Except command not working in databrick sql (spark sql) - hive

I have written this except query to get difference in record from both hive tables from databricks notebook.(I am trying to get result as we get in mssql ie only difference in resultset)
select PreqinContactID,PreqinContactName,PreqinPersonTitle,EMail,City
from preqin_7dec.PreqinContact where filename='InvestorContactPD.csv'
except
select CONTACT_ID,NAME,JOB_TITLE,EMAIL,CITY
from preqinct.InvestorContactPD where contact_id in (
select PreqinContactID from preqin_7dec.PreqinContact
where filename='InvestorContactPD.csv')
But the result set returned is also having matching records.The record which i have shown above is coming in result set but when i checked it separately based on contact_id it is same.so I am not sure why except is returning the matching record also.
Just wanted to know how we can use except or any difference finding command in databrick notebook by using sql.
I want to see nothing in result set if source and target data is same.


EXCEPT works perfectly well in Databricks as this simple test will show:
val df = Seq((3445256, "Avinash Singh", "Chief Manager", "asingh#gmail.com", "Mumbai"))
.toDF("contact_id", "name", "job_title", "email", "city")
// Save the dataframe to a temp view
df.createOrReplaceTempView("tmp")
df.show
The SQL test:
%sql
SELECT *
FROM tmp
EXCEPT
SELECT *
FROM tmp;
This query will yield no results. Is it possible you have some leading or trailing spaces for example? Spark is also case-sensitive so that could also be causing your issue. Try a case-insensitive test by applying the LOWER function to all columns, eg

Related

R: Summary of SQL Tables

I am working with the R programming language.
Normally, when I want to get the summary of a table, I can use something like the "str()" function or the "summary()" function:
str(my_table)
summary(my_table)
However, now I am trying to do this with tables on a server.
For instance, I am trying to get the summaries of variable types for a specific table (e.g. "my_table") on a server. I found a very indirect way to do this:
#load libraries
library(OBDC)
library(RODBC)
library(dbi)
#establish a connection and name it as "dbhandle"
rs <- dbSendQuery(dbhandle, 'select * from my_table limit 1')
dbColumnInfo(rs)
My Question: Is there a more "direct" way to do this? For example, can I get information about each column (e.g. whether the column is integer, character, date, etc.) in a table without first sending the query and then requesting the information? Can I do this directly?
Thanks!

You could try using fetch() from "RMySQL" to turn your SQL query into an R object (e.g. data frame)
library(RMySQL)
rs <- dbSendQuery(dbhandle, 'select * from my_table limit 1')
# Get the results from MySQL into R
my_table = fetch(rs, n=-1)
# clear result
dbClearResult(rs)
rm(rs)
Then use the functions you describe.
str(my_table)
summary(my_table)

Simple, but most likely an incorrect Join is giving a MAX_JOIN_SIZE error

Probably a simple SQL query, but struggling as still learning
The following query runs fine:
SELECT NationalArea. *
FROM NationalArea
WHERE NationalArea.AreaCode = '01922'
This returns about 30 results.
This also runs fine:
SELECT DestinationNames.Name
FROM `DestinationNames`
WHERE DestinationNames.AreaCode = '01922'
This returns just the one
I am trying to run a query that joins the two where the National Area will give a list of area codes and the destination will match those area codes with the names of the towns. The query I have is as follows:
SELECT NationalArea.*, DestinationNames.Name
FROM NationalArea
JOIN DestinationNames
ON NationalArea.AreaCode=DestinationNames.AreaCode
WHERE NationalArea.AreaCode = '01922'
But I get the following error
1104 - The SELECT would examine more than MAX_JOIN_SIZE rows; check your WHERE and use SET SQL_BIG_SELECTS=1 or SET MAX_JOIN_SIZE=# if the SELECT is okay
Thanks in advance

You can display the current value with
SHOW VARIABLES LIKE '%MAX_JOIN_SIZE%';
You can change it with:
SET MAX_JOIN_SIZE = 100
Or skip the check entirely with (run this as a separate command before your query):
SET SQL_BIG_SELECTS = 1
But I would first examine why your join returns more than that. It doesn't look like it should. The default value of max_join_size is 4294967295!

