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请输入英文单字，中文词皆可：

repartition    
n. 分配,区分,摊分
vt. 再分配,再划分

分配,区分,摊分再分配,再划分


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英文字典中文字典相关资料:


  • Spark - repartition () vs coalesce () - Stack Overflow
    Is coalesce or repartition faster? coalesce may run faster than repartition, but unequal sized partitions are generally slower to work with than equal sized partitions You'll usually need to repartition datasets after filtering a large data set I've found repartition to be faster overall because Spark is built to work with equal sized partitions
  • pyspark - Spark: What is the difference between repartition and . . .
    It says: for repartition: resulting DataFrame is hash partitioned for repartitionByRange: resulting DataFrame is range partitioned And a previous question also mentions it However, I still don't understand how exactly they differ and what the impact will be when choosing one over the other?
  • Spark parquet partitioning : Large number of files
    The solution is to extend the approach using repartition( , rand) and dynamically scale the range of rand by the desired number of output files for that data partition
  • dataframe - Spark: Difference between numPartitions in read. jdbc . . .
    Yes: Then is it redundant to invoke repartition method on a DataFrame that was read using DataFrameReader jdbc method (with numPartitions parameter)? Yes Unless you invoke the other variations of repartition method (the ones that take columnExprs param), invoking repartition on such a DataFrame (with same numPartitions) parameter is redundant
  • Strategy for partitioning dask dataframes efficiently
    At the moment I just repartition with npartitions = ncores * magic_number, and set force to True to expand partitions if need be This one size fits all approach works but is definitely suboptimal as my dataset varies in size
  • apache spark sql - Difference between df. repartition and . . .
    What is the difference between DataFrame repartition() and DataFrameWriter partitionBy() methods? I hope both are used to "partition data based on dataframe column"? Or is there any difference?
  • Why is repartition faster than partitionBy in Spark?
    Even though partitionBy is faster than repartition, depending on the number of dataframe partitions and distribution of data inside those partitions, just using partitionBy alone might end up costly
  • Spark repartitioning by column with dynamic number of partitions per . . .
    Spark takes the columns you specified in repartition, hashes that value into a 64b long and then modulo the value by the number of partitions This way the number of partitions is deterministic





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