I have following HQL script which needs to be puti nto pyspark, spark 1.6
insert into table db.temp_avg
select
a,
avg(b) ,
c
from db.temp WHERE flag is not null GROUP BY a, c;
I created few versions of spark code, but I'm stuggling how to get this averaged column into select.
Also I found out that groupped data cannot be write this way:
df3 = df2.groupBy...
df3.write.mode('overwrite').saveAsTable('db.temp_avg')
part of pyspark code:
temp_table = sqlContext.table("db.temp")
df = temp_table.select('a', 'avg(b)', 'c', 'flag').toDF('a', 'avg(b)', 'c', 'flag')
df = df.where(['flag'] != 'null'))
# this ofc does not work along with the avg(b)
df2 = df.groupBy('a', 'c')
df3.write.mode('overwrite').saveAsTable('db.temp_avg')
Thx for your help.
Correct solution:
import pyspark.sql.functions as F
df = sqlContext.sql("SELECT * FROM db.temp_avg").alias("temp")
df = df.select('a', 'b', 'c')\
.filter(F.col("temp.flag").isNotNULL())\
.groupby('a', 'c')\
.agg(F.avg('b').alias("avg_b"))
import pyspark.sql.functions as F
df = sqlContext.sql("select * from db.temp_avg")
df = df.select('a',
b,
'c')\
.filter(F.col("flag").isNotNULL())\
.groupby('a', 'c')\
.agg(F.avg('b').alias("avg_b"))
Then you can save the table by
df.saveAsTable("tabe_name")
Related
I have a long spark code similar to below which has a lot of keywords :
df = spark.sql("""
select * from abc
""")
df.persist()
df2 = spark.sql("""
select * from def
""")
df2.persist()
df3 = spark.sql("""
select * from mno""")
I want to find out all the dataframes which have been persisted and store them in a list.
Output:
l1 = [df, df2]
How can we proceed with this?
Try this:
from pyspark.sql import DataFrame
df = spark.sql("""
select * from abc
""")
df.persist()
df2 = spark.sql("""
select * from def
""")
df2.persist()
df3 = spark.sql("""
select * from mno
""")
dfNameList = []
for k, v in globals().items():
if isinstance(v, DataFrame):
# k is the name of DF, v is DF itself.
if v.storageLevel.useMemory == True:
dfNameList.append(k)
print(dfNameList)
Output:
['df', 'df2']
Loop globals().items();
Find DataFrame instance;
Determine whether DF is persistent in memory;
Collect the DF name and print.
If you want to put all DF in the list instead of DF names, just append the v to list.
Output will like:
[DataFrame[fieldOne: typeOne, fieldTwo: typeTwo, ……], DataFrame[fieldOne: typeOne, fieldTwo: typeTwo，……]]
I have an column id which had type int but later changed to bigint.
It has both types of values.
from pyspark.sql.functions import *
from pyspark.sql.types import *
df = spark.read.parquet('hdfs path')
df = df.select("id", "code")
df=df.withColumn("id1", df["id"].cast(LongType()))
res1=df.select("id1", "code")
res1.show(1, False)
It shows me the data frame but when i try to perform some operations on them
example:
res1.groupBy('code').agg(countDistinct("id1")).show(1, False)
I get Column: [id], Expected: int, Found: INT64
I tried mergeSchema did not work either.
from pyspark.sql.functions import *
from pyspark.sql.types import *
df1 = spark.read.parquet('hdfs path')
df2 = df1.select("id", "code")
df3 = df2.withColumn("id1", df2["id"].cast(LongType()))
res1=df3.select("id1", "code")
res1.show(1, False)
res1.groupBy("code").agg(countDistinct("id1")).show(1, False)
This should work. In spark Dataframes are immutable so you should not assign the value of transformation operation to a same df variable, you should use a different variable name. In scala it would give you compile time error but in python its allowed so you don't notice it.
if you want you could also chain all of your transformation and get a single df variable and perform groupby operation on it as below :
df = spark.read.parquet('hdfs path').select("id", "code").withColumn("id1", col("id").cast(LongType())).select("id1", "code")
df.groupBy("code").agg(countDistinct("id1")).show(1, False)
I have a requirement where I have to generate multiple columns dynamically in pyspark. I have written a similar code as below to accomplish the same.
sc = SparkContext()
sqlContext = SQLContext(sc)
cols = ['a','b','c']
df = sqlContext.read.option("header","true").option("delimiter", "|").csv("C:\\Users\\elkxsnk\\Desktop\\sample.csv")
for i in cols:
df1 = df.withColumn(i,lit('hi'))
df1.show()
However I am missing out columns a and b in the final result. Please help.
