I have a DF A_DF which has among others two columns say COND_B and COND_C. Then I have 2 different df's B_DF with COND_B column and C_DF with COND_C column.
Now I would like to filter A_DF where the value match in one OR the other. Something like:
df = A_DF.filter((A_DF.COND_B == B_DF.COND_B) | (A_DF.COND_C == C_DF.COND_C))
But I found out it is not possible like this.
EDIT
error: Attribute CON_B#264,COND_C#6 is missing from the schema: [... COND_B#532, COND_C#541 ]. Attribute(s) with the same name appear in the operation: COND_B,COND_C. Please check if the right attribute(s) are used.; looks like I can filter only on same DF because of the #number added on the fly..
So I first tried to do a list from B_DF and C_DF and use filter based on that but it was too expensive to use collect() on 100m of records.
So I tried:
AB_DF = A_DF.join(B_DF, 'COND_B', 'left_semi')
AC_DF = A_DF.join(C_DF, 'COND_C', 'left_semi')
df = AB_DF.unionAll(AC_DF).dropDuplicates()
dropDuplicates() I used to removed duplicate records where both conditions where true. But even with that I got some unexpected results.
Is there some other - smoother solution to do it simply? Something like an EXISTS statement in SQL?
EDIT2
I tried SQL based on #mck response:
e.createOrReplaceTempView('E')
b.createOrReplaceTempView('B')
p.createOrReplaceTempView('P')
df = spark.sql("""select * from E where exists (select 1 from B where E.BUSIPKEY = B.BUSIPKEY) or exists (select 1 from P where E.PCKEY = P.PCKEY)""")
my_output.write_dataframe(df)
with error:
Traceback (most recent call last):
File "/myproject/abc.py", line 45, in my_compute_function
df = spark.sql("""select * from E where exists (select 1 from B where E.BUSIPKEY = B.BUSIPKEY) or exists (select 1 from P where E.PCKEY = P.PCKEY)""")
TypeError: sql() missing 1 required positional argument: 'sqlQuery'
Thanks a lot!
Your idea of using exists should work. You can do:
A_DF.createOrReplaceTempView('A')
B_DF.createOrReplaceTempView('B')
C_DF.createOrReplaceTempView('C')
df = spark.sql("""
select * from A
where exists (select 1 from B where A.COND_B = B.COND_B)
or exists (select 1 from C where A.COND_C = C.COND_C)
""")
Related
I have a python list of all the columns of the dataframe as below.
['Timestamp',
'ScheduleCode__VALUE',
'ScheduleCode__i:nil',
'ProductionCode__VALUE',
'ProductionCode__i:nil',
'ProductCode__VALUE',
'ProductCode__i:nil',
'ProductCategory__VALUE',
'ProductCategory__i:nil']
I need to drop all the columns from the above list which ends with __i:nil and rename all the columns with __value to only it's prefix like ProductCode__VALUE should be renamed to ProductCode.
Try this:
column_list = ['Timestamp',
'ScheduleCode__VALUE',
'ScheduleCode__i:nil',
'ProductionCode__VALUE',
'ProductionCode__i:nil',
'ProductCode__VALUE',
'ProductCode__i:nil',
'ProductCategory__VALUE',
'ProductCategory__i:nil']
for element in column_list:
if(element.endswith('__Value')):
df = (
df.withColumnRenamed(element, element.split('__')[0])
)
df = df.drop(*[element for element in column_list if element.endswith('__i:nil')])
Python doesn't like the ampersand below.
I get the error: & is not a supported operation for types str and str. Please review your code.
Any idea how to get this right? I've never tried to join more than 1 column for aliased tables. Thx!!
df_initial_sample = df_crm.alias('crm').join(df_cngpt.alias('cng'), on= (("crm.id=cng.id") & ("crm.cpid = cng.cpid")), how = "inner")
Try using as below -
df_initial_sample = df_crm.alias('crm').join(df_cngpt.alias('cng'), on= (["id"] and ["cpid"]), how = "inner")
Your join condition is overcomplicated. It can be as simple as this
df_initial_sample = df_crm.join(df_cngpt, on=['id', 'cpid'], how = 'inner')
I am running in to this error when I am trying to select a couple of columns from the temporary table.
pd_df = pd.read_sql('select * from abc.cars limit 10', conn)
df = spark.createDataFrame(pd_df)
df.createOrReplaceTempView("cars_tmp")
df.show()
print('***************')
print("Reading from tmp table")
data = spark.sql('select location_id from cars_tmp')
data.show()
AnalysisException: cannot resolve '`location_id`' given input columns: [cars_tmp.abc.product_id, cars_tmp.abc.location_id ...]
When I select all the columns I get the results. So this is successful:
data = spark.sql('select * from cars_tmp')
data.show()
I tried below queries but they fail as well with the same error:
data = spark.sql('select cars_tmp.abc.location_id from cars_tmp')
data.show()
data = spark.sql('select cars_tmp.location_id from cars_tmp')
data.show()
data = spark.sql('select abc.location_id from cars_tmp')
data.show()
I am running these in datbricks.
Databricks runtime version: 7.0
Apache Spark version: 3.0
scala: 2.12
or "spark_version": "7.0.x-scala2.12",
Any help will be highly appreciated.
Thanks
The column name does not exist in the table. select * from cars_tmp works because you do not specify the column name.
Please see this answer https://stackoverflow.com/a/64042756/8913402 with the same error handling.
