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
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'm extracting information of different source files. Each source file corresponds to a given snapshot time of some measurement data. I have a preprocessing function that takes one of these files and outputs a pandas data frame. So I did a spark sc.wholeTextFiles call, which gave me a list of all input files, and then I called map on it, which provided me with an rdd where each element is a pandas data frame. What would now be the best approach to "reshape" this structure such that I have only one resulting data frame consisting of the concatenated smaller data frames?
You can create spark dataframe. Assuming that these files situated in one location and are delimted you can use spark to create a new dataframe having data from all files.
spark.read.option("header", "true").csv("../location/*")
After that you can use lot of transformations available in spark. They are a lot similar to pandas, and works on bigdata and even faster than RDD.
Which Option gives the best performance with pyspark ? A UDF or RDD processing with a map ?
I'm consuming data with spark Structured streaming and for every micro-batch, I'm converting DF to RDD and doing some python graphkit operations and converting again RDD to DF to write to Kafka stream.
I have generally observed that udf is faster than rdd mapping. Depending on your python version, you can use pandas udf, that is definitely faster. Refer here : https://databricks.com/blog/2017/10/30/introducing-vectorized-udfs-for-pyspark.html
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've researched a little on this and found an answer for general Spark applications. However, in structured streaming, you cannot do a join between 2 streaming dataframes (therefore a self join is not possible) and sorting functions cannot be used as well. So is there a way to get the latest entry for each group at all? (I'm on Spark 2.2)
UPDATE: Assuming the dataframe rows are already sorted by time already, we can just take the last entry using for each required row using groupBy then agg with the pyspark.sql.functions.last function.