Much ink has been spilled on the topic of sum types in SQL. The standard solutions are called absorption, separation, and partition; see, e.g.: https://www.inf.unibz.it/~montali/teaching/1415/dpm/slides/4.relational-mapping.pdf .
I want to ask about how to encode open sums. Normal sums allow a field to be one of a fixed set of several different types; with open sums, this set is not fixed.
The basic setup in our program: There is a list of "triggers," where each trigger can be one of many different things. Plugins can be written defining new trigger types, although the set of trigger types can be assumed to be known at compile time.
We want a table of all triggers.
Our current best idea:
Dynamically create a materialized view of the following form:
id | id_in_plugin_table | thing_in_main_program_it_refs | plugin_name
---------------------------------------------------------------------
1 | 27 | 8 | RegexTrigger
2 | 27 | 12 | RidiculouslyUnsafeCustomJSTrigger
This relation is automatically generated from the various plugin tables, each of which have their own ID and a thing_in_main_program_it_refs field.
For illustration, here's what the referenced tables may look like.
RegexTrigger table:
id | thing_in_main_program_it_refs | regex
---------------------------------------------------------------------
27 | 8 | hel*o
RidiculouslyUnsafeCustomJSTrigger
id | thing_in_main_program_it_refs | custom_js
---------------------------------------------------------------------
27 | 12 | (x) => isPrime(x.length())
Either use two roundtrips to lookup the plugin table and then query it, or combine them into a single SQL program which uses EXEC.
I'm happy with part 1, but not with part 2. Neither option sounds efficient, and the latter option uses EXEC.
So, we're looking for either (a) a better way to dynamically select a table in a query, or (b) a different approach to open sums.
Related
Background
I need to compare two tables in two different datacenters to make sure they're the same. The tables can be hundreds of millions, even a billion lines.
An example of this is having a production data pipeline and a development data pipeline. I need to verify that the tables at the end of each pipeline are the same, however, they're located in different datacenters.
The tables are the same if all the values and datatypes for each row and column match. There are primary keys for each table.
Here's an example input and output:
Input
table1:
Name | Age |
Alice| 25.0|
Bob | 49 |
Jim | 45 |
Cal | 52 |
table2:
Name | Age |
Bob | 49 |
Cal | 42 |
Alice| 25 |
Output:
table1 missing rows (empty):
Name | Age |
| |
table2 missing rows:
Name | Age |
Jim | 45 |
mismatching rows:
Name | Age | table |
Alice| 25.0| table1|
Alice| 25 | table2|
Cal | 52 | table1|
Cal | 42 | table2|
Note: The output doesn't need to be exactly like the above format, but it does need to contain the same information.
Question
Is it faster to import these tables into a new, common SQL environment, then use SQL to produce my desired output?
OR
Is it faster to use something like JDBC, retrieve all rows for each table, sort each table, then compare them line by line to produce my desired output?
Edits:
The above solutions would be executed at a datacenter that's hosting one of the tables. In the first solution, the only purpose for creating a new database would be to compare these tables using SQL, there are no other uses.
You should definitively start with the database option. Especially if the databases are connected with a database link you can easy set up the transfer of the data.
Such comparison often leads to a full outer join of the two sources and the experience tell us that DIY joins are notorically less performant that the native database implementation (you can deploy for example a parallel option).
Anyway you may try to implement some sofisticated algoritm that can make the compare without the necessity to transfer the whole table.
An example is based on the Merkle Trees where you first scan both source in their location to recognise which parts are identical (that can be ignored) and transfer and compare only the party with a difference.
So if you expect the tables are nearly identical and have keys that allows some hierarchy such approach could end better than a brute force full compare.
The faster solution is to load both tables to variables (memory) in your programing language and then compare them with your favorite algorithm.
Copy them first to a new table is the more than the double of time in read/write operations to disk, especially the write ones.
it is possible to store a function IN the table to automatically sum a group of columns and store the result in a final column?
ie:
+----+------------+-----------+-------------+------------+
| id | appleCount | pearCount | bananaCount | totalFruit |
+----+------------+-----------+-------------+------------+
| 1 | 300 | 60 | 120 | 480 |
+----+------------+-----------+-------------+------------+
where the column totalFruit is automatically calculated from the previous three columns and updated as the other columns update. in this specific application, there is ONLY going to be the one row. it would be spanky-handy to be able to just push the updated counts and then pull the calculated total out. i seem to recall reading about this ability somewhere, but for the life of me, i can't recall where... :poop:
if there is not way to do this, that's cool. but if there is... :smile:
TIA!