Querying Netezza via SquirrelSQL returns WKT geometry in unknown encoding

I am using SquirrelSQL to write and execute SQL queries on a Netezza database. Using Netezza's spatial capabilities (which are essentially the same as those of PostGIS) I've executed a query and returned a single result that contains a geometry. Here's the query, for reference:
SELECT t.SHAPE
FROM (SELECT * FROM OS_AB_PLUS..E12_ADDRESSBASE WHERE POSTCODE = 'RH1 6NE'
AND PAO_START_NUMBER = '14') as a, OS_TOPO..TOPOGRAPHICAREA as t
WHERE inza..ST_Within(a.shape, t.shape) = TRUE
My issue is that the geometry field, which should contain the polygon coordinates represented as Well-Known Text (WKT), looks instead like this:
g¹ AË Affff¬0AÍÌÌÌî0AÒ 3333Ê AÍÌÌÌî0A» Aë0Afffæ» AffffÒ0A¹ AÒ0A333³¹ A3333¿0AŒ AffffÀ0AÍÌÌLŒ Affff¬0AË A¯0AëQ8Ê A3333í0A3333Ê AÍÌÌÌî0A
I can't seem to find anywhere in SquirrelSQL to specify the encoding of VARCHAR columns, and I've seen the column returned without encoding issues in Aginity (another SQL client). Any suggestions on how to proceed would be much appreciated.

Turns out my issue was not really related to encoding at all. The human-readable version of the geometry in a PostGIS-like database will only be returned when ST_AsText is used in the select statement. So my SQL query becomes:
SELECT inza..ST_AsText(t.SHAPE)
FROM (SELECT * FROM OS_AB_PLUS..E12_ADDRESSBASE WHERE POSTCODE = 'RH1 6NE'
AND PAO_START_NUMBER = '14') as a, OS_TOPO..TOPOGRAPHICAREA as t
WHERE inza..ST_Within(a.shape, t.shape) = TRUE
Which returns. as intended:
POLYGON ((526696.15 148931.9, 526703.94 148932.34, 526703.8 148935.2, 526705.5 148935.3, 526705.4 148937.8, 526695.9 148937.35, 526696.15 148931.9))

Django select only rows with duplicate field values

suppose we have a model in django defined as follows:
class Literal:
name = models.CharField(...)
...
Name field is not unique, and thus can have duplicate values. I need to accomplish the following task:
Select all rows from the model that have at least one duplicate value of the name field.
I know how to do it using plain SQL (may be not the best solution):
select * from literal where name IN (
select name from literal group by name having count((name)) > 1
);
So, is it possible to select this using django ORM? Or better SQL solution?

Try:
from django.db.models import Count
Literal.objects.values('name')
.annotate(Count('id'))
.order_by()
.filter(id__count__gt=1)
This is as close as you can get with Django. The problem is that this will return a ValuesQuerySet with only name and count. However, you can then use this to construct a regular QuerySet by feeding it back into another query:
dupes = Literal.objects.values('name')
.annotate(Count('id'))
.order_by()
.filter(id__count__gt=1)
Literal.objects.filter(name__in=[item['name'] for item in dupes])

This was rejected as an edit. So here it is as a better answer
dups = (
Literal.objects.values('name')
.annotate(count=Count('id'))
.values('name')
.order_by()
.filter(count__gt=1)
)
This will return a ValuesQuerySet with all of the duplicate names. However, you can then use this to construct a regular QuerySet by feeding it back into another query. The django ORM is smart enough to combine these into a single query:
Literal.objects.filter(name__in=dups)
The extra call to .values('name') after the annotate call looks a little strange. Without this, the subquery fails. The extra values tricks the ORM into only selecting the name column for the subquery.

try using aggregation
Literal.objects.values('name').annotate(name_count=Count('name')).exclude(name_count=1)

In case you use PostgreSQL, you can do something like this:
from django.contrib.postgres.aggregates import ArrayAgg
from django.db.models import Func, Value
duplicate_ids = (Literal.objects.values('name')
.annotate(ids=ArrayAgg('id'))
.annotate(c=Func('ids', Value(1), function='array_length'))
.filter(c__gt=1)
.annotate(ids=Func('ids', function='unnest'))
.values_list('ids', flat=True))
It results in this rather simple SQL query:
SELECT unnest(ARRAY_AGG("app_literal"."id")) AS "ids"
FROM "app_literal"
GROUP BY "app_literal"."name"
HAVING array_length(ARRAY_AGG("app_literal"."id"), 1) > 1

