I have requirement to capture the parquet files created as the outcome of a df.write.parquet("s3://bkt/folder", mode="append") command.
I am running this on AWS EMR pyspark.
I can achive this using awswrangler using wr.s3.to_parquet() but this is not really fit for my EMR spark use case.
Is there such functionality ?
I want list of the files from s3://bkt/folder which spark wrote
Thx all
If you want a list of files that spark wrote to particular S3 path you can use either of below approach:
Use input_file_name which will give file path from which the record is originating from and do a distinct operation by selecting filename:
from pyspark.sql.functions import input_file_name
df=spark.read.parquet("s3://bkt/folder")
df.withColumn("filename", input_file_name())
Or you can use boto3 to list the files :
from boto3 import client
conn = client('s3') # again assumes boto.cfg setup, assume AWS S3
for key in conn.list_objects(Bucket='bucket_name')['Contents']:
print(key['Key'])
Related
I would like to "create if not exists" a S3 bucket in YYYY-MM-DD format and store my transformed parquet files there. How do you achieve this in pyspark? Should I use boto3 or does pyspark have something builtin?
I am using the code below to read data from S3. I would like to create S3 and put my transformed files there.
spark_context._jsc.hadoopConfiguration().set("fs.s3a.access.key", config.access_id)
spark_context._jsc.hadoopConfiguration().set("fs.s3a.secret.key", config.access_key)
spark.conf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
Seems like you need to just enable: fs.s3.buckets.create.enabled
I want to check if a delta table in an s3 bucket is actually a delta table. I am trying do this by
from delta import *
from delta.tables import DeltaTable
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
spark = SparkSession.builder\
.appName('test')\
.getOrCreate()
if DeltaTable.isDeltaTable(spark, "s3a://landing-zone/table_name/year=2022/month=2/part-0000-xyz.snappy.parquet"):
print("bla")
else:
print("blabla")
This code runs forever without returning any result. I tested it with a local delta table and there it works. When I trim the path url so it stops after the actual table name, the code shows the same behavior. I also generated a boto3 client and I can see the bucket list when calling s3.list_bucket(). Do I need to parse the client somehow into the if statement?
Thanks a lot in advance!
I am an idiot, I forgot that it is not enough to just create a boto3 client, but I also have to make the actual connection to S3 via
spark._jsc.hadoopConfiguration().set(...)
This question already has answers here:
Spark - Reading JSON from Partitioned Folders using Firehose
(2 answers)
Closed 3 years ago.
I am a newbie in spark.
I have multiple small json files (1kb) in subdirectories of my s3 bucket. I want to merge all the files present in a single directory. Is there any optimized way in doing this using pyspark.
Directory structure:
region/year/month/day/hour/multiple_json_files
I have many directories as indicated above and want to merge all files in a single directory.
P.S: I have tried using python but its taking more time, tried s3distcp but its the same result.
Can anyone please help me in this
you can achieve by below code
First we need to make sure the hadoop aws package is available when we load spark:
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = "--packages=org.apache.hadoop:hadoop-aws:2.7.3 pyspark-shell"
Next we need to make pyspark available in the jupyter notebook:
import findspark
findspark.init()
from pyspark.sql import SparkSession
We need the aws credentials in order to be able to access the s3 bucket. We can use the configparser package to read the credentials from the standard aws file.
import configparser
config = configparser.ConfigParser()
config.read(os.path.expanduser("~/.aws/credentials"))
access_id = config.get(aws_profile, "aws_access_key_id")
access_key = config.get(aws_profile, "aws_secret_access_key")
We can start the spark session and pass the aws credentials to the hadoop configuration:
sc=spark.sparkContext
hadoop_conf=sc._jsc.hadoopConfiguration()
hadoop_conf.set("fs.s3n.impl", "org.apache.hadoop.fs.s3native.NativeS3FileSystem")
hadoop_conf.set("fs.s3n.awsAccessKeyId", access_id)
hadoop_conf.set("fs.s3n.awsSecretAccessKey", access_key)
Finally we can read the data and display it:
df=spark.read.json("s3n://path_of_location/*.json")
df.show()
Are there any method to write spark dataframe directly to xls/xlsx format ????
Most of the example in the web showing there is example for panda dataframes.
but I would like to use spark dataframe for working with my data. Any idea ?
I'm assuming that because you have the "databricks" tag you are wanting to create an .xlsx file within databricks file store and that you are running code within databricks notebooks. I'm also going to assume that your notebooks are running python.
There is no direct way to save an excel document from a spark dataframe. You can, however, convert a spark dataframe to a pandas dataframe then export from there. We'll need to start by installing the xlsxwriter package. You can do this for your notebook environment using a databricks utilites command:
dbutils.library.installPyPI('xlsxwriter')
dbutils.library.restartPython()
I was having a few permission issues saving an excel file directly to dbfs. A quick workaround was to save to the cluster's default directory then sudo move the file into dbfs. Here's some example code:
# Creating dummy spark dataframe
spark_df = spark.sql('SELECT * FROM default.test_delta LIMIT 100')
# Converting spark dataframe to pandas dataframe
pandas_df = spark_df.toPandas()
# Exporting pandas dataframe to xlsx file
pandas_df.to_excel('excel_test.xlsx', engine='xlsxwriter')
Then in a new command, specifying the command to run in shell with %sh:
%sh
sudo mv excel_test.xlsx /dbfs/mnt/data/
It is possible to generate an Excel file from pySpark.
df_spark.write.format("com.crealytics.spark.excel")\
.option("header", "true")\
.mode("overwrite")\
.save(path)
You need to install the com.crealytics:spark-excel_2.12:0.13.5 (or a more recent version of course) library though, for example in Azure Databricks by specifying it as a new Maven library in the libraries list of your cluster (one of the buttons on the left sidebar of the Databricks UI).
