compile time instrumentation Przemysław Delewski February 18, 2025 Warsaw, PolandAgenda 1. Short bio 2. Observability domain 3. OpenTelemetry 4. History of OpenTelemetry go compile time instrumentation to work on brand new navigation stack. I’m also founding member of the OpenTelemetry Go compile-time instrumentation project https://github.com/open-telemetry/community/blo b/main/projects/go-compile-instrumentation Trace represents an execution path - Span represents step in an execution path - Attribute and Event represents additional metadata information carried together with spanHistory of OpenTelemetry go

Real-Time Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified Unified Data Layer? 3. Why Do You Need a Real-Time Unified Data Layer? 4. 5.CrateDB: A Modern Real-Time Unified Data Layer1. Introduction Data teams are facing more challenges than ever. As applications data engineering and architecture teams must design systems that not only scale but also deliver real-time access and insights. However, the complexity isn’t just technical—business expectations have grown

Application([ (r"/", MainHandler), ]) async def main(): app = make_app() app.listen(8888) await asyncio.Event().wait() if __name__ == "__main__": asyncio.run(main()) This example does not use any of Tornado’s ASYNCIO INTEGRATION Tornado is integrated with the standard library asyncio module and shares the same event loop (by default since Tornado 5.0). In general, libraries designed for use with asyncio can be mixed use Tornado’s web framework and HTTP server together. 6.1.2 Asynchronous and non-Blocking I/O Real-time web features require a long-lived mostly-idle connection per user. In a traditional synchronous web

MainHandler), ]) async def main(): app = make_app() app.listen(8888) await asyncio.Event().wait()if __name__ == "__main__": asyncio.run(main()) This example does not use any of Tornado’s asyncio [https://docs.python.org/3/library/asyncio.html#module-asyncio] module and shares the same event loop (by default since Tornado 5.0). In general, libraries designed for use with asyncio [https://docs http1connection – HTTP/1.x client/server implementation Asynchronous networking tornado.ioloop — Main event loop tornado.iostream — Convenient wrappers for non-blocking socketstornado.netutil — Miscellaneous

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1345 78 Logging 1361 78.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1362 78.2 Processing dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM. It is multi-paradigm, combining features

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1345 78 Logging 1361 78.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1362 78.2 Processing dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM. It is multi-paradigm, combining features

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 36.1 REPL-based workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1573 80 Logging 1593 80.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1594 80.2 Processing

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 36.1 REPL-based workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1573 80 Logging 1593 80.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1594 80.2 Processing

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 36.1 REPL-based workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1573 80 Logging 1593 80.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1594 80.2 Processing

Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 33.5 Wall-time Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 33.6 Memory . . . . . . . . . . . . . . 475 35.5 Execution latency, package loading and package precompiling time . . . . . . . . . 482 35.6 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1652 82 Logging 1654 82.1 Log event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1655 82.2 Processing