  700 ) that do not exist. This will likely cause the script to fail and/or the dashboard to be not displayable. The solution suggests that the user check the validity of each script reference to a field in the database table, and update any incorrect references. In some cases, this may involve changing or removing the reference in the script, or adding the referenced field to the database table. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Category 
                 Severity 
                 Description 
                 Solution 
               
               
                   
               
             
            
               
                 Bad table or field 
                 Error 
                 This script uses one or more fields 
                 Update the script to ensure that all field 
               
               
                   
                   
                 that do not exist. 
                 references are valid. 
               
               
                 Invalid user 
                 Error 
                 The Run as user defined for this 
                 Update the collection job Run as field to 
               
               
                   
                   
                 collection job is not valid or 
                 a different user, or ensure that the current 
               
               
                   
                   
                 active. 
                 user record is active and the User ID 
               
               
                   
                   
                   
                 field is populated. 
               
               
                 Bad table or field 
                 Error 
                 There are one or more invalid 
                 Update the KPI to ensure that all 
               
               
                   
                   
                 fields in this KPI&#39;s condition. 
                 conditions reference valid fields. 
               
               
                 Bad table or field 
                 Error 
                 An interactive filter based on Date 
                 In the Interactive Filter record, scroll 
               
               
                   
                   
                 has no tables or fields mapped to 
                 down to the Interactive Filter Dates 
               
               
                   
                   
                 it. 
                 related list and click New to add a new 
               
               
                   
                   
                   
                 target table mapping. 
               
               
                 Bad table or field 
                 Error 
                 An interactive filter based on 
                 In the Interactive Filter record, scroll 
               
               
                   
                   
                 Reference has no tables or fields 
                 down to the Interactive Filter References 
               
               
                   
                   
                 mapped to it. 
                 related list and click New to add a new 
               
               
                   
                   
                   
                 Interactive Filter References record. 
               
               
                 Bad table or field 
                 Error 
                 This script references a table that 
                 Update the script to reference a valid 
               
               
                   
                   
                 does not exist. 
                 table. 
               
               
                 Bad table or field 
                 Error 
                 This breakdown element filter 
                 Update the breakdown element filter to 
               
               
                   
                   
                 contains fields that do not exist. 
                 ensure that all conditions are valid. 
               
               
                 Bad configuration 
                 Error 
                 An interactive filter based on 
                 In the Interactive Filter record, scroll 
               
               
                   
                   
                 Group has no child filters mapped 
                 down to the Interactive Filter Groups 
               
               
                   
                   
                 to it. 
                 related list and click Insert a new row to 
               
               
                   
                   
                   
                 add new Interactive Filter groups. 
               
               
                 Bad table or field 
                 Error 
                 This KPI source is using a table 
                 Update the KPI source to use a valid 
               
               
                   
                   
                 that does not exist. 
                 facts table. 
               
               
                 Bad table or field 
                 Error 
                 A condition used in this 
                 Update the Breakdown Source to ensure 
               
               
                   
                   
                 Breakdown Source is not valid. 
                 that the Facts table and Conditions are 
               
               
                   
                   
                 There are one or more invalid 
                 valid. 
               
               
                   
                   
                 fields in the Conditions. 
               
               
                 Bad table or field 
                 Error 
                 This KPI source contains 
                 Update the KPI source to ensure that the 
               
               
                   
                   
                 Conditions that references one or 
                 Facts table and Conditions are valid. 
               
               
                   
                   
                 more invalid fields from the 
               
               
                   
                   
                 selected Facts table. 
               
               
                 Bad table or field 
                 Error 
                 This breakdown source uses a 
                 Update the breakdown source to use a 
               
               
                   
                   
                 Facts table or Field that does not 
                 valid Facts table and Field. 
               
               
                   
                   
                 exist. 
               
               
                 Bad configuration 
                 Error 
                 This KPI uses unsupported 
                 Update the KPI to use only supported 
               
               
                   
                   
                 condition operators. 
                 condition operators. 
               
