For tracing unexpected events and other problems, it's vital that the monitoring data provides enough information to enable an analyst to trace back to the origins of these issues and reconstruct the sequence of events that occurred. Aggregated data must also support drill-down to enable examination of the performance of the underlying subsystems. The instrumentation data-collection subsystem can actively retrieve instrumentation data from the various logs and other sources for each instance of the application (the pull model). Precise is no different, leveraging the deep Database structure IDERA has expanded Precise into true APM solution. The reasons for unavailability of the system or any subsystems. In a production environment, it's important to be able to track the way in which users use your system, trace resource utilization, and generally monitor the health and performance of your system. At some points, especially when a system has been newly deployed or is experiencing problems, it might be necessary to gather extended data on a more frequent basis. In reality, it can make sense to store the different types of information by using technologies that are most appropriate to the way in which each type is likely to be used. This is called warm analysis. If you're able detect such a decrease, you can take proactive steps to remedy the situation. The pertinent data is likely to be generated at multiple points throughout a system. Overall system availability. To examine system usage, an operator typically needs to see information that includes: An operator should also be able to generate graphs. However, Nastel is a Middleware centric business transaction tool and like most other Application Performance Management Vendors it is focused on that Middleware business transaction. The collection stage of the monitoring process is concerned with retrieving the information that instrumentation generates, formatting this data to make it easier for the analysis/diagnosis stage to consume, and saving the transformed data in reliable storage. For more information, see the Priority Queue pattern. Languages: .Net, Java, PHP, Node.js, Docker Containers, Cloud Foundry, AWS. Monitoring the health of any third-party services that the system uses. To complicate matters further, a single request might be handled by more than one thread as execution flows through the system. (Do services start to fail at a particular time of day that corresponds to peak processing hours?). Effective issue tracking (described later in this section) is key to meeting SLAs such as these. Some elements, such as IIS logs, crash dumps, and custom error logs, are written to blob storage. Virtual machine resources such as processing requirements or bandwidth are monitored with real-time visualization of usage. What you need to do is to break down the business process of the application and then have the software emit events at major business components. This predictive element should be based on critical performance metrics, such as: If the value of any metric exceeds a defined threshold, the system can raise an alert to enable an operator or autoscaling (if available) to take the preventative actions necessary to maintain system health. (For example, an alert can be triggered if the CPU utilization for a node has exceeded 90 percent over the last 10 minutes). You can perform this after the data has been stored, but in some cases, you can also achieve it as the data is collected. It has done no less with its APM solution as well. Incorporate requirements from other monitoring stakeholders, especially line-of-business and application owners. In the case of latency issues affecting performance, an operator should be able to quickly identify the cause of the bottleneck by examining the latency of each step that each request performs. Funnel analysis of multi-step transactions linking directly back to page content data. This data cube can allow complex ad hoc querying and analysis of the performance information. The following sections describe these scenarios in more detail. Security-related information for successful and failing requests should always be logged. 7 Requirements for Monitoring Cloud Apps and Infrastructure 1. In these situations, it might be possible to rework the affected elements and deploy them as part of a subsequent release. Tracking the availability of the system and its component elements. You should also ensure that monitoring for performance purposes does not become a burden on the system. Logging exceptions, faults, and warnings. You should also consider the underlying infrastructure and components on which your system runs. These details can include the tasks that the user was trying to perform, symptoms of the problem, the sequence of events, and any error or warning messages that were issued. Developers should follow a standard approach for tracking the flow of control through their code. In some cases, after the data has been processed and transferred, the original raw source data can be removed from each node. The operator can gather historical information over a specified period and use it in conjunction with the current health data (retrieved from the hot path) to spot trends that might soon cause health issues. Languages: .NET, Java, AJAX, IBM WebSphere WQ. An operator should be able to drill into the reasons for the health event by examining the data from the warm path. This information can also be used to help configure time-based autoscaling. Some types of monitoring generate more long-term data. Alerting is the process of analyzing the monitoring and instrumentation data and generating a notification if a significant event is detected. Data gathered for metering and billing customers might need to be saved indefinitely. Security logs that track all identifiable and unidentifiable network requests. For internal purposes, an organization might also track the number and nature of incidents that caused services to fail. They are great at answering that question of “What did my code just do?”, Read more: Using developer APM tools to find bugs before they get to production. These external systems might provide their own performance counters or other features for