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  • file - What are the pros and cons of the Apache Parquet format compared . . .
    Here's how you can perform this with Pandas if the data is stored in a Parquet file import pandas as pd pd read_parquet('some_file parquet', columns = ['id', 'firstname']) Parquet is a columnar file format, so Pandas can grab the columns relevant for the query and can skip the other columns This is a massive performance improvement
  • How to view Apache Parquet file in Windows? - Stack Overflow
    What is Apache Parquet? 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
  • 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 (?)
  • Extension of Apache parquet files, is it . pqt or . parquet?
    parquet is the most commonly used extension Three letter file extensions are a remnant of the days when file lengths were very restricted It's quite common to see longer names nowadays (e g database sqlite)
  • Python: save pandas data frame to parquet file - Stack Overflow
    Second, write the table into parquet file say file_name parquet # Parquet with Brotli compression pq write_table(table, 'file_name parquet') NOTE: parquet files can be further compressed while writing Following are the popular compression formats Snappy ( default, requires no argument) gzip; brotli; Parquet with Snappy compression
  • Is it better to have one large parquet file or lots of smaller parquet . . .
    Also larger parquet files don't limit parallelism of readers, as each parquet file can be broken up logically into multiple splits (consisting of one or more row groups) 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
  • Unable to infer schema when loading Parquet file
    The documentation for parquet says the format is self describing, and the full schema was available when the parquet file was saved What gives? Using Spark 2 1 1
  • What file extension is the correct way to name parquet files?
    <file-name> parquet : 1) This is the standard and most widely accepted naming convention 2) The compression codec is stored in the Parquet file metadata, not in the filename 3) Tools like Apache Spark, Hive, AWS Athena, and Snowflake expect parquet files regardless of compression –





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