               
                 Bad configuration 
                 Error 
                 An interactive filter based on 
                 In the Interactive Filter record, scroll 
               
               
                   
                   
                 Cascading Filter has no cascading 
                 down to the Cascading Filter related list 
               
               
                   
                   
                 filters mapped to it. 
                 and click New to add a new Cascading 
               
               
                   
                   
                   
                 Filter record. 
               
               
                 Performance 
                 Information 
                 A collection job log is still in the 
                 Check if the job has been restarted. If 
               
               
                 problem 
                   
                 Collecting state but collection is 
                 not, restart it manually. 
               
               
                   
                   
                 no longer running. 
               
               
                 Bad configuration 
                 Information 
                 There are no KPIs defined for a 
                 Create a KPI that uses the KPI source or 
               
               
                   
                   
                 KPI source. 
                 remove the KPI source. 
               
               
                 Bad configuration 
                 Information 
                 A breakdown source is inactive. 
                 Verify that the breakdown source was 
               
               
                   
                   
                   
                 not automatically deactivated by a 
               
               
                   
                   
                   
                 collection job where it is being used. 
               
               
                   
                   
                   
                 This occurs when the breakdown source 
               
               
                   
                   
                   
                 returns more rows than allowed. 
               
               
                   
                   
                   
                 Additionally, make changes to the 
               
               
                   
                   
                   
                 breakdown source if required and then 
               
               
                   
                   
                   
                 (re)activate this breakdown source. 
               
               
                 Bad configuration 
                 Information 
                 An automated KPI is associated 
                 Use the automated KPI in a widget or 
               
               
                   
                   
                 with an active collection job but is 
                 formula KPI. If the automated KPI is 
               
               
                   
                   
                 not used in any widgets or 
                 not used, remove the KPI from the 
               
               
                   
                   
                 formula KPI. 
                 associated collection jobs and delete the 
               
               
                   
                   
                   
                 KPI. 
               
               
                 Bad configuration 
                 Information 
                 A breakdown source is not used 
                 Create a breakdown that uses the 
               
               
                   
                   
                 by any breakdowns. 
                 breakdown source or remove the 
               
               
                   
                   
                   
                 breakdown source. 
               
               
                 Bad configuration 
                 Warning 
                 An inactive breakdown source is 
                 Verify that the breakdown source was 
               
               
                   
                   
                 associated with active KPIs or 
                 not automatically deactivated by a 
               
               
                   
                   
                 collection jobs. 
                 collection job where it is being used. 
               
               
                   
                   
                   
                 This occurs when the breakdown source 
               
               
                   
                   
                   
                 returns more rows than allowed. 
               
               
                   
                   
                   
                 Additionally, make changes to the 
               
               
                   
                   
                   
                 breakdown source if required and then 
               
               
                   
                   
                   
                 (re)activate this breakdown source. 
               
               
                 Bad configuration 
                 Warning 
                 An automated KPI used by a 
                 Associate the automated KPI with a 
               
               
                   
                   
                 widget is not associated with any 
                 collection job and set the Active field on 
               
               
                   
                   
                 collection jobs. 
                 the job KPI record to true. 
               
               
                 Bad configuration 
                 Warning 
                 A breakdown has no breakdown 
                 Define a breakdown mapping for the 
               
               
                   
                   
                 mappings. 
                 breakdown. 
               
               
                 Bad configuration 
                 Warning 
                 An automated KPI is not 
                 Associate the automated KPI with a 
               
               
                   
                   
                 associated with a collection job. 
                 collection job and set the Active field on 
               
               
                   
                   
                   
                 the job KPI record to true. 
               
               
                 Bad configuration 
                 Warning 
                 A script references fields on the 
                 Add the missing fields to the fields list. 
               
               
                   
                   
                 ‘current’ record that are not listed 
               
               
                   
                   
                 in the fields list. 
               
               
                 Performance 
                 Warning 
                 A data collection job ended with a 
                 Check the job logs for warnings to 
               
               
                 problem 
                   
                 collection warning, 
                 identify the issue. 
               
