I have pandas data frame(sample program), converted koalas dataframe, now I am to execute on spark cluster(windows standalone), when i try from command prompt as
spark-submit --master local hello.py, getting error ModuleNotFoundError: No module named 'databricks'
import pandas as pd
from databricks import koalas as ks
workbook_loc = "c:\\2020\Book1.xlsx"
df = pd.read_excel(workbook_loc, sheet_name='Sheet1')
kdf = ks.from_pandas(df)
print(kdf)
What should I change so that I can make use of spark cluster features. My actual program written in pandas does many things, I want to make use of spark cluster to see performance improvements.
You should install koalas via the cluster's admin UI (Libraries/PyPI), if you run pip install koalas on the cluster, it won't work.
Related
I'm trying to plot a subset of a field from a grib file on google colab. The issue I am finding is that due to google colab using an older version of python I can't get enough libraries to work together to 1.) get a field from the grib file and then 2.) extract a subset of that field by lat/lon, and then 3.) be able to plot with matplotlib/cartopy.
I've been able to do each of the above steps on my own PC and there are numerous answers on this forum already that work away from colab, so the issue is related to making it work on the colab environment, which uses python 3.7.
For simplicity, here are some assumptions that could be made for anybody who wants to help.
1.) Use this file, since its been what I have been trying to use:
https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20221113/conus/hrrr.t18z.wrfnatf00.grib2
2.) You could use any field, but I've been extracting this one (output from pygrib):
14:Temperature:K (instant):lambert:hybrid:level 1:fcst time 0 hrs:from 202211131800
3.) You can get this data in zarr format from AWS, but the grib format uploads to the AWS database faster so I need to use it.
Here are some notes on what I've tried:
Downloading the data isn't an issue, it's mostly relating to extracting the data (by lat lon) that is the main issue. I've tried using condacolab or pip to download pygrib, pupygrib, pinio, or cfgrib. I can then use these to download the data above.
I could never get pupygrib or pinio to even download correctly. Cfgrib I was able to get it to work with conda, but then xarray fails when trying to extract fields due to a library conflict. Pygrib worked the best, I was able to extract fields from the grib file. However, the function grb.data(lat1=30,lat2=40,lon1=-100,lon2-90) fails. It dumps the data into 1d arrays instead of 2d as it is supposed to per the documentation found here: https://jswhit.github.io/pygrib/api.html#example-usage
Here is some code I used for the pygrib set up in case that is useful:
!pip install pyproj
!pip install pygrib
# Uninstall existing shapely
!pip uninstall --yes shapely
!apt-get install -qq libgdal-dev libgeos-dev
!pip install shapely --no-binary shapely
!pip install cartopy==0.19.0.post1
!pip install metpy
!pip install wget
!pip install s3fs
import time
from matplotlib import pyplot as plt
import numpy as np
import scipy
import pygrib
import fsspec
import xarray as xr
import metpy.calc as mpcalc
from metpy.interpolate import cross_section
from metpy.units import units
from metpy.plots import USCOUNTIES
import cartopy.crs as ccrs
import cartopy.feature as cfeature
!wget https://noaa-hrrr-bdp-pds.s3.amazonaws.com/hrrr.20221113/conus/hrrr.t18z.wrfnatf00.grib2
grbs = pygrib.open('/content/hrrr.t18z.wrfnatf00.grib2')
grb2 = grbs.message(1)
data, lats, lons = grb2.data(lat1=30,lat2=40,lon1=-100,lon2=-90)
data.shape
This will output a 1d array for data, or lats and lons. That is as far as I can get here because existing options like meshgrib don't work on big datasets (I tried it).
The other option is to get data this way:
grb_t = grbs.select(name='Temperature')[0]
This is plottable, but I don't know of a way to extract a subset of the data from here using lat/lons.
If you can help, feel free to ask me anything I can add more details, but since I've tried like 10 different ways probably no sense in adding every failure. Really, I am open to any way to accomplish this task. Thank you.
I'm looking for a way to debug spark pandas UDF in vscode and Pycharm Community version (place breakpoint and stop inside UDF). At the moment when breakpoint is placed inside UDF debugger doesn't stop.
