Debugging & Managing system performance

The ❌ is a ❌ that developers and system administrators can use to view ❌ data. The [....] consolidates and summarizes the ❌ logs. The log data provides key information about operational and system health that developers and system administrators can use to quickly ❌ performance issues.

Log files capture ❌ ranging from ❌ to ❌ that can cause ❌. Each log is managed by an ❌, which determines the type of events written to the log file. The available log files vary, depending on the ❌ that are configured in the ❌.

The following log files are commonly used on Pega Platform systems:

The ❌ log contains warnings, errors, and information messages about internal operations. This log is also called the console log or system log and is used for debugging the application.
The ❌ log contains performance-related alerts triggered when an operation exceeds the specified performance threshold.
The ❌ log contains alerts (identified by the prefix SECU) that suggest improper configuration of Internet Application Composer facilities, or overt attempts to bypass system security features on the Pega Platform node through URL tampering.
The ❌ log contains entries generated by the Business Intelligence Exchange (BIX) during extract operations.
The ❌ log contains performance data saved from services.
The ❌ log contains information about the setup and run-time behavior of the cluster.

Note: Pega Platform manages logs based on ❌. For example, to log performance data for services, you add ❌ configuration file for the node.

You access log files from the ❌ in Dev Studio. The Log utilities section includes a list of log file ❌. Clicking Log files displays a list of the log file appenders that are ❌. Downloading a log file requires ❌ for the application server that hosts Pega Platform. You can also view log files using an external log viewer, such as ❌.

A system administrator can ❌ in ❌ to control which logging events are displayed in the PEGA log by creating a ❌. Events are recorded only for the ❌.

When debugging an activity, you can use the ❌ with the logging level set to ❌ to add an event to the PEGA log regardless of settings in the ❌. To reduce log entries and improve performance in production systems, remove InfoForced steps or convert them to comments when you finish debugging the activity.

As a Pega Cloud client, you can download log files from the ❌ for internal analysis or to share with ❌. From the My Pega Cloud tab, you can download a ❌ created from your environment during the ❌. Each download you complete using the My Pega Cloud portal supports ❌; to download more additional logs, you ❌ for the additional day's logs. The log bundle is a ❌ you can download to your local computer.

At run time, the Pega ❌ for ❌ application issues. Alerts are ❌ that serve as important notifications about the ❌.

❌ are generated when rule execution exceeds a ❌. ❌ are generated when the security of a ❌. Analyze alerts to identify the root cause of a performance or security issue. You can access alerts from your session from the ❌. The alert format provides information about the alert. You can determine whether the alert is ❌ related, the ❌, the ❌ (for example, a browser), and ❌ that triggered the alert from the alert format.

The most frequently-generated alert is the ❌ alert, which occurs when the elapsed time for an HTTP interaction time exceeds the threshold setting.

The ❌ alert is generated when Pega Platform encounters a thread name in the ❌ that ❌ and whose name contains ❌. This alert allows you to identify potentially malicious scripts that have been inserted into the thread name portion of the URL so that you can research and ❌ or other issues as appropriate.

Used to understand the system resources consumed by processing a single requestor session: ❌
Used to tune the application in case of any database performance issues: ❌
Used to get a detailed trace of performance information about the execution of activities, when rules, and data transforms executed by your requestor session: ❌
Used to actively gather, monitor, and analyze real-time performance and health indicators from all active Pega applications: ❌

The ❌ provides access to the three performance tools: Performance Analyzer (PAL), Database Trace, and Performance Profiler. These tools are also available from the ❌. Performance statistics can help you distinguish between ❌ that arise in the ❌, ❌. In all cases, the statistics can help you determine how to improve performance.

❌: Pega always gathers cumulative resource statistics. Use this tool to understand the system resources consumed by processing a single requestor session. It works on existing data; it does not degrade processing.
❌: This tool is useful to tune the application in case of any database performance issues.
❌: Use this to obtain a detailed trace of performance information about the execution of activities, when condition rules, and data transforms executed by your requestor session.

Performance Analyzer (PAL)
The first step to measuring your application performance is to ❌. Start by clicking ❌ to clear any data in the tool. Because the system continuously monitors performance, you are ❌ entries from your results by resetting data.

There are two options for adding a reading: ❌ and ❌. The only difference between the two readings is the clipboard size, which ❌.

When adding a reading, the best practice is to ❌ that identify ❌. For example, use one reading ❌ or ❌, depending on what process you are measuring. You can download the results to an Excel file by clicking ❌.

The ❌ row displays the totals from ❌ the Performance tool made a reading. This bottom row shows the totals since the ❌ of the Performance tool. Most readings in this row at the outset are ❌. If the readings are ❌, click Reset Data to reset the readings. Each subsequent reading is labeled as a ❌, which indicates the ❌. Delta rows are displayed as you ❌. Each row shows values that ❌ from the ❌. The ❌ reading reflects the ❌ since ❌ ❌.

