I have a PySpark DataFrame. I want to perform some function forearchPartition and then save each result to Hive. The result is a pandas dataframe (within each partition). What is the best way to do this?
I have tried the following without success (gives a serialization error):
def processData(x):
#do something
spark_df = spark.createDataFrame(pandas_df)
spark_df.write.mode("append").format("parquet").saveAsTable(db.table_name)
original_spark_df.rdd.forearchPartition(processData)
I guess, one solution would be to turn pandas into RDD and return it (using mapPartitions instead of forearchPartition), and then use rdd.toDF() and saveAsTable().
Is there some solution to save the pandas to Hive within forearchPartition?
Related
The following code will convert an Apache Spark DataFrame to a Great_Expectations DataFrame. For if I wanted to convert the Spark DataFrame, spkDF to a Great_Expectations DataFrame I would do the following:
ge_df = SparkDFDataset(spkDF)
Can someone let me know how convert a Great_Expectation dataframe to a Spark DataFrame.
So what would I need to do to convert the new Great_Expectations dataframe ge_df back to Spark DataFrame?
According to the official documentation, the class SparkDFDataset holds the original pyspark dataframe:
This class holds an attribute spark_df which is a spark.sql.DataFrame.
So you should be able to access it with :
ge_df.spark_df
I converted parquet file to pandas without issue but had issue converting parquet to spark df and converting spark df to pandas.
after creating a spark session, I ran these code
spark_df=spark.read.parquet('summarydata.parquet')
spark_df.select('*').toPandas()
It returns error
Alternatively, with a parquet object (pd.read_table('summary data.parquet'), how can I convert it to spark df?
The reason I need both spark df and pandas df is that for some smaller DataFrame, I wanna easily use various pandas EDA function, but for some bigger ones I need to use spark sql. And turning parquet to pandas first then to spark df seems a bit of detour.
To convert a Pandas Dataframe into Spark dataframe and viceversa, you will have to use pyarrow which is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and Python processes.
Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call toPandas() and when creating a Spark DataFrame from a Pandas DataFrame with createDataFrame(pandas_df). To use Arrow when executing these calls, users need to first set the Spark configuration spark.sql.execution.arrow.enabled to true. This is disabled by default.
In addition, optimizations enabled by spark.sql.execution.arrow.enabled could fallback automatically to non-Arrow optimization implementation if an error occurs before the actual computation within Spark. This can be controlled by spark.sql.execution.arrow.fallback.enabled.
For more details refer this link PySpark Usage Guide for Pandas with Apache Arrow
import numpy as np
import pandas as pd
# Enable Arrow-based columnar data transfers
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
# Generate a Pandas DataFrame
pdf = pd.DataFrame(np.random.rand(100, 3))
# Create a Spark DataFrame from a Pandas DataFrame using Arrow
df = spark.createDataFrame(pdf)
# Convert the Spark DataFrame back to a Pandas DataFrame using Arrow
result_pdf = df.select("*").toPandas()
I know that you can convert a spark dataframe df into a pandas dataframe with
df.toPandas()
However, this is taking very long, so I found out about a koala package in databricks that could enable me to use the data as a pandas dataframe (for instance, being able to use scikit learn) without having a pandas dataframe. I already have the spark dataframe, but I cannot find a way to make it into a Koalas one.
To go straight from a pyspark dataframe (I am assuming that is what you are working with) to a koalas dataframe you can use:
koalas_df = ks.DataFrame(your_pyspark_df)
Here I've imported koalas as ks.
Well. First of all, you have to understand the reason why toPandas() takes so long :
Spark dataframe are distributed in different nodes and when you run toPandas()
It will pull the distributed dataframe back to the driver node (that's the reason it takes long time)
you are then able to use pandas, or Scikit-learn in the single(Driver) node for faster analysis and modeling, because it's like your modeling on your own PC
Koalas is the pandas API in spark and when you convert it to koalas dataframe : It's still distributed, so it will not shuffle data between different nodes, so you can use pandas' similar syntax for distributed dataframe transformation
I'm using pySpark 2.3, trying to read a csv file that looks like that:
0,0.000476517230863068,0.0008178378961061477
1,0.0008506156837329876,0.0008467260987257776
But it doesn't work:
from pyspark import sql, SparkConf, SparkContext
print (sc.applicationId)
>> <property at 0x7f47583a5548>
data_rdd = spark.textFile(name=tsv_data_path).filter(x.split(",")[0] != 1)
And I get an error:
AttributeError: 'SparkSession' object has no attribute 'textFile'
Any idea how I should read it in pySpark 2.3?
First, textFile exists on the SparkContext (called sc in the repl), not on the SparkSession object (called spark in the repl).
Second, for CSV data, I would recommend using the CSV DataFrame loading code, like this:
df = spark.read.format("csv").load("file:///path/to/file.csv")
You mentioned in comments needing the data as an RDD. You are going to have significantly better performance if you can keep all of your operations on DataFrames instead of RDDs. However, if you need to fall back to RDDs for some reason you can do it like the following:
rdd = df.rdd.map(lambda row: row.asDict())
Doing this approach is better than trying to load it with textFile and parsing the CSV data yourself. If you use the DataFrame CSV loading then it will properly handle all the CSV edge cases for you like quoted fields. Also if only needed some of the columns, you could filter on the DataFrame before converting it to a RDD to avoid needing to bring all that extra data over into the python interpreter.
Currently, I can parse a text file to a Spark DataFrame by way of the RDD API with the following code:
def row_parse_function(raw_string_input):
# Do parse logic...
return pyspark.sql.Row(...)
raw_rdd = spark_context.textFile(full_source_path)
# Convert RDD of strings to RDD of pyspark.sql.Row
row_rdd = raw_rdd.map(row_parse_function).filter(bool)
# Convert RDD of pyspark.sql.Row to Spark DataFrame.
data_frame = spark_sql_context.createDataFrame(row_rdd, schema)
Is this current approach ideal?
Or is there a better way to do this without using the older RDD API.
FYI, Spark 2.0.
Clay,
This is a good approach to load a file that has not specific format instead CSV, JSON, ORC, Parquet or from Database.
If you have any kind of specific logic to work on it, this is the best way to do that. Using RDD is for this kind of situation, when you need to run a specific logic in your data that is not trivial.
You can read here about the uses of the APIs of Spark. And you are in the situation of RDD is the best approach.