Ok, so for some reason none of the above worked for, it always returned <MultilingualQuerySet []>. I use the following, much easier to understand but not so elegant solution:
dupes = []
uniques = []
dupes_query = MyModel.objects.values_list('field', flat=True)
for dupe in set(dupes_query):
if not dupe in uniques:
uniques.append(dupe)
else:
dupes.append(dupe)
print(set(dupes))

If you want to result only names list but not objects, you can use the following query
repeated_names = Literal.objects.values('name').annotate(Count('id')).order_by().filter(id__count__gt=1).values_list('name', flat='true')

What is the best way to run N independent column updates in PostgreSQL? What is the best way to do it in the SQL spec?

I'm looking for a more efficient way to run many columns updates on the same table like this:
UPDATE TABLE table
SET col = regexp_replace( col, 'foo', 'bar' )
WHERE regexp_match( col, 'foo' );
Such that foo, and bar, will be a combination of 40 different regex-replaces. I doubt even 25% of the dataset needs to be updated at all, but what I'm wanting to know is it is possible to cleanly achieve the following in SQL.
A single pass update
A single match of the regex, triggers a single replace
Not running all possible regexp_replaces if only one matches
Not updating all columns if only one needs the update
Not updating a row if no column has changed
I'm also curious, I know in MySQL (bear with me)
UPDATE foo SET bar = 'baz'
Has an implicit WHERE bar != 'baz' clause
However, in PostgreSQL I know this doesn't exist: I think I could at least answer one of my questions if I knew how to skip a single row's update if the target columns weren't updated.
Something like
UPDATE TABLE table
SET col = *temp_var* = regexp_replace( col, 'foo', 'bar' )
WHERE col != *temp_var*

