I have the following table structure :
place_id | parent_place_id | name
---------|-----------------|------------
1 | 2 | child
---------|-----------------|------------
2 | 3 | dad
---------|-----------------|------------
3 | | grandfather
......
I am trying to write a query so that my output data is as follows :
id_Grandfather | name_Grandfather | id_Dad | name_Dad | id_Child | name_child
----------------|------------------|--------|----------|----------|-----------
3 | grandfather | 2 | dad | 1 | child
I have tried many ways but not getting the expected result. Can anyone help me to solve it? Thank !
There is a way to do it with double join. But does it make any sense is totally different question.
SELECT
gf.place_id as id_Grandfather,
gf.name as name_Grandfather,
d.place_id as id_Dad,
d.name as name_Dad,
c.place_id as id_Child,
c.name as name_Child
FROM
your_table c
LEFT JOIN your_table d ON c.parent_place_id = d.place_id
LEFT JOIN your_table gf ON d.parent_lace_id = gf.place_id
-- Add this if you want to have only lines which has Dad and Grandfather fields populated
WHERE d.place_id IS NOT NULL
;
I have the following sample data (items) with some kind of recursion. For the sake of simplicity I limited the sample to 2 level. Matter of fact - they could grow quite deep.
+----+--------------------------+----------+------------------+-------+
| ID | Item - Name | ParentID | MaterializedPath | Color |
+----+--------------------------+----------+------------------+-------+
| 1 | Parent 1 | null | 1 | green |
| 2 | Parent 2 | null | 2 | green |
| 4 | Parent 2 Child 1 | 2 | 2.4 | orange|
| 6 | Parent 2 Child 1 Child 1 | 4 | 2.4.6 | red |
| 7 | Parent 2 Child 1 Child 2 | 4 | 2.4.7 | orange|
| 3 | Parent 1 Child 1 | 1 | 1.3 | orange|
| 5 | Parent 1 Child 1 Child | 3 | 1.3.5 | red |
+----+--------------------------+----------+------------------+-------+
I need to get via SQL all children
which are not orange
for a given starting ID
with either starting ID=1. The result should be 1, 1.3.5. When start with ID=4 the should be 2.4.6.
I read little bit and found the CTE should be used. I tried the following simplified definition
WITH w1( id, parent_item_id) AS
( SELECT
i.id,
i.parent_item_id
FROM
item i
WHERE
id = 4
UNION ALL
SELECT
i.id,
i.parent_item_id
FROM
item, JOIN w1 ON i.parent_item_id = w1.id
);
However, this won't even be executed as SQL-statement. I have several question to this:
CTE could be used with Hibernate?
Is there a way have the result via SQL queries? (more or less as recursive pattern)
I'm somehow lost with the recursive pattern combined with selection of color for the end result.
Your query is invalid for the following reasons:
As documented in the manual a recursive CTE requires the RECURSIVE keyword
Your JOIN syntax is wrong. You need to remove the , and give the items table an alias.
If you need the color column, just add it to both SELECTs inside the CTE and filter the rows in the final SELECT.
If that is changed, the following works fine:
WITH recursive w1 (id, parent_item_id, color) AS
(
SELECT i.id,
i.parent_item_id,
i.color
FROM item i
WHERE id = 4
UNION ALL
SELECT i.id,
i.parent_item_id,
i.color
FROM item i --<< missing alias
JOIN w1 ON i.parent_item_id = w1.id
)
select *
from w1
where color <> 'orange'
Note that the column list for the CTE definition is optional, so you can just write with recursive w1 as ....
I have a table A with columns: id, title, condition
And i have another table B with information about position for some rows from table A. Table B have columns id, next_id, prev_id
How to sort rows from A based on information from table B?
For example,
Table A
id| title
---+-----
1 | title1
2 | title2
3 | title3
4 | title4
5 | title5
Table B
id| next_id | prev_id
---+-----
2 | 1 | null
5 | 4 | 3
I want to get this result:
id| title
---+-----
2 | title2
1 | title1
3 | title3
5 | title5
4 | title4
And after apply this sort, i want to sort by condition column yet.
I've already spent a lot of time looking for a solution, and hope for your help.
You have to add weights to your data, so you can order accordingly. This example uses next_id, not sure if you need to use prev_id, you don't explain the use of it.
