I have a table with 8000 Vendors (Think a Seller on Amazon).
Each Vendor has around 100 items in their purchase history with columns such as price, tax, date.
Each Vendor has columns such as State, Region, Type.
I am creating a Html Table to display Vendors Purchases History by month. However, I also want to compare it to Average by State, Region, and Overall.
My question is, how should I store these aggregate averages. Should I create tables for each one, store them in cache (Redis), calculate in mysql, or calculate in php?
I am using Laravel on the backend. The main problem i have with storing them in their own table is a don't want a bunch of tables like purchase_histroy_state with columns date, state_id, and total. Currently, the server times out when I try to calculate with php.
8,000 vendors x 100 items is 800,000 items that you're asking PHP to trawl through, calculate and store in memory every time it is ran.
If you're wanting a quick and dirty solution - then consider setting up a SQLite database with a denormalized structure, and appropriate indexes. Databases are designed to do this sort of stuff - even SQLite.
I wouldn't recommend this as a long term solution though as it's not really scalable - but it might help in the early development stages to figure out what you need.
Related
I want to love DynamoDB, but the major drawback is the query/scan on the whole DB to pull the results for one query. Would I be better sicking with MySQL or is there another solution I should be aware of?
Uses:
Newsfeed items (Pulls most recent items from table where id in x,x,x,x,x)
User profiles relationships (users follow and friend eachother)
User lists (users can have up to 1,000 items in one list)
I am happy to mix and match database solutions.The main use is lists.
There will be a few million lists eventually, ranging from 5 to 1000 items per list. The list table is formatted as follows: list_id(bigint)|order(int(1))|item_text(varchar(500))|item_text2(varchar(12))|timestamp(int(11))
The main queries on this DB would be on the 'list_relations' table:
Select 'item_text' from lists where list_id=539830
I suppose my main question. Can we get all items for a particular list_id, without a slow query/scan? and by 'slow' do people mean a second? or a few minutes?
Thank you
I'm not going to address whether or not it's a good choice or the right choice, but you can do what you're asking. I have a large dynamoDB instance with vehicle VINs as the Hash, something else for my range, and I have a secondary index on vin and a timestamp field, I am able to make fast queries over thousands of records for specific vehicles over timestamp searches, no problem.
Constructing your schema in DynamoDB requires different considerations than building in MySQL.
You want to avoid scans as much as possible, this means picking your hash key carefully.
Depending on your exact queries, you may also need to have multiple tables that have the same data..but with different hashkeys depending on your querying needs.
You also did not mention the LSI and GSI features of DynamoDB, these also help your query-ability, but have their own sets of drawbacks. It is difficult to advise further without knowing more details about your requirements.
I have a table which stores highscores for a game. This game has many levels where scores are then ordered by score DESC (which is an index) where the level is a level ID. Would partitioning on this level ID column create the same result as create many seperate level tables (one for each level ID)? I need this to seperate out the level data somehow as I'm expecting 10's of millions of entries. I hear partitioning could speed this process up, whilst leaving my tables normalised.
Also, I have an unknown amount of levels in my game (levels may be added or removed at any time). Can I specify to partition on this level ID column and have new partitions automaticaly get created when a new (distinct level ID) is added to the highscore table? I may start with 10 seperate levels but end up with 50, but all my data is still kept in one table, but many partitions? Do I have to index the level ID to make this work?
Thanks in advance for your advice!
Creting an index on a single column is good, but creating an index that contains two columns would be a better solution based on the information you have given. I would run a
alter table highscores add index(columnScore, columnLevel);
This will make performance much better. From a database point of view, no matter what highscores you are looking for, the database will know where to search for them.
On that note, if you can, (and you are using mysami tables) you could also run a:
alter table order by columnScore, columnLevel;
which will then group all your data together, so that even though the database KNOWS where each bit is, it can find all the records that belong to one another nearby - which means less hard drive work - and therefore quicker results.
That second operation too, can make a HUGE difference. My PC at work (horrible old machine that was top of the range in the nineties) has a database with several million records in it that I built - nothing huge, about 2.5gb of data including indexes - and performance was dragging, but ordering the data for the indexes improved query time from about 1.5 minutes per query to around 8 seconds. That's JUST due to hard drive speed in being able to get to all the sectors that contain the data.