I resolved the issue by add each column in the panda select query. So something like this:
pd_df = pd.read_sql('select id, location_id, product_id from abc.cars limit 10', conn)
I have used PySpark SQL to join together two tables, one containing crime location data with longitude and latitude and the other containing postcodes with their corresponding longitude and latitude.
What I am trying to work out is how to tally up how many crimes have occurred within each postcode. I am new to PySpark and my SQL is rusty so I am unsure where I am going wrong.
I have tried to use COUNT(DISTINCT) but that is simply giving me the total number of distinct postcodes.
mySchema = StructType([StructField("Longitude", StringType(),True), StructField("Latitude", StringType(),True)])
bgl_df = spark.createDataFrame(burglary_rdd, mySchema)
bgl_df.registerTempTable("bgl")
rdd2 = spark.sparkContext.textFile("posttrans.csv")
mySchema2 = StructType([StructField("Postcode", StringType(),True), StructField("Lon", StringType(),True), StructField("Lat", StringType(),True)])
pcode_df = spark.createDataFrame(pcode_rdd, mySchema2)
pcode_df.registerTempTable("pcode")
count = spark.sql("SELECT COUNT(DISTINCT pcode.Postcode)
FROM pcode RIGHT JOIN bgl
ON (bgl.Longitude = pcode.Lon
AND bgl.Latitude = pcode.Lat)")
+------------------------+
|count(DISTINCT Postcode)|
+------------------------+
| 523371|
+------------------------+
Instead I want something like:
+--------+---+
|Postcode|Num|
+--------+---+
|LN11 9DA| 2 |
|BN10 8JX| 5 |
| EN9 3YF| 9 |
|EN10 6SS| 1 |
+--------+---+
You can do a groupby count to get a distinct count of values for a column:
group_df = df.groupby("Postcode").count()
You will get the ouput you want.
For an SQL query:
query = """
SELECT pcode.Postcode, COUNT(pcode.Postcode) AS Num
FROM pcode
RIGHT JOIN bgl
ON (bgl.Longitude = pcode.Lon AND bgl.Latitude = pcode.Lat)
GROUP BY pcode.Postcode
"""
count = spark.sql(query)
Also, I have copied in from your FROM and JOIN clause to make the query more relevant for copy-pasta.
I'm not sure why this is happening. In PySpark, I read in two dataframes and print out their column names and they are as expected, but then when do a SQL join I get an error that cannot resolve column name given the inputs. I have simplified the merge just to get it to work, but I will need to add in more join conditions which is why I'm using SQL (will be adding in: "and b.mnvr_bgn < a.idx_trip_id and b.mnvr_end > a.idx_trip_data"). It appears that the column 'device_id' is being renamed to '_col7' in the df mnvr_temp_idx_prev_temp
mnvr_temp_idx_prev = mnvr_3.select('device_id', 'mnvr_bgn', 'mnvr_end')
print mnvr_temp_idx_prev.columns
['device_id', 'mnvr_bgn', 'mnvr_end']
raw_data_filtered = raw_data.select('device_id', 'trip_id', 'idx').groupby('device_id', 'trip_id').agg(F.max('idx').alias('idx_trip_end'))
print raw_data_filtered.columns
['device_id', 'trip_id', 'idx_trip_end']
raw_data_filtered.registerTempTable('raw_data_filtered_temp')
mnvr_temp_idx_prev.registerTempTable('mnvr_temp_idx_prev_temp')
test = sqlContext.sql('SELECT a.device_id, a.idx_trip_end, b.mnvr_bgn, b.mnvr_end \
FROM raw_data_filtered_temp as a \
INNER JOIN mnvr_temp_idx_prev_temp as b \
ON a.device_id = b.device_id')
Traceback (most recent call last): AnalysisException: u"cannot resolve 'b.device_id' given input columns: [_col7, trip_id, device_id, mnvr_end, mnvr_bgn, idx_trip_end]; line 1 pos 237"
Any help is appreciated!
I would recommend renaming the name of the field 'device_id' in at least one of the data frame. I modified your query just a bit and tested it(in scala). Below query works
test = sqlContext.sql("select * FROM raw_data_filtered_temp a INNER JOIN mnvr_temp_idx_prev_temp b ON a.device_id = b.device_id")
[device_id: string, mnvr_bgn: string, mnvr_end: string, device_id: string, trip_id: string, idx_trip_end: string]
Now if you are doing a 'select * ' in above statement, it will work. But if you try to select 'device_id', you will get an error "Reference 'device_id' is ambiguous" . As you can see in the above 'test' data frame definition, it has two fields with the same name(device_id). So to avoid this, I recommend changing field name in one of the dataframes.
mnvr_temp_idx_prev = mnvr_3.select('device_id', 'mnvr_bgn', 'mnvr_end')
.withColumnRenamned("device_id","device")
raw_data_filtered = raw_data.select('device_id', 'trip_id', 'idx').groupby('device_id', 'trip_id').agg(F.max('idx').alias('idx_trip_end'))
Now use dataframes or sqlContext
//using dataframes with multiple conditions
val test = mnvr_temp_idx_prev.join(raw_data_filtered,$"device" === $"device_id"
&& $"mnvr_bgn" < $"idx_trip_id","inner")
//in SQL Context
test = sqlContext.sql("select * FROM raw_data_filtered_temp a INNER JOIN mnvr_temp_idx_prev_temp b ON a.device_id = b.device and a. idx_trip_id < b.mnvr_bgn")
Above queries will work for your problem. And if your data set is too large, I would recommend to not use '>' or '<' operators in Join condition as it causes cross join which is a costly operation if data set is large. Instead use them in WHERE condition.