在不使用pyspark中的数据透视的情况下进行分组的有效方法 [英] effective way to groupby without using pivot in pyspark
问题描述
我有一个查询，我需要使用pyspark计算内存利用率.我已经通过使用数据透视的python pandas实现了这一点，但是现在我需要在pyspark中做到这一点，而数据透视将是一项昂贵的功能，因此我想知道pyspark中是否有此解决方案的替代方案
I have a query where I need to calculate memory utilization using pyspark. I had achieved this with python pandas using pivot but now I need to do it in pyspark and pivoting would be an expensive function so I would like to know if there is any alternative in pyspark for this solution
time_stamp Hostname kpi kpi_subtype value_current
2019/08/17 10:01:05 Server1 memory Total 100
2019/08/17 10:01:06 Server1 memory used 35
2019/08/17 10:01:09 Server1 memory buffer 8
2019/08/17 10:02:04 Server1 memory cached 10
2019/08/17 10:01:05 Server2 memory Total 100
2019/08/17 10:01:06 Server2 memory used 42
2019/08/17 10:01:09 Server2 memory buffer 7
2019/08/17 10:02:04 Server2 memory cached 9
2019/08/17 10:07:05 Server1 memory Total 100
2019/08/17 10:07:06 Server1 memory used 35
2019/08/17 10:07:09 Server1 memory buffer 8
2019/08/17 10:07:04 Server1 memory cached 10
2019/08/17 10:08:05 Server2 memory Total 100
2019/08/17 10:08:06 Server2 memory used 35
2019/08/17 10:08:09 Server2 memory buffer 8
2019/08/17 10:08:04 Server2 memory cached 10
需要转换为
time_stamp Hostname kpi Percentage
2019-08-17 10:05:00 Server1 memory 17
2019-08-17 10:05:00 Server2 memory 26
2019-08-17 10:10:00 Server1 memory 17
2019-08-17 10:10:00 Server2 memory 17
我使用的Python代码
Python code i used
df3 = pd.read_csv('/home/yasin/Documents/IMI/Data/memorry sample.csv')
df3['time_stamp'] = pd.to_datetime(df3['time_stamp'])
ns5min=5*60*1000000000
df3['time_stamp'] = pd.to_datetime(((df3['time_stamp'].astype(np.int64) // ns5min + 1 ) * ns5min))
df4 = df3.pivot_table('value_current' , ['time_stamp' , 'Hostname ' , 'kpi' ], 'kpi_subtype')
df4 = df4.reset_index()
df4['Percentage'] = ((df4['Total'] - (df4['Total'] - df4['used'] + df4['buffer'] + df4['cached'])) / df4['Total']) * 100
寻找一个在pyspark中复制它，并在python中将其作为枢轴的更有效方法是一项昂贵的操作，我需要每5分钟在一个非常大的数据集上执行一次
Looking for a to replicate this in pyspark and a more efficient way in python as pivot is an expensive operation and I need to perform this every 5 mins on a really large dataset
推荐答案
当转换为列的值列表未知时，数据透视很昂贵. Spark有一个重载的pivot方法，该方法将其作为参数.
Pivoting is expensive when the list of values that are translated to columns is unknown. Spark has an overloaded pivot method that takes them as an argument.
def pivot(pivotColumn: String, values: Seq[Any])
如果未知，Spark必须对数据集中的不同值进行排序和收集.否则，逻辑将非常简单明了并描述
In case they aren't known Spark must sort and collect the distinct values from your dataset. Otherwise, the logic is pretty straightforward and described here.
该实现添加了一个新的逻辑运算符(o.a.s.sql.catalyst.plans.logical.Pivot).该逻辑运算符由新的分析器规则(o.a.s.sql.catalyst.analysis.Analyzer.ResolvePivot)转换，该规则当前将其转换为具有许多if语句的聚合，每个枢轴值一个表达式.
The implementation adds a new logical operator (o.a.s.sql.catalyst.plans.logical.Pivot). That logical operator is translated by a new analyzer rule (o.a.s.sql.catalyst.analysis.Analyzer.ResolvePivot) that currently translates it into an aggregation with lots of if statements, one expression per pivot value.
例如，df.groupBy("A"，"B").pivot("C"，Seq(小"，大")).sum("D")将被翻译成df.groupBy("A"，"B").agg(expr("sum(if(C ='small'，D，null))")，expr("sum(if (C ='大'，D，null))))).您本可以完成此操作，但是它会变得很长，并且可能很快就会出错.
For example, df.groupBy("A", "B").pivot("C", Seq("small", "large")).sum("D") would be translated into the equivalent of df.groupBy("A", "B").agg(expr("sum(if(C = ‘small’, D, null))"), expr("sum(if(C = ‘large’, D, null))")). You could have done this yourself but it would get long and possibly error prone quickly.
如果不进行枢纽操作，我会做类似的事情:
Without pivoting I would do something like that:
val in = spark.read.csv("input.csv")
//cast to the unix timestamp
.withColumn("timestamp", unix_timestamp($"time_stamp", "yyyy/MM/dd HH:mm:ss").cast(TimestampType))
.drop($"time_stamp")
现在，我们可以使用主机名按时间窗口对数据集进行分组，并将KPI指标收集到地图中.
answer 有一个很好的描述，
Now we can group our dataset by the time window with hostname and collect KPI metrics into a map.
There is an excellent answer describing just that.
val joinMap = udf { values: Seq[Map[String, Double]] => values.flatten.toMap }
val grouped = in.groupBy(window($"timestamp", "5 minutes"), $"Hostname")
.agg(joinMap(collect_list(map($"kpi_subtype", $"value_current".cast(DoubleType)))).as("metrics"))
输出
+------------------------------------------+--------+-------------------------------------------------------------+
|window |Hostname|metrics |
+------------------------------------------+--------+-------------------------------------------------------------+
|[2019-08-17 10:00:00, 2019-08-17 10:05:00]|Server1 |[Total -> 100.0, used -> 35.0, buffer -> 8.0, cached -> 10.0]|
|[2019-08-17 10:00:00, 2019-08-17 10:05:00]|Server2 |[Total -> 100.0, used -> 42.0, buffer -> 7.0, cached -> 9.0] |
|[2019-08-17 10:05:00, 2019-08-17 10:10:00]|Server1 |[Total -> 100.0, used -> 35.0, buffer -> 8.0, cached -> 10.0]|
|[2019-08-17 10:05:00, 2019-08-17 10:10:00]|Server2 |[Total -> 100.0, used -> 35.0, buffer -> 8.0, cached -> 10.0]|
+------------------------------------------+--------+-------------------------------------------------------------+
现在，我们定义一些别名和一个简单的select语句:
Now we define some aliases and a simple select statement:
val total = col("metrics")("Total")
val used = col("metrics")("used")
val buffer = col("metrics")("buffer")
val cached = col("metrics")("cached")
val result = grouped.select($"window", $"Hostname",
(total - ((total - used + buffer + cached) / total) * 100).as("percentage"))
然后我们开始:
+------------------------------------------+--------+----------+
|window |Hostname|percentage|
+------------------------------------------+--------+----------+
|[2019-08-17 10:00:00, 2019-08-17 10:05:00]|Server1 |17.0 |
|[2019-08-17 10:00:00, 2019-08-17 10:05:00]|Server2 |26.0 |
|[2019-08-17 10:05:00, 2019-08-17 10:10:00]|Server1 |17.0 |
|[2019-08-17 10:05:00, 2019-08-17 10:10:00]|Server2 |17.0 |
+------------------------------------------+--------+----------+
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