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Performance: Challenges and Strategies ArcGIS Enterprise provides a robust and scalable platform for delivering GIS resources for users through services and web applications. Sometimes however, deployments may experience slow performance from the published resource endpoints.   Since ArcGIS is very versatile, there can be different ways, configurations and options when making these resources available for consumption. Obtaining good performance is not always as easy and straight-forward as just toggling a "fast = true" setting. What might be a handy feature for some administrators could be a configuration, that while helpful, may interfere with the performance for others.    In the real-world, performance is often a function of several items and configuration settings. Having strategies to understand and overcome the most common ones may help put the deployment on the road to achieving faster performance. What is Performance? Performance is a description of how fast (or slow) a server or service operates for a particular function of interest. How long it takes an ArcGIS Server service to complete an operation like query, applyEdit or export and then send the response back to the client that requested it would be an example of performance.   This duration is measured in seconds or milliseconds and is commonly referred to as the response time. Although called "response time", there are several important steps that make up the overall time a client like a web browser or ArcGIS Pro spend waiting for a requested server resource: DNS lookup of the server's hostname SSL handshake between client and server TCP/IP connection between client and server Sending the request to server Server processing the request Receiving response from server For large responses, Time-to-first-byte (or TTFB) can be used instead to measure response time Typically, the bulk of the time is spent at 4.1. This is where the server is working on the response. This Community Article will explorer areas that can impact this portion of the response time.  Why is Performance Important? Simply put: the faster the performance, the lower the response time.  The lower the response time, the more requests the server can support at one time. This higher concurrency of requests translates to greater scalability which ultimately means support for more users.   Performance is measured as a unit of time need to complete a single operation at a time (e.g., 0.238 seconds to execute a feature query request).  Scalability on the other hand, is frequently measured as transactions or operations over time (e.g., requests/sec or operations/hour).   When talking about getting better, faster or "more" performance, it implies achieving lower response times. On the other hand, better scalability implies reaching a higher rate of throughput (e.g., more operations/hour).   Note: Of course, for obtaining better performance or throughput, the operations of interest need to execute appropriately and respond with the expected content (e.g., map image, json or pbf data). Errors messages, for example, can be a fast, simple response whose delivery can reach a high rate of throughput. As testers and analysts, this type of throughput is not what we're after...we are interested in the throughput of successful requests. What is Acceptable Performance? It depends.  The criteria or requirement for classifying an item as having fast (or slow) performance can vary greatly by organization, the published services and expected operations users will be calling. It is not uncommon to have different response time goals for ArcGIS Server functions or user application workflows (several requests grouped together to represent one operation). Any number of seconds is fine for a requirement but keep in mind, it may take more hardware as well as more extensive tuning and strategies (this Article) to achieve aggressive goals. How is Performance Measured? Response time is the key metric for determining if performance is meeting or staying within or under a target requirement. Common strategies for measuring are: Single user interaction Through the web browser or ArcGIS Pro This is the easiest place to start If there is no understanding yet on performance, start here Statistically analyzing large volumes of response times Through log analysis tools, the ArcGIS Server Manager Statistics page, or other observability utilities These approaches have the benefit of analyzing real-world requests that users have already executed against the deployment This is discussed in more depth later in the Article Load Testing Can provide an understanding on performance and scalability  More time consuming to setup Online resources exist for getting started Performance Engineering: Load Testing ArcGIS Enterprise  When analyzing logs or running tests, a common strategy is to leverage statistics for breaking down large amounts of response times. The Average and 90th (or 95th) percentile, the Minimum, and Maximum help provide an understanding of the performance the user may have been experiencing. Capturing Response Times -- Web Browser How response times are captured through single user interaction is a fun topic of discussion. The easiest approach to capture response times of REST requests from a web application is with the browser's "developer tools" functionality. All major browsers offer some view of the requests, responses and times being sent and received. This duration can give an idea of how fast a request or operation (potentially multiple requests) performed. Decisions can then be made if this is acceptable or needs to be improved.   Capturing Response Times -- ArcGIS Pro While ArcGIS Pro also communicates with ArcGIS Enterprise via REST, it does not have a built-in equivalent of the developer tools. For capturing responses times, you'll need a separate HTTP debugger. There are many available, a popular choice is Fiddler. With Fiddler installed to the same machine as ArcGIS Pro, it can be configured to intercept the traffic. Request parameters, response content and times can be similarly captured and examined. Are Goals Required for Improving Performance? Absolutely not. GIS Administrators can always analyze, tune and apply best practices to the system even if no official performance requirements are in place. However, it is highly recommended to understand what performance your system is initially delivering (e.g., these are typically referred to as baseline response time numbers) before adjustments are made. This way you can determine if the applied changes are having a positive effect. Common Performance Challenges and Potential Strategies Service Pool Types and Instances One of the most frequent areas that ArcGIS administrators encounter performance challenges with is setting the appropriate number of instances for dedicated services. But first, let's review the different types. There are 3 service types, each with their own strengths. Dedicated Hosted Shared As a GIS Administrator it is important to be able to identify the instance type for a service. This can be easily viewed within ArcGIS Server