I'm new to databricks and trying to create partition.I've 3 columns available for partition i.e name,value and date.The required condition is that partition of date should be on weekly basis.
I've done something like this :
df\
.write\
.format("delta")\
.partitionBy(["name","value" and "date"]).
.save(writePath)
I don't know how to partition date by week ? I came across repartitionByRange but not sure how to apply for my condition ?
You can create new columns week and year from the date column and use them in partitionby:
from pyspark.sql import functions as F
df.withColumn("week", F.weekofyear("date")) \
.withColumn("year", F.year("date")) \
.write \
.format("delta") \
.partitionBy("year", "week") \
.save(writePath)
Note that the week number alone is not sufficient as it depends also on the year.
Related
I am loading the delta tables into S3 delta lake. the table schema is product_code,date,quantity,crt_dt.
i am getting 6 months of Forecast data, for example if this month is May 2022, i will get May, June, July, Aug, Sept, Oct quantities data. What is the issue i am facing here is the data is getting duplicated every month. i want only a single row in the delta table based on the recent crt_dt as shown in below screenshot. Can anyone help me with the solution i should implement?
The data is partitioned by crt_dt.
Thanks!
If you want to get the recent crt_dt normally this code will do the trick
w3 = Window.partitionBy("product_cat").orderBy(col("crt_dt").desc())
df.withColumn("row",row_number().over(w3)) \
.filter(col("row") == 1).drop("row") \
.show()
for more details check this https://sparkbyexamples.com/pyspark/pyspark-select-first-row-of-each-group/
You have a dataset that you'd like to filter and then write out to a Delta table.
Another poster told you how to filter the data to meet your requirements. Here's how to filter the data and then write it out.
filtered_df = df.withColumn("row",row_number().over(w3)) \
.filter(col("row") == 1).drop("row") \
.show()
filtered_df.write.format("delta").mode("append").save("path/to/delta_lake")
You can also do this with SQL if you aren't using the Python API.
timestampbefore timestamp after #pissall's code I have a Timestamp column with 0.5Hz frequency, that results in millions of rows. I am willing to reduce this data size by having a timestamp in an hourly manner. i.e 24 observations for a particular day.
I already reduced the data size by filtering the data by year, month and day. but as it is still very big i want to reduce it now to hourly basis.
I am working on Databricks and using PySpark for the same.
i used following command to reduce my data size from years to a Day.
df = df.filter(df.Timestamp.between('2019-09-03 00:00:00','2019-09-04 00:00:00'))
I would appreciate your help.
Thanks
Java.util.gregori...
You can replace the minutes and seconds part of your datetime using a UDF. Might not be the best solution, but here you go:
import pyspark.sql.functions as F
from pyspark.sql.types import TimestampType
date_replace_udf = F.udf(lambda date: date.replace(minute=0, second=0, microsecond=0),TimestampType())
df = df.withColumn("Timestamp", date_replace_udf(F.col("Timestamp")))
Another reference: How to truncate the time on a DateTime object in Python?
I'm using a Databricks notebook with Spark and Scala to read data from S3 into a DataFrame:
myDf = spark.read.parquet(s"s3a://data/metrics/*/*/*/). where * wildcards represent year/month/day.
Or I just hardcode it: myDf = spark.read.parquet(s"s3a://data/metrics/2018/05/20/)
Now I want to add an hour parameter right after the day. The idea is to obtain data from S3 for the most recently available hour.
If I do myDf = spark.read.parquet(s"s3a://data/metrics/2018/05/20/*) then I'll get data for all hours of may 20th.
How is it possible to achieve this in a Databricks notebook without hardcoding the hour?
Use timedate function
from datetime import datetime, timedelta
latest_hour = datetime.now() - timedelta(hours = 1)
You can also split them by year, month, day, hour
latest_hour.year
latest_hour.month
latest_hour.day
latest_hour.hour
quick question that I did not find on google.
What is the best way to create a Spark Dataframe with time stamps
lets say I have startpoint endpoint and a 15 min interval. What would be the best way to resolve this on spark ?
try SparkSQL inside udf like this DF.withColumn("regTime",current_timestamp) ,it's can replace a column ,
you also can convert a String to date or timestamp use DF.withColumn("regTime", to_date(spark_temp_user_day("dateTime")))
regTime is a column of DF
Suppose i have a dataframe in which Timestamp column is present.
Timestamp
2016-04-19T17:13:17
2016-04-20T11:31:31
2016-04-20T18:44:31
2016-04-20T14:44:01
I have to check whether current timsetamp is greater than Timestamp + 1 (i.e addition of 1 day to it) column in Scala
DataFrame supports two types of current_ on date and timestamp
Let's consider a DataFrame df with id and event_date columns.
We can perform the following filter operations :
import sqlContext.implicits._
import org.apache.spark.sql.functions._
// the event_date is before the current timestamp
df.filter('event_date.lt(current_timestamp()))
// the event_date is after the current timestamp
df.filter('event_date.gt(current_timestamp()))
I advice you to read the associated scala doc for more information here. You have a whole section on dates and timestamps operations.
EDIT: As discussed in the comments, in order to add a day to your event_date column, you can use the date_addfunction :
df.filter(date_add('event_date,1).lt(current_timestamp()))
You can do it liked this.
df.filter(date_add('column_name', 1).lt(current_timestamp()))