               
                 Bad configuration 
                 Warning 
                 A collection job does not have any 
                 Add an automated KPI to the data 
               
               
                   
                   
                 KPIs associated with it. 
                 collection job. 
               
               
                 Bad configuration 
                 Warning 
                 Multiple automated KPI have the 
                 Update references, such as in widgets 
               
               
                   
                   
                 same KPI source, Conditions, 
                 and formula KPI, to use only one of the 
               
               
                   
                   
                 Aggregate, and Field or Script 
                 duplicate automated KPI, and then delete 
               
               
                   
                   
                 values. 
                 the unused duplicate KPI. 
               
               
                 Bad configuration 
                 Warning 
                 A KPI is associated with multiple 
                 Associate the KPI with only one 
               
               
                   
                   
                 active scheduled data collection 
                 scheduled data collection job. You 
               
               
                   
                   
                 jobs. 
                 should only associate the KPI with 
               
               
                   
                   
                   
                 multiple on-demand jobs that do not run 
               
               
                   
                   
                   
                 on a schedule. 
               
               
                 Performance 
                 Warning 
                 A data collection job ended with a 
                 Check the job logs for errors to identify 
               
               
                 problem 
                   
                 collection error. 
                 the issue. 
               
               
                 Bad configuration 
                 Warning 
                 A breakdown has multiple 
                 Remove one of the breakdown mappings 
               
               
                   
                   
                 breakdown mappings using the 
                 containing the duplicate facts table. 
               
               
                   
                   
                 same facts table. 
               
               
                 Bad configuration 
                 Warning 
                 A widget references a non- 
                 Update the widget to reference a valid 
               
               
                   
                   
                 existent KPI. 
                 KPI. 
               
               
                 Bad configuration 
                 Warning 
                 A formula KPI contains one or 
                 Update the formula to ensure that all 
               
               
                   
                   
                 more invalid KPIs, breakdowns, 
                 KPIs, breakdowns, and breakdown 
               
               
                   
                   
                 or breakdown elements, or the 
                 elements are valid and that the formula 
               
               
                   
                   
                 formula KPI references itself. 
                 KPI does not reference itself. 
               
               
                 Performance 
                 Warning 
                 A scheduled collection job has an 
                 Check the job logs to identify possible 
               
               
                 problem 
                   
                 average execution time that is too 
                 sources of slowness. The following 
               
               
                   
                   
                 long. 
                 values may help identify the root cause. 
               
               
                   
                   
                   
                 The score collection relative start and 
               
               
                   
                   
                   
                 end interval 
               
               
                   
                   
                   
                 The number of different KPI sources 
               
               
                   
                   
                   
                 used by the KPIs 
               
               
                   
                   
                   
                 If the job includes inefficient scripted 
               
               
                   
                   
                   
                 KPIs or breakdowns 
               
               
                   
                   
                   
                 If there are slow queries during the job 
               
               
                   
                   
                   
                 execution. 
               
               
                 Bad configuration 
                 Warning 
                 A widget references a non- 
                 Update the widget to reference a valid 
               
               
                   
                   
                 existent breakdown in the 
                 breakdown. 
               
               
                   
                   
                 Breakdown or 2nd Breakdown 
               
               
                   
                   
                 field. 
               
               
                 Bad configuration 
                 Warning 
                 A formula or manual KPI is 
                 Remove all formula and manual KPIs 
               
               
                   
                   
                 associated with a collection job. 
                 from collection jobs. Associate only 
               
               
                   
                   
                   
                 automated KPIs with collection jobs. 
               