Manager, under Manage Services:   Selecting the Appropriate Type Choosing a dedicated service type is ideal when achieving the most performance and scalability control is desired. With this type, the administrator can: Set the maximum number of instances to the number of CPU cores to take full advantage of the available processing capability ArcGIS Server machine (via the maximum) Conserve memory when idle (via the minimum) Adjust for predicable performance by setting the min and max to the same value Dedicated services are ideal for heavily requested services or services where performance is paramount. Hosted services do not utilization ArcSOC instances and auto-scale as needed. However, the ArcGIS capabilities available to it (Hosted) are limited as it is used primarily with feature queries. Shared services are great for accessing items that are requested less frequently. They typically have more ArcGIS capabilities available to them but not all ArcGIS functionally is available (e.g., branch versioning). A shared instance pool is the default type when publishing a service with ArcGIS Pro. Note: It is important to reiterate that if the selected service type is set to dedicated, the (minimum and maximum) number of instances should be evaluated to ensure they are optimal. The default when publishing in ArcGIS Pro is to only use a maximum of 2 (instances). This might be too low and inadeqaute for services where performance/scalability are important.  Focus the Map Optimizing the map is not a new strategy but a relatively easy one to follow for getting better performance from your deployment. When the map is focused on its primary purpose and presentation, the system does not have to do unnecessary work. Remember, the web is a multiuser platform. Making the display of the dynamic data as streamlined as possible is key to good performance (and scalability). With potentially many requests occurring at the same time, the content being shared needs to be as efficient as possible. Map Strategies Choose an Optimal Default Extent If the map is providing data on Los Angeles, the default extent should not be showing all of California If many different map scales are required, use scale dependencies and generalization to limit the detail to when you need most (e.g., the largest scales) Remove unneeded layers Consider removing nice-to-have data layers At the very least, unselect them and have user opt-in to enable Purposely limit what users can do with a service Avoid projecting on the fly Use the same coordinate system for data frame and data Definition queries Ensure indexes are in place if comparison logic is applied to attribute columns  Note: "Focusing the map" applies to maps, apps and services Software Releases A particular version of ArcGIS Enterprise (and its related solutions) can have a handful of patches after its initial base release. These patches can offer performance improvements as well as functionality and security fixes.  It is highly recommended to periodically check the Esri Patches and Updates site or run the "Check for ArcGIS Enterprise Updates" tool. Then, apply the updates at the appropriate time. Resource Contention and Expansion There are times when best practices and strategies for performance are applied but lower response times and higher scalability are still required. Perhaps the current hardware running ArcGIS Server is simply exhausted where the processing power or memory have become the bottleneck for improving the user experience. For such situations, you need to consider expansion and/or upgrading the hardware. Scalability For the ArcGIS Server and ArcGIS Web Adaptor tiers of the deployment, you generally have the following options for improving scalability using hardware:  Scaling up Adding more resources to the existing machine (e.g., additional processing cores) Additional memory can also assist with scaling by allowing the deployment to have more ArcSOC instances running concurrently  Ideal for deployments such as: cloud, virtualization, Kubernetes Scaling out Adding more machines of equal resource capacity Ideal for deployments such as: on-premise, cloud, virtualization, Kubernetes Performance To improve performance with hardware, there is typically just one option: Obtaining faster processing cores  Ideal for deployments such as: cloud, Kubernetes The system's paging configuration can also impact scalability even when ample memory resources have been added to a system. Although this is operating system software setting and not hardware, it can play a crucial part in the running of many concurrent ArcSOC instances. Be sure to set this accordingly to handle a workload with many instances or instances with a large memory footprint. There can be situations where performance is limited and appears to be CPU bound (e.g., a bottleneck due to limited processing power). Further inspection may reveal the "culprit" to be one or more slow queries which were suboptimal to begin with or an expensive operation called too frequently (through a periodic administrative tasks). In such a case, it may be more effective to address to the "bad" queries to improve performance and scalability. Note: These expansion strategies are for overcoming general processing and memory limitations. But, disk and network (bandwidth, latency) resources can also be bottlenecks. For some environments, these can be more complicated to expand upon requiring additional steps to upgrade. A General Approach to Scaling For ArcGIS Server it is can be easier to scale up first, then out. The reason is due to the Site configuration and directories which would need to be migrated to shared storage if the architecture is switched from a single machine to a multi-machine deployment.     How much should you scale up...two servers, five servers? Without details on average response times and the anticipated number of users to support, it’s difficult to provide a concise answer. However, a simplistic, general approach would be to just try doubling the current amount (memory and/or physical processing cores) and observing the impact. For many cases, this probably works well up to 16 CPUs. At that point, adding another machine may be more advantageous.   Note: Your current product license may impact how many physical cores you can use with ArcGIS Server. Check with your Esri Account Manager for more details.   Note: For support with capacity planning or architecture design (which can help provide a detailed understanding and estimate on the required hardware resources for a given set of workflows), contact Todd Jarrard ([email protected]) in Esri Professional Services. Observability "Quantifying ArcGIS" is a great strategy...a personal favorite. It defines what resources were being requested and how fast were the responses to fulfill these requests. It is important for obtaining an understanding of general system performance. If system resource utilization can also be captured, the analysis can be further elevated. There are many utilities available for periodically examining your system. Some tools may read the access logs and primarily focus on the statistical request performance made by users. Others may poll Server's statistics page and capture the CPU utilization (of ArcGIS Server or the database) for that duration of time. Which approach is best? If observability is currently not taking place then most likely, any one of them. would be a good addition. They all help provide some type of insight to the performance and health of the deployment.  