I want to read zip files that have csv files. I have tried many ways but I have not succeeded. In my case, the path where I should read the file is in Azure Storage Explorer.
For example, when I have to read a csv in databricks I use the following code:
dfDemandaBilletesCmbinad = spark.read.csv("/mnt/data/myCSVfile.csv", header=True)
So, the Azure Storage path that I want is "/mnt/data/myZipFile.zip" , which inside I have some csv files.
Is it possible to read csv files coming from Azure storage via pySpark in databricks?
I think that the only way to do this is with Pandas, openpyxl and zip library for python, as there're no similar library for pySpark.
import pandas as pd
import openpyxl, zipfile
#Unzip and extract in file. Maybe, could be better to unzip in memory with StringIO.
with zipfile.ZipFile('/dbfs/mnt/data/file.zip', 'r') as zip_ref:
zip_ref.extractall('/dbfs/mnt/data/unzipped')
#read excel
my_excel = openpyxl.load_workbook('/dbfs/mnt/data/unzipped/file.xlsx')
ws = my_excel['worksheet1']
# create pandas dataframe
df = pd.DataFrame(ws.values)
# create spark dataframe
spark_df = spark.createDataFrame(df)
The problem is that this only is being executed in the driver VM of the cluster.
Please keep in mind that the Azure Storage Explorer does not store any data. It's a tool that lets you access your Azure storage account from any device and on any platform. Data always stored in an Azure storage account.
In your scenario, it appears that your Azure storage account is already mounted to the Databricks DBFS file path. Since it is mounted, you can use spark.read command access the file directly from Azure storage account
Sample df = spark.read.text("dbfs:/mymount/my_file.txt")
Reference: https://docs.databricks.com/data/databricks-file-system.html
and regarding ZIP file please refer
https://learn.microsoft.com/en-us/azure/databricks/_static/notebooks/zip-files-python.html
Related
Unity Catalog have recently been set up in my databricks account, and I am trying to stream from an Azure container containing parquet files to a service catalog, using a notebook that ran before.
I do however now get the following
py4j.security.Py4JSecurityException: Method public org.apache.spark.sql.streaming.DataStreamReader org.apache.spark.sql.streaming.DataStreamReader.format(java.lang.String) is not whitelisted on class class org.apache.spark.sql.streaming.DataStreamReader
when trying to run the following spark command from my Notebook:
df = (spark
.readStream
.format("cloudFiles")
.option("cloudFiles.format", "parquet")
.option("cloudFiles.useNotifications", "false") # useNotifications determines if we efficiently scan the new files or if we set up a subscription to listen to new file events
.option("cloudFiles.schemaEvolutionMode", "failOnNewColumns") # schemaEvolutionMode determines what happens when the schema changes
.option("cloudFiles.schemaLocation", schemaPath)
.load(dataPath)
)
where schemaPathand dataPath contain the paths to the parquet schema and data files.
The closest related error I have found is the following pre-Unity Catalog error, suggesting that I should disable table access control on my clusters:
https://kb.databricks.com/en_US/streaming/readstream-is-not-whitelisted
All table access control are disabled in my Admin Console.
Are there some other settings that should be set to ensure white-listing from Azure files now that Unity Catalog is set up?
------ Edit -----
Using a Single User cluster on Databricks runtime version 11.3 beta, I get the following error instead:
com.databricks.sql.cloudfiles.errors.CloudFilesIOException: Failed to write to the schema log at location
followed by the location to the azure schema in my storage location. I also get this error message by spawning new job clusters from azure datafactory.
I am trying to parse Json data with multi nested level. I am using the approach is giving filename and using open(File-name) to load the data. when I am providing datalake path, it is throwing error that file path not found. I am able to read data in dataframes but How can I read file from data lake without converting to dataframes and reading it as a file and open it?
Current code approach on local machine which is working:
f = open(File_Name.Json)
data = json.load(f)
Failing scenario when provding datalake path:
f = open(Datalake path/File_Name.Json)
data = json.load(f)
You need to mount the data lake folder to a location in dbfs (in Databricks), although mounting is a security risk. Anyone with access to Databricks resource will have access to all mounted locations.
Documentation on mounting to dbfs: https://docs.databricks.com/data/databricks-file-system.html#mount-object-storage-to-dbfs
The open function works only with local files, not understanding (out of box) the cloud file paths. You can of course try to mount the cloud storage, but as it was mentioned by #ARCrow, it would be a security risk (until you create so-called passthrough mount that will control access on the cloud storage level).
