spark.read.format("delta").load("/tmp/delta/people10m")
How to give correct path to adls delta folder?
spark.read.format("delta").load("/tmp/delta/people10m")
tried above command, but not working
if you mean the direct access to ADLS, then you need to give a full path, like, abfss://<container>#<storage-acct>.dfs.core.windows.net/<path> - just following documentation about it (there are different authentication options available, so select what works for you).
Related
I am facing with one problem in Azure Databricks. In my notebook I am executing simple write command with partitioning:
df.write.format('parquet').partitionBy("startYear").save(output_path,header=True)
And I see something like this:
Can someone explain why spark is creating this additional empty files for every partition and how to disable it?
I tried different mode for write, different partitioning and spark versions
I reproduced the above and got the same results when I use Blob Storage.
Can someone explain why spark is creating this additional empty files
for every partition and how to disable it?
Spark won't create these types of files. Blob Storage creates the blobs like above when we create parquet files by partitions.
We cannot avoid these if you use Blob Storage. You can avoid it by using ADLS Storage.
These are my Results with ADLS:
I'm currently using databricks and in order to test my databricks code I'm using databricks connect in VS code. While I'm using databricks connect, since yesterday suddenly it started behaving strange, while I'm submitting a code from VS Code, the databricks-connect is taking the access of the person who has created the databricks cluster, now he doesn't have adequate access over all the resources and my test cases/ code is failing due to access issue.
Some more inputs: I have a function which does an update operation in a delta table, so this means i can't use a normal hive or temp table as they doesn't support update operation.
I have tried delta-lake in local, but that seems tobe not working and also not that convenient, as i wouldn't be able to access my ADLs location (until i specifically do the configuration change)
So, mu question is , how you guys are doing a spark specific testing? (I'm using pytest).
I found we don't have much material for a databricks code testing in the internet..any help?
Using Python-3, I am trying to compare an Excel (xlsx) sheet to an identical spark table in Databricks. I want to avoid doing the compare in Databricks. So I am looking for a way to read the spark table via the Databricks api. Is this possible? How can I go on to read a table: DB.TableName?
There is no way to read the table from the DB API as far as I am aware unless you run it as a job as LaTreb already mentioned. However, if you really wanted to, you could use either the ODBC or JDBC drivers to get the data through your databricks cluster.
Information on how to set this up can be found here.
Once you have the DSN set up you can use pyodbc to connect to databricks and run a query. At this time the ODBC driver will only allow you to run Spark-SQL commands.
All that being said, it will probably still be easier to just load the data into Databricks, unless you have some sort of security concern.
I can recomend you write pyspark code in notebook, call the notebook from previously defined job, and establish connection between your local machine and databricks workspace.
You could perfom comaprision directly on spark or convert data frames to pandas if you wish. If noteebok will end comaprision, could retrun result from particular job. I think that sending all databricks tables could be impossible because of API limitation you have spark cluster to perform complex operation, API should be use to send small messages.
Officical documentation:
https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/jobs#--runs-get-output
Retrieve the output and metadata of a run. When a notebook task
returns a value through the dbutils.notebook.exit() call, you can use
this endpoint to retrieve that value. Azure Databricks restricts this
API to return the first 5 MB of the output. For returning a larger
result, you can store job results in a cloud storage service.
is there anyway of reading files located in my local machine other than navigating to 'Data'> 'Add Data' on Databricks.
in my past experience using Databricks, when using s3 buckets, I was able to just read and load a dataframe by just specifying the path like so: i.e
df = spark.read.format('delta').load('<path>')
is there any way i can do something like this using databricks to read local files?
If you use the Databricks Connect client library you can read local files into memory on a remote Databricks Spark cluster. See details here.
The alternative is to use the Databricks CLI (or REST API) and push local data to a location on DBFS, where it can be read into Spark from within a Databricks notebook. A similar idea would be to use the AWS CLI to put local data into an S3 bucket that can be accessed from Databricks.
It sounds like what you are looking for is Databricks Connect, which works with many popular IDEs.
I need some clarity on Databricks DBFS.
In simple basic terms, what is it, what is the purpose of it and what does it allow me to do?
The documentation on databricks, says to this effect..
"Files in DBFS persist to Azure Blob storage, so you won’t lose data even after you terminate a cluster."
Any insight will be helpful, haven't been able to find documentation that goes into the details of it from architecture and usage perspective
I have experience with DBFS, it is a great storage which is holding data which you can upload from your local computer using DBFS CLI! The CLI setup a bit tricky, but when you manage, you can easily move whole folders around in this environment (remember using -overwrite! )
create folders
upload files
modify, remove files and folders
With Scala you can easily pull in the data you store in this storage with a code like this:
val df1 = spark
.read
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("dbfs:/foldername/test.csv")
.select(some_column_name)
Or read in the whole folder to process all csv the files available:
val df1 = spark
.read
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("dbfs:/foldername/*.csv")
.select(some_column_name)
I think it is easy to use and learn, I hope you find this info helpful!
Databricks File System (DBFS) is a distributed file system mounted into a Databricks workspace and available on Databricks clusters.
DBFS is an abstraction on top of scalable object storage and offers the following benefits:
1) Allows you to mount storage objects so that you can seamlessly access data without requiring credentials.
2) Allows you to interact with object storage using directory and file semantics instead of storage URLs.
Persists files to object storage(Blob), so you won’t lose data after you terminate a cluster.
Below link will help you to get more understanding on the Databricks utils commands:
databricks-file-system link
A few points in addition to the other answers worth mentioning:
AFAIK, You don’t pay for storage costs associated with DBFS. Instead you pay an hourly fee to run jobs on DBX.
Even though it is storing the data in blob/s3 in the cloud, you can’t access that storage directly. That means you have to use the DBX APIs or cli to access this storage.
Which leads to the third and obvious point, Using DBFS will more tightly couple your spark applications to DBX. Which may or may not be what you want to do.