I am trying to read a Hive Table having a date column with datatype as timestamp, length=9.
my code looks something like the following -
df = hc.sql("select * from schema.table")
It can read all other columns (datatype = varchar).
It either reads null or gives none in date column
I have printed the df.dtypes and found that the dataframe schema was inferred correctly and date columns have timestamp datatype
surprisingly the same code works in notebook, only fails in spark-shell environment.
Can someone guide me what could be wrong or limitation that renders this error and how could I rectify it?
I have seen this problem in spark, Where It will display as null when the datatype is timestamp, Its a bug. There is a way to get around it where you have to read that date column as string using something like this to_char(Column_name ,'YYYY-MM-DD HH-MM-SS') as column_name, and then cast it to timestamp. If you can tell me the source type and the tool you used to pull the data like sqoop or are you getting the data in some form of files ?? I can help you better.
Related
I am reading a parquet file into a dataframe. My goal is to verify that my time data (column type in parquet : timestamp) are ISO 8601.
The dates in time column look like this : 2021-03-13T05:34:27.100Z or 2021-03-13T05:34:27.100+0000
But when I read my dataframe, pyspark transform 2021-03-13T05:34:27.100Z into 2021-03-13 05:34:27.100
I want to keep the original format, but I can't figure out how to stop pyspark from doing this. I tried to use a custom schema with string for dates but I get this error: Parquet column cannot be converted in file file.snappy.parquet. Column: [time], Expected: string, Found: INT96
Also I tried using conf parameters but it didn't work for me.
Using spark read jdbc option , i am reading oracle table and one of the column type is 'Number' type. After reading and writing into s3 bucket, dataframe printschema is showing decimal(38,10). I know cast to int type can help but issue with we created redshift table with Intger type and decimal value(data frame value) is not allowing to do copy command. Is there any solution other than cast option. ?
Running into an issue on Spark 2.4 on EMR 5.20 in AWS.
I have a string column as a partition, which has date values. My goal is to have the max value of this column be referenced as a filter. The values look like this 2019-01-01 for January 1st, 2019.
In this query, I am trying to filter to a certain date value (which is a string data type), and Spark ends up reading all directories, not just the resulting max(value).
spark.sql("select mypartitioncolumn, column1, column2 from mydatabase.mytable where mypartitioncolumn= (select max(mypartitioncolumn) from myothertable) group by 1,2,3 ").show
However, in this instance, If I hardcode the value, it only reads the proper directory.
spark.sql("select mypartitioncolumn, column1, column2 from mydatabase.mytable where mypartitioncolumn= '2019-01-01' group by 1,2,3 ").show
Why is Spark not recognizing both methods in the same way? I made sure that if I run the select max(mypartitioncolumn) from myothertable query, it shows the exact same value as my hardcoded method (as well as the same datatype).
I can't find anything in the documentation that differentiates partition querying other than data type differences. I checked to make sure that my schema in both the source table as well as value are string types, and also tried to cast my value as a string as well cast( (select max(mypartitioncolumn) from myothertable) as string), it doesn't make any difference.
Workaround by changing configuration
sql("set spark.sql.hive.convertMetastoreParquet = false")
Spark docs
"When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the spark.sql.hive.convertMetastoreParquet configuration, and is turned on by default."
I have a parquet file with 400+ columns, when I read it, the default datatypes attached to a lot of columns is String (may be due to the schema specified by someone else).
I was not able to find a parameter similar to
inferSchema=True' #for spark.read.parquet, present for spark.read.csv
I tried changing
mergeSchema=True #but it doesn't improve the results
To manually cast columns as float, I used
df_temp.select(*(col(c).cast("float").alias(c) for c in df_temp.columns))
this runs without error, but converts all the actual string column values to Null. I can't wrap this in a try, catch block as its not throwing any error.
Is there a way where i can check whether the columns contains only 'integer/ float' values and selectively cast those columns to float?
Parquet columns are typed, so there is no such thing as schema inference when loading Parquet files.
Is there a way where i can check whether the columns contains only 'integer/ float' values and selectively cast those columns to float?
You can use the same logic as Spark - define preferred type hierarchy and attempt to cast, until you get to the point, where you find the most selective type, that parses all values in the column.
How to force inferSchema for CSV to consider integers as dates (with "dateFormat" option)?
Spark data type guesser UDAF
There's no easy way currently,
there's a Github issue already existing which can be referred
https://github.com/databricks/spark-csv/issues/264
somthing like https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
existing for scala this can be created for pyspark
is there a way, how to convert an input string field into orc table with column specified as varchar(xx) in sparksql select query? Or I have to use some workaroud? I'm using Spark 1.6.
I found on Cloudera forum, Spark does not care about length, it saves the value as string with no size limit.
The table is inserted into Hive OK, but I'm little bit worried about data quality.
temp_table = sqlContext.table(ext)
df = temp_table.select(temp_dable.day.cast('string'))
I would like to see something like that :)))
df = temp_table.select(temp_dable.day.cast('varchar(100)'))
Edit:
df.write.partitionBy(part).mode('overwrite').insertInto(int)
Table I'm inserting into is saved as an ORC file (the line above probably should have .format('orc')).
I found here, that If I specify a column as a varchar(xx) type, than the input string will be cutoff to the xx length.
Thx