Anyway, here's a code example:
-- Temporal Data for the test:
CREATE TEMP TABLE table_a(id integer,tittle text);
CREATE TEMP TABLE table_b(id integer,next_id integer, prev_id integer);
INSERT INTO table_a VALUES
(1,'title1'),
(2,'title2'),
(3,'title3'),
(4,'title4'),
(5,'title5');
INSERT INTO table_b VALUES
(2,1,null),
(5,4,3);
-- QUERY:
SELECT
id,tittle,
CASE -- Adding weight
WHEN next_id IS NULL THEN (id + 0.1)
ELSE next_id
END AS orden
FROM -- Joining tables
(SELECT ta.*,tb.next_id
FROM table_a ta
LEFT JOIN table_b tb
ON ta.id=tb.id)join_a_b
ORDER BY orden
And here's the result:
id | tittle | orden
--------------------------
2 | title2 | 1
1 | title1 | 1.1
3 | title3 | 3.1
5 | title5 | 4
4 | title4 | 4.1
Situation
Using Python 3, Django 1.9, Cubes 1.1, and Postgres 9.5.
These are my datatables in pictorial form:
The same in text format:
Store table
------------------------------
| id | code | address |
|-----|------|---------------|
| 1 | S1 | Kings Row |
| 2 | S2 | Queens Street |
| 3 | S3 | Jacks Place |
| 4 | S4 | Diamonds Alley|
| 5 | S5 | Hearts Road |
------------------------------
Product table
------------------------------
| id | code | name |
|-----|------|---------------|
| 1 | P1 | Saucer 12 |
| 2 | P2 | Plate 15 |
| 3 | P3 | Saucer 13 |
| 4 | P4 | Saucer 14 |
| 5 | P5 | Plate 16 |
| and many more .... |
|1000 |P1000 | Bowl 25 |
|----------------------------|
Sales table
----------------------------------------
| id | product_id | store_id | amount |
|-----|------------|----------|--------|
| 1 | 1 | 1 |7.05 |
| 2 | 1 | 2 |9.00 |
| 3 | 2 | 3 |1.00 |
| 4 | 2 | 3 |1.00 |
| 5 | 2 | 5 |1.00 |
| and many more .... |
| 1000| 20 | 4 |1.00 |
|--------------------------------------|
The relationships are:
Sales belongs to Store
Sales belongs to Product
Store has many Sales
Product has many Sales
What I want to achieve
I want to use cubes to be able to do a display by pagination in the following manner:
Given the stores S1-S3:
-------------------------
| product | S1 | S2 | S3 |
|---------|----|----|----|
|Saucer 12|7.05|9 | 0 |
|Plate 15 |0 |0 | 2 |
| and many more .... |
|------------------------|
Note the following:
Even though there were no records in sales for Saucer 12 under Store S3, I displayed 0 instead of null or none.
I want to be able to do sort by store, say descending order for, S3.
The cells indicate the SUM total of that particular product spent in that particular store.
I also want to have pagination.
What I tried
This is the configuration I used:
"cubes": [
{
"name": "sales",
"dimensions": ["product", "store"],
"joins": [
{"master":"product_id", "detail":"product.id"},
{"master":"store_id", "detail":"store.id"}
]
}
],
"dimensions": [
{ "name": "product", "attributes": ["code", "name"] },
{ "name": "store", "attributes": ["code", "address"] }
]
This is the code I used:
result = browser.aggregate(drilldown=['Store','Product'],
order=[("Product.name","asc"), ("Store.name","desc"), ("total_products_sale", "desc")])
I didn't get what I want.
I got it like this:
----------------------------------------------
| product_id | store_id | total_products_sale |
|------------|----------|---------------------|
| 1 | 1 | 7.05 |
| 1 | 2 | 9 |
| 2 | 3 | 2.00 |
| and many more .... |
|---------------------------------------------|
which is the whole table with no pagination and if the products not sold in that store it won't show up as zero.
My question
How do I get what I want?
Do I need to create another data table that aggregates everything by store and product before I use cubes to run the query?
Update
I have read more. I realised that what I want is called dicing as I needed to go across 2 dimensions. See: https://en.wikipedia.org/wiki/OLAP_cube#Operations
Cross-posted at Cubes GitHub issues to get more attention.