Once analysis has been conducted for a deployment, reports can be typically generated that highlight which map services might be of interest due to: The observed response times being slower than expected The number of requests issued for the resource Both response time and number of requests For an ArcGIS Site with many services, knowing which ones are statistically slow or are consuming the most resources help focus tuning efforts. With such reports, the GIS administrator has turned data into valuable information and are now better informed at making decisions for improving the user experience. That said, examining logs and statistics is just one (important) slice of the analysis pie. A Challenge with Common Observability Tools Many tools for system observability and monitoring focus the analysis on requests and responses for services. This is a good approach and definitely assists administrators with quantifying ArcGIS, but it can have a limitation. The limitation can appear with an assumption that a slow map service can be "fixed" by simply adding processing cores. More cores might improve some aspects of the situation, but it is recommended that the service be examined (or even reexamined) in more depth before more resources are obtained. This goes back to the "Focus the Map" section, for example: Ensure the data for the service is not being shown at too small of scales Avoid suboptimal queries Detailed query analysis can help show the occurrence of these behaviors that might get masked with general service reporting. However, while the break down of service request parameters and the underlying queries can improve the analysis it can add complexity to the reporting itself (e.g., more time to execute, more views of what to look at, the understanding of the views). Additionally, not all observability tools perform this type of inspection. Some recent efforts that are gaining traction are attempting to tackle this issue. They are based on a bottom up approach of analysis where the starting point is on the underlying database queries themselves through mechanism known as "query datastore". Query datastore analysis is powerful and does not impact the database performance like a trace can but it does require some knowledge of the queries themselves and their purpose. Look to this type of analysis capability in the future to help get the most from your observability tools. Conclusion There is no single item to easily adjust for boosting performance and scalability of an ArcGIS Enterprise Site. However, this Article lists some common strategies that can be applied together for improving it. It is also important to understand these are items that should be periodically revisited and acted upon. User habits change over time as does the popularity of a web application or service. Resources that were assigned to a particular service can be reevaluated or reduced to make room for the next featured item in your Site. ArcGIS performance analysis can be fun but it also a continuous effort for maintaining the best user experience.     Attribution   Resource: File:Grayson_running_the_4x100.jpg Description: English:Grayson running the first leg of the 4x100 at the 2010 Tigered invite Author: Graysonbay Created: 02:02, 29 November 2010 License: This file is licensed under the Creative Commons Attribution 3.0 Unported license   Resource: File:Kurvimeter_1_fcm.jpg Author: Frank C. Müller, Baden-Baden License: This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.   Resource: File:My_Opera_Server.jpg Description: A server used for the My Home Author: William Viker, [email protected] (c) 2006 License: The copyright holder of this file allows anyone to use it for any purpose, provided that the copyright holder is properly attributed. Redistribution, derivative work, commercial use, and all other use is permitted.   Resource: File:Samsung-1GB-DDR2-Laptop-RAM.jpg Description: A 1 gigabyte stick of DDR2 667 MHz (PC2-5300) laptop RAM, made by Samsung and pulled from a 2007 MacBook laptop. Author: Evan-Amos Created: 1 August 2018 License: Public Domain   ... View more

ArcSOC Availability and Utilization Optimizing the ArcSOC instance availability and utilization for your service is a good strategy for helping users obtain fast response times and lower wait times from their dynamic requests to your Site. It can also benefit server resource utilization like memory as the service is not running a lot of instances that it will never use. But optimizing the minimum and maximum number of instances for your dedicated services is not a one-time job. Usage patterns of your services can change over time so the task of collecting this information is something that you will want and to revisit periodically as a GIS administrator. Before diving into how to observe ArcSOC instance activity statistics, let’s review some of the key details of the two ArcSOC-based service types in ArcGIS Server and how they play into this discussion: Dedicated Shared Note: Wait time is the duration the request spends in a “queue” on the server until an ArcSOC instance is available to start working on it. Dedicated Instance Pool Services Dedicated services (e.g., non-hosted and non-shared services) are a mainstay ArcGIS resource in deployments as many applications depend on the capabilities such as geoprocessing, Branch Version editing, and Utility Network workflows (all which require dedicated services). While such services are very versatile and are a major pillar in ArcGIS because of the functionality they provide, as a GIS administrator you need to periodically examine, adjust, and configure the number of ArcSOC instances (minimum and maximum) to take full advantage of your available resources as your Site and users grow. To learn more about ArcSOC instances see: Understand service instances Limitations of Shared Instance Pool Services Shared service instances are great! They are truly a game changer for helping admins manage the demand for many services with finite resources. However, their restrictions and requirements limit which service capabilities can be used with them.  