WR!
Yes, it is possible. But is it worth it? It is simple enough to do
SELECT ...
appleCount + pearCount + bananaCount AS totalFruit
...
See MariaDB Generated Columns for how to generate the extra column -- either as a real extra column or "virtual". What version of MariaDB?--There are a number of changes over time.
(MySQL users: 5.7.6 has a similar MySQL Generated Columns.)
I am sure this question has been asked before, but I'm so new to SQL, I can't even combine the correct search terms to find an answer! So, apologies if this is a repetition.
The db I'm creating has to be created at run-time, then the data is entered after creation. Some fields will have a varying number of entries, but the number is unknown at creation time.
I'm struggling to come up with a db design to handle this variation.
As an (anonymised) example, please see below:
| salad_name | salad_type | salad_ingredients | salad_cost |
| apple | fruity | apple | cheap |
| unlikely | meaty | sausages, chorizo | expensive |
| normal | standard | leaves, cucumber, tomatoes | mid |
As you can see, the contents of "salad_ingredients" varies.
My thoughts were:
just enter a single, comma-separated string and separate at run-time. Seems hacky, and couldn't search by salad_ingredients!
have another table, for each salad, such as "apple_ingredients", which could have a varying number of rows for each ingredient. However, I can't do this, because I don't know the salad_name at creation time! :(
Have a separate salad_ingredients table, where each row is a salad_name, and there is an arbitrary number of ingredients fields, say 10, so you could have up to 10 ingredients. Again, seems slightly hacky, as I don't like to unused fields, and what happens if a super-complicated salad comes along?
Is there a solution that I've missed?
Thanks,
Dan
based on my experience the best solution is based on a normalized set of tables
table salads
id
salad_name
salad_type
salad_cost
.
table ingredients
id
name
and
table salad_ingredients
id
id_salad
id_ingredients
where id_salad is the corresponding if from salads
and id_ingredients is the corresponding if from ingredients
using proper join you can get (select) and filter (where) all the values you need
While trying to build a data warehousing application using Talend, we are faced with the following scenario.
We have two tables tables that look like
Table master
ID | CUST_NAME | CUST_EMAIL
------------------------------------
1 | FOO | FOO_BAR#EXAMPLE.COM
Events Table
ID | CUST_ID | EVENT_NAME | EVENT_DATE
---------------------------------------
1 | 1 | ACC_APPLIED | 2014-01-01
2 | 1 | ACC_OPENED | 2014-01-02
3 | 1 | ACC_CLOSED | 2014-01-02
There is a one-to-many relationship between master and the events table.Since, given a limited number of event names I proposing that we denormalize this structure into something that looks like
ID | CUST_NAME | CUST_EMAIL | ACC_APP_DATE_ID | ACC_OPEN_DATE_ID |ACC_CLOSE_DATE_ID
-----------------------------------------------------------------------------------------
1 | FOO | FOO_BAR#EXAMPLE.COM | 20140101 | 20140102 | 20140103
THE DATE_ID columns refer to entries inside the time dimension table.
First question : Is this a good idea ? What are the other alternatives to this scheme ?
Second question : How do I implement this using Talend Open Studio ? I figured out a way in which I moved the data for each event name into it's own temporary table along with cust_id using the tMap component and later linked them together using another tMap. Is there another way to do this in talend ?
To do this in Talend you'll need to first sort your data so that it is reliably in the order of applied, opened and closed for each account and then denormalize it to a single row with a single delimited field for the dates using the tDenormalizeRows component.
After this you'll want to use tExtractDelimitedFields to split the single dates field.
Yeah, this is a good idea, this is called a cumulative snapshot fact. http://www.kimballgroup.com/2012/05/design-tip-145-time-stamping-accumulating-snapshot-fact-tables/
Not sure how to do this in Talend (dont know the tool) but it would be quite easy to implement in SQL using a Case or Pivot statement
Regarding only your first question, it's certainly a good idea -- unless there is any possibility of the same persons applying-opening-closing their account more than once AND you want to keep all this information in their history (so UPDATE wouldn't help).