               
                   
               
            
           
         
       
     
     C. Example Graphical User Interfaces 
       FIGS. 8A-8E  depict example GUIs that may be displayed in conjunction with a diagnostics program (e.g., one of diagnostics programs  714 ) for a performance analytics software application (e.g., one of performance analytics programs  704 ). For instance, these GUIs may be displayed, on a client device disposed within a managed network, when the client device invokes the diagnostic program to be executed on a computational instance of a remote network management program. 
       FIG. 8A  depicts GUI  800  including a tabular menu through which one or more diagnostic tests can be selected. GUI  800  includes menu bar  802 , header row  804 , and example diagnostic test  806 . 
     Menu bar  802  includes “New” button  802 A, “Execute All” button  802 B, and search box  802 C. Activation of “New” button  802 A may cause a new GUI or window to appear in which a new diagnostic test can be defined. This functionality allows users to define their own diagnostic tests. Activation of “Execute All” button  802 B may cause all diagnostic tests to be performed, including those listed on GUI  800  as well as any others not shown therein. In some embodiments, one or more selected diagnostic tests can be executed by way of another control on GUI  800 . Entry of data (e.g., a text string) into search box  802 C may cause the list of diagnostic tests on GUI  800  to be filtered so that only those with fields containing the entered data are displayed on GUI  800 . 
     Header bar  804  provides column titles for the diagnostic tests displayed in GUI  800 , including a name header  804 A, description header  804 B, severity header  804 C, and active header  804 D. 
     Name header  804 A is disposed above a column containing the names of the diagnostic tests. Note that these names are omitted from Table 1 for sake of brevity. 
     Description header  804 B is disposed above a column containing descriptions of the diagnostic tests. In some embodiments, these descriptions may correspond to the descriptions of diagnostic tests in the description column of Table 1. 
     Severity header  804 C is disposed above a column containing the severity of the problems checked for by the diagnostic tests. These severities may correspond to the “Severity” column of Table 1. 
     Active header  804 D is disposed above a column containing an indication of whether each diagnostic test is active. In some embodiments, the values in this column may either be “true” to indicate that the diagnostic test is active or “false” to indicate that the diagnostic test is inactive. An active diagnostic test can be performed by the diagnostic program, and an inactive diagnostic test might not be able to be performed by diagnostic program. 
     The diagnostic tests may be sortable by each of these column headers. For instance, in GUI  800 , the diagnostic tests are sorted alphabetically by name. 
     Example diagnostic test  806  is one of possibly many such diagnostic tests, each individually selectable for execution by the diagnostic program. As shown in  FIG. 8A , example diagnostic test  806  includes name indicator  806 A, description indicator  806 B, severity indicator  806 C, and active indicator  806 D. 
       FIG. 8B  depicts GUI  810  through which a diagnostic test can be viewed and edited. In particular, GUI  810  includes details relating to example diagnostic test  806 . Navigation from GUI  800  to GUI  810  may occur when a user clicks on or otherwise activates any of name indicator  806 A, description indicator  806 B, severity indicator  806 C, or active indicator  806 D. 
     GUI  810  may include name text box  812 , severity text box  814 , description text box  816  and active checkbox  818 . These GUI components may be user-editable and correspond to name indicator  806 A, description indicator  806 B, severity indicator  806 C, and active indicator  806 D, respectively. 
     Table indicator  820  may provide the name of the database table that is used by script  824 . Thus, table indicator  820  may refer to a database table in performance analytics database  700 . 
     Advanced checkbox  822  allows the user to control the type of GUI that is used to view and/or edit a diagnostic test. In  FIG. 8B , this checkbox is checked, indicating that GUI  810  depicts an advanced editing mode. Unchecking this box may result in navigation from GUI  810  to GUI  830  in  FIG. 8C . 
     Code editor  824  of  FIG. 8B  may be a large text box that allows a user to view and/or edit script code. In this fashion, the user may customize the script to perform diagnostic tests in accordance with technical requirements or best practices, for example. 
       FIG. 8C  depicts GUI  830  through which a diagnostic test can be viewed and edited. In particular, GUI  830  includes details relating to example diagnostic test  806 . As noted above, navigation from GUI  810  to GUI  830  may occur when a user unchecks advanced checkbox  822 . Similarly, navigation from GUI  830  to GUI  810  may occur when a user checks advanced checkbox  822 . 
     Similar to GUI  810 , GUI  830  depicts name text box  812 , severity text box  814 , description text box  816  and active checkbox  818 . As noted, these GUI components may be user-editable and correspond to name indicator  806 A, description indicator  806 B, severity indicator  806 C, and active indicator  806 D, respectively. GUI  830  also depicts table indicator  820 . 
