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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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repartition查看　repartition　在百度字典中的解释百度英翻中〔查看〕
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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
  • 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:
  • 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
  • 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
  • Spark : how can evenly distribute my records in all partition
    Repartition to N partitions using an identity partitionFunc, moving item to partition index % N Take only the values, dropping the index in the tuple Note that this is slower than the default hash-based repartitioning, because it requires another Spark stage during zipWithIndex() to count the size of each partition
  • 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 –
  • 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
  • Spark repartitioning by column with dynamic number of partitions per . . .
    df repartition(8, $"country", rand): This will create up to 8 partitions for each country, so it should create 8 partitions for China, but the France Cuba partitions are unknown France could be in 8 partitions and Cuba could be in up to 5 partitions See this answer for more details Here's the repartition() documentation:
  • 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 =
  • Difference between repartition (1) and coalesce (1) - Stack Overflow
    In any scenario where you're reducing the data down to a single partition (or really, less than half your number of executors), you should almost always use repartition over coalesce because of this The shuffle caused by repartition is a small price to pay compared to the single-threaded operation of a call to coalesce(1)





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