Memory profiling enables you to understand the memory allocation and behavior of your blockchain applications over time in Substrate-based clients. It identifies method calls in the context of how memory was allocated, combining this information with the number of allocated objects. In addition, profiling can be used to analyze memory leaks, identify where memory consumption is happening, define temporary allocations, and investigate excessive memory fragmentation within applications. When running Substrate locally, it also accounts for runtime.
We recommend koute's memory profiler. This assumes you have installed rust stable, GCC, and yarn globally.
- clone the repository
- compile the library and the cli
git clone https://github.com/koute/memory-profiler cd memory-profiler cargo build --release -p memory-profiler # builds the library cargo build --release -p memory-profiler-cli # builds the cli
Using the Profiler on Substrate
Build the client in release mode in your terminal (
cargo build --release). After the command is
completed, run the build binary with the memory profiler preloaded. This step assumes the
memory-profiler directory is next to the Substrate directory and will only function if the
directory is correctly called and placed:
You can pass cli arguments in substrate as usual, configuring the memory profiler via environment variables.
This will create a
.dat file in the directory which you defined and maintain the traces for
analyzing. Additionally, you can utilize the memory-profiler server to do the same thing using a
web ui. To execute this, place the memory-profiler in a directory as follows:
../memory-profiler/target/release/memory-profiler-cli server *.dat
This will run the analyzing. On standard hardware this takes approximately a minute per gigabyte of data. Substrate creates about 1.6Gb/hour runtime during normal operation and more during the initial compile. Once successfully complied, it will generate a URL. This creates a visual means to inspect the data (the deduplication warnings can be ignored):
[2020-05-06T08:57:09Z WARN cli_core::loader] Duplicate allocation of 0x00007F58BC20CA90; old backtrace = BacktraceId(21363), new backtrace = BacktraceId(19194) [2020-05-06T08:57:09Z WARN cli_core::loader] Duplicate allocation of 0x00007F5864230040; old backtrace = BacktraceId(21321), new backtrace = BacktraceId(21321) [2020-05-06T08:57:09Z WARN cli_core::loader] Duplicate allocation of 0x00007F5864202D70; old backtrace = BacktraceId(21322), new backtrace = BacktraceId(21322) [2020-05-06T08:59:20Z INFO cli_core::loader] Loaded data in 315s 820 [2020-05-06T08:59:20Z INFO actix_server::builder] Starting 8 workers [2020-05-06T08:59:20Z INFO actix_server::builder] Starting server on 127.0.0.1:8080
Now open your browser and point it to the URL (here
http://localhost:8080/) and select the session
you want to investigate. This will propagate a graph and may need a few minutes to load if a lot of
data is being parsed. Substrate tracing becomes significantly large quickly, and longer term or
super-in-depth introspection for specific runtime calls are sometimes hard to source.
Via the top via the REST API you can also download other versions of the data and analyze it with a secondary tool.
The heaptrack file and heaptrack GUI can be used to investigate specific calls and stack traces suspected to leaking memory.
Tips & Tricks
While the profiler generates huge logs, especially if Substrate has been running for a while, the
analyzer might itself run out of memory during the process and experience a segmentation fault. To
circumvent this, create smaller logs by toggling the memory profiler with the
Whenever this call is sent, it restarts and creates a new log file. Additionally, a simpler way to
have the tracing split in thirty minute chunks – around ~800MB each – is to use
PID=12345 watch -n 1800 'kill -USR1 $PID && sleep 1 && kill -USR1 $PID'.
- The memory profiler currently core dumps when running substrate with
- Clicking on
flamegraphmakes the server crash.