Geoprocessing, Branch Version editing, and Utility Network, for example, are currently not supported through shared services (or through hosted services). This leaves dedicated services as the only choice for such functionality. Configured ArcSOC Instance Availability vs Instance Demand For services that are very popular, critical and/or have the requirement of running under the dedicated service type, understanding the optimal instance setting is important for several reasons. If the maximum number of active instances is too high, memory is wasted (as well as cost). The over allocation of ArcSOC instances is an important but unique case as its impact would not show up in the analysis of just response times. Alternatively, having the maximum too low can impact performance (in the form of higher response times and longer wait times) as users might be frequently waiting for an already busy ArcSOC instance to become free. Of course, setting the instance minimum and maximum to different values has a trade-off too. For critical services where performance is paramount, having the user’s request wait while an instance needs to be started can be time consuming and will affect performance. So, for predictable performance on essential services, it is recommended to set the minimum and maximum number of instances to the same value. As listed on Introduction to service instances: Accordingly, it's important for ArcGIS Server administrators to monitor the number of instances their site is running, and to limit running instances when performance is inhibited by memory usage. Configuring the service’s availability (through its instance minimums and maximums) and the impact of those settings from the user’s demand on the service is key to an optimally running Site. There is a mutual relationship between configuring the service’s availability (through the instance minimums and maximums) and the direct affect that has to requests coming in to a dedicated service. While finding the optimal setting is an ongoing task, there are some tools and resources to help administrators tackle the challenge. ArcGIS Server Service Report The ArcGIS Server Service Report (introduced in 10.1) is one of those lesser-known REST Admin API gems. This resource can help monitor a Site by providing a configurable summary of all the services in a folder. It is Generally a fast-performing request (depending on the number of services in the folder being requested). The instance service statistics section of the returned response is extremely valuable as it lists details on the ArcSOC instances (min, max, busy) across the whole deployment (e.g., the ArcGIS Server Site). By periodically polling this endpoint, one can get up-to-the-second insight of the service instance configuration vs the demand…as it happens. With such information, better decisions can be made about optimizing machine and service resources. In turn, this can help improving response times and lower wait times. Note: In ArcGIS Server, instance service statistics information and the Statistics page in Manager are actually different resources though they are providing similar views of the same data. The service statistics provide raw access to the instance values (Site-wide and per machine) as well as more detail. The Statistics page is an interface for creating reports from some of that information. Automating the Service Report Collection with Soccer Any script, program or tool that regularly observes the Service Report endpoint of the folder of interest would suffice. However, if you are looking for a free, existing tool then  Soccer is recommended. (Arc)SOC ScannER or Soccer is a utility for scanning and reading the services' statistics on a specific ArcGIS Server folder. It parses the collected data and writes it out to a CSV file for additional post-capture analysis (e.g., creating charts in a spreadsheet to visual the usage). It takes advantage of the REST Admin's Service Report resource in ArcGIS Server to gather this info. The original goal of soccer was to capture the report endpoint output of a specific folder in ArcGIS Server and save the ArcSOC instance statistics (e.g., Running, Busy, Maximum, etc...) for each service. Currently, soccer is a command-line only utility. It is made available in the .NET 6.0 portable runtime for Windows (win-x64), Linux (linux-x64) and macOS (osx-x64). For simplicity, running soccer only requires 3 inputs (other parameters can be passed in to extend functionality): soccer.exe -s "[https://ArcGISServer/ServerWebAdaptor]" -f [FolderToScan] -t "[PreGeneratedArcGISToken]" For example: soccer.exe -s "https://gisserver.domain.com/server" -f "Gas" -t "APLeyWOcKZp9stZ_C01DQ.." Note: A pre-generated ArcGIS Server token can be obtained from Portal. Typically, the generateToken URL is: https://gisserver.domain.com/portal/sharing/rest/generateToken and the Webapp URL would then be: https://gisserver.domain.com/server/admin. Set the Expiration value to something appropriate for your expected monitoring duration. Standard Out while running from a command-window: Connected to: "https://gisserver.domain.com/server " (Gas) Press Ctrl-C twice to stop... Sleeping 5 seconds... When running, soccer connects to the Service Report endpoint of the ArcGIS Server folder specified, collects the data, writes it to a local CSV file, then sleeps. After the sleep duration has elapsed, it repeats the process. Soccer will keep collecting until the process has been manually stopped (Ctrl-C). Note: To collect on the root ArcGIS Server folder, use either: -f “/” or -f “” Analyzing the CSV File Sample contents as seen from a simple text viewer: DateTime,Epoch,IntervalSeconds,Host,Folder,ServiceName,Type,Provider,Running,Busy,Maximum,Free,NotCreated,Initializing,Transactions,TotalBusyTime,ServicesCollected,ResponseTimeMilliseconds,ContentLength,ConfiguredState,RealTimeState,Message 5/2/2023 1:12:45 AM,1682989965310,5,gisserver.domain.com,Gas,Gas_Utility_Network,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,121.6224,6065,STARTED,STARTED,success 5/2/2023 1:12:45 AM,1682989965310,5,gisserver.domain.com,Gas,Landbase_PostgreSQL,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,121.6224,6065,STARTED,STARTED,success 5/2/2023 1:12:50 AM,1682989970469,10,gisserver.domain.com,Gas,Gas_Utility_Network,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,117.901,6065,STARTED,STARTED,success 5/2/2023 1:12:50 AM,1682989970469,10,gisserver.domain.com,Gas,Landbase_PostgreSQL,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,117.901,6065,STARTED,STARTED,success 5/2/2023 1:12:55 AM,1682989975606,15,gisserver.domain.com,Gas,Gas_Utility_Network,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,109.6187,6065,STARTED,STARTED,success 5/2/2023 1:12:55 AM,1682989975606,15,gisserver.domain.com,Gas,Landbase_PostgreSQL,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,109.6187,6065,STARTED,STARTED,success 5/2/2023 1:13:00 AM,1682989980733,20,gisserver.domain.com,Gas,Gas_Utility_Network,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,125.1686,6065,STARTED,STARTED,success 5/2/2023 1:13:00 AM,1682989980733,20,gisserver.domain.com,Gas,Landbase_PostgreSQL,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,125.1686,6065,STARTED,STARTED,success 5/2/2023 1:13:05 AM,1682989985874,25,gisserver.domain.com,Gas,Gas_Utility_Network,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,116.3949,6065,STARTED,STARTED,success 5/2/2023 1:13:05 AM,1682989985874,25,gisserver.domain.com,Gas,Landbase_PostgreSQL,MapServer,ArcObjects11,32,0,32,32,0,0,0,0,2,116.3949,6065,STARTED,STARTED,success Note: By design, columns such as IntervalSeconds and ResponseTimeMilliseconds will show duplicate values if more than one service exists in the folder being observed. The collected data is a typical CSV, but there are some