But if you're able to read file into dataframe, then it means that cluster has all necessary settings for accessing the cloud storage - in this case you can just use dbutils.fs.cp command to copy file from the cloud storage to local disk, and then open it with open function. Something like this:
dbutils.fs.cp("Datalake path/File_Name.Json", "file:///tmp/File_Name.Json")
with open("/tmp/File_Name.Json", "r") as f:
data = json.load(f)
I'm looking to export Azure Monitor data from Log Analytics to a storage account and the read the JSON files into Databricks using PySpark.
The blob path for the Log Analytics export contains an equals (=) sign and Databricks throws and exception when using the path.
WorkspaceResourceId=/subscriptions/subscription-id/resourcegroups/<resource-group>/providers/microsoft.operationalinsights/workspaces/<workspace>/y=<four-digit numeric year>/m=<two-digit numeric month>/d=<two-digit numeric day>/h=<two-digit 24-hour clock hour>/m=<two-digit 60-minute clock minute>/PT05M.json
Log Analytics Data Export
Is there a way to escape the equals sign so that the JSON files can be loaded from the blob location?
I tried the similar use case referring from Microsoft Documentation, below are the steps:
Mount the storage container. We can do it with python code as below, make sure you pass all the parameters correct, because incorrect parameters will lead to multiple different errors.
dbutils.fs.mount(
source = "wasbs://<container-name>#<storage-account-name>.blob.core.windows.net",
mount_point = "/mnt/<mount-name>",
extra_configs = {"<conf-key>":dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>")})
Below are the parameters description:
<storage-account-name> is the name of your Azure Blob storage account.
<container-name> is the name of a container in your Azure Blob storage account.
<mount-name> is a DBFS path representing where the Blob storage container or a folder inside the container (specified in source) will be mounted in DBFS.
<conf-key> can be either fs.azure.account.key.<storage-account-name>.blob.core.windows.net or fs.azure.sas.<container-name>.<storage-account-name>.blob.core.windows.net
dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>") gets the key that has been stored as a secret in a secret scope.
Then you can access those files as below:
df = spark.read.text("/mnt/<mount-name>/...")
df = spark.read.text("dbfs:/<mount-name>/...")
Also there are multiple ways in accessing the file, all of these were mentioned clearly in the doc.
And check this Log Analytics workspace doc to understand about exporting the data to Azure Storage.
I have ADLS Gen2 Datalake with "Blob Containers" and "File Shares". I have mounted the Blob containers in my Databricks notebook, so I can read everything there inside my databricks notebooks.
I also have some files in the "File Share", but I am not able to read these files into a dataframe thorugh Databricks using pyspark.
I have created an Access Signature for the File share and I have got the url for one of the files inside the Share as well. That url works fine through Postman. I can download that file using the url.
The sample url is shown below:
https://somedatalakename.file.core.windows.net/file_share_name/Data_20200330_1030.csv?sv=yyyy-mm-dd&si=somename&sr=s&sig=somerandomsignature%3D
How to read the same csv, which is inside this file share, into a dataframe through databricks using pyspark?
I also tried
from pyspark import SparkFiles
spark.sparkContext.addFile(uri)
call_df = spark.read.format("csv").option("header", "true").load("file://" + SparkFiles.get("Data_" + date_str + "_1030.csv"))
And I get the below error:
org.apache.spark.sql.AnalysisException: Path does not exist: file:/local_disk0/spark-ce42ed1b-5d82-4559-9000-d1bf3621539e/userFiles-eaf0fd36-68aa-409e-8610-a7909635b006/Data_20200330_1030.csv
Please give me some pointers on how to solve this problem. Thanks.
The problem with your load syntax. file: does not works in Databricks so you need to replace it with dbfs i.e. Databricks file system.
Command to load the file:
spark.read.format("csv").option("header","true").load(f"dbfs:/path/to/your/directory/FileName.csv")
I have a zip file containing a relatively large dataset (1Gb) stored in a zip file in Google Cloud Storage instance.
I need to use Notebook hosted in Google Cloud Datalab to access that file and the data contained there. How do I go about this?
Thank you.
Can you try the following?
import pandas as pd
# Path to the object in Google Cloud Storage that you want to copy
sample_gcs_object = 'gs://path-to-gcs/Hello.txt.zip'
# Copy the file from Google Cloud Storage to Datalab
!gsutil cp $sample_gcs_object 'Hello.txt.zip'
# Unzip the file
!unzip 'Hello.txt.zip'
# Read the file into a pandas DataFrame
pandas_dataframe = pd.read_csv('Hello.txt')