coalesce

R: coalescing a large data frame

由 二次信任 提交于 2020-11-24 17:20:26
问题 Say I create a data frame, foo : foo <- data.frame(A=rep(NA,10),B=rep(NA,10)) foo$A[1:3] <- "A" foo$B[6:10] <- "B" which looks like, A B 1 A <NA> 2 A <NA> 3 A <NA> 4 <NA> <NA> 5 <NA> <NA> 6 <NA> B 7 <NA> B 8 <NA> B 9 <NA> B 10 <NA> B I can coalesce this into a single column, like this: data.frame(AB = coalesce(foo$A, foo$B)) giving, AB 1 A 2 A 3 A 4 <NA> 5 <NA> 6 B 7 B 8 B 9 B 10 B which is nice. Now, say my data frame is huge with lots of columns. How do I coalesce that without naming each

R: coalescing a large data frame

由 蓝咒 提交于 2020-11-24 17:15:26
问题 Say I create a data frame, foo : foo <- data.frame(A=rep(NA,10),B=rep(NA,10)) foo$A[1:3] <- "A" foo$B[6:10] <- "B" which looks like, A B 1 A <NA> 2 A <NA> 3 A <NA> 4 <NA> <NA> 5 <NA> <NA> 6 <NA> B 7 <NA> B 8 <NA> B 9 <NA> B 10 <NA> B I can coalesce this into a single column, like this: data.frame(AB = coalesce(foo$A, foo$B)) giving, AB 1 A 2 A 3 A 4 <NA> 5 <NA> 6 B 7 B 8 B 9 B 10 B which is nice. Now, say my data frame is huge with lots of columns. How do I coalesce that without naming each

COALESCE - guaranteed to short-circuit?

由 こ雲淡風輕ζ 提交于 2020-07-04 12:03:38
问题 From this question, a neat answer about using COALESCE to simplify complex logic trees. I considered the problem of short circuiting. For instance, in functions in most languages, arguments are fully evaluated and are then passed into the function. In C: int f(float x, float y) { return x; } f(a, a / b) ; // This will result in an error if b == 0 That does not appear to be a limitation of the COALESCE "function" in SQL Server: CREATE TABLE Fractions ( Numerator float ,Denominator float )

COALESCE - guaranteed to short-circuit?

由 蹲街弑〆低调 提交于 2020-07-04 12:02:23
问题 From this question, a neat answer about using COALESCE to simplify complex logic trees. I considered the problem of short circuiting. For instance, in functions in most languages, arguments are fully evaluated and are then passed into the function. In C: int f(float x, float y) { return x; } f(a, a / b) ; // This will result in an error if b == 0 That does not appear to be a limitation of the COALESCE "function" in SQL Server: CREATE TABLE Fractions ( Numerator float ,Denominator float )

Avoid nested aggregate error using coalesce()

由 浪子不回头ぞ 提交于 2020-07-03 13:41:27
问题 I currently have a query using coalesce that worked in SQL server,however, it is not working in Amazon Redshift. Is there a way I can more appropriately write this to use in Redshift: coalesce(sum(Score)/nullif(sum(ScorePrem),0),0) as percent 回答1: Consider running the aggregate query as a subquery or CTE, then handle transformation or secondary calculations in an outer main query. WITH agg AS ( SELECT calendar_month_id ,day_of_month ,month_name ,DaysRemaining ,RPTBRANCH ,0 AS TotalGrp ,SUM

Unexpected BLOB results with MySQL testing NULL variable with IFNULL, COALESCE

由 耗尽温柔 提交于 2020-06-17 09:48:06
问题 In trying to test whether a variable has been defined, I discovered that the IF, IFNULL, and COALESCE statements return simply BLOB rather than the value I expected when (a) the variable has not been defined or (b) it has been explicitly set to NULL before being assigned a value in the session. I've verified this in MySQL versions 5.7 and 8.0. SELECT IF(@p IS NULL, 'is null', 'not null'); # 'is null' SELECT IF(@p IS NULL, 'is null', @p); # BLOB SELECT IFNULL(@p, 'is null'); # BLOB SELECT

hive 的判断条件（if、coalesce、case）

由 穿精又带淫゛_ 提交于 2020-04-07 17:13:37
CONDITIONAL FUNCTIONS IN HIVE Hive supports three types of conditional functions. These functions are listed below: IF( Test Condition, True Value, False Value ) The IF condition evaluates the “Test Condition” and if the “Test Condition” is true, then it returns the “True Value”. Otherwise, it returns the False Value. Example: IF(1=1, 'working', 'not working') returns 'working' COALESCE( value1,value2,... ) The COALESCE function returns the fist not NULL value from the list of values. If all the values in the list are NULL, then it returns NULL. Example: COALESCE(NULL,NULL,5,NULL,4) returns 5

Spark2.0-RDD分区原理分析

由 纵饮孤独 提交于 2020-03-24 09:00:02
3 月，跳不动了？>>> Spark分区原理分析 介绍 分区是指如何把RDD分布在spark集群的各个节点的操作。以及一个RDD能够分多少个分区。 一个分区是大型分布式数据集的逻辑块。 那么思考一下：分区数如何映射到spark的任务数？如何验证？分区和任务如何对应到本地的数据? Spark使用分区来管理数据，这些分区有助于并行化分布式数据处理，并以最少的网络流量在executors之间发送数据。 默认情况下，Spark尝试从靠近它的节点读取数据到RDD。由于Spark通常访问分布式分区数据，为了优化transformation（转换）操作，它创建分区来保存数据块。 存在在HDFS或Cassandra中的分区数据是一一对应的（由于相同的原因进行分区）。 默认情况下，每个HDFS的分区文件（默认分区文件块大小是64M）都会创建一个RDD分区。 默认情况下，不需要程序员干预，RDD会自动进行分区。但有时候你需要为你的应用程序，调整分区的大小，或者使用另一种分区方案。 你可以通过方法 def getPartitions: Array[Partition] 来获取RDD的分区数量。 在spark-shell中执行以下代码： val v = sc.parallelize(1 to 100) scala> v.getNumPartitions res2: Int = 20 /
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