I am pipelining csv's from an S3 bucket to AWS's Athena using Glue and the titles of the columns are just the default 'col0', 'col1' etc, while the true titles of the columns are found in the first row entry. Is there a way, either in the pipeline process or in an early postgreSQL query, to make the first row entry the column names? Ideally avoiding directly hardcoding in the column names in the Glue crawler.
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format.html
Use withHeader=True while reading data using Glue Api
Related
Oversimplified Scenario:
A process which generates monthly data in a s3 file. The number of fields could be different in each monthly run. Based on this data in s3,we load the data to a table and we manually (as number of fields could change in each run with addition or deletion of few columns) run a SQL for few metrics.There are more calculations/transforms on this data,but to have starter Im presenting the simpler version of the usecase.
Approach:
Considering the schema-less nature, as the number of fields in the s3 file could differ in each run with addition/deletion of few fields,which requires manual changes every-time in the SQL, Im planning to explore Spark/Scala, so that we can directly read from s3 and dynamically generate SQL based on the fields.
Query:
How I can achieve this in scala/spark-SQL/dataframe? s3 file contains only the required fields from each run.Hence there is no issue reading the dynamic fields from s3 as it is taken care by dataframe.The issue is how can we generate SQL dataframe-API/spark-SQL code to handle.
I can read s3 file via dataframe and register the dataframe as createOrReplaceTempView to write SQL, but I dont think it helps manually changing the spark-SQL, during addition of a new field in s3 during next run. what is the best way to dynamically generate the sql/any better ways to handle the issue?
Usecase-1:
First-run
dataframe: customer,1st_month_count (here dataframe directly points to s3, which has only required attributes)
--sample code
SELECT customer,sum(month_1_count)
FROM dataframe
GROUP BY customer
--Dataframe API/SparkSQL
dataframe.groupBy("customer").sum("month_1_count").show()
Second-Run - One additional column was added
dataframe: customer,month_1_count,month_2_count) (here dataframe directly points to s3, which has only required attributes)
--Sample SQL
SELECT customer,sum(month_1_count),sum(month_2_count)
FROM dataframe
GROUP BY customer
--Dataframe API/SparkSQL
dataframe.groupBy("customer").sum("month_1_count","month_2_count").show()
Im new to Spark/Scala, would be helpful if you can provide the direction so that I can explore further.
It sounds like you want to perform the same operation over and over again on new columns as they appear in the dataframe schema? This works:
from pyspark.sql import functions
#search for column names you want to sum, I put in "month"
column_search = lambda col_names: 'month' in col_names
#get column names of temp dataframe w/ only the columns you want to sum
relevant_columns = original_df.select(*filter(column_search, original_df.columns)).columns
#create dictionary with relevant column names to be passed to the agg function
columns = {col_names: "sum" for col_names in relevant_columns}
#apply agg function with your groupBy, passing in columns dictionary
grouped_df = original_df.groupBy("customer").agg(columns)
#show result
grouped_df.show()
Some important concepts can help you to learn:
DataFrames have data attributes stored in a list: dataframe.columns
Functions can be applied to lists to create new lists as in "column_search"
Agg function accepts multiple expressions in a dictionary as explained here which is what I pass into "columns"
Spark is lazy so it doesn't change data state or perform operations until you perform an action like show(). This means writing out temporary dataframes to use one element of the dataframe like column as I do is not costly even though it may seem inefficient if you're used to SQL.
The Copy Data activity in Azure Data Factory appears to be limited to copying to only a single destination table. I have a spreadsheet containing rows that should be expanded out to multiple tables which reference each other - what would be the most appropriate way to achieve that in Data Factory?
Would multiple copy tasks running sequentially be able to perform this task, or does it require calling a custom stored procedure that would perform the inserts? Are there other options in Data Factory for transforming the data as described above?
If the columnMappings in your source and sink dataset don't against the error conditions mentioned in this link,
1.Source data store query result does not have a column name that is specified in the input dataset "structure" section.
2.Sink data store (if with pre-defined schema) does not have a column name that is specified in the output dataset "structure" section.
3.Either fewer columns or more columns in the "structure" of sink dataset than specified in the mapping.
4.Duplicate mapping.
you could connect the copy activities in series and execute them sequentially.
Another solution is Stored Procedure which could meet your custom requirements.About configuration,please refer to my previous detailed case:Azure Data Factory mapping 2 columns in one column
I have some data which I need to pivot in Talend. This is a sample:
brandname,metric,value
A,xyz,2
B,xyz,2
A,abc,3
C,def,1
C,ghi,6
A,ghi,1
Now I need this data to be pivoted on the metric column like this:
brandname,abc,def,ghi,xyz
A,3,null,1,2
B,null,null,null,2
C,null,1,6,null
Currently I am using tPivotToColumnsDelimited to pivot the data to a file and reading back from that file. However having to store data on an external file and reading back is messy and unnecessary overhead.
Is there a way to do this with Talend without writing to an external file? I tried to use tDenormalize but as far as I understand, it will return the rows as 1 column which is not what I need. I also looked for some 3rd party component in TalendExchange but couldn't find anything useful.
Thank you for your help.
Assuming that your metrics are fixed, you can use their names as columns of the output. The solution to do the pivot has two parts: first, a tMap that transposes the value of each input-row in into the corresponding column in the output-row out and second, a tAggregate that groups the map's output-rows according to the brandname.
For the tMap you'd have to fill the columns conditionally like this, example for output colum named "abc":
out.abc = "abc".equals(in.metric)?in.value:null
In the tAggregate you'd have to group by out.brandname and aggregate each column as sum ignoring nulls.
Our cluster has Spark 1.3 and Hive
There is a large Hive table that I need to add randomly selected rows to.
There is a smaller table that I read and check a condition, if that condition is true, then I grab the variables I need to then query for the random rows to fill. What I did was do a query on that condition, table.where(value<number), then make it an array by using take(num rows). Then since all of these rows contain the information I need on which random rows are needed from the large hive table, I iterate through the array.
When I do the query I use ORDER BY RAND() in the query (using sqlContext). I created a var Hive table ( to be mutable) adding a column from the larger table. In the loop, I do a unionAll newHiveTable = newHiveTable.unionAll(random_rows)
I have tried many different ways to do this, but am not sure what is the best way to avoid CPU and temp disk use. I know that Dataframes aren't intended for incremental adds.
One thing I have though now to try is to create a cvs file, write the random rows to that file incrementally in the loop, then when the loop is finished, load the cvs file as a table, and do one unionAll to get my final table.
Any feedback would be great. Thanks
I would recommend that you create an external table with hive, defining the location, and then let spark write the output as csv to that directory:
in Hive:
create external table test(key string, value string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ';'
LOCATION '/SOME/HDFS/LOCATION'
And then from spark with the aide of https://github.com/databricks/spark-csv , write the dataframe to csv files and appending to the existing ones:
df.write.format("com.databricks.spark.csv").save("/SOME/HDFS/LOCATION/", SaveMode.Append)
I currently have a redshift table in our database that has 10 columns, and I want to add another. It's trivial to do an alter table to do this.
My question - When I do this, will all my old CSV files fail to insert into redshift (via COPY from S3) given they won't have this new column?
I was hoping the columns would just be NULL vs. it failing on import, but I haven't seen any documentation on this.
Ideally I wish I could specify the actual column name in the header row of the CSV, but I haven't seen if that is possible anywhere.
FILLRECORD in COPY command does that: 'Allows data files to be loaded when contiguous columns are missing at the end of some of the records'.