I would like to save a huge pyspark dataframe as a Hive table. How can I do this efficiently? I am looking to use saveAsTable(name, format=None, mode=None, partitionBy=None, **options) from pyspark.sql.DataFrameWriter.saveAsTable.
# Let's say I have my dataframe, my_df
# Am I able to do the following?
my_df.saveAsTable('my_table')
My question is which formats are available for me to use and where can I find this information for myself? Is OrcSerDe an option? I am still learning about this. Thank you.
Following file formats are supported.
text
csv
ldap
json
parquet
orc
Referece: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
So I was able to write the pyspark dataframe to a compressed Hive table by using a pyspark.sql.DataFrameWriter. To do this I had to do something like the following:
my_df.write.orc('my_file_path')
That did the trick.
https://spark.apache.org/docs/1.6.0/api/python/pyspark.sql.html#pyspark.sql.DataFrame.write
I am using pyspark 1.6.0 btw
Related
Are there any method to write spark dataframe directly to xls/xlsx format ????
Most of the example in the web showing there is example for panda dataframes.
but I would like to use spark dataframe for working with my data. Any idea ?
I'm assuming that because you have the "databricks" tag you are wanting to create an .xlsx file within databricks file store and that you are running code within databricks notebooks. I'm also going to assume that your notebooks are running python.
There is no direct way to save an excel document from a spark dataframe. You can, however, convert a spark dataframe to a pandas dataframe then export from there. We'll need to start by installing the xlsxwriter package. You can do this for your notebook environment using a databricks utilites command:
dbutils.library.installPyPI('xlsxwriter')
dbutils.library.restartPython()
I was having a few permission issues saving an excel file directly to dbfs. A quick workaround was to save to the cluster's default directory then sudo move the file into dbfs. Here's some example code:
# Creating dummy spark dataframe
spark_df = spark.sql('SELECT * FROM default.test_delta LIMIT 100')
# Converting spark dataframe to pandas dataframe
pandas_df = spark_df.toPandas()
# Exporting pandas dataframe to xlsx file
pandas_df.to_excel('excel_test.xlsx', engine='xlsxwriter')
Then in a new command, specifying the command to run in shell with %sh:
%sh
sudo mv excel_test.xlsx /dbfs/mnt/data/
It is possible to generate an Excel file from pySpark.
df_spark.write.format("com.crealytics.spark.excel")\
.option("header", "true")\
.mode("overwrite")\
.save(path)
You need to install the com.crealytics:spark-excel_2.12:0.13.5 (or a more recent version of course) library though, for example in Azure Databricks by specifying it as a new Maven library in the libraries list of your cluster (one of the buttons on the left sidebar of the Databricks UI).
For more info see https://github.com/crealytics/spark-excel.
I believe you can do it like this.
sourcePropertySet.write
.format("com.databricks.spark.csv")
.option("header", "true")
.save("D:\\resultset.csv")
I'm not sure you can write directly to Excel, but Excel can definitely consume a CSV. This is almost certainly the easiest way of doing this kind of thing and the cleanest as well. In Excel you have all kinds of formatting, which can throw errors when used in some systems (think of merged cells).
You can not save it directly but you can have it as its stored in temp location and move it to your directory. My code piece is:
import xlsxwriter import pandas as pd1
workbook = xlsxwriter.Workbook('data_checks_output.xlsx')
worksheet = workbook.add_worksheet('top_rows')
Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd1.ExcelWriter('data_checks_output.xlsx', engine='xlsxwriter')
output = dataset.limit(10)
output = output.toPandas()
output.to_excel(writer, sheet_name='top_rows',startrow=row_number)
writer.save()
Below code does the work of moving files.
%sh
sudo mv data_checks_output.xlsx /dbfs/mnt/fpmount/
Comment if anyone has new update or better way to do it.
Yet Pyspark does not offer any method to save excel file. But you can save csv file, then it can be read in Excel.
From pyspark.sql module version 2.3 you have write.csv:
df.write.csv('path/filename'))
Documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html?highlight=save
I have got a file in HDFS (/user/username/Project/data/file.xlsx) that I want to read into a DataFrame. (I do not care if it is a PySpark DataFrame or Pandas, but Pandas is preferred.)
I am using a Zeppelin Notebook to do my code.
Is it possible to get data from this file?
I have already tried the following commands, but none of them worked:
df = pd.read_excel("/user/username/Project/data/file.xlsx")
df = pd.read_excel("hdfs:///user/username/Project/data/file.xlsx")
df = pd.read_excel("hdfs://user/username/Project/data/file.xlsx")
I don't think you can read files stored in hdfs directly with pandas.
You probably have to either :
load the file into spark then use toPandas()
df = spark.read.format("excel").load("hdfs:xxx").toPandas()
use some alternative to enable pandas to read directly, as described here
It seems export and import commands in Python Interpreter in Apache Zeppellin can be only realised through "pd.read_csv" and "to_csv" modules.
I have and RDD[String], each String is a hive text format row data, and the hive table is in hive database so I can get the schema, is there way to let spark parse RDD[String] to a DataFrame with the schema so I don't need to it manually.
If in your RDD[String], each string represent a particular structure like (id,name,salary). You can create the case class in scala and convert your RDD[String] to RDD[case class] and then use toDF() function to convert RDD to DataFrame.
If your file is delimited then you can use csv package to create the DataFrame on delimited file if you are using spark 2.x or later. Or if you are using spark 1.6.x or earlier you can use external spark-csv package for the same.
Hope it helps.
Regards,
Neeraj
My data on HDFS is in Sequence file format. I am using PySpark (Spark 1.6) and trying to achieve 2 things:
Data path contains a timestamp in yyyy/mm/dd/hh format that I would like to bring into the data itself. I tried SparkContext.wholeTextFiles but I think that might not support Sequence file format.
How do I deal with the point above if I want to crunch data for a day and want to bring in the date into the data? In this case I would be loading data like yyyy/mm/dd/* format.
Appreciate any pointers.
If stored types are compatible with SQL types and you use Spark 2.0 it is quite simple. Import input_file_name:
from pyspark.sql.functions import input_file_name
Read file and convert to a DataFrame:
df = sc.sequenceFile("/tmp/foo/").toDF()
Add file name:
df.withColumn("input", input_file_name())
If this solution is not applicable in your case then universal one is to list files directly (for HDFS you can use hdfs3 library):
files = ...
read one by one adding file name:
def read(f):
"""Just to avoid problems with late binding"""
return sc.sequenceFile(f).map(lambda x: (f, x))
rdds = [read(f) for f in files]
and union:
sc.union(rdds)
I'm running a Spark Notebook to save a DataFrame as a Parquet File in the Bluemix Object Storage.
I want to overwrite the Parquet File, when rerunning the Notebook. But actually it's just appending the data.
Below a sample of the iPython Code:
df = sqlContext.sql("SELECT * FROM table")
df.write.parquet("swift://my-container.spark/simdata.parquet", mode="overwrite")
I'm not the python guy,but SaveMode work for dataframe like this
df.write.mode(SaveMode.Overwrite).parquet("swift://my-container.spark/simdata.parquet")
I think the blockstorage replace only the 'simdata.parquet' the 'PART-0000*' remains cuz was 'simdata.parquet' with the 'UUID' of app-id, when you try to read, the DF read all files with the 'simdata.parquet*'