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Go Monitoring

WhaTap Go Application Monitoring provides the monitoring service for the Go applications.

  • It supports the web framework. It collects the data such as web transaction URL, response time, TPS information, error message, etc.
  • It continuously collects data from the Go Runtime package. It also collects data including memory, goroutine, and GC related data.
  • When multiple API/RPC services are called in the MSA environment, the call relationships are collected through link tracing.

WhaTap's Application Monitoring can monitor application in real time without reproducing failures.

Key features​

  • Real-time transaction monitoring

    WhaTap displays the status of active transactions in real time. Any application issue manifests itself as an increase of active transactions. If you can immediately check SQLs, outbound calls, and method stacks being executed the very moment when transactions increase, you can recognize or analyze the problem very fast.

  • Transaction performance Deep Dive

    WhaTap can collect and analyze the performance history of all transactions. Transaction performance can be traced down to the method level as well as SQLs and HTTP calls.

  • MSA call pattern analysis

    WhaTap visualizes the ratio of calls based on the transaction URL for the relationships between complex applications that have the MSA structure. This enables you to view the call relations and change of call ratios.

  • Analysis of AI-based response distribution patterns

    WhaTap automatically recognizes specific patterns through machine learning and sends alert notifications to operators. This can inform you of the issues that the operator did not recognize. (Please contact support for on-premise type)
    Application number: 10-2020-0037381

  • Analysis of the multi-project transaction linkage

    WhaTap provides the transaction-based tracing in the MSA environment. All transactions can be traced to determine which applications were delayed.

  • Post analysis (Cube)

    You can check various metrics at a time based on the time axis. Because it is possible to specify the time zone where the response time, error count, or throughput is high, you can identify problematic factors in the same time zone or accurately view the service status.