I am trying to include null values in collect_list while using pyspark, however the collect_list operation excludes nulls. I have looked into the following post Pypsark - Retain null values when using collect_list . However, the answer given is not what I am looking for.
I have a dataframe df like this.
| id | family | date |
----------------------------
| 1 | Prod | null |
| 2 | Dev | 2019-02-02 |
| 3 | Prod | 2017-03-08 |
Here's my code so far:
df.groupby("family").agg(f.collect_list("date").alias("entry_date"))
This gives me an output like this:
| family | date |
-----------------------
| Prod |[2017-03-08]|
| Dev |[2019-02-02]|
What I really want is as follows:
| family | date |
-----------------------------
| Prod |[null, 2017-03-08]|
| Dev |[2019-02-02] |
Can someone please help me with this? Thank you!
A possible workaround for this could be to replace all null-values with another value. (Perhaps not the best way to do this, but it's a solution nonetheless)
df = df.na.fill("my_null") # Replace null with "my_null"
df = df.groupby("family").agg(f.collect_list("date").alias("entry_date"))
Should give you:
| family | date |
-----------------------------
| Prod |[my_null, 2017-03-08]|
| Dev |[2019-02-02] |
Related
I'm trying to fill a table with data to test a system.
I have two tables
User
+----+----------+
| id | name |
+----+----------+
| 1 | Majikaja |
| 2 | User 2 |
| 3 | Markus |
+----+----------+
Goal
+----+----------+---------+
| id | goal | user_id |
+----+----------+---------+
I want to insert into goal one record for every user only using their IDs (they have to exists) and some fixed or random value.
I was thinking in something like this:
INSERT INTO Goal (goal, user_id) values ('Fixed value', select u.id from user u)
So it will generate:
Goal
+----+-------------+---------+
| id | goal | user_id |
+----+-------------+---------+
| 1 | Fixed value | 1 |
| 2 | Fixed value | 2 |
| 3 | Fixed value | 3 |
+----+-------------+---------+
I could just write a simple PHP script to achieve it but I wonder if is it possible to do using raw SQL only.
I am quite new in regular expression so I am having confusion in replacing numbers inside a string, in which several id numbers (from 1 to 5 digits, up to now) are separated by commas and closed between curly brackets.
I need to put before each written number a fixed code like p1_ in order to distinguish in future different types of objects' ids.
I have postgres database with a column "maintainance" in text format which can contains values like the followings (cells CAN'T BE null or empty):
+---------------+
| maintainance |
+---------------+
| {12541,2,4} |
+---------------+
| {12,131,9999} |
+---------------+
| {54} |
+---------------+
| {1} |
+---------------+
| {12500,65} |
+---------------+
and I'd need to replace values putting before each number "p1_" like this:
+------------------------+
| maintainance |
+------------------------+
| {p1_12541,p1_2,p1_4} |
+------------------------+
| {p1_12,p1_131,p1_9999} |
+------------------------+
| {p1_54} |
+------------------------+
| {p1_1} |
+------------------------+
| {p1_12500,p1_65} |
+------------------------+
can you please suggest me how to write replace command using regular expressions ?
Thanks in advance
regexp_replace(col, '[0-9]+', 'p_1\&', 'g')
Hello I'm fairly new to spark and I need help with this little exercise. I want to find certain values in another dataframe but if those values aren't present I want to reduce the length of each value until I find the match. I have these dataframes:
----------------
|values_to_find|
----------------
| ABCDE |
| CBDEA |
| ACDEA |
| EACBA |
----------------
------------------
| list | Id |
------------------
| EAC | 1 |
| ACDE | 2 |
| CBDEA | 3 |
| ABC | 4 |
------------------
And I expect the next output:
--------------------------------
| Id | list | values_to_find |
--------------------------------
| 4 | ABC | ABCDE |
| 3 | CBDEA | CBDEA |
| 2 | ACDE | ACDEA |
| 1 | EAC | EACBA |
--------------------------------
For example ABCDE isn't present so I reduce its length by one (ABCD), again it doesn't match any so I reduce it again and this time I get ABC, which matches so I use that value to join and form a new dataframe. There is no need to worry about duplicates values when reducing the length but I need to find the exact match. Also, I would like to avoid using a UDF if possible.
I'm using a foreach to get every value in the first dataframe and I can do a substring there (if there is no match) but I'm not sure how to lookup these values in the 2nd dataframe. What's the best way to do it? I've seen tons of UDFs that could do the trick but I want to avoid that as stated before.
df1.foreach { values_to_find =>
df1.get(0).toString.substring(0, 4)}
Edit: Those dataframes are examples, I have many more values, the solution should be dynamic... iterate over some values and find their match in another dataframe with the catch that I need to reduce their length if not present.
Thanks for the help!
