May I know what is the best way to create a table structure in SQL Server that would store large amount of data as well retrieve the data easily, taking performance into consideration.
For example:
Candidates to Jobs (one-to-many or many-to-one)
Candidates Submitted to Jobs (many-to-many)
Candidates Placed to Job (one-to-one)
I've got 2 tables with few columns - like this:
+-------------+ +------------+
| Candidates | | Jobs |
+-------------+ +------------+
| CandidateID | | JobID |
| DisplayName | | JobTitle |
| JobTitle | | UserID |
| UserID | | CreateDate |
| CreateDate | +------------+
+-------------+
Now, I would like to create another table to store submissions and placements of Candidate(s) to Job(s).
Could someone give me few examples of how I can store this data.
Related
I'm trying to design a database that allows for filtering according to if a specific resource fills certain categories. I've gotten to the point where I can input data that seems to be how it should be filled out but I'm not sure how I should pull it out again.
The main resource table looks like this:
Table1 - resources
| resourceID | AutoNum |
| title | short text |
| author | short text |
| publish date | date |
| type | short text |
Table2 - Department Categories
| ID | AutoNum |
| 1 | Yes/No |
| 2 | Yes/No |
| fID| Number |
Table3 - Categories
| ID | AutoNum |
| cat | Yes/No |
| dog | Yes/No |
| bird | Yes/No |
| fID | Number |
I have built a form where you can fill in items to the resource ID, and at the same time check off the Yes/No boxes in tables 2 & 3.
I'm trying to use the primary key ID from table 1 and copy it into the table 2 & 3 with referential integrity to cascade deletes, updates. Which I think is the right way to do this.
Currently, I've learnt that I can implement a search function for the columns in table 1, this seems to work fine. However I am stuck with applying the relevant columns in table 2 and 3 as filters.
apply search>
[X] - Cats
Should only return records from table 1 where in table 3 the relevant column has a tick in the Yes/No box.
I hope I have explained this properly, very new to Access and databases so if you need clarity, don't mind offering.
As the questions states, what is the best way when designing a database for types and categories?
Scenario:
I have x amount of database-tables e.g. users, feedback, facts and countries, and all these tables have a type-attribute. What I've found is that a lot of people tend to just create type-tables for each and one of these. E.g. user_types, feedback_types, fact_types and country_types.
I'm currently working on a project where I don't want to create a bunch of extra tables just to handle their individual types. Therefore I'm trying to come up with a database-design-solution that fits all tables.
My best thought of solution:
At first I thought I might just create a polymorphic table that has id, type_id, typable_id and typable_type and a types table. Then i figured that I have to specify in the types table which type-attribute belongs to which table. Then it hit me, I can create a self-referencing table where the parent name is the table name.
E.g.
---------------------------------------------
|id | parent_id | name | description |
---------------------------------------------
| 1 | null | feedback | something |
---------------------------------------------
| 2 | 1 | general | something |
---------------------------------------------
| 3 | 1 | bug | something |
---------------------------------------------
| 4 | 1 | improvement | something |
---------------------------------------------
| 5 | null | countries | something |
---------------------------------------------
| 4 | 5 | europe | something |
---------------------------------------------
| 4 | 5 | asia | something |
---------------------------------------------
| etc... |
---------------------------------------------
Is this a ok design? I'm thinking a lot about the parent names in this table, I haven't seen anyone else use table-names as parents.
If thinking about it in a front-end point of view, it's easier to get the correct types depending on which types you're looking for.
Please give me feedback on this. I'm struggling to find a good design.
So I have a bunch of chatty http requests in my Angular 1 app which are bottle necking many of the other requests.
Imagine I have a list of Users from a totally different data source and I make calls to 5 different tables such as:
user.signup
+-----+------------+
| uid | date |
+-----+------------+
| 1 | 2016-12-13 |
| 2 | 2016-12-01 |
+-----+------------+
user.favourite_color
+-----+-------+
| uid | color |
+-----+-------+
| 1 | red |
| 5 | blue |
| 7 | green |
+-----+-------+
user.location
+-----+-----------+
| uid | location |
+-----+-----------+
| 2 | uk |
| 3 | france |
| 9 | greenland |
+-----+-----------+
The reason they are in different tables are because the fields are optional.
