I have a column in spark dataframe of timestamp type with date format like '2019-06-13T11:39:10.244Z'
My goal is to convert this column into EST time(subtracting 4 hours) keeping the same format.
I tried it using from_utc_timestamp api but it seems it is converting the UTC time to my local timezone (+5:30) and adding it to the timestamp then subtracting 4 hours from it. I tried to use Joda time but for some reason it is adding 33 days to the EST time
innput = 2019-06-13T11:39:10.244Z
using from_utc_timestamp api:
val tDf = df.withColumn("newTimeCol", to_utc_timestamp(col("timeCol"), "America/New_York"))
output = 2019-06-13T13:09:10.244Z+5:30
using Joda time package:
val coder : (String => String) = (arg: String) => {
new DateTime(arg, DateTimeZone.UTC).minusHours(4).toString("yyyy-mm-dd'T'HH:mm:s.SS'Z'")}
val sqlfunc = udf(coder)
val tDf = df.withColumn("newTime", sqlfunc(col("_c20")))
output = 2019-39-13T07:39:10.244Z
desired output = 2019-06-13T07:39:10.244Z
Kindly advise how should I proceed. Thanks in advance
There is a typo in your format string when creating the output.
Your format string should be yyyy-MM-dd'T'HH:mm:s.SS'Z' but it is yyyy-mm-dd'T'HH:mm:s.SS'Z'.
mm is the format char for minutes while MM is the format char for the months. You can check all format chars here.
Related
I am reading a CSV that contains two types of date:
dd-MMM-yyyy hh:mm:ss -> 13-Dec-2019 17:10:00
dd/MM/yyyy hh:mm -> 11/02/2020 17:33
I am trying to transform all dates of the first type into the second type but I can't find a good solution. I am trying this:
val pr_date = readeve.withColumn("Date", when(to_date(col("Date"),"dd-MMM-yyyy hh:mm:ss").isNotNull,
to_date(col("Date"),"dd/MM/yyyy hh:mm")))
pr_date.show(25)
And I get the entire Date column as null values:
I am trying with this function:
def to_date_(col: Column,
formats: Seq[String] = Seq("dd-MMM-yyyy hh:mm:ss", "dd/MM/yyyy hh:mm")) = {
coalesce(formats.map(f => to_date(col, f)): _*)
}
val p2 = readeve.withColumn("Date",to_date_(readeve.col(("Date")))).show(125)
And in the first type of date i get nulls too:
What am I doing wrong? (new with Scala Spark)
Scala version: 2.11.7
Spark version: 2.4.3
Try code below? Note that 17 is HH, not hh. Also try to_timestamp instead of to_date because you want to keep the time.
val pr_date = readeve.withColumn(
"Date",
coalesce(
date_format(to_timestamp(col("Date"),"dd-MMM-yyyy HH:mm:ss"),"dd/MM/yyyy HH:mm"),
date_format(to_timestamp(col("Date"),"dd/MM/yyyy HH:mm"),"dd/MM/yyyy HH:mm")
)
)
I am receiving time data into my source as a csv file in the format (HHMMSSHS). I am not sure about what HS in the format stands for. example data will be like 15110708.
I am creating table in databricks table with received columns and data. I want to convert this field to time while processing in scala.
I am using UDF to do formating on any data on the go. But for this i am totally stuck while writing a UDF for parsing only time.
The final output should be 15:11:07:08 or any time format suitable for this string.
I tried with java.text.SimpleDateFormat and faced issue with unparsable string.
Is there any way to convert the above given string to a time format?
I am storing this value as acolumn in databricks notebook table. Is there any other format other than string to save only time values?
Have you tried?:
import java.time.LocalTime
val dtf : DateTimeFormatter = DateTimeFormatter.ofPattern("HHmmssSS")
val localTime = udf { str : String =>
LocalTime.parse(str, dtf).toString
}
that gives:
+---------+------------+
|Timestamp|converted |
+---------+------------+
|15110708 |15:11:07.080|
|15110708 |15:11:07.080|
+---------+------------+
I have data in hive table in the below format.
2019-11-21 18:19:15.817
I wrote a sql query as below to get the above column value into epoch format.
val newDF = spark.sql(f"""select TRIM(id) as ID, unix_timestamp(sig_ts) as SIG_TS from table""")
And I am getting the output column SIG_TS as 1574360296 which is not having milliseconds.
How to get the epoch timestamp of a date with milliseconds?
