Redshift Distribution By Child Columns - amazon-redshift

My Situation
I have some tables in my redshift cluster that all break down into either an order_id, shipment_id, or shipment_item_id depending on how granular the table is. order_id is a 1 to many relationship on shipment_id and shipment_id is a 1 to many on shipemnt_item_id.
My Question
I distribute on order_id, so all shipment_id and shipment_item_id records should be on the same nodes across the tables since they are grouped by order_id. My question is, when I have to join on shipment_id or shipment_item_id then will redshift know that the records are on the same nodes, or will it still broadcast the tables since they aren't joined on order_id?
Example Tables
unified_order shipment_details
+----------+-------------+------------------+ +-------------+-----------+--------------+
| order_id | shipment_id | shipment_item_id | | shipment_id | ship_day | ship_details |
+----------+-------------+------------------+ +-------------+-----------+--------------+
| 1 | 1 | 1 | | 1 | 1/1/2017 | stuff |
| 1 | 1 | 2 | | 2 | 5/1/2017 | other stuff |
| 1 | 1 | 3 | | 3 | 6/14/2017 | more stuff |
| 1 | 2 | 4 | | 4 | 5/13/2017 | less stuff |
| 1 | 2 | 5 | | 5 | 6/19/2017 | that stuff |
| 1 | 3 | 6 | | 6 | 7/31/2017 | what stuff |
| 2 | 4 | 7 | | 7 | 2/5/2017 | things |
| 2 | 4 | 8 | +-------------+-----------+--------------+
| 3 | 5 | 9 |
| 3 | 5 | 10 |
| 4 | 6 | 11 |
| 5 | 7 | 12 |
| 5 | 7 | 13 |
+----------+-------------+------------------+
Distribution
distribution_by_node
+------+----------+-------------+------------------+
| node | order_id | shipment_id | shipment_item_id |
+------+----------+-------------+------------------+
| 1 | 1 | 1 | 1 |
| 1 | 1 | 1 | 2 |
| 1 | 1 | 1 | 3 |
| 1 | 1 | 2 | 4 |
| 1 | 1 | 2 | 5 |
| 1 | 1 | 3 | 6 |
| 1 | 5 | 7 | 12 |
| 1 | 5 | 7 | 13 |
| 2 | 2 | 4 | 7 |
| 2 | 2 | 4 | 8 |
| 3 | 3 | 5 | 9 |
| 3 | 3 | 5 | 10 |
| 4 | 4 | 6 | 11 |
+------+----------+-------------+------------------+

The Amazon Redshift documentation does not go into detail how information is shared between nodes, but it is doubtful that it "broadcasts the tables".
Rather, information is probably sent between nodes based on need -- only the relevant columns would be shared, and possibly only sub-ranges of the data.
Rather than worrying too much about the internal implementation, you should test various DISTKEY and SORTKEY strategies against real queries to determine performance.
Follow the recommendations from Choose the Best Distribution Style to minimize the amount of data that needs to be sent between nodes and consult Amazon Redshift Best Practices for Designing Queries to improve queries.

You can EXPLAIN your query to see how data will be distributed (or not) during the execution. In this doc you'll see how to read the query plan:
Evaluating the Query Plan

