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 have two tables products and product_attributs. One Product can have one or many attributs and these are filled by a dynamic web form (name and value inputs) added by the user as needed. For example for a drill the user could decide to add two attributs : color=blue and power=100 watts. For another product it could be 3 or more different attribus and for another it could have no special attributs.
products
| id | name | identifier | identifier_type | active
| ----------|--------------|-------------|------------------|---
| 1 | Drill | AD44 | barcode | true
| 2 | Polisher | AP211C | barcode | true
| 3 | Jackhammer | AJ2133 | barcode | false
| 4 | Screwdriver | AS4778 | RFID | true
product_attributs
|id | name | value | product_id
|----------|--------------|-------------|----------
|1 | color | blue | 1
|2 | power | 100 watts | 1
|3 | size | 40 cm | 2
|4 | energy | electrical | 3
|4 | price | 35€ | 3
so attributs could be anything which are set dynamically by the user. My need is to generate a report on CSV which contain all products with their attributs. Without a good experience in SQL I generated the following basic request :
SELECT pr.name, pr.identifier_type, pr.identifier, pr.active, att.name, att.value
FROM products as pr
LEFT JOIN product_attributs att ON pr.id = att.product_id
as you know the result will contain for the same product as many rows as attributs it has and this is not ideal for reporting. The ideal would be this :
|name | identifier_type | identifier | active | name | value | name | value
|-----------|-----------------|------------|--------|--------|-------|------ |------
|Drill | barcode | AD44 | true | color | blue | power | 100 w
|Polisher | barcode | AP211C | true | size | 40 cm | null | null
|Jackhammer | barcode | AJ2133 | true | energy | elect | price | 35 €
|Screwdriver| barcode | AS4778 | true | null | null | null | null
here I only showed a max of two attributes per product but it could be more if needed. Well I did some research and came across the pivot with crosstab function on Postgres but the problem it requests static values but this does not match my need.
thanks lot for your help and sorry for duplicates if any.
Thanks Laurenz Albe for your help. array_agg solved my problem. Here is the query if someone may be interested in :
SELECT
pr.name, pr.description, pr.identifier_type, pr.identifier,
pr.internal_identifier, pr.active,
ARRAY_TO_STRING(ARRAY_AGG (oa.name || ' = ' || oa.value),', ') attributs
FROM
products pr
LEFT JOIN product_attributs oa ON pr.id = oa.product_id
GROUP BY
pr.name, pr.description, pr.identifier_type, pr.identifier,
pr.internal_identifier, pr.active
ORDER BY
pr.name;
Googling for a definition either returns results for a column oriented DB or gives very vague definitions.
My understanding is that wide column stores consist of column families which consist of rows and columns. Each row within said family is stored together on disk. This sounds like how row oriented databases store their data. Which brings me to my first question:
How are wide column stores different from a regular relational DB table? This is the way I see it:
* column family -> table
* column family column -> table column
* column family row -> table row
This image from Database Internals simply looks like two regular tables:
The guess I have as to what is different comes from the fact that "multi-dimensional map" is mentioned along side wide column stores. So here is my second question:
Are wide column stores sorted from left to right? Meaning, in the above example, are the rows sorted first by Row Key, then by Timestamp, and finally by Qualifier?
Let's start with the definition of a wide column database.
Its architecture uses (a) persistent, sparse matrix, multi-dimensional
mapping (row-value, column-value, and timestamp) in a tabular format
meant for massive scalability (over and above the petabyte scale).
A relational database is designed to maintain the relationship between the entity and the columns that describe the entity. A good example is a Customer table. The columns hold values describing the Customer's name, address, and contact information. All of this information is the same for each and every customer.
A wide column database is one type of NoSQL database.
Maybe this is a better image of four wide column databases.
My understanding is that the first image at the top, the Column model, is what we called an entity/attribute/value table. It's an attribute/value table within a particular entity (column).
For Customer information, the first wide-area database example might look like this.
Customer ID Attribute Value
----------- --------- ---------------
100001 name John Smith
100001 address 1 10 Victory Lane
100001 address 3 Pittsburgh, PA 15120
Yes, we could have modeled this for a relational database. The power of the attribute/value table comes with the more unusual attributes.
Customer ID Attribute Value
----------- --------- ---------------
100001 fav color blue
100001 fav shirt golf shirt
Any attribute that a marketer can dream up can be captured and stored in an attribute/value table. Different customers can have different attributes.
The Super Column model keeps the same information in a different format.
Customer ID: 100001
Attribute Value
--------- --------------
fav color blue
fav shirt golf shirt
You can have as many Super Column models as you have entities. They can be in separate NoSQL tables or put together as a Super Column family.
The Column Family and Super Column family simply gives a row id to the first two models in the picture for quicker retrieval of information.
Most (if not all) Wide-column stores are indeed row-oriented stores in that every parts of a record are stored together. You can see that as a 2-dimensional key-value store. The first part of the key is used to distribute the data across servers, the second part of the key lets you quickly find the data on the target server.
Wide-column stores will have different features and behaviors. However, Apache Cassandra, for example, allows you to define how the data will be sorted. Take this table for example:
| id | country | timestamp | message |
|----+---------+------------+---------|
| 1 | US | 2020-10-01 | "a..." |
| 1 | JP | 2020-11-01 | "b..." |
| 1 | US | 2020-09-01 | "c..." |
| 2 | CA | 2020-10-01 | "d..." |
| 2 | CA | 2019-10-01 | "e..." |
| 2 | CA | 2020-11-01 | "f..." |
| 3 | GB | 2020-09-01 | "g..." |
| 3 | GB | 2020-09-02 | "h..." |
|----+---------+------------+---------|
If your partitioning key is (id) and your clustering key is (country, timestamp), the data will be stored like this:
[Key 1]
1:JP,2020-11-01,"b..." | 1:US,2020-09-01,"c..." | 1:US,2020-10-01,"a..."