Changed the code like below. its working now, but wanted to know if there is a better way of handling it.
cols = ['a','b','c']
cols_add = []
flg_first = 'Y'
df = sqlContext.read.option("header","true").option("delimiter", "|").csv("C:\\Users\\elkxsnk\\Desktop\\sample.csv")
for i in cols:
print('start'+str(df.columns))
if flg_first == 'Y':
df1 = df.withColumn(i,lit('hi'))
cols_add.append(i)
flg_first = 'N'
else:enter code here
df1 = df1.select(df.columns+cols_add).withColumn(i,lit('hi'))
cols_add.append(i)
print('end' + str(df1.columns))
df1.show()
I am brand new to pyspark and want to translate my existing pandas / python code to PySpark.
I want to subset my dataframe so that only rows that contain specific key words I'm looking for in 'original_problem' field is returned.
Below is the Python code I tried in PySpark:
def pilot_discrep(input_file):
df = input_file
searchfor = ['cat', 'dog', 'frog', 'fleece']
df = df[df['original_problem'].str.contains('|'.join(searchfor))]
return df
When I try to run the above, I get the following error:
AnalysisException: u"Can't extract value from original_problem#207:
need struct type but got string;"
In pyspark, try this:
df = df[df['original_problem'].rlike('|'.join(searchfor))]
Or equivalently:
import pyspark.sql.functions as F
df.where(F.col('original_problem').rlike('|'.join(searchfor)))
Alternatively, you could go for udf:
import pyspark.sql.functions as F
searchfor = ['cat', 'dog', 'frog', 'fleece']
check_udf = F.udf(lambda x: x if x in searchfor else 'Not_present')
df = df.withColumn('check_presence', check_udf(F.col('original_problem')))
df = df.filter(df.check_presence != 'Not_present').drop('check_presence')
But the DataFrame methods are preferred because they will be faster.
I want to filter a Pyspark DataFrame with a SQL-like IN clause, as in
sc = SparkContext()
sqlc = SQLContext(sc)
df = sqlc.sql('SELECT * from my_df WHERE field1 IN a')
where a is the tuple (1, 2, 3). I am getting this error:
java.lang.RuntimeException: [1.67] failure: ``('' expected but identifier a found
which is basically saying it was expecting something like '(1, 2, 3)' instead of a.
The problem is I can't manually write the values in a as it's extracted from another job.
How would I filter in this case?
String you pass to SQLContext it evaluated in the scope of the SQL environment. It doesn't capture the closure. If you want to pass a variable you'll have to do it explicitly using string formatting:
df = sc.parallelize([(1, "foo"), (2, "x"), (3, "bar")]).toDF(("k", "v"))
df.registerTempTable("df")
sqlContext.sql("SELECT * FROM df WHERE v IN {0}".format(("foo", "bar"))).count()
## 2
Obviously this is not something you would use in a "real" SQL environment due to security considerations but it shouldn't matter here.
In practice DataFrame DSL is a much better choice when you want to create dynamic queries:
from pyspark.sql.functions import col
df.where(col("v").isin({"foo", "bar"})).count()
## 2
It is easy to build and compose and handles all details of HiveQL / Spark SQL for you.
reiterating what #zero323 has mentioned above : we can do the same thing using a list as well (not only set) like below
from pyspark.sql.functions import col
df.where(col("v").isin(["foo", "bar"])).count()
Just a little addition/update:
choice_list = ["foo", "bar", "jack", "joan"]
If you want to filter your dataframe "df", such that you want to keep rows based upon a column "v" taking only the values from choice_list, then
from pyspark.sql.functions import col
df_filtered = df.where( ( col("v").isin (choice_list) ) )
You can also do this for integer columns:
df_filtered = df.filter("field1 in (1,2,3)")
or this for string columns:
df_filtered = df.filter("field1 in ('a','b','c')")
A slightly different approach that worked for me is to filter with a custom filter function.
def filter_func(a):
"""wrapper function to pass a in udf"""
def filter_func_(col):
"""filtering function"""
if col in a.value:
return True
return False
return udf(filter_func_, BooleanType())
# Broadcasting allows to pass large variables efficiently
a = sc.broadcast((1, 2, 3))
df = my_df.filter(filter_func(a)(col('field1'))) \
from pyspark.sql import SparkSession
import pandas as pd
spark=SparkSession.builder.appName('Practise').getOrCreate()
df_pyspark=spark.read.csv('datasets/myData.csv',header=True,inferSchema=True)
df_spark.createOrReplaceTempView("df") # we need to create a Temp table first
spark.sql("SELECT * FROM df where Departments in ('IOT','Big Data') order by Departments").show()