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TrackMemory.CPU.IO.Bottlenecks

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Broken down
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by method name, class name, and line number. Without complex overhead, in any language or framework.

IO31%Address:0x4553e01415161718192021222324252627282930313233343536 < n> ( std::array<std::array<, n>, n> &board, &row, &col) { i = , j = ; (i = ; i < col; i++) { (board[row][i]) { ; } } // Check upper diagonal on left side (i = row, j = col; i >= && j >= ; i--, j--) { (board[i][j]) { ; } } (i = row, j = col; j >= && i < n; i++, j--) { (board[i][j]) { ; }templateboolconstintconst intconst intintforifreturnforifreturnforifreturnsize_t000false00false0falseisSafe// Check this row on left side// Check lower diagonal on left sideC++

Thanks to eBPF's nature, Parca Agent operates in Linux kernel space allowing it to grab exactly the data needed at low overhead.

Get a full picture of how your app performs in production.

  • Multi-dimensional data model

  • Optimized, built-in storage

  • Support for pushing and
    pulling profiles from targets

  • Query engine specifically
    designed for profiling data

  • Targets are discovered via service
    discovery or static configuration

  • Label-selector based query
    language

Never miss the important data with a continuous profiling.

You never know at which point in time you are going to need profiling data, so always collect it at low overhead.

Learn more
Profiling data

Build faster and more reliable apps with Parca

  • Reduce cost

    Many organizations have 20-30% of resources wasted in easily optimized code paths. The Parca Agent aims to lower the bar of starting to profile by requiring zero-instrumentation for the whole infrastructure. Deploy in your infrastructure and get started!

  • Improve Performance

    Using profiling data collected over time, Parca can (with confidence and statistical significance) determine hot paths to optimize. Additionally, it can show differences between any query, such as comparing versions of software or any other dimension.

  • Fast debugging

    Profiling data provides unique insight and depth into what code a process executed over time. Situations, traditionally difficult to troubleshoot, memory leaks, but also momentary spikes in CPU or I/O causing unexpected behavior can be easily understood with continuous profiling.

  • Catch regressions

    The latest deploy of your application has a performance regression? Understand with a single query where CPU time is spent differently now.

Parca dashboard

Many organizations have 20-30% of resources wasted in easily optimized code paths. The Parca Agent aims to lower the bar of starting to profile by requiring zero-instrumentation for the whole infrastructure.

Install binaryDeploy to Kubernetes
  • ParcaServer
  • ParcaWeb UI
  • ParcaAgent
Parca is Open Source.
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Parca was originally created by Polar Signals
and people who contributed to projects such as Prometheus, Thanos, Kubernetes,
Drone and more. All components are available under the Apache 2 License on GitHub.

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community!

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