Snowflaking is definitely not a good option if you are going to design a data warehouse. So, denormalizing will certainly be a good choice in this case. Following article almost fits perfectly to clear the air over such scenarios,
http://www.kimballgroup.com/2008/09/design-tip-105-snowflakes-outriggers-and-bridges/
let's assume i have a self referencing hierarchical table build the classical way like this one:
CREATE TABLE test
(name text,id serial primary key,parent_id integer
references test);
insert into test (name,id,parent_id) values
('root1',1,NULL),('root2',2,NULL),('root1sub1',3,1),('root1sub2',4,1),('root
2sub1',5,2),('root2sub2',6,2);
testdb=# select * from test;
name | id | parent_id
-----------+----+-----------
root1 | 1 |
root2 | 2 |
root1sub1 | 3 | 1
root1sub2 | 4 | 1
root2sub1 | 5 | 2
root2sub2 | 6 | 2
What i need now is a function (preferrably in plain sql) that would take the id of a test record and
clone all attached records (including the given one). The cloned records need to have new ids of course. The desired result
would like this for example:
Select * from cloningfunction(2);
name | id | parent_id
-----------+----+-----------
root2 | 7 |
root2sub1 | 8 | 7
root2sub2 | 9 | 7
Any pointers? Im using PostgreSQL 8.3.
Pulling this result in recursively is tricky (although possible). However, it's typically not very efficient and there is a much better way to solve this problem.
Basically, you augment the table with an extra column which traces the tree to the top - I'll call it the "Upchain". It's just a long string that looks something like this:
name | id | parent_id | upchain
root1 | 1 | NULL | 1:
root2 | 2 | NULL | 2:
root1sub1 | 3 | 1 | 1:3:
root1sub2 | 4 | 1 | 1:4:
root2sub1 | 5 | 2 | 2:5:
root2sub2 | 6 | 2 | 2:6:
root1sub1sub1 | 7 | 3 | 1:3:7:
It's very easy to keep this field updated by using a trigger on the table. (Apologies for terminology but I have always done this with SQL Server). Every time you add or delete a record, or update the parent_id field, you just need to update the upchain field on that part of the tree. That's a trivial job because you just take the upchain of the parent record and append the id of the current record. All child records are easily identified using LIKE to check for records with the starting string in their upchain.
What you're doing effectively is trading a bit of extra write activity for a big saving when you come to read the data.
When you want to select a complete branch in the tree it's trivial. Suppose you want the branch under node 1. Node 1 has an upchain '1:' so you know that any node in the branch of the tree under that node must have an upchain starting '1:...'. So you just do this:
SELECT *
FROM table
WHERE upchain LIKE '1:%'
This is extremely fast (index the upchain field of course). As a bonus it also makes a lot of activities extremely simple, such as finding partial trees, level within the tree, etc.
I've used this in applications that track large employee reporting hierarchies but you can use it for pretty much any tree structure (parts breakdown, etc.)
Notes (for anyone who's interested):
I haven't given a step-by-step of the SQL code but once you get the principle, it's pretty simple to implement. I'm not a great programmer so I'm speaking from experience.
If you already have data in the table you need to do a one time update to get the upchains synchronised initially. Again, this isn't difficult as the code is very similar to the UPDATE code in the triggers.
This technique is also a good way to identify circular references which can otherwise be tricky to spot.
The Joe Celko's method which is similar to the njreed's answer but is more generic can be found here:
Nested-Set Model of Trees (at the middle of the article)
Nested-Set Model of Trees, part 2
Trees in SQL -- Part III
#Maximilian: You are right, we forgot your actual requirement. How about a recursive stored procedure? I am not sure if this is possible in PostgreSQL, but here is a working SQL Server version:
CREATE PROCEDURE CloneNode
#to_clone_id int, #parent_id int
AS
SET NOCOUNT ON
DECLARE #new_node_id int, #child_id int
INSERT INTO test (name, parent_id)
SELECT name, #parent_id FROM test WHERE id = #to_clone_id
SET #new_node_id = ##IDENTITY
DECLARE #children_cursor CURSOR
SET #children_cursor = CURSOR FOR
SELECT id FROM test WHERE parent_id = #to_clone_id
OPEN #children_cursor
FETCH NEXT FROM #children_cursor INTO #child_id
WHILE ##FETCH_STATUS = 0
BEGIN
EXECUTE CloneNode #child_id, #new_node_id
FETCH NEXT FROM #children_cursor INTO #child_id
END
CLOSE #children_cursor
DEALLOCATE #children_cursor
Your example is accomplished by EXECUTE CloneNode 2, null (the second parameter is the new parent node).
This sounds like an exercise from "SQL For Smarties" by Joe Celko...
I don't have my copy handy, but I think it's a book that'll help you quite a bit if this is the kind of problems you need to solve.