If you plan to store data for different users, what about having 2 tables - one with all the information about different levels, another with one row for every user alongside with his scores in XML/json?
I am using PHP, MySQL to develop a website to be used in an educational institution.
One function of this is to allocate lectures when creating a batch. To allocate lectures to the starting batch, system will prompt available lectures based on their availability and qualifications. Then the course coordinator will take the decision.
My problem is how to check the availability of the lecture on a particular week day given time slot.(time slot is varying not fixed durations)
I am planning to keep lecture schedules in a table where it shows lecture_Id, Batch_Id, day, start_time, end_time, start_day , end_day.
Then when availability checking I need to write a complex query to check the available lecture_Id's. I couldn't figure out it yet.
Is there any other smart ways to do this?
Thanks
The way you're proposing to store the info is the first way I'd think of doing it - the complex query isn't so bad though.
So if I understand correctly, you have lectures stored in another table, with a one - to -many relationship with your lecture_schedules table?
If so, this will get the lecture details for a given time range.
Something like this:
SELECT * FROM lecture_schedules
INNER JOIN lectures USING(lecture_id)
WHERE start_day<=DAY(yourstarttime) AND end_day>=DAY(yourendtime)
AND start_time<=yourstarttime AND end_time>=yourendtime;
Note you will need to edit table names and column names to reflect your actual schema, and replace yourstarttime and yourendtime with the time range values.
Hope this helps
EDIT:
This query makes several assumptions about the columns and datatypes of those columns in your schema - don't just copy & paste and expect it to work first time :)
I'm looking to create an SQL query (in MySQL) that will display 6 random, yet popular entries in my web application.
My database has the following tables:
favorites
submissions
submissions_tags
tags
users
submissions_tags and tags are cross-referencing tables that give each submission a certain number of tags.
submissions contains boolean featured, int downloads, and int views, all three of which I'd like to use to weight this query with.
The favorites table is again a cross-reference table with the fields submission_id and user_id. Counting the number of times each submission has been favorited would be good to weigh the results with.
So basically I want to select 6 random rows weighted with these four variables - featured, downloads, views, and favorite count. Each time the user refreshes the page, I want a new random 6 to be selected. So maybe the query could limit it to 12 most-recent but only pluck 6 random results out to show. Is that a sensible idea in terms of processing etc.?
So my question is, how can I go about writing this query? Where should I begin? I am using PHP/CodeIgniter to drive this site with. Is it possible to get the entire lot in one query, or will I have to use multiple queries to do this? Or, do I need to simplify my ideas?
Thanks,
Jack
I've implemented something similar to this before. The route I took was to have a script run on the server every XX minutes to fill a table with a pool of items (say 20-30 items). Then the query to use in your application would be randomly pick 5 or so from that table.
Just need to setup an algorithm to select those 20-30 items. #Emmerman's is similar to what I used before to calculate a popularity_number where I took weights of multiple associations to the item (views, downloads, etc) to get an overall number. We also used an age to make sure the pool of items stayed up-to-date. You'll have to tinker with the algorithm over time to make sure the relevant items are being populated.
The idea is to calc some popularity which can be for e.g.
popularity = featured*W1 + downloads*W2 + views*W3 + fcount*W4
Where W1-W4 are constant weights.
Then add some random number to popularity and sort for it.
I need to store about 73,200 records per day consisting of 3 points of data: id, date, and integer.
Some members of my team suggest creating tables using month's as the table name (september_2010), while others are suggesting having one table with lots of data in it...
Any suggestions on how to deal with this amount of data? Thanks.
========== Thank you to all the feedback.
I recommend against that. I call this antipattern Metadata Tribbles. It creates multiple problems:
You need to remember to create a new table every year or else your app breaks.
Querying aggregates against all rows regardless of year is harder.
Updating a date potentially means moving a row from one table to another.
It's harder to guarantee the uniqueness of pseudokeys across multiple tables.
My recommendation is to keep it in one table until and unless you've demonstrated that the size of the table is becoming a genuine problem, and you can't solve it any other way (e.g. caching, indexing, partitioning).
Seems like it should be just fine holding everything in one table. It will make retrieval much easier in the future to maintain 1 table, as opposed to 12 tables per year. At 73,200 records per day it will take you almost 4 years to hit 100,000,000 which is still well within MySQLs capabilities.
Absolutely not.
