I have a table with a column that includes a handful of numbers delimited by a comma. I need to select * rows that include a particular value. I am using SQL Server and C# so it can be in SQL or LINQ.
The data in my channels column (varchar) looks something like this: 1,5,8,22,27,33
My Media table looks like this:
MediaID MediaName MediaDate ChannelIDs
------- --------- --------- ----------
1 | The Cow Jumped Over The Moon | 01/18/2015 | 1,5,8,22,27,33
2 | The Cat In The Hat | 01/18/2015 | 2,4,9,25,28,31
3 | Robin Hood The Thief | 01/18/2015 | 3,5,6,9,22,33
4 | Jingle Bells Batman Smells | 01/18/2015 | 6,7,9,24,25,32
5 | Up The River Down The River | 01/18/2015 | 5,6,10,25,26,33
etc...
My Channels Table looks like this:
ChannelID ChannelName
--------- -----------
1 Animals
2 Television
3 Movies
4 Nursery Rhymes
5 Holidays
etc...
Each row of Media could contain multiple channels.
Should I be using a contains search like this?
SELECT * FROM Media WHERE CONTAINS (Channels,'22')
This would require me to full-text index this column but I don't really want to include this column in my full-text index.
Is there a better way to do this?
Thanks
You should fix your data format so you are not storing numbers as comma-delimited strings. SQL has a great data structure for lists, it is called a table not a string. In particular, you want a junction table with one row per "media" entity and id.
That said, sometimes you are stuck with a particular data structure. If so, you can use like:
where ','+channels+',' like '%,22,%'
Note: this cannot take advantage of regular indexes, so performance will not be good. Fix the data structure if you have a large table and need better performance.
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.
I have an array type I want to store in Postgres. One of the major use cases I have is to see if any of the records has an array which has a string in it.
eg.
| A | ["NY", "Paris", "Milan"] |
| B | ["Paris", "NY"] |
| C | [] |
| D | ["Milan"] |
Does there exist a row with Paris in the array? Which rows have Milan in the array? and so on.
I have 2 options on how to store the column. I can either make it a type text[] or convert it into a json as {"cities": ["NY", "Paris", "Milan"]} and then store as a JSONB field
However, I am not sure what would allow the fastest querying for the use case I have. Is there any one obviously better way of doing this? Am I tying myself down in any way by choosing one over the other? If I choose one over the other then how can I query the DB?
As you seem to be storing simple lists of values, I would recommend to use datataype Array over JSON, which better fits more complex cases (nested datastructures, associative arrays, ...).
To check for the value of an element at any position in the array, you can use array function ANY().
Here is a query that will return all records where the array stored in column cities contains 'Paris' :
SELECT t.* FROM mytable t WHERE 'Paris' = ANY(t.cities);
Yields :
id cities
---------------------------
A ["NY","Paris","Milan"]
B ["Paris","NY"]
Demo on DB Fiddle
For more information :
Postgres Arrays Documentation
Postgres Arrays Tutorial
I've noticed it is better to query JSONB, if it is a simple key-value store.
As in for instance you want to store arbitrary info on a row that your not sure what the columns(keys) would be.
info = {"a":"apple", "b":"ball"}
For use cases like yours, it would be better if you could design the db with simple tables so you could use JOINS and Indexes to your advantage.
You could restructure the tables like :
Location
id | name
----------
1 | Paris
2 | NY
3 | Milan
Other Table (with foreign key on location table)
user | location_id
--------------------
A | 1
A | 3
B | 2
Using these set of tables it would be easy to query all users with location paris using JOINS.
My client wants the possibility to match a set of data against an array of regular expressions, meaning:
table:
name | officeId (foreignkey)
--------
bob | 1
alice | 1
alicia | 2
walter | 2
and he wants to do something along those lines:
get me all records of offices (officeId) where there is a member with
ANY name ~ ANY[.*ob, ali.*]
meaning
ANY of[alicia, walter] ~ ANY of [.*ob, ali.*] results in true
I could not figure it out by myself sadly :/.
Edit
The real Problem was missing form the original description:
I cannot use select disctinct officeId .. where name ~ ANY[.*ob, ali.*], because:
This application, stored data in postgres-xml columns, which means i do in fact have (after evaluating xpath('/data/clients/name/text()'))::text[]):
table:
name | officeId (foreignkey)
-----------------------------------------
[bob, alice] | 1
[anthony, walter] | 2
[alicia, walter] | 3
There is the Problem. And "you don't do that, that is horrible, why would you do it like this, store it like it is meant to be stored in a relation database, user a no-sql database for Document-based storage, use json" are no options.
I am stuck with this datamodel.
