Comments (4)
Not really a bug (nor is this really a feature request?). Perhaps there isn't much improvement to be made?
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I look at the profile of this. This is all thrift decoding the metadata. Which is a known flaw of parquet.
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@ritchie46 but I'd at least expect all my CPU cores to be pinned, but it appears to almost be single threaded? The metadata per file should be individually parseable right?
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Looks like arrow-rs apache/arrow-rs#5853 has been investigating this issue, maybe parquet v3 will also help fix this flaw.
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Related Issues (20)
- Polars use nest_asyncio
- Cannot scan cloud files containing spaces in path name HOT 8
- DataFrame rename method is extremely slow for dictionary argument
- Support for Business Day Lengths in group_by_dynamic
- Support for making all possible combinations of elements in a df, multiIndex from the cartesian product of multiple iterables? HOT 2
- Slow rolling_skew performance and inconsistent signature vs other rolling methods HOT 1
- sum() and cum_sum() do not cast to UInt64 when data overflows UInt32 HOT 3
- Support casting String to Time HOT 5
- `pl.col('str_col').cast(pl.UInt8).fill_null(pl.lit(0))` gets converted to Int16 type HOT 4
- add `with_source_column` to `scan_parquet` and `read_parquet`
- Rolling_mean with weights gives wrong result when min_periods is not None HOT 1
- json_decode with majority-null columns often fails schema unification HOT 4
- ComputeError: parquet: File out of specification: Invalid thrift: protocol error HOT 2
- List(string) cannot be coerced with String anymore since 1.0 in Concat HOT 2
- Support `Decimal` in `read_csv` HOT 3
- `pl.struct` named expr combined with `.name.keep()` PanicException
- pl.col() mixes strangely with selectors HOT 3
- LazyFrame OOM on CSVs with rows that contains large amount of data HOT 2
- `pl.from_pandas` with columns that mix strings and NAs results in `TypeError` HOT 3
- ComputeError: this expression cannot run in the group_by context HOT 1
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