Clicking INIT, DELTA, or FULL displays more details about the reading. Many different results are available to you for analyzing the performance. Be mindful of ❌, as it can skew initial readings. To obtain results unaffected by FUA, run through the process once to ensure that ❌ before taking any measurements.
❌: Sequence number of the most recent completed interaction.
❌: Total number of interactions.
❌: Total elapsed time for this interaction.
❌: Rules assembly elapsed time.
❌: Elapsed time spent finding rules.
❌: Elapsed time executing SQL operations against the PegaRULES database; ❌ for the PegaRULES database or external databases.
❌: Time spent waiting for an Connect rule execution ❌.
❌: Elapsed time accessing ❌ instances from the PegaRULES database plus the elapsed time ❌.
❌: Total CPU time used.
❌: CPU time spent assembling Java classes.

Database Trace
Run Database Trace if ❌ indicate performance issues in the ❌. Database Trace can trace ❌ like ❌ that are performed.

The Database Trace produces a ❌ containing ❌ ❌ ❌ of your requestor session with the ❌ or ❌. A basic familiarity of SQL Operations, (for example read, write, and insert), and BLOB is useful to interpret the output. Downloaded data contains a ❌ of trace events.

Operating the Database Trace tool can ❌ by producing a large volume output. Use the tool for ❌, in ❌ appropriate for debugging.

Click ❌ to open the settings window. The settings window lists ❌ to trace. If an event is ❌, remove it from the list to streamline the results. You also have the option to ❌. Generating the stack trace is an ❌ process and should only be used when required.

Performance Profiler
Use the Profiler to obtain a ❌ of performance information about the execution of ❌, and ❌ by ❌. The Profiler traces ❌ of rules of ❌ in ❌. The Performance Profiler should be run in conjunction with ❌ to narrow down the specific step of the cause. When you use the Performance Profiler, you ❌ of the application's performance, and then you ❌ to identify problems.

The Performance Profiler requires ❌. Stop the Performance Profiler as soon as your data collection is complete.

In the Performance Profiler results table, click the Download icon to save the results in a ❌. The results can then be opened by using any spreadsheet program, such as Excel, to review the Performance Profiler readings.

❌: Marks the beginning of each activity step or other row.
❌: Sequence number of the interaction, as recorded in the Performance tool. Starts with 1 on the initial display of the login form.
❌: Handle of the activity, when condition, or data transform.
❌: Handle of the activity that called the current rule.
❌: Step number. For when condition rules, this value is always ❌. For data transforms, this value is always ❌.
❌: For activities, ❌. For when condition rules, ❌. For data transforms, ❌.
❌: Total CPU time in seconds for this step. If this step contains a ❌, the CPU time of ❌.
❌: Total CPU time in seconds for this step, excluding time for called processing. If this step contains a Call or Java instruction, the CPU time of ❌.
❌: Elapsed time in seconds to complete this step. Values reflect the impact of other users on this server, waits for database operations and external events, and so on.
❌: Elapsed time (wall time) in seconds, for this step, ❌. (This value is useful for isolating the performance of ❌ that may perform extensive processing in addition to calling activities.)

Pega Predictive Diagnostic Cloud
The PDC is a Software as a Service (SaaS) tool that runs on Pega Cloud and actively ❌, ❌ and ❌ from ❌ such as alerts and guardrail violations. PDC allows you to monitor ❌ Pega applications.

Pega sends ❌ to PDC by using only the clipboard parameter data that is ❌. The data is sent ❌ to minimize any performance impact on the monitored Pega implementation. Data with ❌ is not sent due to privacy concerns, and PDC ❌ from the monitored application.

Pega gathers the following data for PDC:
❌: Identifies the type of alert and the metadata captured in an interaction.
❌: Describes the parameters from the current parameter page, including important contextual information about the functions that run in the monitored application. All remaining parameters are filtered out and excluded.
❌: Details the database query, excluding business data values.
❌: Contains some contextual data that is a subset of the fields sent for alerts.
❌: Includes statistics for average response time and unique user count. PDC uses these statistics to identify overall performance and performance trends for monitored applications.
❌: Includes database index information. PDC uses database index information to generate recommendations to improve query performance.

PDC ❌ the data generated from Pega applications to produce ❌. The trending dashboards provide a ❌ of daily ❌, ❌ ❌. The presentation of data in ❌ helps to ❌ ❌ by presenting behavior over time. PDC empowers ❌ and IT administrators to take preventative action by predicting potential system performance and business logic issues and providing remediation suggestions.

Use ❌ to view a summary of issues that affect the performance of your monitored system. Use ❌ to conduct an in-depth investigation.

Pega Predictive Diagnostic Cloud generates ❌ that you can receive as messages to help you address any issues that might occur. By default, PDC sends notifications by email. However you can configure PDC to send individual notifications by ❌.