[Key2]
2:CA,2019-10-01,"e..." | 2:CA,2020-10-01,"d..." | 2:CA,2020-11-01,"f..."
[Key3]
3:GB,2020-09-01,"g..." | 3:GB,2020-09-02,"h..."
Or in table form:
| id | country | timestamp | message |
|----+---------+------------+---------|
| 1 | JP | 2020-11-01 | "b..." |
| 1 | US | 2020-09-01 | "c..." |
| 1 | US | 2020-10-01 | "a..." |
| 2 | CA | 2019-10-01 | "e..." |
| 2 | CA | 2020-10-01 | "d..." |
| 2 | CA | 2020-11-01 | "f..." |
| 3 | GB | 2020-09-01 | "g..." |
| 3 | GB | 2020-09-02 | "h..." |
|----+---------+------------+---------|
If you change the primary key (composite of partitioning and clustering key) to (id, timestamp) WITH CLUSTERING ORDER BY (timestamp DESC) (id is the partitioning key, timestamp is the clustering key in descending order), the result would be:
[Key 1]
1:US,2020-09-01,"c..." | 1:US,2020-10-01,"a..." | 1:JP,2020-11-01,"b..."
[Key2]
2:CA,2019-10-01,"e..." | 2:CA,2020-10-01,"d..." | 2:CA,2020-11-01,"f..."
[Key3]
3:GB,2020-09-01,"g..." | 3:GB,2020-09-02,"h..."
Or in table form:
| id | country | timestamp | message |
|----+---------+------------+---------|
| 1 | US | 2020-09-01 | "c..." |
| 1 | US | 2020-10-01 | "a..." |
| 1 | JP | 2020-11-01 | "b..." |
| 2 | CA | 2019-10-01 | "e..." |
| 2 | CA | 2020-10-01 | "d..." |
| 2 | CA | 2020-11-01 | "f..." |
| 3 | GB | 2020-09-01 | "g..." |
| 3 | GB | 2020-09-02 | "h..." |
|----+---------+------------+---------|
I suspect this question is already well-answered but perhaps due to limited SQL vocabulary I have not managed to find what I need. I have a database with many code:description mappings in a single 'parameter' table. I would like to define a query or procedure to return the descriptions for all (or an arbitrary list of) coded values in a given 'content' table with their descriptions from the parameter table. I don't want to alter the original data, I just want to display friendly results.
Is there a standard way to do this?
Can it be accomplished with SELECT or are other statements required?
Here is a sample query for a single coded field:
SELECT TOP (5)
newid() as id,
B.BRIDGE_STATUS,
P.SHORTDESC
FROM
BRIDGE B
LEFT JOIN PARAMTRS P ON P.TABLE_NAME = 'BRIDGE'
AND P.FIELD_NAME = 'BRIDGE_STATUS'
AND P.PARMVALUE = B.BRIDGE_STATUS
ORDER BY
id
I want to produce 'decoded' results like:
| id | BRIDGE_STATUS |
|--------------------------------------|------------ |
| BABCEC1E-5FE2-46FA-9763-000131F2F688 | Active |
| 758F5201-4742-43C6-8550-000571875265 | Active |
| 5E51634C-4DD9-4B0A-BBF5-00087DF71C8B | Active |
| 0A4EA521-DE70-4D04-93B8-000CD12B7F55 | Inactive |
| 815C6C66-8995-4893-9A1B-000F00F839A4 | Proposed |
Rather than original, coded data like:
| id | BRIDGE_STATUS |
|--------------------------------------|---------------|
| F50214D7-F726-4996-9C0C-00021BD681A4 | 3 |
| 4F173E40-54DC-495E-9B84-000B446F09C3 | 3 |
| F9C216CD-0453-434B-AFA0-000C39EFA0FB | 3 |
| 5D09554E-201D-4208-A786-000C537759A1 | 1 |
| F0BDB9A4-E796-4786-8781-000FC60E200C | 4 |
but for an arbitrary number of columns.
This is a hardcoded example of what I'm trying to achieve:
SELECT id FROM places
WHERE metadata->'route'='Route 23'
OR metadata->'route'='Route 22'
OR metadata->'region'='Northwest'
OR metadata->'territory'='Territory A';
Metadata column is an hstore column and I'm wanting to build up the WHERE clause dynamically based on another query from a different table. The table could either be:
id | metadata
---------+----------------------------
1647 | "region"=>"Northwest"
1648 | "route"=>"Route 23"
1649 | "route"=>"Route 22"
1650 | "territory"=>"Territory A"
or
id | key | value
----+-------------+-------+---
1 | route | Route 23
2 | route | Route 22
3 | region | Northwest
4 | territory | Territory A
Doesnt really matter, just whatever works to build up that where clause. It could potentially have 1 to n number of OR's in it based on the other query.
Ended up with a solution using the 2nd table (distribution table):
id | metadata
---------+----------------------------
1647 | "region"=>"Northwest"
1648 | "route"=>"Route 23"
1649 | "route"=>"Route 22"
1650 | "territory"=>"Territory A"
Used the following join, which the #> sees if the places.metadata contains the distributions.metadata
SELECT places.id, places.metadata
FROM places INNER JOIN distributions
ON places.metadata #> distributions.metadata
WHERE distributions.some_other_column = something;