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安裝中文字典英文字典辭典工具!
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- What are the pros and cons of the Apache Parquet format compared to . . .
"Overall, Parquet showed either similar or better results on every test [than Avro] The query-performance differences on the larger datasets in Parquet’s favor are partly due to the compression results; when querying the wide dataset, Spark had to read 3 5x less data for Parquet than Avro
- Reading Fixing a corrupt parquet file - Stack Overflow
Either the file is corrupted or this is not a parquet file when I tried to construct a ParquetFile instance I assume appending PAR1 to the end of the file could help this? But before that, I realized that the ParquetFile constructor optionally takes an "external" FileMetaData instance, which has properties that I may be able to estimate (?)
- How to view Apache Parquet file in Windows? [closed]
Apache Parquet is a binary file format that stores data in a columnar fashion Data inside a Parquet file is similar to an RDBMS style table where you have columns and rows But instead of accessing the data one row at a time, you typically access it one column at a time Apache Parquet is one of the modern big data storage formats
- Is it possible to read parquet files in chunks? - Stack Overflow
If your parquet file was not created with row groups, the read_row_group method doesn't seem to work (there is only one group!) However if your parquet file is partitioned as a directory of parquet files you can use the fastparquet engine, which only works on individual files, to read files then, concatenate the files in pandas or get the
- Python: save pandas data frame to parquet file - Stack Overflow
import pyarrow as pa import pyarrow parquet as pq First, write the dataframe df into a pyarrow table # Convert DataFrame to Apache Arrow Table table = pa Table from_pandas(df_image_0) Second, write the table into parquet file say file_name parquet # Parquet with Brotli compression pq write_table(table, 'file_name parquet')
- Is it better to have one large parquet file or lots of smaller parquet . . .
The only downside of larger parquet files is it takes more memory to create them So you can watch out if you need to bump up Spark executors' memory row groups are a way for Parquet files to have vertical partitioning Each row group has many row chunks (one for each column, a way to provide horizontal partitioning for the datasets in parquet)
- Extension of Apache parquet files, is it . pqt or . parquet?
I wonder if there is a consensus regarding the extension of parquet files I have seen a shorter pqt extension, which has typical 3-letters (like in csv, tsv, txt, etc) and then there is a rather long (therefore unconventional(?)) parquet extension which is widely used
- Spark parquet partitioning : Large number of files
First I would really avoid using coalesce, as this is often pushed up further in the chain of transformation and may destroy the parallelism of your job (I asked about this issue here : Coalesce reduces parallelism of entire stage (spark))
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