You can load the dataframe as temporary view and write the SQL. Is the above scenario you are implementing for the first time in Spark or already did in the previous code ( i mean before spark have you implemented in the legacy system). With Spark you have the freedom to write udf in scala or use SQL. Sorry i don't have solution handy so just giving a pointer.
the following will help you.
val dataDF1 = Seq((4,"ABC"),(3,"CBDEA"),(2,"ACDE"),(1,"EAC")).toDF("Id","list")
val dataDF2 = Seq(("ABCDE"),("CBDEA"),("ACDEA"),("EACBA")).toDF("compare")
dataDF1.createOrReplaceTempView("table1")
dataDF2.createOrReplaceTempView("table2")
spark.sql("select * from table1 inner join table2 on table1.list like concat('%',SUBSTRING(table2.compare,1,3),'%')").show()
Output:
+---+-----+-------+
| Id| list|compare|
+---+-----+-------+
| 4| ABC| ABCDE|
| 3|CBDEA| CBDEA|
| 2| ACDE| ACDEA|
| 1| EAC| EACBA|
+---+-----+-------+
I have Spark 2.3 very big dataframe like this:
-------------------------
| col_key | col1 | col2 |
-------------------------
| AA | 1 | 2 |
| AB | 2 | 1 |
| AA | 2 | 3 |
| AC | 1 | 2 |
| AA | 3 | 2 |
| AC | 5 | 3 |
-------------------------
I need to "split" this dataframe by values in col_key column and save each splitted part in separate csv file, so I have to get smaller dataframes like
-------------------------
| col_key | col1 | col2 |
-------------------------
| AA | 1 | 2 |
| AA | 2 | 3 |
| AA | 3 | 2 |
-------------------------
and
-------------------------
| col_key | col1 | col2 |
-------------------------
| AC | 1 | 2 |
| AC | 5 | 3 |
-------------------------
and so far.
Every result dataframe I need to save as different csv file.
Count of keys is not big (20-30) but total count of data is (~200 millions records).
I have the solution where in the loop is selected every part of data and then saved to file:
val keysList = df.select("col_key").distinct().map(r => r.getString(0)).collect.toList
keysList.foreach(k => {
val dfi = df.where($"col_key" === lit(k))
SaveDataByKey(dfi, path_to_save)
})
It works correct, but bad issue of this solution is that every selection of data by every key couse full passing through whole dataframe, and it get too many time.
I think must be faster solution, where we pass through dataframe only once and during this put every record to "rigth" result dataframe (or directly to separate file). But I don't know how can to do it :)
May be, someone have ideas about it?
Also I prefer to use Spark's DataFrame API because it provides fastest way of data processing (so using RDD's is not desirable, if possible).
You need to partition by column and save as csv file. Each partition save as one file.
yourDF
.write
.partitionBy("col_key")
.csv("/path/to/save")
Why don't you try this ?
I have the following table that I have loaded in Tableau (It has only one column CreatedOnDate)
+-----------------+
| CreatedOnDate |
+-----------------+
| 1/1/2016 |
| 1/2/2016 |
| 1/3/2016 |
| 1/4/2016 |
| 1/5/2016 |
| 1/6/2016 |
| 1/7/2016 |
| 1/8/2016 |
| 1/9/2016 |
| 1/10/2016 |
| 1/11/2016 |
| 1/12/2016 |
| 1/13/2016 |
| 1/14/2016 |
+-----------------+
I want to be able to find the maximum date in the table, compare it with every date in the table and get the difference in days. For the above table, the maximum date in table is 1/14/2016. Every date is compared to 1/14/2016 to find the difference.
Expected Output
+-----------------+------------+
| CreatedOnDate | Difference |
+-----------------+------------+
| 1/1/2016 | 13 |
| 1/2/2016 | 12 |
| 1/3/2016 | 11 |
| 1/4/2016 | 10 |
| 1/5/2016 | 9 |
| 1/6/2016 | 8 |
| 1/7/2016 | 7 |
| 1/8/2016 | 6 |
| 1/9/2016 | 5 |
| 1/10/2016 | 4 |
| 1/11/2016 | 3 |
| 1/12/2016 | 2 |
| 1/13/2016 | 1 |
| 1/14/2016 | 0 |
+-----------------+------------+
My goal is to create this Difference calculated field. I am struggling to find a way to do this using DATEDIFF.
And help would be appreciated!!
woodhead92, this approach would work, but means you have to use table calculations. Much more flexible approach (available since v8) is Level of Details expressions:
First, define a MAX date for the whole dataset with this calculated field called MaxDate LOD:
{FIXED : MAX(CreatedOnDate) }
This will always calculate the maximum date on table (will overwrite filters as well, if you need to reflect them, make sure you add them to context.
Then you can use pretty much the same calculated field, but no need for ATTR or Table Calculations:
DATEDIFF('day', [CreatedOnDate], [MaxDate LOD])
Hope this helps!