requesting performance data. This data might take several forms in the raw data, and the analysis process must be provided with sufficient instrumentation data to be able to map these different forms. You can obtain this information by: For metering purposes, you also need to be able to identify which users are responsible for performing which operations, and the resources that these operations use. And it can generate reports, graphs, and charts to provide a historical view of the data that can help identify long-term trends. To help with our role, we deployed an application ⦠The same instrumentation data might be required for more than one purpose. Operational reporting typically includes the following aspects: Security reporting is concerned with tracking customers' use of the system. Capturing data at this level of detail can impose an additional load on the system and should be a temporary process. It is designed to help developers optimize the performance of their applications in QA and “retrace” application problems in production via very detailed code level transactions traces. Analyze the progress of user requests to break down the overall response time of a request into the response times of the individual work items in that request. For example, performance counters can be used to provide a historical view of system performance over time. This will help to correlate events for operations that span hardware and services running in different geographic regions. Log information might also be held in more structured storage, such as rows in a table. The instrumentation data must be aggregated and correlated to support the following types of analysis: You can calculate the percentage availability of a service over a period of time by using the following formula: This is useful for SLA purposes. From data collection to processing and then deriving knowledge from your data, AppDynamics provides full visibility into exactly how application performance is affecting your business. Tracing execution of user requests. Don't mix log messages with different security requirements in the same log file. This data is typically provided through low-level performance counters that track information such as: All visualizations should allow an operator to specify a time period. If possible, capture information about all retry attempts and failures for any transient errors that occur. An operator can also use this information to ascertain which features are infrequently used and are possible candidates for retirement or replacement in a future version of the s⦠They are also being used more and more by developers and not just IT operations for application performance monitoring. Application monitoring is conducted by real-time packet scanning of I/O requests across a cloud network. Treat instrumentation as an ongoing iterative process and review logs regularly, not just when there is a problem. Team Center provides a good dashboard for quickly navigating the details you to dig into the issues. The availability of the order-placement part of the system is therefore a function of the availability of the repository and the payment subsystem. As described in the section Consolidating instrumentation data, the data for each part of the system is typically captured locally, but it generally needs to be combined with data generated at other sites that participate in the system. One source well summarizes the purpose of APM as follows: âTo translate IT metrics into an End-User-Experience that provides value back to the business.â Application monitoring ⦠Usage monitoring tracks how the features and components of an application are used. This information can then be used to determine whether (and how) to spread the load more evenly across devices, and whether the system would perform better if more devices were added. There is a lot of gray area as to what APM is and who it benefits within an organization. Endpoint monitoring. Synthetic user monitoring. Performance data often has a longer life so that it can be used for spotting performance trends and for capacity planning. Auditing can provide evidence that links customers to specific requests. Monitoring the day-to-day usage of the system and spotting trends that might lead to problems if they're not addressed. The details provided to the alerting system should also include any appropriate summary and context information. In this case, an isolated, single performance event is unlikely to be statistically significant. These actions might involve adding resources, restarting one or more services that are failing, or applying throttling to lower-priority requests. Examples include the analyses that are required for alerting and some aspects of security monitoring (such as detecting an attack on the system). In some cases, an alert can also be used to trigger an automated process that attempts to take corrective actions, such as autoscaling. (In an e-commerce system, a failure in the system might prevent a customer from placing orders, but the customer might still be able to browse the product catalog.). IDERA is known for having an intuitive dashboard and allow for quick insights, Precise uses these dashboards to make it one of the best APM Monitoring Tools available today. Trace logs might be better stored in Azure Cosmos DB. To support debugging, the system can provide hooks that enable an operator to capture state information at crucial points in the system. The data from a series of events should provide a more reliable picture of system performance. It might also be possible to inject diagnostics dynamically by using a diagnostics framework. To some extent, a degree of connectivity failure is normal and might be due to transient errors. Join us for a 15 minute, group Retrace session, How to Troubleshoot IIS Worker Process (w3wp) High CPU Usage, How to Monitor IIS Performance: From the Basics to Advanced IIS Performance Monitoring, SQL Performance Tuning: 7 Practical Tips for Developers, Looking for New Relic Alternatives & Competitors? For example, you might start with measuring many factors to determine system health. Check our free transaction tracing tool, Tip: Find application errors and performance problems instantly