Related

Multiple Joins with Filters in DAX

I'm trying to recreate simple SQL query in DAX. The output Query needs to work in Power BI Report Builder and I have been trying all day reading all sorts of PowerBI / DAX online resources to rewrite this.
A little bit about the data:
The data is structured in three tables, CustomCar, Engine and Chassis.
Basically "CarId" is the key that connects all three tables.
Let's assume all tables have more than 20 columns. so only a few of the columns are needed in the final output.
All three tables (CustomCar, Chassis and Engine) have an IsActive property (the relationship between Engine/Chassis to CustomCar is MANY-TO-ONE. Because an engine might blow up and they change it therefore somehow we want to track which Engine is on the car today and what engine was on it last year, however, at any time, there is only one active engine for each car.. The same goes for Chassis)
Both Engine and Chassis have 'Manufacturer' and 'Model' columns so in the output query they need to be identified from each other.
I am not trying to sum any sort of sales number, just a list of cars with their current configuration.
Any help is appreciated.
Select
CC.Name, CC.Model as 'CustomCarModel', CC.MaxSpeed,
Ch.Manufacturer as 'ChassisManufacturer', Ch.Model as 'ChassisModel', Ch.ManufacturedDate as 'ChassisManfDate',
E.Manufactuer as 'EngineManufacturer', E.Model as 'EngineModel', E.Power, E.CylCount, E.ManufacturedDate
From CustomCars CC
Join Chassis Ch on Ch.CarID = CC.CarId
Join Engine E on E.CarID = CC.CarID
where
CC.IsActive = 1 and CC.FirstTestDriveYear < 1980 and
Ch.IsActive = 1 and
E.IsActive = 1
More info, here are my tables.
Classic Car:
CarId (Primary Key) | Model | MaxSpeed | NumOfPax | TankCapacity | IsActive | FirstTestDriveYear |....
1 | SuperChev | 220 | 2 | 60 | 1 | 1985 |
2 | CustomBranco | 185 | 2 | 90 | 1 | 1979 |
3 | RebuiltToyo | 251 | 4 | 20 | 0 | 1990 |
Chassis:
ChassisId (Primary Key) | CarId (Foreign Key)| IsActive | Manufacturer | Model | ManufacturedDate | ...
1 | 1 | 0 | ACME Chassis | M1 | '04-Jan-1985' | ...
2 | 1 | 1 | SuperChassis | T5 | '03-Feb-1987' | ...
3 | 2 | 0 | Ford | S2 | '25-Mar-1965' | ...
4 | 2 | 0 | Ford | S2 | '25-Mar-1968' | ...
5 | 3 | 0 | JapanChass | X123 | '25-Feb-1988' | ...
6 | 2 | 1 | Ford | S8 | '08-Jul-1978' | ...
7 | 2 | 0 | Ford | S2 | '25-Mar-1968' | ...
8 | 3 | 1 | JapanChass | Y765 | '25-Feb-1992' | ...
Engine:
EngineId (Primary Key) | CarId (Foreign Key)| IsActive | Manufacturer | Model | ManufacturedDate | Power | CylCount | ...
1 | 1 | 0 | GM | AB1 | '04-Jan-1985' | 320 | 8 | ...
2 | 1 | 1 | Bently | ZY2 | '03-Feb-1987' | 285 | 8 | ...
3 | 2 | 0 | Ford | S2 | '25-Mar-1965' | 290 | 6 | ...
4 | 2 | 0 | Ford | S2 | '25-Mar-1968' | 292 | 6 | ...
5 | 3 | 0 | Toyota | X123 | '25-Feb-1988' | 180 | 4 | ...
6 | 2 | 1 | Ford | S8 | '08-Jul-1978' | 222 | 8 | ...
7 | 2 | 0 | Ford | S2 | '25-Mar-1968' | 320 | 8 | ...
8 | 3 | 1 | Toyota | Y765 | '25-Feb-1992' | 211 | 6 | ...
I have found a work around for this. I added the query when adding the data pipeline in Power BI dashboard and will use the values from the query as is.

Is there a V-lookup effect in Microsoft Access?

I am a novice self-teaching Microsoft Access.
I have an MS Access database with a table of students (Table1).
Table1
+----+-----------+----------+------------+------------+
| id | firstname | lastname | Year_Group | Form_Group |
+----+-----------+----------+------------+------------+
| 2 | mnb | nbgfv | 7 | 1 |
| 3 | jhg | uhgf | 8 | 2 |
| 4 | poi | ijuy | 9 | 2 |
| 5 | tgf | tgfd | 10 | 2 |
| 6 | wer | qwes | 11 | 2 |
+----+-----------+----------+------------+------------+
Every day students days are recorded sort of like Table2.
Table2
+----------+----+-----------+----------+------------+--------+-----------+----------+
| Date | id | firstname | lastname | Year_Group | Effort | Behaviour | Homework |
+----------+----+-----------+----------+------------+--------+-----------+----------+
| 28/02/19 | 2 | mnb | nbgfv | 7 | Good | Good | Y |
| 28/02/19 | 3 | jhg | uhgf | 8 | OK | OK | Y |
| 28/02/19 | 4 | poi | ijuy | 9 | Bad | Bad | N |
| 01/03/19 | 5 | tgf | tgfd | 10 | Good | OK | Y |
| 01/03/19 | 6 | wer | qwes | 11 | Good | Good | Y |
+----------+----+-----------+----------+------------+--------+-----------+----------+
Is there a way (when using a list box or combo box) to select a student from Table1 so that their information is used for the corresponding columns in Table2?
Or is there a more efficient way to do this?
Firstly, you should normalise your data.
Currently, you are repeating the firstname, lastname, and Year_Group data in two separate tables, which not only bloats your database, but also means that such data must be maintained in two separate places, potentially leading to inconsistencies and then uncertainty as to which is the master.
Instead, I would suggest that your Students table should contain all information pertaining to the characteristics of a student:
Students
+----+-----------+----------+------------+------------+
| id | firstname | lastname | Year_Group | Form_Group |
+----+-----------+----------+------------+------------+
| 2 | mnb | nbgfv | 7 | 1 |
| 3 | jhg | uhgf | 8 | 2 |
| 4 | poi | ijuy | 9 | 2 |
| 5 | tgf | tgfd | 10 | 2 |
| 6 | wer | qwes | 11 | 2 |
+----+-----------+----------+------------+------------+
And the information pertaining to each school day should only reference the student ID in the Students table:
SchoolDays
+----------+----+--------+-----------+----------+
| Date | id | Effort | Behaviour | Homework |
+----------+----+--------+-----------+----------+
| 28/02/19 | 2 | Good | Good | Y |
| 28/02/19 | 3 | OK | OK | Y |
| 28/02/19 | 4 | Bad | Bad | N |
| 01/03/19 | 5 | Good | OK | Y |
| 01/03/19 | 6 | Good | Good | Y |
+----------+----+--------+-----------+----------+
Then, if you want to display the data in its entirety, you would use a query which joins the two tables, e.g.:
select
t2.date,
t1.firstname,
t1.lastname,
t1.year_group,
t2.effort,
t2.behaviour,
t2.homework
from
students t1 inner join schooldays t2 on t1.id = t2.id