     GUI  830  differs from GUI  810  at least in its depiction of condition configurator  832 , message text box  834 , and solution description text box  836  (note that GUI  810  may also contain message text box  834  and solution description text box  836  positioned below code editor  824 , for instance). Condition configurator  832  may include text boxes, drop down menus, and/or other GUI components that allow a user to specify one or more rules of a diagnostic test. Message text box  834  is user-editable and contains text to be stored or displayed when this diagnostic test detects a problem. This text describes the problem. Solution description text box  836  contains text to be stored or displayed when this diagnostic test detects a problem. This text provides a possible solution for the problem. 
       FIG. 8D  depicts GUI  840  including a tabular menu that displays a list of diagnostic test suites that are executing or have been executed. GUI  840  includes text box  842 , header row  844 , and example diagnostic test suite results  846 . Each diagnostic test suite may include execution of more than one diagnostic test. For instance, when multiple diagnostic tests are selected by way of GUI  800 , the results of all selected diagnostic tests may be displayed as one row in GUI  840 . 
     Entry of data into text box  842  may cause the list of diagnostic test suites displayed on GUI  840  to be filtered by this data. For instance, if the date “2017-10-02” is entered into text box  842 , only diagnostic tests executed on this date may be displayed. Alternatively, the drop down menu to the left of text box  842  may be used to select other header columns in which the search takes place. 
     Header bar  844  provides column titles for the diagnostic test suites displayed in GUI  840 , including an execution date header  844 A, state header  844 B, error messages header  844 C, warning messages header  844 D, and information messages header  844 E. 
     Execution date header  844 A is disposed above a column containing the dates and times of when execution began for the listed diagnostic test suites. Alternatively, this column may contain the dates and times of when execution completed for these diagnostic test suites. 
     State header  844 B is disposed above a column containing state descriptions of the diagnostic test suites. These descriptions may indicate, for example, that a diagnostic test suite is executing or has completed execution. 
     Error messages header  844 C is disposed above a column containing a number of error conditions found while executing each of the diagnostic test suites. This count may relate to the number of individual diagnostic tests in the suite that reported an error. 
     Warning messages header  844 D is disposed above a column containing a number of warning conditions found while executing each of the diagnostic test suites. This count may relate to the number of individual diagnostic tests in the suite that reported a warning. 
     Information messages header  844 E is disposed above a column containing a number of informational conditions found while executing each of the diagnostic test suites. This count may relate to the number of individual diagnostic tests in the suite that reported an informational condition. 
     The diagnostic test suite results in  FIG. 8D  may be sortable by each of these column headers. For instance, in GUI  840 , the diagnostic test suites are sorted in descending order of the number of warning messages. 
     Example diagnostic test suite result  846  is one of possibly many such diagnostic test suite results, each individually selectable for further display. As shown in  FIG. 8D , example diagnostic test suite result  846  includes execution date  846 A (indicating that the diagnostic test suite began execution at 3:40:34 PM on Oct. 4, 2017), state  846 B (indicating that the diagnostic test suite has completed execution), error messages  846 C (indicating no errors), warning messages  846 D (indicating  1  warning), and information messages  846 E (indicating  5  informational messages). 
     If a user clicks on or otherwise activates any of execution date  846 A, state  846 B, error messages  846 C, warning messages  846 D, or information messages  846 E, a GUI displaying detailed information about example diagnostic test suite result  846  may be displayed. 
     To that point,  FIG. 8E  depicts GUI  850  including a tabular menu that displays details of diagnostic test suite results. The results shown in GUI  850  are, for example, the details of diagnostic test suite result  846 . As shown in various components of GUI  850 , the information displayed therein matches that of example diagnostic test suite result  846 . 
     For instance, name indicator  852 , execution date indicator  854 , total messages indicator  858 , error messages indicator  860 , warning messages indicator  862 , and information messages indicator  864  all match those of example diagnostic test suite result  846 . Diagnostics executed indicator  856  specifies that three individual diagnostic tests were performed as part of this suite. 
     Entry of data into text box  866  may cause the list of diagnostic tests displayed on GUI  850  to be filtered by this data. For instance, if the text “widget” is entered into text box  866 , only diagnostic tests with a description containing the term “widget” may be displayed. Alternatively, the drop down menu to the left of text box  866  may be used to select other header columns in which the search takes place. 