important fields which will be helpful for quickly analyzing the ArcSOC activity from out service(s) of interest: IntervalSeconds ServiceName Running Busy Maximum By opening the CSV file in a spreadsheet, other services residing in the same folder can be easily filtered out through the ServiceName column. The IntervalSeconds, Running, Busy, Maximum values can then be plotted to visualize (e.g., through the Scatter with Smooth Line chart ) and show the instance configuration versus incoming demand. In this case, the Gas_Utility_Network service was set to min/max instance configuration of 32/32. The "polished" chart below: Note: The Maximum and Running represent the ArcSOC service configuration instance maximum and minimum, respectively. In this case, Running has the same values and is plotted “behind” Maximum. Busy represents the number of instances that were active (due to requests from users) across all the machines in the Site. The “goal” of tuning and optimization in this case, would be to avoid the Busy values from constantly reaching the Maximum. If this were to happen, it means there was not enough ArcSOCs available for the service to meet the user demand as the instances were always busy. User requests would then most likely be encountering increased response times and wait times in the process. If the requests wait too long in the system, they timeout (typically after 60 seconds). Having a lot of service request timeouts would negatively impact the user experience. Based on the observations from this monitored duration, the rise and fall of the Busy column values did not come anywhere close to the maximum number of instances that were available…which from one perspective was good. However, this also indicated that there was a measurable number of instances running and taking up memory but not being used…which was not ideal. To optimize system resources going forward, the instance configuration of this service could have been set to a lower value that is closer to the (expected) peak usage (e.g., somewhere between 18 – 24). Use 18 to conserve memory with the potential of encountering several instances where user wait longer for their requests to be fulfilled Use 24 to favor performance over memory usage Final Thoughts Having an “optimized” service setting for the minimum and maximum number of instances does not guarantee that users will never encounter slow performance. User needs and habits change over time, so monitoring this information is something that will need to be done periodically. In the real-world, there are conditions where service wait times can still be encountered in a deployment…even with ample instances available and plenty of system resources. It is not realistic to eliminate service wait time completely but is instead more practical to reduce it. Optimizing the service instances is something admins can directly control that has an impact on wait times. Understanding and periodically evaluating the ArcSOC instance configuration (for dedicated services) and its relationship to the user demand is key to helping GIS administrators better plan and manager their Site by optimizing performance and efficiently utilizing resources.     ... View more

Capturing Hardware Utilization During an Apache JMeter Load Test Measured transaction throughput and response times are critical data of any ArcGIS Enterprise load test, but captured hardware utilization of the deployment machines provide vital information as well. Together, these test artifacts allow for the proper analysis of the capabilities and efficiencies of the Site. Determining that an application or feature service has achieved a particular level of throughput is good, but confirming the scalability characteristics while also examining the captured processor utilization of the tested workload is even better. This Article will discuss several ways to capture the machine hardware utilization. This resource usage is a great compliment to the results of an Apache JMeter load test of ArcGIS Enterprise and can help further the analysis. The Article will focus on the most common scenarios using free tools and utilities for Windows and Linux. Capturing Strategies Unfortunately, there is no one size fits all for capturing hardware utilization in a load test. Given the potential for a multitude of different environments (Windows, Linux, Cloud, Kubernetes, Docker, etc...) or lack of required access and permissions, there may be cases where you will need to alter your strategy and methodology in order to capture and monitor the usage information from your hardware while the Apache JMeter tests run. For example, perfmon (e.g. Performance Monitor) is free and included with every instance of Microsoft Windows and can work great when all the machines in the environment are on Windows. It is the frontend for several Windows technologies (e.g. WMI, DCOM) and all of them together offer a versatile framework for monitoring hardware utilization in "real-time" with graphs or scripting a resource capture to a data file. However, perfmon is not available for Linux so another tool (e.g. dstat) would be needed to capture the equivalent information. Of course, even with an all Windows setup, challenges can still arise with the perfmon approach, such as with restrictive cloud environments. But, are there any other ways to record this valuable machine data? We'll explore the answer to that question later in this Article. What Information Should the Load Test Capture? When GIS work such as generating a map image or calculating a Geoprocessing service request is sent to an ArcGIS Enterprise deployment, the server machines that work on the response will utilize various hardware resources to complete the job. Ideally, most of the work happens in the central processor unit (CPU) but this is not always the case. Regardless of the technology used to monitor and capturing usage for your load test, there are four primary hardware counter categories that all server machines have: Processor Memory Network Disk Each category has a variety of metrics that can be used for the showing utilization of that resource. Its not necessary (or recommended) to capture every available processor metric during a test...just the vital ones. In other words, the ones needed to answer typical load test questions or the particular story you are after. Typically, some important metrics include the following: Processor Utilization percentage Memory Available (MB or bytes) Used (MB or bytes) Used percentage Network Send (MB or bytes) Received (MB or bytes) Disk Idle percentage Queue length Read (MB or bytes) Write (MB or bytes) Note: If your servers have multiple disks, it is highly recommend to collect on all of them but separately. In other words, treat the C drive, D drive and E drive as different resources when capturing using. It technically possible to have multiple active Network devices but it is not very common.   