In the reference below there is described Local mode and Distributed mode.
I'm trying at least to debug in Local mode. Pycharm/VS Code there should be a way to debug local enc by "Attach to Local Process". Just I can not figure out how.
At the moment I can not find any answer how to attach pyspark debugger to local process inside UDF in VS Code(my dev ide).
I found only examples below in Pycharm.
Attache to local process How can PySpark be called in debug mode?
When I try to attach to process I'm getting message below in Pycharm. In VS Code I'm getting msg that process can not be attached.
Attaching to a process with PID=33,692
/home/usr_name/anaconda3/envs/yf/bin/python3.8 /snap/pycharm-community/223/plugins/python-ce/helpers/pydev/pydevd_attach_to_process/attach_pydevd.py --port 40717 --pid 33692
WARNING: The 'kernel.yama.ptrace_scope' parameter value is not 0, attach to process may not work correctly.
Please run 'sudo sysctl kernel.yama.ptrace_scope=0' to change the value temporary
or add the 'kernel.yama.ptrace_scope = 0' line to /etc/sysctl.d/10-ptrace.conf to set it permanently.
Process finished with exit code 0
Server stopped.
pyspark_xray https://github.com/bradyjiang/pyspark_xray
With this package, it is possible to debug rdds running on worker, but I was not able to adjust package to debug UDFs
Example code, breakpoint doesn't stop inside UDF pandas_function(url_json):
import pandas as pd
import pyspark
from pyspark.sql import Row
from pyspark.sql.types import StructType, StructField, IntegerType,StringType
spark = pyspark.sql.SparkSession.builder.appName("test") \
.master('local[*]') \
.getOrCreate()
sc = spark.sparkContext
# Create initial dataframe respond_sdf
d_list = [('api_1',"{'api': ['api_1', 'api_1', 'api_1'],'A': [1,2,3], 'B': [4,5,6] }"),
(' api_2', "{'api': ['api_2', 'api_2', 'api_2'],'A': [7,8,9], 'B': [10,11,12] }")]
schema = StructType([
StructField('url', StringType(), True),
StructField('content', StringType(), True)
])
jsons = sc.parallelize(rdd_list)
respond_sdf = spark.createDataFrame(jsons, schema)
# Pandas UDF
def pandas_function(url_json):
# Here I want to place breakpoint
df = pd.DataFrame(eval(url_json['content'][0]))
return df
# Pnadas UDF transformation applied to respond_sdf
respond_sdf.groupby(F.monotonically_increasing_id()).applyInPandas(pandas_function, schema=schema).show()
This example demonstrates how to use excellent pyspark_exray library to step into UDF functions passed into Dataframe.mapInPandas function
https://github.com/bradyjiang/pyspark_xray/blob/master/demo_app02/driver.py
I'm trying to convert a Pandas dataframe to a Pyspark dataframe, and getting the following pyarrow-related error:
import pandas as pd
import numpy as np
data = np.random.rand(1000000, 10)
pdf = pd.DataFrame(data, columns=list("abcdefghij"))
df = spark.createDataFrame(pdf)
/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/session.py:714: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true; however, failed by the reason below:
'JavaPackage' object is not callable
Attempting non-optimization as 'spark.sql.execution.arrow.fallback.enabled' is set to true.
I've tried different versions of pyarrow (0.10.0, 0.14.1, 0.15.1 and more) but with the same result. How can I debug this?
I had the same issue, changed the cluster setting to emr-5.30.1 and arrow version to 0.14.1 and it resolved the issue
Can you try upgrading your pyspark to >= 3.0.0? I had the above error with all versions of arrow, but bumping to the newer pyspark fixed it for me.
There is a version conflict with older versions of Spark (ex: 2.4.x) and newer versions of arrow.
Does any one has idea how to run pandas program on spark standalone cluster machine(windows)? the program developed using pycharm and pandas?
Here the issue is i am able to run from command prompt using spark-submit --master spark://sparkcas1:7077 project.py and getting results. but the activity(status) I am not seeing # workers and also Running Application status and Completed application status from spark web UI: :7077
in the pandas program I just included only one statement " from pyspark import SparkContext
import pandas as pd
from pyspark import SparkContext
# reading csv file from url
workbook_loc = "c:\\2020\Book1.xlsx"
df = pd.read_excel(workbook_loc, sheet_name='Sheet1')
# converting to dict
print(df)
What could be the issue?
