Gluten offloads most of Spark SQL execution to native engine. We can use gperftools or jemalloc to analyze the offheap memory and cpu profile.

Profile with gperftools

gperftools is a collection of a high-performance multi-threaded malloc() implementation, plus some pretty nifty performance analysis tools, see more: https://github.com/gperftools/gperftools/wiki.

Build and install gperftools

Download gperftools from https://github.com/gperftools/gperftools/releases, build and install.

wget https://github.com/gperftools/gperftools/releases/download/gperftools<version>/gperftools-<version>.tar.gz
tar xzvf gperftools-<version>.tar.gz
cd gperftools-<version>
./configure
make && make install

Then we can find the tcmalloc libraries in $GPERFTOOLS_HOME/.lib.

Run Gluten with gperftools

Configure --files or spark.files for Spark.

--files /path/to/gperftools/libtcmalloc_and_profiler.so
or
spark.files /path/to/gperftools/libtcmalloc_and_profiler.so

Use LD_PRELOAD to preload tcmalloc library, and enable heap profile with HEAPPROFILE or cpu profile with CPUPROFILE.

Example of enabling heap profile in spark executor:

spark.executorEnv.LD_PRELOAD  ./libtcmalloc_and_profiler.so

# Specifies dump profile path. ${CONTAINER_ID} is only used to distinguish the result files when running on yarn.
spark.executorEnv.HEAPPROFILE /tmp/gluten_heap_perf_${CONTAINER_ID} 

Finally, profiling files prefixed with /tmp/gluten_heap_perf_${CONTAINER_ID} will be generated for each spark executor.

Analyze profiling output

Prepare the required native libraries. Assume static build is used for Gluten, there is no other shared dependency libs.

jar xf gluten-velox-bundle-spark3.5_2.12-centos_7_x86_64-1.2.0.jar relative/path/to/libvelox.so ralative/path/to/libgluten.so
mv libvelox.so libgluten.so /path/to/gluten_lib_prefix

Generate a GIF of the analysis result:

# `/usr/bin/java` indicates the program used by running spark executor
pprof --show_bytes --gif --lib_prefix=/path/to/gluten_lib_prefix /usr/bin/java /path/to/gluten_heap_perf_XXX > result.gif

Result like:

Or display analysis result in TEXT:

pprof --text --lib_prefix=/path/to/gluten_lib_prefix /usr/bin/java /path/to/gluten_heap_perf_XXX

Result like:

** Get more help from https://github.com/gperftools/gperftools/wiki#documentation.

Profile with jemalloc

jemalloc is a general purpose malloc(3) implementation that emphasizes fragmentation avoidance and scalable concurrency support. We can also use it to analyze Gluten performance. Getting Started with jemalloc: https://github.com/jemalloc/jemalloc/wiki/Getting-Started.

Build and install jemalloc

Download jemalloc from https://github.com/jemalloc/jemalloc/releases, build and install.

cd /path/to/jemalloc
./autogen.sh --enable-prof
make && make install

Then we can find the jemalloc library in $JEMALLOC_HOME/.lib.

Run Gluten with jemalloc

Configure --files or spark.files for Spark.

--files /path/to/jemalloc/libjemalloc.so
or
spark.files /path/to/jemalloc/libjemalloc.so

Example of enabling heap profile in spark executor:

spark.executorEnv.LD_PRELOAD  ./libjemalloc.so
spark.executorEnv.MALLOC_CONF prof:true,lg_prof_interval:30,prof_prefix:/tmp/gluten_heap_perf

Finally, profiling files prefixed with /tmp/gluten_heap_perf.${PID} will be generated for each spark executor.

Analyze profiling output

Prepare the required native libraries. Assume static build is used for Gluten, so there is no other shared dependency libs.

jar xf gluten-velox-bundle-spark3.5_2.12-centos_7_x86_64-1.2.0.jar relative/path/to/libvelox.so relative/path/to/libgluten.so
mv libvelox.so libgluten.so /path/to/gluten_lib_prefix

Generate a GIF of the analysis result:

# `/usr/bin/java` indicates the program used by running spark executor
jeprof --show_bytes --gif --lib_prefix=/path/to/gluten_lib_prefix /usr/bin/java /path/to/gluten_heap_perf_XXX > result.gif

Or display analysis result in TEXT:

jeprof --text --lib_prefix=/path/to/gluten_lib_prefix /usr/bin/java /path/to/gluten_heap_perf_XXX

** Get more help from https://jemalloc.net/jemalloc.3.html.