This is a pure SQL solution using crosstab() from the additional tablefunc module to pivot the aggregated data. It typically performs better than any client-side alternative. If you are not familiar with crosstab(), read this first:
PostgreSQL Crosstab Query
And this about the "extra" column in the crosstab() output:
Pivot on Multiple Columns using Tablefunc
SELECT product_id, product
, COALESCE(s1, 0) AS s1 -- 1. ... displayed 0 instead of null
, COALESCE(s2, 0) AS s2
, COALESCE(s3, 0) AS s3
, COALESCE(s4, 0) AS s4
, COALESCE(s5, 0) AS s5
FROM crosstab(
'SELECT s.product_id, p.name, s.store_id, s.sum_amount
FROM product p
JOIN (
SELECT product_id, store_id
, sum(amount) AS sum_amount -- 3. SUM total of product spent in store
FROM sales
GROUP BY product_id, store_id
) s ON p.id = s.product_id
ORDER BY s.product_id, s.store_id;'
, 'VALUES (1),(2),(3),(4),(5)' -- desired store_id's
) AS ct (product_id int, product text -- "extra" column
, s1 numeric, s2 numeric, s3 numeric, s4 numeric, s5 numeric)
ORDER BY s3 DESC; -- 2. ... descending order for S3
Produces your desired result exactly (plus product_id).
To include products that have never been sold replace [INNER] JOIN with LEFT [OUTER] JOIN.
SQL Fiddle with base query.
The tablefunc module is not installed on sqlfiddle.
Major points
Read the basic explanation in the reference answer for crosstab().
I am including with product_id because product.name is hardly unique. This might otherwise lead to sneaky errors conflating two different products.
You don't need the store table in the query if referential integrity is guaranteed.
ORDER BY s3 DESC works, because s3 references the output column where NULL values have been replaced with COALESCE. Else we would need DESC NULLS LAST to sort NULL values last:
PostgreSQL sort by datetime asc, null first?
For building crosstab() queries dynamically consider:
Dynamic alternative to pivot with CASE and GROUP BY
I also want to have pagination.
That last item is fuzzy. Simple pagination can be had with LIMIT and OFFSET:
Displaying data in grid view page by page
I would consider a MATERIALIZED VIEW to materialize results before pagination. If you have a stable page size I would add page numbers to the MV for easy and fast results.
To optimize performance for big result sets, consider:
SQL syntax term for 'WHERE (col1, col2) < (val1, val2)'
Optimize query with OFFSET on large table
I'm using PostgreSQL 9.1 to query hierarchical tree-structured data, consisting of edges (or elements) with connections to nodes. The data are actually for stream networks, but I've abstracted the problem to simple data types. Consider the example tree table. Each edge has length and area attributes, which are used to determine some useful metrics from the network.
CREATE TEMP TABLE tree (
edge text PRIMARY KEY,
from_node integer UNIQUE NOT NULL, -- can also act as PK
to_node integer REFERENCES tree (from_node),
mode character varying(5), -- redundant, but illustrative
length numeric NOT NULL,
area numeric NOT NULL,
fwd_path text[], -- optional ordered sequence, useful for debugging
fwd_search_depth integer,
fwd_length numeric,
rev_path text[], -- optional unordered set, useful for debugging
rev_search_depth integer,
rev_length numeric,
rev_area numeric
);
CREATE INDEX ON tree (to_node);
INSERT INTO tree(edge, from_node, to_node, mode, length, area) VALUES
('A', 1, 4, 'start', 1.1, 0.9),
('B', 2, 4, 'start', 1.2, 1.3),
('C', 3, 5, 'start', 1.8, 2.4),
('D', 4, 5, NULL, 1.2, 1.3),
('E', 5, NULL, 'end', 1.1, 0.9);
Which can be illustrated below, where the edges represented by A-E are connected with nodes 1-5. The NULL to_node (Ø) represents the end node. The from_node is always unique, so it can act as PK. If this network flows like a drainage basin, it would be from top to bottom, where the starting tributary edges are A, B, C and the ending outflow edge is E.
The documentation for WITH provide a nice example of how to use search graphs in recursive queries. So, to get the "forwards" information, the query starts at the end, and works backwards:
WITH RECURSIVE search_graph AS (
-- Begin at ending nodes
SELECT E.from_node, 1 AS search_depth, E.length
, ARRAY[E.edge] AS path -- optional
FROM tree E WHERE E.to_node IS NULL
UNION ALL
-- Accumulate each edge, working backwards (upstream)
SELECT o.from_node, sg.search_depth + 1, sg.length + o.length
, o.edge|| sg.path -- optional
FROM tree o, search_graph sg
WHERE o.to_node = sg.from_node
)
UPDATE tree SET
fwd_path = sg.path,
fwd_search_depth = sg.search_depth,
fwd_length = sg.length
FROM search_graph AS sg WHERE sg.from_node = tree.from_node;
SELECT edge, from_node, to_node, fwd_path, fwd_search_depth, fwd_length
FROM tree ORDER BY edge;
edge | from_node | to_node | fwd_path | fwd_search_depth | fwd_length
------+-----------+---------+----------+------------------+------------
A | 1 | 4 | {A,D,E} | 3 | 3.4
B | 2 | 4 | {B,D,E} | 3 | 3.5
C | 3 | 5 | {C,E} | 2 | 2.9
D | 4 | 5 | {D,E} | 2 | 2.3
E | 5 | | {E} | 1 | 1.1
The above makes sense, and scales well for large networks. For example, I can see edge B is 3 edges from the end, and the forward path is {B,D,E} with a total length of 3.5 from the tip to the end.