The way I see it I have 3 options:
Put them in 1 table
So I could just group them all in 1 table and have a bunch of null columns but that just doesn't sit right with me in terms of DB design.
+-----+------------+-----------+-------+
| uid | date | location | color |
+-----+------------+-----------+-------+
| 1 | 2016-12-13 | null | red |
| 2 | 2016-12-01 | uk | null |
| 3 | null | greenland | null |
| 5 | null | null | blue |
+-----+------------+-----------+-------+
Join them all with 1 request
So I could just have one query that joins all these tables but the way I see it they would have to be full joins with the expectation that some uid's wouldn't exist in some tables. e.g.
+------+------------+-------+-----------+-------+-------+
| uid | date | l_uid | location | c_uid | color |
+------+------------+-------+-----------+-------+-------+
| 1 | 2016-12-13 | null | null | 1 | red |
| 2 | 2016-12-01 | 2 | uk | null | null |
| null | null | 3 | greenland | null | null |
| null | nul | null | null | 5 | blue |
+------+------------+-------+-----------+-------+-------+
which is probably even worse!
Change the way the requests are made?
Maybe make some clever changes how the requests are made:
function activate() {
$q.all([requestSignupDate(), requestFaveColor(), requestLocation(), ....])
.then(function (data) {
//do a bunch of stuff with the data
});
}
which I want to change to:
function activate() {
requestUserData();
}
Any suggestions?
This is a typical ORM problem - precisely this database does not provide a better way of storing the user as an entity.
The entity properties are spread out in multiple tables and due to being optional you are taxed to do left joins with multiple tables.
So you have to essentially solve that problem first. You have several options or (non-options without knowing requirements.)
Put them in one table
If you can use nullable columns and refactor your application - this is preferable. I see that your other tables have just one or two more fields. Heavily normalized tables saves some space and with no data repetition other normalization benefits are moot.
Join them all with 1 request
Only if your query stays performant. Can you use left join ?
You would do this if the above option is difficult. Use this only as a quick fix.
Other options To solve Database Problem
Use server side caching if feasible.
If feasible use a different NoSQL database (e.g. MongoDB)
Change the way the requests are made?
Do you really need all the properties upfront ? The web is asynchronous so why not keep things async. Use $q.all only if you need all the properties. For example the user may not even navigate/scroll to certain part of the page to see stuff so why may queries in the first place.
Along with this you can cluster your server side and the database so that these queries fall on multiple machines and load gets distributed. You may get some items retrieved in parallel.
If the number of columns are all that you have and the tables are all that you mentioned i.e. the supplemental tables have fewer properties I would go with Put them in 1 table option.
Why doesn't using nullable fields sit right with you? NULL exists because it's useful, and a profile table with optional values for a fixed set of fields is practically the textbook case for invoking it. If fields can be dynamically redefined (eg swapping out "favorite color" for "favorite food"), it's another story, but that's not a requirement in what you've described.
I'm trying to index a database table using a <cfindex>. I also want to categorize the index based on the values of a column of the same table. No documentation I have come across clearly mention what the "category" attribute takes. Is it a column name or just any desire value and if the later then how do the index determine which record belongs to what category?
Thanks a lot in Advance.
| ID | CATEGORY | NAME | DETAILS | DATE |
| 1 | people | John | John details | 01/23/1980 |
| 2 | animal | Dog | dogs details | 02/22/1990 |
| 3 | people | Ben | Ben's details | 10/10/2006 |
| 4 | animal | panda | panda's details | 07/17/2009 |
The docs didn't make it clear, but if you are indexing a database and there is a column that you want to use for the category, just pass the name of the db column to the category attribute.
I need to regularly import large (hundreds of thousands of lines) tsv files into multiple related SQL Server 2008 R2 tables.
The input file looks something like this (it's actually even more complex and the data is of a different nature, but what I have here is analogous):
January_1_Lunch.tsv
+-------+----------+-------------+---------+
| Diner | Beverage | Food | Dessert |
+-------+----------+-------------+---------+
| Nancy | coffee | salad_steak | pie |
| Joe | milk | soup_steak | cake |
| Pat | coffee | soup_tofu | pie |
+-------+----------+-------------+---------+
Notice that one column contains a character-delimited list that needs preprocessing to split it up.