The Performance Monitor interface collecting from various hardware metrics that have been added manually added (to perfmon): Note: Some methodologies for capturing the utilization will save the values in bytes or while other use megabytes. Either is fine, but it is important to understand which one is being captured for analysis and report presentation of the data. Note: Together with the transaction throughput (e.g. transactions/sec) and transaction response times (e.g. seconds), the metrics listed above make up the bulk of a load test's Key Performance Indicators (KPIs). KPIs help evaluate and benchmark the success of a particular test or effort against defined test goals (e.g. the Test Plan). Sample Interval Typically, a load test (with a primary focus on scalability) does not run more than an hour or two. Since that is considered a relatively short duration, it makes sense to capture the hardware utilization with a fast interval (e.g. short frequency) of 5 to 10 seconds. This helps ensure that enough data is obtained for performing a thorough analysis. If the capture interval is 1 minute or longer, some significant impacts to the hardware resources (from the test) might be missed. Note: Monitoring software may already be running on your network that is periodically collecting hardware utilization from your ArcGIS Enterprise servers. If any generated reports are available, they can be used for load test analysis as long as the capture interval is on a short frequency (e.g. 5 to 10 seconds). For load test analysis, you are interested in good metric detail for a few hours as opposed to broad metric coverage of a full day. The Difference Between Capturing and Monitoring The terms "capturing" and "monitoring" hardware utilization are often used interchangeably. However, for Apache JMeter testing purposes, this Article is focused on capturing the usage over monitoring it. The key difference here is the capturing means we are recording or saving the information for easier post-test analysis.  Test Clients (Load Generator Machines) When capturing hardware utilization from a load test against ArcGIS Enterprise, it is a best testing practice to also measure usage from the Test Client machine(s). Understandably, most of the focus is put into observing the server resources, but the hardware from the test clients can be a bottleneck too. If these machines do not have enough capacity (e.g. processor, memory, network or disk) the ability to generate load against your deployment will be negatively impacted! Understand Your Server Baselines If you are not very familiar with the hardware of your ArcGIS Enterprise deployment, it is recommended to watch the resource utilization while the system is idle. This "usage" will help paint a mental picture of the best case scenario. In other words, what does the machine look like when the software and services are running but no one is using it. In such a scenario, resource activity from all four hardware categories should be minimal. A load tests does not need to record this information of the "idle state" but it is good to be aware of what it looks like. Note: Using tools like Task Manager (for Windows) or top (for Linux) are great tools for manually getting a quick view of system resource utilization. However, these are not ideal saving the usage from all the categories to a file. Task Manager's view of system resource utilization (Windows): top's view of system resource utilization (Linux): Common Utilization Capturing Examples Using Perfmon (To Capture Utilization Outside of Load Test) Assuming the required permissions are available for your user account in Windows, perfmon is one of the easiest ways to record hardware utilization for load testing analysis. Performance Monitor (and the supporting technologies) can capture the usage from the local machine or multiple remote servers, simultaneously. Note: As a reminder, the metrics available to perfmon within each hardware group are plentiful. There is also a large amount of software metrics that can be captured. But in this case, less is more. Only a small handful of counters are needed for most situations. As mentioned earlier, where you can run into considerations: The ArcGIS Enterprise deployment is running on Linux perfmon is not available for Linux The easiest workaround is to use another methodology to collect the information (discussed later) The deployment is in the cloud Running erfmon in the cloud to collect from other deployment machines (in the cloud) is technically possible but behind-the-scenes it uses networking protocols that are typically blocked in the environment The easiest workaround here is to run perfmon manually on each cloud machine and collect the generated outputs when the test has completed Launching perfmon in Windows is as simple as executing: Run --> perfmon From there you can add each machine as well as each respective hardware metric and counter. While this is very powerful, it can be time consuming and potentially prone to typos! Instead, the recommended strategy for this methodology is to use a PowerShell script where all of these items are predefined. The following will gather key perfmon metrics (that are already defined) from the four primary hardware categories and will do this for four different machines (that you can specify):   # ============================================================================== # Updated: 2022-08-02 # Filename: ps_get_all_continuous.ps1 # Version: 0.1.0 # Desc: Continuously collect various metrics and save (binary) values to a file # Notes: It is recommended to start the metric collection 20 seconds before test starts # as counter initialization can take some time # ============================================================================== Write-Output -InputObject 'Collecting all metrics (continuous)...' # Variables # Windows Perfmon Counter(s) $counters = "Processor(_Total)\% Processor Time","Memory\Available MBytes","Memory\Committed Bytes","Memory\% Committed Bytes In Use","Network Interface(*)\Bytes Received/sec","Network Interface(*)\Bytes Sent/sec","LogicalDisk(_Total)\% Idle Time","LogicalDisk(_Total)\Disk Read Bytes/sec","LogicalDisk(_Total)\Disk Write Bytes/sec" # Machines to collect from $computers = @('server1.domain.com','server2.domain.com','server3.domain.com','server4.domain.com') # Time in seconds between each sample collection $sampleinterval = 10 $date = (Get-Date) $dyndate = '{0:yyyyMMddTHHmmss}' -f $date # 20220303T140044 # File name $filename = "perfmon_all_continuous" # Output file type (choices are blg, csv, or tsv) $outputfiletype = "blg" # Output file $outputfile = '{0}_{1}.{2}' -f $filename, $dyndate, $outputfiletype # Get-counter Write-Output -InputObject ('Start DateTime: {0}' -f $date) Get-counter -Computername $computers -Counter $counters -Continuous -Sampleinterval $sampleinterval | Export-counter -Force -FileFormat $outputfiletype -Path $outputfile   The metrics are collected every 10 seconds until you hit Ctrl-C in the PowerShell window. The information is saved in a binary formatted file that you can open in Windows (for further analysis or transformation): Note: It is recommended to start this Powershell script about 20 or 30 seconds before your start the load test as counter initialization is not immediate and takes a moment or two. You can open the *.blg file at any time during the capture, but the latest information is not flushed and saved to the file until you hit Ctrl-C from the PowerShell window where the script is running. Double-clicking and opening the *.blg file should look similar to the following: The end result is an output that looks very similar to what we captured. However, in this case, the process has been automated (which is a good thing). From this interface in Performance Monitor, you can select/deselect metrics for specific isolation, export the graph as an image, save the raw data to another format like CSV, or conduct usage analysis from all of the collected machines. Pretty powerful! Note: Performance Monitor scripts do not directly integrate into JMeter.  