with Stackify Retrace. Enforce quotas. This is the mechanism that Azure Diagnostics implements. Auto-discovers all application components and dependencies end-to-end. To examine system performance, an operator typically needs to see information that includes: It can also be helpful to provide tools that enable an operator to help spot correlations, such as: Along with this high-level functional information, an operator should be able to obtain a detailed view of the performance for each component in the system. AppDynamics caters to larger enterprises and offers a SaaS APM option as well as an on-premise option. Rather than saving old data in its entirety, it might be possible to down-sample the data to reduce its resolution and save storage costs. Through its agent is provides auto-discovered topology visualizations of applications and their components. In addition, exceptions can arise as a result of a fault in any level of the system. (See those sections for more details.) As described in the section Information for correlating data, you must ensure that the raw instrumentation data includes sufficient context and activity ID information to support the required aggregations for correlating events. Each instance of an Azure web or worker role can be configured to capture diagnostic and other trace information that's stored locally. Don't write all trace data to a single log, but use separate logs to record the trace output from different operational aspects of the system. This information should be tied back to the release pipeline so that problems with a specific version of a component can be tracked quickly and rectified. Adopt well-defined schemas for this information to facilitate automated processing of log data across systems, and to provide consistency to operations and engineering staff reading the logs. This mechanism is described in more detail in the "Availability monitoring" section. This information might take a variety of formats. Nonrepudiation is an important factor in many e-business systems to help maintain trust be between a customer and the organization that's responsible for the application or service. Log all critical exceptions, but enable the administrator to turn logging on and off for lower levels of exceptions and warnings. Capturing this information is simply a matter of providing a means to retrieve and store it where it can be processed and analyzed. This data can help reduce the possibility that false-positive events will trip an alert. Alternatively, you can use different channels (such as Service Bus topics) to direct data to different destinations depending on the form of analytical processing that's required. If a user reports an issue that has a known solution in the issue-tracking system, the operator should be able to inform the user of the solution immediately. Information about the health and performance of your deployments not only helps your team react to issues, it also gives them the security to make changes with confidence. A single instance of a metric is usually not useful in isolation. Computers operating in different time zones and networks might not be synchronized. Transaction tracking shows where the issues are occurring. Analyze the percentage time availability of the individual components and services in the system. For more information, see the section Supporting hot, warm, and cold analysis later in this document. Ideally, the dashboard should also display related information, such as the source of each request (the user or activity) that's generating this I/O. If possible, you should also capture performance data for any external systems that the application uses. Warm analysis can also be used to help diagnose health issues. Logging exceptions, faults, and warnings. When the problem is resolved, the customer can be informed of the solution. For example, an entry to a method can emit a trace message that specifies the name of the method, the current time, the value of each parameter, and any other pertinent information. The progress of the debugging effort should be recorded against each issue report. Azure Diagnostics gathers data from the following sources for each compute node, aggregates it, and then uploads it to Azure Storage: For more information, see the article Azure: Telemetry Basics and Troubleshooting. Security issues might occur at any point in the system. Real-time monitoring with sub-second analytics, Pre-built processing rules and dashboards, Complex Event Processing (CEP) engine for advanced application analytics and rules, Intuitive, easily defined dashboards provide insights at a glance. Distributed applications and services running in the cloud are, by their nature, complex pieces of software that comprise many moving parts. Typical high-level indicators that can be depicted visually include: All of these indicators should be capable of being filtered by a specified period of time. New Relic also provides APM for mobile apps, advanced browser performance monitoring and most recently added infrastructure monitoring. This involves incorporating tracing statements at key points in the application code, together with timing information. (Other infrastructure will be covered in the next section.) Collecting ambient performance information, such as background CPU utilization or I/O (including network) activity. Instrumentation is a critical part of the monitoring process. In addition, availability data can be obtained from performing endpoint monitoring. For example, a dashboard that depicts the overall disk I/O for the system should allow an analyst to view the I/O rates for each individual disk to ascertain whether one or more specific devices account for a disproportionate volume of traffic. For example, remove the ID and password from any database connection strings, but write the remaining information to the log so that an analyst can determine that the system is accessing the correct database. But you can prioritize messages to accelerate them through the queue if they contain data that must be handled more quickly. In proactive application monitoring the problems are found and dealt with before the consumer even knows there is a problem. Do not disclose sensitive information