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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
    The repartition method makes new partitions and evenly distributes the data in the new partitions (the data distribution is more even for larger data sets) Difference between coalesce and repartition coalesce uses existing partitions to minimize the amount of data that's shuffled repartition creates new partitions and does a full shuffle
  • Why is repartition faster than partitionBy in Spark?
    repartition("partition") \ write format("json") \ Here, you are repartitioning the existing dataframe based on the column "partition" which has 100 distinct values So the existing dataframe will incur a full shuffle bringing down the number of partitions from 10K to 100
  • Spark parquet partitioning : Large number of files
    repartition(numPartitions, $"some_col", rand) is an elegant solution but does not handle small data partitions well It will write out numPartitions files for every data partition, even if they are tiny
  • Databricks - How to change a partition of an existing Delta table?
    I have a table in Databricks delta which is partitioned by transaction_date I want to change the partition column to view_date I tried to drop the table and then create it with a new partition co
  • Strategy for partitioning dask dataframes efficiently
    Unless you are sure there are no object dtype columns I would suggest specifying deep=True, that is, repartition using: df repartition(npartitions= 1+df memory_usage(deep=True) sum() compute() n ) Where n is your target partition size in bytes Adding 1 ensures the number of partitions is always greater than 1 ( performs floor division)
  • What, exactly happens when a repartition occurs in a kafka stream?
    @JRibkr: For starters, "repartition" is mentioned 88 times in KStream javadoc I assume I've got the gist of it, but I haven't seen any detailed description, and the scope of the "internal" topic might be open to interpretation Also, your link points to interactive queries, which is not what I'm talking about –
  • dataframe - Spark: Difference between numPartitions in read. jdbc . . .
    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 However, I'm not sure if forcing same degree of parallelism on an already-parallelized DataFrame also invokes shuffling of data among executors unnecessarily
  • Is there a way to repartition the input topic in Kafka streams?
    Yes you can You set a new key and afterwards pipe the data through another topic repartition() will create the required topic automatically for your, with the same number of partitions as your input topic; it's also possible to set the number of partitions explicitly to scale in out via `repartitioned(Repartitioned numberOfPartitions( ))` KStream stream =
  • pyspark - Spark: What is the difference between repartition and . . .
    Represents a partitioning where rows are distributed evenly across output partitions by starting from a random target partition number and distributing rows in a round-robin fashion This partitioning is used when implementing the DataFrame repartition() operator When using repartition by column expression:
  • When should I repartition an RDD? - Stack Overflow
    How many partitions should I use for a given file? For example, suppose I have a 10GB parquet file, 3 executors with 2 cores and 3gb memory each Should I repartition? How many partitions should I use? What is the better way to make that choice? Are all data types (ie txt, parquet, etc ) repartitioned by default if no partitioning is provided?





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