For more info see https://github.com/crealytics/spark-excel.
I believe you can do it like this.
sourcePropertySet.write
.format("com.databricks.spark.csv")
.option("header", "true")
.save("D:\\resultset.csv")
I'm not sure you can write directly to Excel, but Excel can definitely consume a CSV. This is almost certainly the easiest way of doing this kind of thing and the cleanest as well. In Excel you have all kinds of formatting, which can throw errors when used in some systems (think of merged cells).
You can not save it directly but you can have it as its stored in temp location and move it to your directory. My code piece is:
import xlsxwriter import pandas as pd1
workbook = xlsxwriter.Workbook('data_checks_output.xlsx')
worksheet = workbook.add_worksheet('top_rows')
Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd1.ExcelWriter('data_checks_output.xlsx', engine='xlsxwriter')
output = dataset.limit(10)
output = output.toPandas()
output.to_excel(writer, sheet_name='top_rows',startrow=row_number)
writer.save()
Below code does the work of moving files.
%sh
sudo mv data_checks_output.xlsx /dbfs/mnt/fpmount/
Comment if anyone has new update or better way to do it.
Yet Pyspark does not offer any method to save excel file. But you can save csv file, then it can be read in Excel.
From pyspark.sql module version 2.3 you have write.csv:
df.write.csv('path/filename'))
Documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html?highlight=save
I have a requirement -
1. To convert parquet file present in s3 to csv format and place it back in s3. The process should exclude the use of EMR.
2. Parquet file has more than 100 cols i need to just extract 4 cols from that parquet file and create the csv in s3.
Does anyone has any solution to this?
Note - Cannot use EMR or AWS Glue
Assuming you want to keep things easy within the AWS environment, and not using Spark (Glue / EMR), you could use AWS Athena in the following way:
Let's say your parquet files are located in S3://bucket/parquet/.
You can create a table in the Data Catalog (i.e. using Athena or a Glue Crawler), pointing to that parquet location. For example, running something like this in the Athena SQL console:
CREATE EXTERNAL TABLE parquet_table (
col_1 string,
...
col_100 string)
PARTITIONED BY (date string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
LOCATION 's3://bucket/parquet/' ;
Once you can query your parquet_table table, which will be reading parquet files, you should be able to create the CSV files in the following way, using Athena too and choosing only the 4 columns you're interested in:
CREATE TABLE csv_table
WITH (
format = 'TEXTFILE',
field_delimiter = ',',
external_location = 's3://bucket/csv/'
)
AS SELECT col_1, col_2, col_3, col_4
FROM parquet_table ;
After this, you can actually drop the csv temporary table and only use the CSV files, under s3://bucket/csv/, and do more, for example by having an S3-trigger Lambda function and doing something else or similar.
Remember that all this can be achieved from Lambda, interacting with Athena (example here) and also, bear in mind it has an ODBC connector and PyAthena to use it from Python, or more options, so using Athena through Lambda or the AWS Console is not the only option you have, in case you want to automate this in a different way.
I hope this helps.
Additional edit, on Sept 25th, 2019:
Answering to your question, about doing this in Pandas, I think the best way would be using Glue Python Shell, but you mentioned you didn't want to use it. So, if you decide to, here it is a basic example of how to:
import pandas as pd
import boto3
from awsglue.utils import getResolvedOptions
from boto3.dynamodb.conditions import Key, Attr
args = getResolvedOptions(sys.argv,
['region',
's3_bucket',
's3_input_folder',
's3_output_folder'])
## #params and #variables: [JOB_NAME]
## Variables used for now. Job input parameters to be used.
s3Bucket = args['s3_bucket']
s3InputFolderKey = args['s3_input_folder']
s3OutputFolderKey = args['s3_output_folder']
## aws Job Settings
s3_resource = boto3.resource('s3')
s3_client = boto3.client('s3')
s3_bucket = s3_resource.Bucket(s3Bucket)
for s3_object in s3_bucket.objects.filter(Prefix=s3InputFolderKey):
s3_key = s3_object.key
s3_file = s3_client.get_object(Bucket=s3Bucket, Key=s3_key)
df = pd.read_csv(s3_file['Body'], sep = ';')
partitioned_path = 'partKey={}/year={}/month={}/day={}'.format(partKey_variable,year_variable,month_variable,day_variable)
s3_output_file = '{}/{}/{}'.format(s3OutputFolderKey,partitioned_path,s3_file_name)
# Writing file to S3 the new dataset:
put_response = s3_resource.Object(s3Bucket,s3_output_file).put(Body=df)
Carlos.
It all depends upon your business requirement, what sort of action you want to take like Asynchronously or Synchronous call.
You can trigger a lambda github example on s3 bucket asynchronously when a parquet file arrives in the specified bucket. aws s3 doc
You can configure s3 service to send a notification to SNS or SQS as well when an object is added/removed form the bucket which in turn can then invoke a lambda to process the file Triggering a Notification.
You can run a lambda Asynchronously every 5 minutes by scheduling the aws cloudwatch events The finest resolution using a cron expression is a minute.
Invoke a lambda Synchronously over HTTPS (REST API endpoint) using API Gateway.
Also worth checking how big is your Parquet file as lambda can run max 15 min i.e. 900 sec.
Worth checking this page as well Using AWS Lambda with Other Services
It is worth taking a look at CTAS queries in Athena recently: https://docs.aws.amazon.com/athena/latest/ug/ctas-examples.html
We can store the query results in a different format using CTAS.