However, I cannot figure out a good way to build a reverse query. That is, from each edge, what are the accumulated "upstream" edges, lengths and areas. Using WITH RECURSIVE, the best I have is:
WITH RECURSIVE search_graph AS (
-- Begin at starting nodes
SELECT S.from_node, S.to_node, 1 AS search_depth, S.length, S.area
, ARRAY[S.edge] AS path -- optional
FROM tree S WHERE from_node IN (
-- Starting nodes have a from_node without any to_node
SELECT from_node FROM tree EXCEPT SELECT to_node FROM tree)
UNION ALL
-- Accumulate edges, working forwards
SELECT c.from_node, c.to_node, sg.search_depth + 1, sg.length + c.length, sg.area + c.area
, c.edge || sg.path -- optional
FROM tree c, search_graph sg
WHERE c.from_node = sg.to_node
)
UPDATE tree SET
rev_path = sg.path,
rev_search_depth = sg.search_depth,
rev_length = sg.length,
rev_area = sg.area
FROM search_graph AS sg WHERE sg.from_node = tree.from_node;
SELECT edge, from_node, to_node, rev_path, rev_search_depth, rev_length, rev_area
FROM tree ORDER BY edge;
edge | from_node | to_node | rev_path | rev_search_depth | rev_length | rev_area
------+-----------+---------+----------+------------------+------------+----------
A | 1 | 4 | {A} | 1 | 1.1 | 0.9
B | 2 | 4 | {B} | 1 | 1.2 | 1.3
C | 3 | 5 | {C} | 1 | 1.8 | 2.4
D | 4 | 5 | {D,A} | 2 | 2.3 | 2.2
E | 5 | | {E,C} | 2 | 2.9 | 3.3
I would like to build aggregates into the second term of the recursive query, since each downstream edge connects to 1 or many upstream edges, but aggregates are not allowed with recursive queries. Also, I'm aware that the join is sloppy, since the with recursive result has multiple join conditions for edge.
The expected result for the reverse / backwards query is:
edge | from_node | to_node | rev_path | rev_search_depth | rev_length | rev_area
------+-----------+---------+-------------+------------------+------------+----------
A | 1 | 4 | {A} | 1 | 1.1 | 0.9
B | 2 | 4 | {B} | 1 | 1.2 | 1.3
C | 3 | 5 | {C} | 1 | 1.8 | 2.4
D | 4 | 5 | {A,B,D} | 3 | 3.5 | 3.5
E | 5 | | {A,B,C,D,E} | 5 | 6.4 | 6.8
How can I write this update query?
Note that I'm ultimately more concerned about accumulating accurate length and area totals, and that the path attributes are for debugging. In my real-world case, forwards paths are up to a couple hundred, and I expect reverse paths in the tens of thousands for large and complex catchments.
UPDATE 2:
I rewrote the original recursive query so that all accumulation/aggregation is done outside the recursive part. It should perform better than the previous version of this answer.
This is very much alike the answer from #a_horse_with_no_name for a similar question.
WITH
RECURSIVE search_graph(edge, from_node, to_node, length, area, start_node) AS
(
SELECT edge, from_node, to_node, length, area, from_node AS "start_node"
FROM tree
UNION ALL
SELECT o.edge, o.from_node, o.to_node, o.length, o.area, p.start_node
FROM tree o
JOIN search_graph p ON p.from_node = o.to_node
)
SELECT array_agg(edge) AS "edges"
-- ,array_agg(from_node) AS "nodes"
,count(edge) AS "edge_count"
,sum(length) AS "length_sum"
,sum(area) AS "area_sum"
FROM search_graph
GROUP BY start_node
ORDER BY start_node
;
Results are as expected:
start_node | edges | edge_count | length_sum | area_sum
------------+-------------+------------+------------+------------
1 | {A} | 1 | 1.1 | 0.9
2 | {B} | 1 | 1.2 | 1.3
3 | {C} | 1 | 1.8 | 2.4
4 | {D,B,A} | 3 | 3.5 | 3.5
5 | {E,D,C,B,A} | 5 | 6.4 | 6.8