Note: For cloud deployments, you may have to run the perfmon script on each respective server manually. Using dstat (To Capture Utilization Outside of Load Test) dstat is a free command line tool (written by [email protected]) available for (or with) most Linux distributions that captures (and monitors) hardware utilization. Although the utility has not been updated for several years, it is fairly robust and easy to script. dstat terminal output with running it with no options: The following will run on your Linux server and capture hardware usage to a csv file every 5 second until you manually stop it with Ctrl-C: [gisadmin@pesrv04 ~]$ dstat -tTcmn --disk-util -d --nocolor --noheaders --output dstat_20220823.csv 5 Note: With dstat, the captured processor usage is "idle time percentage", not usage percentage. To get the usage percentage, subtract 100 from the value . However, the disk utilization is in a usage percentage. Additionally, the output above includes a presentation format so the raw values (not shown) for memory, network and disk are saved as bytes. Note: dstat scripts would need to be run manually and separately on every Linux machine in the deployment. They do not directly integrate into JMeter. Once a load test is done and the scripts stopped, the CSV files can be collected and analyzed (e.g. plotted or charted in a spreadsheet program). Using Apache JMeter (To Capture Utilization Directly From Load Test) Although it has a few more steps (as well as its own hurdles) than perfmon and dstat scripts, using Apache JMeter to capture hardware utilization has one main advantage: Consistency. The same JMeter Test Plan can be used to capture the same hardware metrics from Windows and Linux (and Mac) This is accomplished through several free pieces of software: The jp@gc - PerfMon Metrics Collector extension that is added to JMeter  This component is used to define what metrics to collect and from which machines The PerfMon Server Agent tool that runs in a Java Runtime Environment (JRE) on each server in the deployment  This component does the actual collection on each server and sends the data back to the JMeter The jp@gc - PerfMon Metrics Collector component can be easily added to the Test Plan through the JMeter Plugins Manager (found under "PerfMon (Servers Performance Monitoring): The PerfMon Server Agent component is relatively easy to add but a few extra steps to get it to work right. Note: Due to a bug in the sigar-amd64-winnt library, the ServerAgent crashes if you use the out-of-the-box library with the JDK or JRE greater than version 8. The workaround is to patch the library or use an up-to-date version 8 JRE. OpenLogic provides free, periodically updated v8 OpenJDK (and JRE) releases to download for Windows, Linux and Mac. Although the JDK would work, the JRE is recommended in this case as it is a smaller package to download but still provides the environment needed for the agent to run. Note: Either the 32bit or 64bit JRE will work for the ServerAgent. Note: The OpenLogic JDK or JRE can also be used to run Apache JMeter itself. The ServerAgent and OpenLogic JRE unzipped in the same folder:  The OpenLogic JRE moved into the ServerAgent folder: Open the startAgent.bat file in an editor and update it with the name of the JRE directory that was copied into the ServerAgent folder:   off set JAVAPATH=.\openlogic-openjdk-jre-8u342-b07-windows-64\bin %JAVAPATH%\java -jar %0\..\CMDRunner.jar --tool PerfMonAgent %*   Save the changes The C:\JMeterAgent\ServerAgent-2.2.3 folder now has a self-contained ServerAgent that can be copied over to the appropriate machines for collecting their hardware utilization To start the agent in Windows, double-click on the startAgent.bat The agent will launch into a command window: The agent will not start "collecting" metrics until directed by the Apache JMeter Test Plan By default, the agent and JMeter will communicate with each other on TCP and UDP port 4444 Note: The jp@gc - PerfMon Metrics Collector uses the name "perfmon" but does not actually utilize Windows Performance Monitor technology. The SampleWorldCities Test Plan With Metric Collection Support To download the Apache JMeter Test Plan used in this Article see: sampleworldcities5.zip   Opening the Test Plan in Apache JMeter should look similar to the following: Adjust the User Defined Variables to fit your environment This test defines four different machines to collect hardware utilization from: A web adaptor The requests are also sent to this endpoint An ArcGIS Server A Portal for ArcGIS The Test Client (the machine running JMeter) The hostname of this machine is auto-detected by JMeter when the test runs Components of the Test Plan The jp@gc - PerfMon Metrics Collector extension is the only element of interest here as this test is practically identical to previous Article Test Plans for SampleWorldCities. jp@gc - PerfMon Metrics Collector Extension As mentioned earlier, the extension is what tells each running agent what it want them to collect. The Test Plan available in this Article has already added the counters for each of the four primary hardware categories.  Note: The metrics parameters will work for Linux and Mac. However, the Disks IO metrics are in the extension are configured to collect on the C drive (e.g. C\:\) which does not exist in Linux. This would need to be changed to /. Validate the JMeter-to-ServerAgent Connectivity Start the test from the GUI by click the play button (green triangle) If all the ServerAgents are running and reachable on all the intended machines in the ArcGIS Enterprise deployment, they should be able to collect utilization and send the information back to the Test Plan As JMeter receives this information it will display the information in the Chart tab Let this collect for a few second to validate connectivity then stop the test Note: When the test is run from the command line, the Filename containing the data of the collected metrics will be based on variables in the bat script that define the "results_" file. Once the test has completed, you can use this "jp@gc - PerfMon Metrics Collector" element to load the raw values and either isolate particular counters or generate chart images. This file will start with the string "perfmon_". The results and perfmon files both end with a *.jtl extension but are just CSV files. Test Execution The load test should be run in the same manner as a typical JMeter Test Plan. See the runMe_withP.bat script included with the sampleworldcities5.zip project for an example on how to run a test as recommended by the Apache JMeter team.  