     Header bar  868  provides column titles for the diagnostic tests displayed in GUI  850 , including a diagnostic name header  868 A, severity header  868 B, message header  868 C, problem table header  868 D, problem record header  868 E, and solution description header  868 F. 
     Diagnostic name header  868 A is disposed above a column containing a brief description of the problems detected by each diagnostic test, if a problem was detected. Severity header  868 B is disposed above a column containing the severity of the detected problems, each in accordance with the severity column of Table 1 (e.g., error, warning, or information). Message header  868 C is disposed above a column containing a longer description of the detected problems, each in accordance with the description column of Table 1. Problem table header  868 D is disposed above a column containing a reference to the database table in which each problem was encountered (if applicable). Problem record header  868 E is disposed above a column containing a reference to the database record in which each problem was encountered (if applicable). Solution description header  868 F is disposed above a column containing a recommended solution for each of the detected problems, in accordance with the solution column of Table 1. 
     Example diagnostic test result  870  is one of possibly many such diagnostic test results. As shown in  FIG. 8E , example diagnostic test result  870  includes diagnostic indicator  870 A, severity indicator  870 B, message indicator  870 C, problem table indicator  870 D, problem record indicator  870 E, and solution description indicator  870 F. Example diagnostic test result  870  relates to the diagnostic test with the description “A widget references a non-existent KPI” in Table 1. 
     VII. EXAMPLE OPERATIONS 
       FIG. 9  is a flow chart illustrating an example embodiment. The process illustrated by  FIG. 9  may be carried out by a computing device, such as computing device  100 , and/or a cluster of computing devices, such as server cluster  200 . However, the process can be carried out by other types of devices or device subsystems. For example, the process could be carried out by a portable computer, such as a laptop or a tablet device. 
     The embodiments of  FIG. 9  may be simplified by the removal of any one or more of the features shown therein. Further, these embodiments may be combined with features, aspects, and/or implementations of any of the previous figures or otherwise described herein. 
     These embodiments may involve diagnostic testing of a performance analytics software application. A performance analytics database, a diagnostic database, and a computing device may be disposed within a computational instance of a remote network management platform that remotely manages a managed network. 
     Block  900  may involve obtaining, by the computing device and from the diagnostic database, a representation of a particular test from a plurality of tests stored therein, where the particular test is configured to determine compliance with a plurality of pre-defined consistency, configuration, and performance rules for (i) KPIs associated with the managed network and (ii) dashboards that are configured to specify, on a performance analytics GUI within the managed network, graphical representations of the KPIs, where representations of the KPIs and dashboards are stored in the performance analytics database. The particular test may be, for example, any one of the diagnostic tests listed in Table 1, variations thereof, or another diagnostic test entirely. 
     Block  902  may involve applying, by the computing device, each of the plurality of the pre-defined consistency, configuration, and performance rules to the KPIs and the dashboards. 
     Block  904  may involve, when applying at least one of the plurality of the pre-defined consistency, configuration, and performance rules indicates a problem, writing, by the computing device, an associated severity, problem description, and solution to the diagnostic database as output of the particular test. 
     In some embodiments, the associated severity, problem description, and solution are defined in the diagnostic database as part of the representation of the particular test. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules specifies that a database table is required to be present in the performance analytics database in order for at least one of the dashboards to be displayable. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules specifies that a field within a database table is required to be present in the performance analytics database in order for at least one of the dashboards to be displayable. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules specifies that a field within a database table of the performance analytics database is required to contain a particular value in order for at least one of the dashboards to be displayable. In alternative or additional embodiments, the performance analytics database may be required to contain a particular value within a particular range of values in order for at least one of the dashboards to be displayable. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules determines whether collection, by the performance analytics software application, of one or more values of a KPI has failed. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules determines whether collection, by the performance analytics software application, of one or more values of a KPI is still executing. 