The schema is highly normalized -- each record has multiple many-to-many foreign key relationships. Nothing too unusual here...
Meals
+----+-----------------+
| id | name |
+----+-----------------+
| 1 | January_1_Lunch |
+----+-----------------+
Beverages
+----+--------+
| id | name |
+----+--------+
| 1 | coffee |
| 2 | milk |
+----+--------+
Food
+----+-------+
| id | name |
+----+-------+
| 1 | salad |
| 2 | soup |
| 3 | steak |
| 4 | tofu |
+----+-------+
Desserts
+----+------+
| id | name |
+----+------+
| 1 | pie |
| 2 | cake |
+----+------+
Each input column is ultimately destined for a separate table.
This might seem an unnecessarily complex schema -- why not just have a single table that matches the input? But consider that a diner may come into the restaurant and order only a drink or a dessert, in which case there would be many null rows. Considering that this DB will ultimately store hundreds of millions of records, that seems like a poor use of storage. I also want to be able to generate reports for just beverages, just desserts, etc., and I figure those will perform much better with separate tables.
The orders are tracked in relationship tables like this:
BeverageOrders
+--------+---------+------------+
| mealId | dinerId | beverageId |
+--------+---------+------------+
| 1 | 1 | 1 |
| 1 | 2 | 2 |
| 1 | 3 | 1 |
+--------+---------+------------+
FoodOrders
+--------+---------+--------+
| mealId | dinerId | foodId |
+--------+---------+--------+
| 1 | 1 | 1 |
| 1 | 1 | 3 |
| 1 | 2 | 2 |
| 1 | 2 | 3 |
| 1 | 3 | 2 |
| 1 | 3 | 4 |
+--------+---------+--------+
DessertOrders
+--------+---------+-----------+
| mealId | dinerId | dessertId |
+--------+---------+-----------+
| 1 | 1 | 1 |
| 1 | 2 | 2 |
| 1 | 3 | 1 |
+--------+---------+-----------+
Note that there are more records for Food because the input contained those nasty little lists that were split into multiple records. This is another reason it helps to have separate tables.
So the question is, what's the most efficient way to get the data from the file into the schema you see above?
Approaches I've considered:
Parse the tsv file line-by-line, performing the inserts as I go. Whether using an ORM or not, this seems like a lot of trips to the database and would be very slow.
Parse the tsv file to data structures in memory, or multiple files on disk, that correspond to the schema. Then use SqlBulkCopy to import each one. While it's fewer transactions, it seems more expensive than simply performing lots of inserts, due to having to either cache a lot of data or perform many writes to disk.
Per How do I bulk insert two datatables that have an Identity relationship and Best practices for inserting/updating large amount of data in SQL Server 2008, import the tsv file into a staging table, then merge into the schema, using DB functions to do the preprocessing. This seems like the best option, but I'd think the validation and preprocessing could be done more efficiently in C# or really anything else.
Are there any other possibilities out there?
The schema is still under development so I can revise it if that ends up being the sticking point.
You can import you file in the table of the following structure: Diner, Beverage, Food, Dessert, ID (identity, primary key NOT CLUSTERED - for performance issues).
After this simply add the following columns: Dinner_ID, Beverage_ID, Dessert_ID and fill them according to your separate tables (it's simple to group each of the columns and to add the missing data to lookup tables as Beverages, Desserts, Meals and, after this, to fix the imported table with the IDs for existent and newly added records).
The situation with Food table is more complex because of ability to combine the foods, but the same trick can be used: you can also add the data to your lookup table and, among this, store the combinations of foods in the additional temp table (with the unique ID) and separation on the single dishes.
When the parcing will be finished, you will have 3 temp tables:
table with all your imported data and IDs for all text columns
table with distinct food lists (with IDs)
table with IDs of food per food combination
From the above tables you can perform the insertion of the parsed values to either structure as you want.
In this case only 1 insert (bulk) will be done to the DB from the code side. All other data manipulations will be performed in the DB.