data-partitioning

问题 How can i export mails adresses in CSV files in a range of 30 users for each one . I have already try this $users = Get-ADUser -Filter * -Properties Mail $nbCsv = [int][Math]::Ceiling($users.Count/30) For($i=0; $i -le $nbCsv; $i++){ $arr=@() For($j=(0*$i);$j -le ($i + 30);$j++){ $arr+=$users[$j] } $arr|Export-Csv -Path ($PSScriptRoot + "\ASSFAM" + ("{0:d2}" -f ([int]$i)) + ".csv") -Delimiter ";" -Encoding UTF8 -NoTypeInformation } It works but, i think there is a better way to achieve this

问题 I have 2 issues. When I try to split my data into test and train sets, using sample.split as below, the sampling is done rather unclearly. What I mean is that the data d, has a length of 392 and so, 4:1 division should show 0.8*392= 313.6 i.e. 313 or 314 rows in test set, but the shown length is 304. Is there something that I might be missing? require(caTools) set.seed(101) samplev = sample.split(d[,], SplitRatio= 0.80) train = subset(d, samplev == TRUE) test = subset(d, samplev == FALSE) I'm

问题 I try to optimize a join query between two spark dataframes, let's call them df1, df2 (join on common column "SaleId"). df1 is very small (5M) so I broadcast it among the nodes of the spark cluster. df2 is very large (200M rows) so I tried to bucket/repartition it by "SaleId". In Spark, what is the difference between partitioning the data by column and bucketing the data by column? for example: partition: df2 = df2.repartition(10, "SaleId") bucket: df2.write.format('parquet').bucketBy(10,

问题 According to the Apache Flink documentation, KeyBy transformation logically partitions a stream into disjoint partitions. All records with the same key are assigned to the same partition. Is KeyBy 100% logical transformation? Doesn't it include physical data partitioning for distribution across the cluster nodes? If so, then how it can guarantee that all the records with the same key are assigned to the same partition? For instance, assuming that we are getting a distributed data stream from

问题 The schema I'm working on has a small amount of customers, with lots of data per customer. In determining a partitioning strategy, my first thought was to partition by customer_id and then subpartition by range with a day interval. However you cannot use interval in subpartitions. Ultimately I would like a way to automatically create partitions for new customers as they are created, and also have automatic daily subpartitions created for the customers' data. All application queries are at the

问题 The schema I'm working on has a small amount of customers, with lots of data per customer. In determining a partitioning strategy, my first thought was to partition by customer_id and then subpartition by range with a day interval. However you cannot use interval in subpartitions. Ultimately I would like a way to automatically create partitions for new customers as they are created, and also have automatic daily subpartitions created for the customers' data. All application queries are at the

问题 Related questions: Algorithm to Divide a list of numbers into 2 equal sum lists divide list in two parts that their sum closest to each other Let's assume I have a list, which contains exactly 2k elements. Now, I'm willing to split it into two parts, where each part has a length of k while trying to make the sum of the parts as equal as possible. Quick example: [3, 4, 4, 1, 2, 1] might be splitted to [1, 4, 3] and [1, 2, 4] and the sum difference will be 1 Now - if the parts can have

问题 Hello following on from my question: Windows Azure table access latency Partition keys and row keys selection about the way I have organised data in my Azure storage account. I have a table storage scheme designed to store info about entities. There are about 4000 - 5000 entities. There are 6 entity types and the types are roughly evenly distributed. so around 800'ish each. ParitionKey: entityType-Date Row key: entityId As the question details I have been sufferring latency issues where

问题 Problem Statement: I have the following problem: There are more than a billion points in 3D space. The goal is to find the top N points which has largest number of neighbors within given distance R. Another condition is that the distance between any two points of those top N points must be greater than R. The distribution of those points are not uniform. It is very common that certain regions of the space contain a lot of points. Goal: To find an algorithm that can scale well to many

问题 Problem Statement: I have the following problem: There are more than a billion points in 3D space. The goal is to find the top N points which has largest number of neighbors within given distance R. Another condition is that the distance between any two points of those top N points must be greater than R. The distribution of those points are not uniform. It is very common that certain regions of the space contain a lot of points. Goal: To find an algorithm that can scale well to many