     In some embodiments, a particular rule of the plurality of the pre-defined consistency, configuration, and performance rules determines whether one of the dashboards references a KPI that is undefined. 
     In some embodiments, a client device disposed within the managed network invokes the diagnostic software program and displays a diagnostics GUI. The diagnostic software program may be further configured to: prior to obtaining the representation of the particular test, cause the diagnostics GUI on the client device to display, in tabular form, a selectable list of the plurality of tests; and receive, from the client device, selection of the particular test. 
     In some of these embodiments, the diagnostic software program may be further configured to: in response to receiving selection of the particular test, cause the diagnostics GUI on the client device to display a definition of the particular test that includes an editable representation of executable script code that carries out at least part of the particular test. 
     In some of these embodiments, the diagnostic software program is further configured to: in response to receiving selection of the particular test, cause the diagnostics GUI on the client device to display a definition of the particular test that includes a configurable representation of the particular test, where the configurable representation allows menu-based specification of rules that check properties of particular database tables of the performance analytics database. 
     In some of these embodiments, the diagnostic software program is further configured to: when applying at least one of the plurality of the pre-defined consistency, configuration, and performance rules indicates a problem, cause the diagnostics GUI on the client device to display, in tabular form, the associated severity, problem description, and solution description. 
     Although the embodiments herein have been described in the context of a performance analytics application that collects data, defines dashboards, and transmits the data and dashboards to a client device for display, these embodiments may also apply to other applications. For example, any application that defines associations or relationships between data and graphical user interfaces on which this data is presented can benefit from the diagnostic tools described herein. Additionally, these embodiments can also operate on any database table to determine compliance between the records therein and the applications that make use of these records. 
     VIII. CONCLUSION 
     The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those described herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. 
     The above detailed description describes various features and operations of the disclosed systems, devices, and methods with reference to the accompanying figures. The example embodiments described herein and in the figures are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. 
     With respect to any or all of the message flow diagrams, scenarios, and flow charts in the figures and as discussed herein, each step, block, and/or communication can represent a processing of information and/or a transmission of information in accordance with example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and/or messages can be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved. Further, more or fewer blocks and/or operations can be used with any of the message flow diagrams, scenarios, and flow charts discussed herein, and these message flow diagrams, scenarios, and flow charts can be combined with one another, in part or in whole. 
     A step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data). The program code can include one or more instructions executable by a processor for implementing specific logical operations or actions in the method or technique. The program code and/or related data can be stored on any type of computer readable medium such as a storage device including RAM, a disk drive, a solid state drive, or another storage medium. 
     The computer readable medium can also include non-transitory computer readable media such as computer readable media that store data for short periods of time like register memory and processor cache. The computer readable media can further include non-transitory computer readable media that store program code and/or data for longer periods of time. Thus, the computer readable media may include secondary or persistent long term storage, like ROM, optical or magnetic disks, solid state drives, compact-disc read only memory (CD-ROM), for example. The computer readable media can also be any other volatile or non-volatile storage systems. A computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device. 
     Moreover, a step or block that represents one or more information transmissions can correspond to information transmissions between software and/or hardware modules in the same physical device. However, other information transmissions can be between software modules and/or hardware modules in different physical devices. 
     The particular arrangements shown in the figures should not be viewed as limiting. It should be understood that other embodiments can include more or less of each element shown in a given figure. Further, some of the illustrated elements can be combined or omitted. Yet further, an example embodiment can include elements that are not illustrated in the figures. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purpose of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.