I am trying use pyspark to analyze my data on databricks notebooks. Blob storage has been mounted on the databricks cluster and after ananlyzing, would like to write csv back into blob storage. As pyspark working in distributed fashion, csv file is broken into small blocks and written on the blob storage. How to overcome this and write as a single csv file on blob when we do analysis using pyspark. Thanks.
Do you really want a single file? If yes, the only way you can overcome it by merging all the small csv files into a single csv file. You can make use of map function on the databricks cluster to merge it or may be you can use some background job to do the same.
Have a look here: https://forums.databricks.com/questions/14851/how-to-concat-lots-of-1mb-cvs-files-in-pyspark.html
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 want to get a list of all parquet file names from a directory in Azure datalake in Pyspark.
The long file names starting with 'part-'
How to achieve this?
I reproduced this and got below results.
These are my parquet files in ADLS container.
To get these files in synapse first mount the ADLS to synapse using ADLS linked service.
After mounting, use the below code to get the parquet files that starts with part.
files_list=mssparkutils.fs.ls("abfss://<container_name>#<storageaccount_name>.dfs.core.windows.net/")
print("Total files list : ",files_list)
flist=[]
for i in range(0,len(files_list)):
if(files_list[i].name.startswith('part')):
flist.append(files_list[i].path)
print("\n \n File paths that starts with part",flist)
My Execution for your reference:
If you want to read all files you can just use wild card path part* in the file path like this.
df=spark.read.parquet("abfss://<container_name><storageaccount_name>.dfs.core.windows.net/part*.parquet")
I'd like to use Data fusion on GCP as my ETL pipeline manager and store the raw data in GCS using the delta format. Has anyone done this, or does a plugin exist?
Data Fusion has a plugin to read files/objects from a path in a Google Cloud Storage bucket and it does support the Parquet format. One approach can be to use cloud function to convert the delta to parquet and then use it in the data fusion pipeline.
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.
Do you need to ingest excel and other proprietary formats using glue or allow glue to work crawl your s3 bucket to use these data formats within your data lake?
I have gone through the "Data Lake Foundation on the AWS Cloud" document and am left scratching my head about getting data into the lake. I have a Data Provider with a large set of data stored on their system as excel and access files.
Based on the process flow they would upload the data into the submission s3 bucket, which would set off a series of actions, but there is no etl of the data into a format that would work with the other tools.
Would using these files require using glue on the data that is submitted in the bucket or is there another way to make this data available to other tools such as Athena and redshift spectrum?
Thank you for any light you can shed on this topic.
-Guido
I'm not seeing that can take excel data directly to Data Lake. You might need to convert into CSV/TSV/Json or other formats before loading into Data Lake.
Formats Supported by Redshift Spectrum:
http://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-data-files.html -- Again I don't see Excel as of now.
Athena Supported File Formats:
http://docs.aws.amazon.com/athena/latest/ug/supported-formats.html -- I don't see Excel also not supported here.
You need to upload the files to S3 either to Use Athena or Redshift Spectrum or even Redshift storage itself.
Uploading Files to S3:
If you have bigger files, you need to use S3 multipart upload to upload quicker. If you want more speed, you need to use S3 accelerator to upload your files.
Querying Big Data with Athena:
You can create external tables with Athena from S3 locations. Once you create external tables, use Athena Sql reference to query your data.
http://docs.aws.amazon.com/athena/latest/ug/language-reference.html
Querying Big Data with Redshift Spectrum:
Similar to Athena, you can create external tables with Redshift. Start querying those tables and get the results on Redshift.
Redshift has lot of commercial tools, I use SQL Workbench. It is free open source and rock solid, supported by AWS.
SQL WorkBench: http://www.sql-workbench.net/
Connecting your WorkBench to Redshift: http://docs.aws.amazon.com/redshift/latest/mgmt/connecting-using-workbench.html
Copying data to Redshift:
Also if you want to take the data storage to Redshift, you can use the copy command to pull the data from S3 and its gets loaded to Redshift.
Copy Command Examples:
http://docs.aws.amazon.com/redshift/latest/dg/r_COPY_command_examples.html
Redshift Cluster Size and Number of Nodes:
Before creating Redshift Cluster, check for required size and number of nodes needed. More number of nodes gets query parallely running. One more important factor is how well your data is distributed. (Distribution key and Sort keys)
I have a very good experience with Redshift, getting up to the speed might take sometime.
Hope it helps.