The runMe_withP.bat script contains a jmeterbin variable that will need to be set to the appropriate value for your environment This script contains a little more logic than the runMe.bat found in other Test Plan Articles This extra logic was added so the results file and the file containing the collected hardware end with the same runname Contents of the runMe_withP.bat script:   echo off rem Scripted JMeter Test Plan execution rem Utilizing ApacheJMeter.jar for invocation rem rem 2022/08/25.1 rem rem With environment variable to JMeter support (for PerfMon extension) rem ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ rem *** Variables *** rem Set JMeter memory to min/max of 4GB (adjust based on your test client resources) set heap=-Xms4g -Xmx4g -XX:MaxMetaspaceSize=256m rem Location of %JAVA_HOME%\bin set javadir=%JAVA_HOME%\bin rem Location of Apache JMeter bin set jmeterbin=C:\apache-jmeter-5.5\bin rem Location of JMeter Test Plan root folder (e.g. the folder where the Test Plan resides) set projectdir=%~dp0 rem Name of the JMeter Test Plan (without the JMX file extension) set testname=sampleworldcities5 rem String appended to results file of each test run set runname=testrun1 rem Proxy settings rem set proxyhost=http://localhost rem set proxyport=8888 rem ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ rem *** Start your engines *** color 20 rem Set environment variable to reference in test (e.g. ${__P(env.run)}) set jvm_args="-Denv.runname=%testname%_%runname%" echo on rem *** Test started *** %javadir%\java.exe --illegal-access=warn %jvm_args% %heap% -jar %jmeterbin%\ApacheJMeter.jar -Jjmeter.save.saveservice.autoflush=false -n -f -t "%projectdir%\%testname%.jmx" ^ -l "%projectdir%\results\results_%testname%_%runname%.jtl" ^ -j "%projectdir%\logs\debug_%testname%_%runname%.log" ^ -e -o "%projectdir%\reports\%testname%_%runname%" ^ rem uncomment as needed rem -H %proxyhost% -P %proxyport% ^ rem *** Test completed *** echo off color 40 ping localhost -n 5 color 07   Note: It is always recommended to coordinate the load test start time and duration with the appropriate personnel of your organization. This ensures minimal impact to users and other colleagues that may also need to use your on-premise ArcGIS Enterprise Site. Additionally, this helps prevent system noise from other activity and use which may "pollute" the test results. Note: For several reasons, it is strongly advised to never load test services provided by ArcGIS Online. General Methodology Guide This guide is not intended to be comprehensive covering every hardware utilization capture scenario. Scenario #1  Servers Windows Environment On-premise Collecting Machine (Test Client) Windows Description Capturing Windows usage from Windows Difficulty Easy/Moderate Recommended Methodology Windows Perfmon (via PowerShell script) Scenario #2  Servers Windows/Linux Environment On-premise Collecting Machine (Test Client) Windows Description Capturing Windows/Linux usage from Windows Difficulty Moderate Recommended Methodology JMeter Perfmon Extension and ServerAgent Scenario #3  Servers Linux Environment On-premise Collecting Machine (Test Client) Windows/Linux Description Capturing Linux usage from Windows/Linux Difficulty Easy/Moderate Recommended Methodology dstat command or script Scenario #4  Servers Windows/Linux Environment Cloud Collecting Machine (Test Client) Windows Description Capturing Windows/Linux usage from Windows Difficulty Moderate Recommended Methodology Cloud Watch/Azure Monitor (not covered in this Article) Scenario #5  Servers Windows Environment Cloud Collecting Machine (Test Client) Windows Description Capturing Windows usage from Windows Difficulty Moderate Recommended Methodology Run Windows Perfmon (via PowerShell script) on each machine in the deployment Scenario #6  Servers Linux Environment Kubernetes Collecting Machine (Test Client) Windows Description Capturing Linux contain/pod usage from Windows Difficulty Moderate/Hard Recommended Methodology Environment dashboard or custom resource endpoint (not covered in this Article) Common Utilization Collection Challenges Capturing hardware utilization of deployment machines for a load test is a best testing practice but it is not always possible. The most typical challenges are: Permissions The most common Not having been granted access to or the ability to capture usage from remote servers is the most common challenge Environment/Location Collecting utilization from machines on the local network is one thing, collecting usage from machines in the cloud is another thing Operation System Sometimes the OS presents its own *hurdles* for capturing the utilization Technical Certain environments (e.g. Kubernetes/Docker) may not have the same APIs for collecting the usage as a traditional (physical or virtual) machine Final Thoughts Simply put, there are many ways to capture hardware utilization information from the machines in an ArcGIS Enterprise deployment to analyze with your load test results. Which is the best way to capture it...any way that works best were you can quickly record the info and use it for effectively analysis. Although no one Article can cover every situation, environment and scenario, this one lists several methodologies for capturing this data for common ones.    ... View more

System Log Parser's ServiceDetails Analysis Type Looking for a quick way to summarize the details of all your ArcGIS Enterprise services? If so, then System Log Parser's ServiceDetails Analysis Type is highly recommended. This feature performs a scan of your Site through the REST Admin API and presents the gathered information into a spreadsheet report for easy digestion. The report is a great resource to find details on your service provider statistics, service configuration info, or data source analysis. Selecting ServiceDetails from the GUI The ServiceDetails Analysis Type can be selected from the type right of the GUI: Note: The ServiceDetails Analysis Type is only available with an ArcGIS Server (Web) log query since it must make use of the REST Admin API Selecting ServiceDetails from the Command Line The ServiceDetails Analysis Type is also available from the command-line. For example:  slp.exe -s https://yourwebadaptor.domain.com/server -u gisadmin -p Myp@ssword -pfl true -validate true -a servicedetails The ServiceDetails Report The upper section in the Summary worksheet of the ServiceDetails report: The lower section detailing Service Provider Statistics:  The Site Details worksheet provides a listing of the hardware (CPU and Memory) from the machines running ArcGIS Server There is also table containing a verbose breakdown on the configured parameters of each service Additionally, there are two Datasource worksheets (not shown) providing information and analysis on where the services are looking for their respective data Latest Version Bug fixes and new features are always being added to SLP. The latest version can be found here: System Log Parser  ... View more