Is there an equivalent to Databricks' DBFS FileStore system in Azure Synapse? Is it possible to upload csv files and read them into pandas dataframes within Azure Synapse notebooks? Ideally I'd like to not load the csv into a database; looking for something as simple as DBFS' FileStore folder.
In Databricks: pd.read_csv('/dbfs/FileStore/name_of_file.csv')
In Synapse: ?
I don't see anywhere to upload csv files directly like in DBFS:
The azure synapse equivalent of using FileStore in Databricks would be to use the data lake file system linked to your synapse workspace. Once you go to your synapse studio, navigate to Data->Linked where you can find the linked storage account. This storage account was created/assigned when you create your workspace.
This primary data lake functions close to the FileStore in azure Databricks. You can use the UI shown in the above image to upload required files. You can right click on any of the files and load it into a Dataframe. As you can see in the image below, you can right click on the file and then choose new notebook -> Load to DataFrame.
The UI automatically provides a code which helps to load the csv file to a spark Dataframe. You can modify this code to load the file as a pandas Dataframe.
'''
#this is provided by synapse when you select file and choose to load to Dataframe
df = spark.read.load('abfss://data#datalk1506.dfs.core.windows.net/sample_1.csv', format='csv'
## If header exists uncomment line below
##, header=True
)
display(df.limit(10))
'''
#Use this following code to load as pandas dataframe
import pandas as pd
df = pd.read_csv('abfss://data#datalk1506.dfs.core.windows.net/sample_1.csv')
This data lake storage will be linked to the workspace with the help of the linked service (Can be viewed in Manage->Linked services). This is created by default from the data lake and file system information provided by the user (mandatory) while creating the synapse workspace.
Related
My question is around : storing Azure blob key in config file.
Below is the picture of my package overview. I'm trying to pull data from Oracle source and put a flat file on Azure blob storage (in csv format). That is the scope of this SSIS package.
Right side of picture shares that I can execute the package via commandline if **Protection level =' EncryptSensistiveWithUserKey' **
But NOW packages need to be run via a service account and not developer, architects accounts.
Looping back into my question: HOW do I store blob key in config file by setting the **Protection level = 'DONOTSAVESENSITIVE'** . Config file code after the package overview.
Config File currently being used
<?xml version="1.0"?><DTSConfiguration><DTSConfigurationHeading><DTSConfigurationFileInfo GeneratedBy="BI\MonkeyMan" GeneratedFromPackageName="Package" GeneratedFromPackageID="{9BDF0000-CAC9-4823-A6D8-EE59C3BB31A0}" GeneratedDate="4/6/2020 6:48:57 PM"/></DTSConfigurationHeading><Configuration ConfiguredType="Property" Path="\Package.Connections[Prod v18].Properties[ConnectionString]" ValueType="String">
<ConfiguredValue>SERVER=1.1.1.01:1521/DB;USERNAME=MonkeyMan;WINAUTH=0;data source=1.1.1.01:1521/DB;user id=MonkeyMan;password=isharemypasswords;
</ConfiguredValue></Configuration></DTSConfiguration>
Finally picture of when I try to run the package via dtexec with protection level set to DO NOT SAVE SENSITIVE.
Thanks for all your help around this.
The easiest way might be to use the builtin config-editor and select the properties you want to save to the config file.
Here's one tutorial to set it up: https://www.tutorialgateway.org/ssis-package-configuration-using-xml-configuration-file/
I would like write reqular CSV file to Storage, but what I get is folder "sample_file.csv" and 4 files under it. How to create normal csv file from data frame to Azure Storage Gen2?
I'm happy with any advice or link to article.
df.coalesce(1).write.option("header", "true").csv(TargetDirectory + "/sample_file.csv")
I am trying to write data to a csv files and store the file on Azure Data Lake Gen2 and run into job aborted error message. This same code used to work fine previously.
Error Message:
org.apache.spark.SparkException: Job aborted.
Code:
import requests
response = requests.get('https://myapiurl.com/v1/data', auth=('user', 'password'))
data = response.json()
from pyspark.sql import *
df=spark.createDataFrame([Row(**i) for i in data])
df.write.format(source).mode("overwrite").save(path) #error line
I summarize the solution below
If you want to access Azure data lake gen2 in Azure databricks, you have two choices to do that.