Simple way: Create an UDF since spark's built-in function truncates at seconds.
import java.sql.Timestamp
val fullTimestampUDF = udf{t: Timestamp => t.getTime}
val df = Seq("2019-11-21 18:19:15.817").toDF("sig_ts")
.withColumn("sig_ts_ut", unix_timestamp($"sig_ts"))
.withColumn("sig_ts_ut_long", fullTimestampUDF($"sig_ts"))
df.show(false)
+-----------------------+----------+--------------+
|sig_ts |sig_ts_ut |sig_ts_ut_long|
+-----------------------+----------+--------------+
|2019-11-21 18:19:15.817|1574356755|1574356755817 |
+-----------------------+----------+--------------+
How do you convert a timestamp column to epoch seconds?
var df = sc.parallelize(Seq("2018-07-01T00:00:00Z")).toDF("date_string")
df = df.withColumn("timestamp", $"date_string".cast("timestamp"))
df.show(false)
DataFrame:
+--------------------+---------------------+
|date_string |timestamp |
+--------------------+---------------------+
|2018-07-01T00:00:00Z|2018-07-01 00:00:00.0|
+--------------------+---------------------+
If you have a timestamp you can cast it to a long to get the epoch seconds
df = df.withColumn("epoch_seconds", $"timestamp".cast("long"))
df.show(false)
DataFrame
+--------------------+---------------------+-------------+
|date_string |timestamp |epoch_seconds|
+--------------------+---------------------+-------------+
|2018-07-01T00:00:00Z|2018-07-01 00:00:00.0|1530403200 |
+--------------------+---------------------+-------------+
Use unix_timestamp from org.apache.spark.functions. It can a timestamp column or from a string column where it is possible to specify the format. From the documentation:
public static Column unix_timestamp(Column s)
Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds), using the default timezone and the default locale, return null if fail.
public static Column unix_timestamp(Column s, String p)
Convert time string with given pattern (see http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html) to Unix time stamp (in seconds), return null if fail.
Use as follows:
import org.apache.spark.functions._
df.withColumn("epoch_seconds", unix_timestamp($"timestamp")))
or if the column is a string with other format:
df.withColumn("epoch_seconds", unix_timestamp($"date_string", "yyyy-MM-dd'T'HH:mm:ss'Z'")))
It can be easily done with unix_timestamp function in spark SQL like this:
spark.sql("SELECT unix_timestamp(inv_time) AS time_as_long FROM agg_counts LIMIT 10").show()
Hope this helps.
You can use the function unix_timestamp and cast it into any datatype.
Example:
val df1 = df.select(unix_timestamp($"date_string", "yyyy-MM-dd HH:mm:ss").cast(LongType).as("epoch_seconds"))
I am facing an issue when i am trying to find the number of months between two dates using 'months_between'function. when my input date format is 'dd/mm/yyyy' or any other date format then the function is returning the correct output. however when i am passing the input date format as yyyymmdd then i am getting the below error.
Code:
val df = spark.read.option("header", "true").option("dateFormat", "yyyyMMdd").option("inferSchema", "true").csv("MyFile.csv")
val filteredMemberDF = df.withColumn("monthsBetween", functions.months_between(col("toDate"), col("fromDT")))
error:
cannot resolve 'months_between(toDate, fromDT)' due to data type mismatch: argument 1 requires timestamp type,
however, 'toDate' is of int type. argument 2 requires timestamp type, however, 'fromDT' is of int type.;
When my input is as below,
id fromDT toDate
11 16/06/2008 16/08/2008
12 13/07/2008 13/10/2008
getting expected output,
id fromDT toDate monthsBetween
11 16/6/2008 16/8/2008 2
12 13/7/2008 13/10/2008 3
when i am passing the below data, facing the above said error.
id fromDT toDate
11 20150930 20150930
12 20150930 20150930
You first need to use to_date function to convert those numbers to DateTimes.
import org.apache.spark.sql.functions._
val df = spark.read
.option("header", "true")
.option("dateFormat", "yyyyMMdd")
.option("inferSchema", "true")
.csv("MyFile.csv")
val dfWithDates = df
.withColumn("toDateReal", to_date(concat(col("toDate")), "yyyyMMdd"))
.withColumn("fromDateReal", to_date(concat(col("fromDT")), "yyyyMMdd"))
val filteredMemberDF = dfWithDates
.withColumn("monthsBetween", months_between(col("toDateReal"), col("fromDateReal")))