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)
Related
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 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
My program relies on local system storage to write a file that is being generated by the program itself. Hence executing the job in "DirectPipelineRunner" mode. Below is the flow,
One of my function - Makes multiple REST API requests and creates/appends to a file(Output.txt) in local system storage.
Pipeline: a) Upload generated file to GCS 2) Read the file from GCS c) Perform transformation d) Write to BigQuery.
Since, my program writes/appends API response to local system storage, I'm executing the pipeline in DirectPipelineRunner mode.
Is it possible to have temporary space in cloud to remove dependency on local file system So that I can execute the pipleline in DataflowPipelineRunner mode?
I guess Custom Sources and Sinks can be used here. Can someone add some light on this problem statement?
How can I move a file from one bucket to another with the Cloud Storage Java API? I can find examples of file creation but not copying or deletion - and I imagine I'd have to copy the file and delete it in order to execute a move from one bucket to another.
You're correct. Do the copy and then delete the original after. There are some examples on GitHub. Here's the gist of it:
CopyWriter copyWriter = originalBlob.copyTo(BlobId.of(bucketName, blobName));
Blob copiedBlob = copyWriter.getResult();
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')