Pandas code runs only on the driver and no workers are involved in this. So there is no point of using pandas code inside spark.
If you are using spark 3.0 you can run your pandas code distributed by converting the spark df as koalas
I'm doing calculations on a cluster and at the end when I ask summary statistics on my Spark dataframe with df.describe().show() I get an error:
Serialized task 15:0 was 137500581 bytes, which exceeds max allowed: spark.rpc.message.maxSize (134217728 bytes). Consider increasing spark.rpc.message.maxSize or using broadcast variables for large values
In my Spark configuration I already tried to increase the aforementioned parameter:
spark = (SparkSession
.builder
.appName("TV segmentation - dataprep for scoring")
.config("spark.executor.memory", "25G")
.config("spark.driver.memory", "40G")
.config("spark.dynamicAllocation.enabled", "true")
.config("spark.dynamicAllocation.maxExecutors", "12")
.config("spark.driver.maxResultSize", "3g")
.config("spark.kryoserializer.buffer.max.mb", "2047mb")
.config("spark.rpc.message.maxSize", "1000mb")
.getOrCreate())
I also tried to repartition my dataframe using:
dfscoring=dfscoring.repartition(100)
but still I keep on getting the same error.
My environment: Python 3.5, Anaconda 5.0, Spark 2
How can I avoid this error ?
i'm in same trouble, then i solve it.
the cause is spark.rpc.message.maxSize if default set 128M, you can change it when launch a spark client, i'm work in pyspark and set the value to 1024, so i write like this:
pyspark --master yarn --conf spark.rpc.message.maxSize=1024
solve it.
I had the same issue and it wasted a day of my life that I am never getting back. I am not sure why this is happening, but here is how I made it work for me.
Step 1: Make sure that PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.
Turned out that python in worker(2.6) had a different version than in driver(3.6). You should check if environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.
I fixed it by simply switching my kernel from Python 3 Spark 2.2.0 to Python Spark 2.3.1 in Jupyter. You may have to set it up manually. Here is how to make sure your PySpark is set up correctly https://mortada.net/3-easy-steps-to-set-up-pyspark.html
STEP 2: If that doesn't work, try working around it:
This kernel switch worked for DFs that I haven't added any columns to:
spark_df -> panda_df -> back_to_spark_df .... but it didn't work on the DFs where I had added 5 extra columns. So what I tried and it worked was the following:
# 1. Select only the new columns:
df_write = df[['hotel_id','neg_prob','prob','ipw','auc','brier_score']]
# 2. Convert this DF into Spark DF:
df_to_spark = spark.createDataFrame(df_write)
df_to_spark = df_to_spark.repartition(100)
df_to_spark.registerTempTable('df_to_spark')
# 3. Join it to the rest of your data:
final = df_to_spark.join(data,'hotel_id')
# 4. Then write the final DF.
final.write.saveAsTable('schema_name.table_name',mode='overwrite')
Hope that helps!
I had the same problem but using Watson studio. My solution was:
sc.stop()
configura=SparkConf().set('spark.rpc.message.maxSize','256')
sc=SparkContext.getOrCreate(conf=configura)
spark = SparkSession.builder.getOrCreate()
I hope it help someone...
I had faced the same issue while converting the sparkDF to pandasDF.
I am working on Azure-Databricks , first you need to check the memory set in the spark config using below -
spark.conf.get("spark.rpc.message.maxSize")
Then we can increase the memory-
spark.conf.set("spark.rpc.message.maxSize", "500")
For those folks, who are looking for AWS Glue script pyspark based way of doing this. The below code snippet might be useful
from awsglue.context import GlueContext
from pyspark.context import SparkContext
from pyspark import SparkConf
myconfig=SparkConf().set('spark.rpc.message.maxSize','256')
#SparkConf can be directly used with its .set property
sc = SparkContext(conf=myconfig)
glueContext = GlueContext(sc)
..
..