Mount Azure data lake gen2 as Azure databricks's file system. After doing that, you can read and write files with the path /mnt/<>. And We just need to run the code one time.
a. Create a service principal and assign Storage Blob Data Contributor to the sp in the scope of the Data Lake Storage Gen2 storage account
az login
az ad sp create-for-rbac -n "MyApp" --role "Storage Blob Data Contributor" \
--scopes /subscriptions/<subscription>/resourceGroups/<resource-group>/providers/Microsoft.Storage/storageAccounts/<storage-account>
b. code
configs = {"fs.azure.account.auth.type": "OAuth",
"fs.azure.account.oauth.provider.type": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
"fs.azure.account.oauth2.client.id": "<appId>",
"fs.azure.account.oauth2.client.secret": "<clientSecret>",
"fs.azure.account.oauth2.client.endpoint": "https://login.microsoftonline.com/<tenant>/oauth2/token",
"fs.azure.createRemoteFileSystemDuringInitialization": "true"}
dbutils.fs.mount(
source = "abfss://<container-name>#<storage-account-name>.dfs.core.windows.net/folder1",
mount_point = "/mnt/flightdata",
extra_configs = configs)
Access directly using the storage account access key.
We can add the code spark.conf.set( "fs.azure.account.key.<storage-account-name>.dfs.core.windows.net", "<storage-account-access-key-name>") to our script. Then we can read and write files with path abfss://<file-system-name>#<storage-account-name>.dfs.core.windows.net/.
for example
from pyspark.sql.types import StringType
spark.conf.set(
"fs.azure.account.key.testadls05.dfs.core.windows.net", "<account access key>")
df = spark.createDataFrame(["10", "11", "13"], StringType()).toDF("age")
df.show()
df.coalesce(1).write.format('csv').option('header', True).mode('overwrite').save('abfss://test#testadls05.dfs.core.windows.net/result_csv')
For more details, please refer to here
I am running a Hive QL through HD Insight on-demand cluster which does the following
Spool the data from a hive view
Create a folder by name abcd inside a Blob storage container
named XYZ
Store the view data in a file inside the abcd folder
However, when the hive QL is run, there is an empty file with the name abcd that is getting created outside the abcd folder
Any idea why this is happening and how do we stop it from happening. Please suggest
Thanks,
Surya
You get this because the Azure storage you are mounting does not have a hierarchical file system. For example, the mount is a blob storage of type StorageV2 but you have not ticked the Use hierarchical filesystem at creation time. A version 2 blob with hierarchical file system is known as Azure Data Lake Storage generation 2 (ADLS Gen2), where they basically get rid of the blob - lake difference you had with ADLS Gen 1 vs older blob generations.
According to the blob API you are using, a number of tricks are used to give you the illusion of a hierarchical FS even when you don't have one. Like creating empty files, or hidden ones. The main is that the hierarchy is flat (i.e. there is none), so you can't just create an empty folder, you have to put something there.
For example, if you mount a v2 blob with the wasbs:// driver in Databricks, and you do a mkdir -p /dbfs/mnt/mymount/this/is/a/path from a %sh cell you will see something like this:
this folder, this empty file
this/is folder, this/is empty file
etc.
Finally, while this is perfectly file for Azure blob itself, it might cause trouble to anything else not expecting it, even %sh ls.
Just recreate the storage as ADLS Gen2, or update it live enabling the hierarchical FS.
Thanks,
Can any1 provide me an Idea, How to implement unzipping of .gz format file through Worker. If i try to write unzipping of file then, where i need to store unzipped file(i.e one text file
) , Will it be loaded in any location in azure. how can i specify the path in Windows Azure Worker process like current execting directory. If this approach doesnot work, then i need to create one more blob to store unzipped .gz file i.e txt.
-mahens
In your Worker Role, it is up to you how a .gz file arrive (downloaded from Azure Blob storage) however on the file is available you can use GZipStream to compress/uncompress a .GZ file. You can also find code sample in above link with Compress and Decompress function.
This SO discussion shares a few tools and code to explain how you can unzip .GZ using C#:
Unzipping a .gz file using C#
Next when you will use Decompress/Compress code in a Worker Role you have ability to store it directly to local storage (as suggested by JcFx) or use MemoryStream to store directly to Azure Blob Storage.
The following SO article shows how you can use GZipStream to store unzipped content into MemoryStream and then use UploadFromStream() API to store directly to Azure Blob storage:
How do I use GZipStream with System.IO.MemoryStream?
If you don't have any action related to your unzipped file then storing directly to Azure Blob storage is best however if you have to do something with unzipped content you can save locally as well as storage to Azure Blob storage back for further usage.
This example, using SharpZipLib, extracts a .gzip file to a stream. From there, you could write it to Azure local storage, or to blob storage:
http://wiki.sharpdevelop.net/GZip-and-Tar-Samples.ashx