[SPARK-48798][PYTHON] Introduce spark.profile.render for SparkSession-based profiling
#47202
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HyukjinKwon
approved these changes
Jul 3, 2024
xinrong-meng
approved these changes
Jul 3, 2024
xinrong-meng
reviewed
Jul 3, 2024
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LGTM, thank you for working on that! |
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The test failures seem not related to this PR. |
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Thanks! merging to master. |
ericm-db
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Jul 10, 2024
…on-based profiling ### What changes were proposed in this pull request? Introduces `spark.profile.render` for SparkSession-based profiling. It uses [`flameprof`](https://github.com/baverman/flameprof/) for the default renderer. ``` $ pip install flameprof ``` run `pyspark` on Jupyter notebook: ```py from pyspark.sql.functions import pandas_udf spark.conf.set("spark.sql.pyspark.udf.profiler", "perf") df = spark.range(10) pandas_udf("long") def add1(x): return x + 1 added = df.select(add1("id")) added.show() spark.profile.render(id=2) ``` <img width="1103" alt="pyspark-udf-profile" src="https://github.com/apache/spark/assets/506656/795972e8-f7eb-4b89-89fc-3d8d18b86541"> On CLI, it will return `svg` source string. ```py '<?xml version="1.0" standalone="no"?>\n<!DOCTYPE svg ... ``` Currently only `renderer="flameprof"` for `type="perf"` is supported as a builtin renderer. You can also pass an arbitrary renderer. ```py def render_perf(stats): ... spark.profile.render(id=2, type="perf", renderer=render_perf) def render_memory(codemap): ... spark.profile.render(id=2, type="memory", renderer=render_memory) ``` ### Why are the changes needed? Better debuggability. ### Does this PR introduce _any_ user-facing change? Yes, `spark.profile.render` will be available. ### How was this patch tested? Added/updated the related tests, and manually. ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#47202 from ueshin/issues/SPARK-48798/render. Authored-by: Takuya Ueshin <ueshin@databricks.com> Signed-off-by: Takuya Ueshin <ueshin@databricks.com>
jingz-db
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Jul 22, 2024
…on-based profiling ### What changes were proposed in this pull request? Introduces `spark.profile.render` for SparkSession-based profiling. It uses [`flameprof`](https://github.com/baverman/flameprof/) for the default renderer. ``` $ pip install flameprof ``` run `pyspark` on Jupyter notebook: ```py from pyspark.sql.functions import pandas_udf spark.conf.set("spark.sql.pyspark.udf.profiler", "perf") df = spark.range(10) pandas_udf("long") def add1(x): return x + 1 added = df.select(add1("id")) added.show() spark.profile.render(id=2) ``` <img width="1103" alt="pyspark-udf-profile" src="https://github.com/apache/spark/assets/506656/795972e8-f7eb-4b89-89fc-3d8d18b86541"> On CLI, it will return `svg` source string. ```py '<?xml version="1.0" standalone="no"?>\n<!DOCTYPE svg ... ``` Currently only `renderer="flameprof"` for `type="perf"` is supported as a builtin renderer. You can also pass an arbitrary renderer. ```py def render_perf(stats): ... spark.profile.render(id=2, type="perf", renderer=render_perf) def render_memory(codemap): ... spark.profile.render(id=2, type="memory", renderer=render_memory) ``` ### Why are the changes needed? Better debuggability. ### Does this PR introduce _any_ user-facing change? Yes, `spark.profile.render` will be available. ### How was this patch tested? Added/updated the related tests, and manually. ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#47202 from ueshin/issues/SPARK-48798/render. Authored-by: Takuya Ueshin <ueshin@databricks.com> Signed-off-by: Takuya Ueshin <ueshin@databricks.com>
attilapiros
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Oct 4, 2024
…on-based profiling ### What changes were proposed in this pull request? Introduces `spark.profile.render` for SparkSession-based profiling. It uses [`flameprof`](https://github.com/baverman/flameprof/) for the default renderer. ``` $ pip install flameprof ``` run `pyspark` on Jupyter notebook: ```py from pyspark.sql.functions import pandas_udf spark.conf.set("spark.sql.pyspark.udf.profiler", "perf") df = spark.range(10) pandas_udf("long") def add1(x): return x + 1 added = df.select(add1("id")) added.show() spark.profile.render(id=2) ``` <img width="1103" alt="pyspark-udf-profile" src="https://github.com/apache/spark/assets/506656/795972e8-f7eb-4b89-89fc-3d8d18b86541"> On CLI, it will return `svg` source string. ```py '<?xml version="1.0" standalone="no"?>\n<!DOCTYPE svg ... ``` Currently only `renderer="flameprof"` for `type="perf"` is supported as a builtin renderer. You can also pass an arbitrary renderer. ```py def render_perf(stats): ... spark.profile.render(id=2, type="perf", renderer=render_perf) def render_memory(codemap): ... spark.profile.render(id=2, type="memory", renderer=render_memory) ``` ### Why are the changes needed? Better debuggability. ### Does this PR introduce _any_ user-facing change? Yes, `spark.profile.render` will be available. ### How was this patch tested? Added/updated the related tests, and manually. ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#47202 from ueshin/issues/SPARK-48798/render. Authored-by: Takuya Ueshin <ueshin@databricks.com> Signed-off-by: Takuya Ueshin <ueshin@databricks.com>
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What changes were proposed in this pull request?
Introduces
spark.profile.renderfor SparkSession-based profiling.It uses
flameproffor the default renderer.run
pysparkon Jupyter notebook:On CLI, it will return
svgsource string.Currently only
renderer="flameprof"fortype="perf"is supported as a builtin renderer.You can also pass an arbitrary renderer.
Why are the changes needed?
Better debuggability.
Does this PR introduce any user-facing change?
Yes,
spark.profile.renderwill be available.How was this patch tested?
Added/updated the related tests, and manually.
Was this patch authored or co-authored using generative AI tooling?
No.