about the system or personal information about users. After analytical processing, the results can be sent directly to the visualization and alerting subsystem. This context provides valuable information about the application state at the time that the monitoring data was captured. Ideally, users should not be aware that such a failure has occurred. It can include: In many cases, batch processes can generate reports according to a defined schedule. For maximum coverage, you should use a combination of these techniques. Metrics will generally be a measure or count of some aspect or resource in the system at a specific time, with one or more associated tags or dimensions (sometimes called a sample). In some cases, it might be necessary to move the analysis processing to the individual nodes where the data is held. You can also use multiple instances of the test client as part of a load-testing operation to establish how the system responds under stress, and what sort of monitoring output is generated under these conditions. It's also important to understand how the data that's captured in different metrics and log files is correlated, because this information can be key to tracking a sequence of events and help diagnose problems that arise. In the last few years, APM tools have become affordable and a must have for all businesses. This information must be sufficient to enable an analyst to diagnose the root cause of any problems. After that, it can be archived or discarded. A feature of security monitoring is the variety of sources from which the data arises. So even if a specific system is unavailable, the remainder of the system might remain available, although with decreased functionality. If you need to perform more analytical operations or require full-text search capabilities on the data, it might be more appropriate to use data storage that provides capabilities that are optimized for specific types of queries and data access. Red for unhealthy (the system has stopped), Yellow for partially healthy (the system is running with reduced functionality). Activity logs recording the operations that are performed either by all users or for selected users during a specified period. Information such as the number of failed and/or successful sign-in requests can be displayed visually to help detect whether there is a spike in activity at an unusual time. For example, Visual Studio Team Services Build Service defines downtime as the period (total accumulated minutes) during which Build Service is unavailable. The number of successful/failing application requests. For example, it might not be possible to clean the data in any way. The date and time when the error occurred, together with any other environmental information such as the user's location. As with health monitoring, the raw data that's required to support availability monitoring can be generated as a result of synthetic user monitoring and logging any exceptions, faults, and warnings that might occur. No reporting across apps. The schema effectively specifies a contract that defines the data fields and types that the telemetry system can ingest. Note that in some cases, the raw instrumentation data can be provided to the alerting system. Determine which features are heavily used and determine any potential hotspots in the system. All commercial systems that include sensitive data must implement a security structure. Troubleshooting can involve tracing all the methods (and their parameters) invoked as part of an operation to build up a tree that depicts the logical flow through the system when a customer makes a specific request. The schema might also include domain fields that are relevant to a particular scenario that's common across different applications. Cost: $15 per month per server + data charges. Cons: No reporting per SQL query. Alerting can also be used to invoke system functions such as autoscaling. Recording the entry and exit times can also prove useful. But they have limitations in the operations that you can perform by using them, and the granularity of the data that they hold is quite different. This might involve running the system under a simulated load in a test environment and gathering the appropriate data before deploying the system to a production environment. Guaranteeing that the system meets any service-level agreements (SLAs) established with customers. The average processing time for requests. A good dashboard does not only display information, it also enables an analyst to pose ad hoc questions about that information. A key feature to consider in this solution is the ability to support multiple protocol analytics (e.g., XML, SQL, PHP) since most companies have more than just web-based applications to support. Determining the efficiency of the application in terms of the deployed resources, and understanding whether the volume of resources (and their associated cost) can be reduced without affecting performance unnecessarily. Include the call stack if possible. Entire application topology is visualized in an interactive infographic. You can envisage the entire monitoring and diagnostics process as a pipeline that comprises the stages shown in Figure 1. For these reasons, you should take a holistic view of monitoring and diagnostics. This information can be used to determine which requests have succeeded, which have failed, and how long each request takes. Additionally, your code and/or the underlying infrastructure might raise events at critical points. The application code can generate its own monitoring data at notable points during the lifecycle of a client request. Monitoring the exceptions that have occurred throughout the system or in specified subsystems during a specified period. The most common way to visualize data is to use dashboards that can display information as a series of charts, graphs, or some other illustration. This allows administrators to see the percentage of CPU engaged on each VM or the fluctuation of network traffic requests by bandwidth and IP addresses over time. The considerations will vary from metric to metric. What has caused an intense I/O loading at the system level at a specific time? 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