Error Center¶

TrueWatch Error Center is a full-stack solution designed to help teams systematically track and manage application errors. It automatically aggregates error information from performance monitoring, user access, and logs, categorizing massive amounts of discrete error reports into manageable tasks and providing a complete analysis and handling workflow.

Core Concepts¶

Error¶

Refers to an abnormal event unit identified from APM, RUM, and logs, containing three essential elements: error type, error message, and error stack.

Error Issue¶

The system uses an intelligent fingerprint algorithm to aggregate multiple error reports (Errors) with the same root cause into a unique Error Issue (problem unit). Each Issue has an independent lifecycle status, following a standard flow from "To be assigned (Triage)" to "Resolved (Resolved)", and is associated with complete analysis context (including performance traces, user sessions, source code mapping, and log metrics).

Error Delivery Logic¶

The Error Center employs an Intelligent Aggregation Mechanism for unified management of error data. The system only processes error data that can generate a unique identifier (issue_id). This identifier is generated by combining the error fingerprint and service context, ensuring continuous tracking of the same error.

Error Aggregation Mechanism¶

The system implements intelligent error aggregation through the following steps:

1. Error Fingerprint Generation

a. The system generates a unique fingerprint identifier (fingerprint) for each error:

b. Data Integrity Requirements:

The generation of an error fingerprint is based on data integrity. APM, RUM, and Logs data must contain fields at the above paths to generate a fingerprint; if fields are missing, that piece of data is skipped.

c. Standardization Processing:

• Before calculating the fingerprint, the system performs standardization processing, automatically identifying and filtering variable content (such as timestamps, user IDs, UUIDs, etc.) in the error message (error_message), ensuring consistent fingerprints for identical logical errors.

• Stack information (error_stack) undergoes optimization processing, retaining only key business call lines and filtering out framework internal calls and third-party library calls.

• The system strictly validates field integrity; data missing required fields will be excluded.

2. Error Issue Identifier (Issue ID Generation)

a. Based on the error fingerprint, the system combines the context in which the error occurred to generate a unique issue identifier (issue_id), used for tracking and managing that issue within the Error Center. The specific generation logic is as follows:

• APM Errors: service + fingerprint (Service name + fingerprint)
• RUM Errors: app_name + fingerprint (Application name + fingerprint)
• Log Errors: service + fingerprint (Service name + fingerprint)

b. Lifecycle Initiation:

When the system first generates a new issue_id for a particular error combination, it automatically records the first occurrence time of that issue in the workspace and begins full lifecycle status tracking and aggregated analysis for it.

3. Error Data Storage

Typical Use Cases¶

Use Case 1: Backend Service Exception Aggregation¶

An e-commerce platform's order service generates tens of thousands of NullPointerException errors during a promotion period. Traditionally, these are scattered into alerts due to containing different order IDs.

Solution:

1. Configure an APM error rule to monitor the order-service.
2. The Error Center automatically extracts fingerprints, aggregating exceptions from the same code location into a single Issue.
3. The development team only needs to address one root cause issue; after fixing it, all related alerts disappear.

👆🏻 Transition from "alert storms" to "precise localization", significantly improving handling efficiency.

Use Case 2: Frontend Error Tracing¶

Users report occasional white screens on the checkout page, but the issue cannot be reproduced. Traditional logs lack client-side context.

Solution:

1. Configure a RUM error rule to collect Web application errors.
2. Filter the error list by RUM and application type, then enter the details page.
3. View "Session Replay" to reconstruct the user's operation path: click coupon → parsing failure → white screen.
4. Examine the error stack; SourceMap automatically maps to line 42 of the source code, the JSON parsing logic.

👆🏻 Transition from "unreproducible" to "second-level localization", reducing the user experience issue resolution cycle from days to hours.

Use Case 3: Log Exception Correlation Analysis¶

Application logs frequently show "Connection Timeout", but it's unclear whether it's a network issue or a downstream service problem.

Solution:

1. Configure a log error rule to collect logs containing error_type:DBError.
2. In the error details page, view "Correlated Traces". The trace error details show the timeout occurred during the payment-service call.
3. Check "Infrastructure Dependencies" to confirm that the database connection pool monitoring shows connection exhaustion.
4. Root cause identified: Insufficient thread pool configuration in the downstream payment service, not a network issue.

👆🏻 Transition from "surface phenomenon" to "root cause localization", avoiding misjudgments leading to ineffective optimizations.

Getting Started¶

Prerequisites¶

Before you begin, please ensure your data is already connected to TrueWatch:

• APM: Services are connected and reporting trace data.
• RUM: Web or other applications have the SDK integrated.
• Logs: Logs are indexed and contain error fields.

Start Configuration¶

The Error Center operates in a configuration-driven manner.

1. Before use, you need to create an error delivery rule to explicitly specify the data scope to monitor (specific log indexes, APM services, or RUM applications).
2. After configuration, the system will only perform intelligent analysis and aggregation processing on errors generated from these selected data sources. You can go to the Error List to view and manage aggregated error Issues, filtering by status, source, etc.
3. You can further analyze Error Details, delving into individual errors, and correlating traces, logs, and sessions for root cause analysis.