Do it in code. Open up a cursor, then: grab a row, run it through the 40 regular expressions, and if it changed, save it back. Repeat until the cursor doesn't give you any more rows.
Whether you do it that way or come up with the magical SQL expression, it's still going to be a row scan of the entire table, but the code will be much simpler.
Experimental Results
In response to criticism, I ran an experiment. I inserted 10,000 lines from a documentation file into a table with a serial primary key and a varchar column. Then I tested two ways to do the update. Method 1:
in a transaction:
opened up a cursor (select for update)
while reading 100 rows from the cursor returns any rows:
for each row:
for each regular expression:
do the gsub on the text column
update the row
This takes 1.16 seconds with a locally connected database.
Then the "big replace," a single mega-regex update:
update foo set t =
regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(regexp_replace(t,
E'\bcommit\b', E'COMMIT'),
E'\b9acf10762b5f3d3b1b33ea07792a936a25e45010\b',
E'9ACF10762B5F3D3B1B33EA07792A936A25E45010'),
E'\bAuthor:\b', E'AUTHOR:'),
E'\bCarl\b', E'CARL'), E'\bWorth\b',
E'WORTH'), E'\b\b',
E''), E'\bDate:\b',
E'DATE:'), E'\bMon\b', E'MON'),
E'\bOct\b', E'OCT'), E'\b26\b',
E'26'), E'\b04:53:13\b', E'04:53:13'),
E'\b2009\b', E'2009'), E'\b-0700\b',
E'-0700'), E'\bUpdate\b', E'UPDATE'),
E'\bversion\b', E'VERSION'),
E'\bto\b', E'TO'), E'\b2.9.1\b',
E'2.9.1'), E'\bcommit\b', E'COMMIT'),
E'\b61c89e56f361fa860f18985137d6bf53f48c16ac\b',
E'61C89E56F361FA860F18985137D6BF53F48C16AC'),
E'\bAuthor:\b', E'AUTHOR:'),
E'\bCarl\b', E'CARL'), E'\bWorth\b',
E'WORTH'), E'\b\b',
E''), E'\bDate:\b',
E'DATE:'), E'\bMon\b', E'MON'),
E'\bOct\b', E'OCT'), E'\b26\b',
E'26'), E'\b04:51:58\b', E'04:51:58'),
E'\b2009\b', E'2009'), E'\b-0700\b',
E'-0700'), E'\bNEWS:\b', E'NEWS:'),
E'\bAdd\b', E'ADD'), E'\bnotes\b',
E'NOTES'), E'\bfor\b', E'FOR'),
E'\bthe\b', E'THE'), E'\b2.9.1\b',
E'2.9.1'), E'\brelease.\b',
E'RELEASE.'), E'\bThanks\b',
E'THANKS'), E'\bto\b', E'TO'),
E'\beveryone\b', E'EVERYONE'),
E'\bfor\b', E'FOR')
The mega-regex update takes 0.94 seconds to update.
At 0.94 seconds compared to 1.16, it's true that the mega-regex update is faster, running in 81% of the time of doing it in code. It is not, however a lot faster. And ye Gods, look at that update statement. Do you want to write that, or try to figure out what went wrong when Postgres complains that you dropped a parenthesis somewhere?
Code
The code used was:
def stupid_regex_replace
sql = Select.new
sql.select('id')
sql.select('t')
sql.for_update
sql.from(TABLE_NAME)
Cursor.new('foo', sql, {}, #db) do |cursor|
until (rows = cursor.fetch(100)).empty?
for row in rows
for regex, replacement in regexes
row['t'] = row['t'].gsub(regex, replacement)
end
end
sql = Update.new(TABLE_NAME, #db)
sql.set('t', row['t'])
sql.where(['id = %s', row['id']])
sql.exec
end
end
end
I generated the regular expressions dynamically by taking words from the file; for each word "foo", its regular expression was "\bfoo\b" and its replacement string was "FOO" (the word uppercased). I used words from the file to make sure that replacements did happen. I made the test program spit out the regex's so you can see them. Each pair is a regex and the corresponding replacement string:
[[/\bcommit\b/, "COMMIT"],
[/\b9acf10762b5f3d3b1b33ea07792a936a25e45010\b/,
"9ACF10762B5F3D3B1B33EA07792A936A25E45010"],
[/\bAuthor:\b/, "AUTHOR:"],
[/\bCarl\b/, "CARL"],
[/\bWorth\b/, "WORTH"],
[/\b<cworth#cworth.org>\b/, "<CWORTH#CWORTH.ORG>"],
[/\bDate:\b/, "DATE:"],
[/\bMon\b/, "MON"],
[/\bOct\b/, "OCT"],
[/\b26\b/, "26"],
[/\b04:53:13\b/, "04:53:13"],
[/\b2009\b/, "2009"],
[/\b-0700\b/, "-0700"],
[/\bUpdate\b/, "UPDATE"],
[/\bversion\b/, "VERSION"],
[/\bto\b/, "TO"],
[/\b2.9.1\b/, "2.9.1"],
[/\bcommit\b/, "COMMIT"],
[/\b61c89e56f361fa860f18985137d6bf53f48c16ac\b/,
"61C89E56F361FA860F18985137D6BF53F48C16AC"],
[/\bAuthor:\b/, "AUTHOR:"],
[/\bCarl\b/, "CARL"],
[/\bWorth\b/, "WORTH"],
[/\b<cworth#cworth.org>\b/, "<CWORTH#CWORTH.ORG>"],
[/\bDate:\b/, "DATE:"],
[/\bMon\b/, "MON"],
[/\bOct\b/, "OCT"],
[/\b26\b/, "26"],
[/\b04:51:58\b/, "04:51:58"],
[/\b2009\b/, "2009"],
[/\b-0700\b/, "-0700"],
[/\bNEWS:\b/, "NEWS:"],
[/\bAdd\b/, "ADD"],
[/\bnotes\b/, "NOTES"],
[/\bfor\b/, "FOR"],
[/\bthe\b/, "THE"],
[/\b2.9.1\b/, "2.9.1"],
[/\brelease.\b/, "RELEASE."],
[/\bThanks\b/, "THANKS"],
[/\bto\b/, "TO"],
[/\beveryone\b/, "EVERYONE"],
[/\bfor\b/, "FOR"]]
If this were a hand-generated list of regex's, and not automatically generated, my question is still appropriate: Which would you rather have to create or maintain?

For the skip update, look at suppress_redundant_updates - see http://www.postgresql.org/docs/8.4/static/functions-trigger.html.
This is not necessarily a win - but it might well be in your case.
Or perhaps you can just add that implicit check as an explicit one?