This looks pretty horrific, but the only way I can think of doing such a thing would be a hybrid of a cross-join and a semi join. On small data sets this would probably work pretty well. On large datasets, I imagine the cross-join component could hit you pretty hard.
Check it out and let me know if it works against your real data:
with patterns as (
select unnest(array['.*ob', 'ali.*']) as pattern
)
select
o.name, o.officeid
from
office o
where exists (
select null
from patterns p
where o.name ~ p.pattern
)
The semi-join helps protect you from cases where you have a name like "alicia nob" that would meet multiple search patterns would otherwise come back for every match.
You could cast the array to text.
SELECT * FROM workers WHERE (xpath('/data/clients/name/text()', xml_field))::text ~ ANY(ARRAY['wal','ant']);
When casting a string array into text, strings containing special characters or consisting of keywords are enclosed in double quotes kind of like {jimmy,"walter, james"} being two entries. Also when matching with ~ it is matched against any part of the string, not the same as LIKE where it's matched against the whole string.
Here is what I did in my test database:
test=# select id, (xpath('/data/clients/name/text()', name))::text[] as xss, officeid from workers WHERE (xpath('/data/clients/name/text()', name))::text ~ ANY(ARRAY['wal','ant']);
id | xss | officeid
----+-------------------------+----------
2 | {anthony,walter} | 2
3 | {alicia,walter} | 3
4 | {"walter, james"} | 5
5 | {jimmy,"walter, james"} | 4
(4 rows)
I have a table with data along the (massively simplified) lines of:
User | Value
-----|------
UsrA | 100
UsrA | 102
UsrB | 100
UsrA | 100
UsrB | 101
and, for reasons far to obscure to go into, I need to store the COUNT of each value in a table for future retrieval - ending up with something like
User | Value100Count | Value101Count | Value102Count
-----|---------------|---------------|--------------
UsrA | 2 | 0 | 1
UsrB | 1 | 1 | 0
However, there could be up to 255 different Values - meaning potentially 255 different ValueXCount columns. I know this is a horrible way to do things, but is there an easy way to get the data into a format that can be easily INSERTed into the destination table? Is there a better way to store the COUNT of values per user (unfortunately I do need to store this information; grabbing it from the source table each time isn't an option)?
The whole thing isn't very pretty, but you know that, rather than your table with 255 columns I'd consider setting up another table with:
User | Value | CountOfValue
And set a primary key over User and Value.
You could then insert the count's for given user/value combos into the CountOfValue field
As I said, the design is horrible and it feels like you would be better off starting from scratch, normalizing and doing counts live.
Check out indexed views. You can maintain the table automatically, with integrity and as a bonus it can get used in queries that already do count(*) on that data.
I'm going to start work on a medium sized application, and i'm planning it's db design.
One thing that I'm not sure about is this.
I will have many tables which will need internationalization, such as: "membership_options, gender_options, language_options etc"
Each of these tables will share common i18n fields, like:
"title, alternative_title, short_description, description"
In your opinion which is the best way to do it?
Have an i18n table with the same fields for each of the tables that will need them?
or do something like:
Membership table Gender table
---------------- --------------
id | created_at id | created_at
1 - 22.03.2001 1 - 14.08.2002
2 - 22.03.2001 2 - 14.08.2002
General translation table
-------------------------
record_id | table_name | string_name | alternative_title| .... |id_language
1 - membership regular null 1 (english)
1 - membership normale null 2 (italian)
1 - gender man null 1(english)
1 -gender uomo null 2(italian)
This would avoid me repeating something like:
membership_translation table
-----------------------------
membership_id | name | alternative_title | id_lang
1 regular null 1
1 normale null 2
gender_translation table
-----------------------------
gender_id | name | alternative_title | id_lang
1 man null 1
1 uomo null 2
and so on, so i would probably reduce the number of db tables, but i'm not sure about performance.I'm not much of a DB designer, so please let me know.
The most common way I've seen this done is with two tables, membership and membership_ml, with one storing the base values and the ml table storing the localized strings. This is similar to your second option. Most of the systems I see like this are made that way because they weren't designed with internationalization in mind from the get go, so the extra _ml tables were "tacked on" later.
What I think is a better option is similar to your first option, but a little bit different. You would have a central table for storing all the translations, but instead of putting the table name and field name in there, you would use tokens and a central "Content" table to store all the translations. That way you can enforce some kind of RI between the tokens in the base table and the translations in the Content table if you want as well.
I actually asked a question about this very thing a while back, so you can have a look at that for some more info (rather than repasting the schema examples here).
I also think the best solution is to keep translations on different table. This approach use Open Cart which is open source and you can take a look the way it deals with the problem. Another source of information is here "http://www.gsdesign.ro/blog/multilanguage-database-design-approach/" especially on the comments sections