count continuously postgresql data

i need help with counting some data
this what i want
| user_id | action_id | count |
-------------------------------------
| 1 | 1 | 1 |
| 2 | 2 | 1 |
| 3 | 2 | 2 |
| 4 | 3 | 1 |
| 5 | 3 | 2 |
| 6 | 3 | 3 |
| 7 | 4 | 1 |
| 8 | 5 | 1 |
| 9 | 5 | 2 |
| 10 | 6 | 1 |
this is what i have
| user_id | action_id | count |
-------------------------------
| 1 | 1 | 1 |
| 2 | 2 | 1 |
| 3 | 2 | 1 |
| 4 | 3 | 1 |
| 5 | 3 | 1 |
| 6 | 3 | 1 |
| 7 | 4 | 1 |
| 8 | 5 | 1 |
| 9 | 5 | 1 |
| 10 | 6 | 1 |
i really need it for create some research about second action from users
how do i do it?
thank you
Using ROW_NUMBER should work here:
SELECT
user_id,
action_id,
ROW_NUMBER() OVER (PARTITION BY action_id ORDER BY user_id) count
FROM yourTable
ORDER BY
user_id;
Demo

Spotfire - Calculate average only if there are minimum 3 values

I want to create a cross table in Spotfire where in which Average is calculated only when there are at least 3 values. If there are no values or less than 3 values the average should be blank.
+-------+-----+---------+
| Month | Age | Average |
+-------+-----+---------+
| 1 | 10 | |
| 2 | 11 | |
| 3 | 2 | 7.7 |
| 4 | | |
| 5 | 13 | |
| 6 | 14 | |
| 7 | | |
| 8 | 19 | |
| 9 | 20 | |
| 10 | 21 | 20 |
+-------+-----+---------+
If I'm understanding you correctly, you want to group by Month, and then have something like this as your aggregation:
If(Count()>2,Avg([Age]),null) as [AverageAge_3Min]

Are pg_stat_database and pg_stat_activity really listing the same stuff aka how do I get a list of all backends

In this answer to the question Right query to get the current number of connections in a PostgreSQL DB the poster implies that
SELECT sum(numbackends) FROM pg_stat_database;
and
SELECT count(*) FROM pg_stat_activity;
give the same results.
However, if I do this on my db the first one says 119 and the second one 30.
This is the difference as shown by summing numbackends and counting:
+------+-------------+-------+
| | numbackends | count |
+------+-------------+-------+
| db1 | 1 | 1 |
| db2 | 1 | 1 |
| db3 | 1 | 1 |
| db4 | 1 | 1 |
| db5 | 2 | 2 |
| db6 | 2 | 2 |
| db7 | 12 | 3 | <--
| db8 | 4 | 4 |
| db9 | 5 | 5 |
| db10 | 78 | 35 | <--
+------+-------------+-------+
Why does this difference exist?
How can I list each of the 119-30=89 backends not shown in pg_stat_activity?