It will ruin relationship between tables.
Table relations being built based on field values, not table names.
Especially for this very table that will grow by just 300Mb/year
so in 100 days you have 7.3 M rows, about 25M a year or so. 25M rows isn't a lot anymore. MySQL can handle tables with millions of rows. It really depends on your hardware and your query types and query frequency.
But you should be able to partition that table (if MySQL supports partitioning), what you're describing is an old SQL Server method of partition. After building those monthly tables you'd build a view that concatenates them together to look like one big table... which is essentially what partitioning does but it's all under-the-covers and fully optimized.
Usually this creates more trouble than it's worth, it's more maintenance , your queries need more logic, and it's painful to pull data from more than one period.
We store 200+ million time based records in one (MyISAM) table, and queries are blazingly still fast.
You just need to ensure there's an index on your time/date column and that your queries makes use of the index (e.g. a query that messes around with DATE_FORMAT or similar on a date column will likely not use an index. I wouldn't put them in separate tables just for the sake of retreival performance.
One thing that gets very painful with such a large number of records is when you have to delete old data, this can take a long time (10 minutes to 2 hours for e.g. wiping a month worth of data in tables with hundreds of mullions rows). For that reason we've partitioning the tables, and use a time_dimension(see e.g. the time_dimension table a bit down here) relation table for managing the periods instead of simple date/datetime columns or strings/varchars representing dates.
Some members of my team suggest creating tables using month's as the table name (september_2010), while others are suggesting having one table with lots of data in it...
Don't listen to them. You're already storing a date stamp, what about different months makes it a good idea to split the data that way? The engine will handle the larger data sets just fine, so splitting by month does nothing but artificially segregate the data.
My first reaction is: Aaaaaaaaahhhhhhhhh!!!!!!
Table names should not embed data values. You don't say what the data means, but supposing for the sake of argument it is, I don't know, temperature readings. Just imagine trying to write a query to find all the months in which average temperature increased over the previous month. You'd have to loop through table names. Worse yet, imagine trying to find all 30-day periods -- i.e. periods that might cross month boundaries -- where temperature increased over the previous 30-day period.
Indeed, just retrieving an old record would go from a trivial operation -- "select * where id=whatever" -- would become a complex operation requiring you to have the program generate table names from the date on the fly. If you didn't know the date, you would have to scan through all the tables searching each one for the desired record. Yuck.
With all the data in one properly-normalized table, queries like the above are pretty trivial. With separate tables for each month, they're a nightmare.
Just make the date part of the index and the performance penalty of having all the records in one table should be very small. If the size of table really becomes a performance problem, I could dimply comprehend making one table for archive data with all the old stuff and one for current data with everything you retrieve regularly. But don't create hundreds of tables. Most database engines have ways to partition your data across multiple drives using "table spaces" or the like. Use the sophisticated features of the database if necessary, rather than hacking together a crude simulation.
Depends on what searches you'll need to do. If normally constrained by date, splitting is good.
If you do split, consider naming the tables like foo_2010_09 so the tables will sort alphanumerically.
what is your DB platform?
In SQL Server 2K5+ you can partition on date.
My bad, I didnt notice the tag. #thetaiko is right though and this is well within MySQL capabilities to deal with this.
I would say it depends on how the data is used. If most queries are done over the complete data, it would be an overhead to always join the tables back together again.
If you most times only need a part of the data (by date), it is a good idea to segment the tables into smaller pieces.
For the naming i would do tablename_yyyymm.
Edit: For sure you should then also think about another layer between the DB and your app to handle the segmented tables depending on some date given. Which can then get pretty complicated.
I'd suggest dropping the year and just having one table per month, named after the month. Archive your data annually by renaming all the tables $MONTH_$YEAR and re-creating the month tables. Or, since you're storing a timestamp with your data, just keep appending to the same tables. I assume by virtue of the fact that you're asking the question in the first place, that segregating your data by month fits your reporting requirements. If not, then I'd recommend keeping it all in one table and periodically archiving off historical records when performance gets to be an issue.
I agree with this idea complicating your database needlessly. Use a single table. As others have pointed out, it's not nearly enough data to warrent extraneous handling. Unless you use SQLite, your database will handle it well.
However it also depends on how you want to access it. If the old entries are really only there for archival purposes, then the archive pattern is an option. It's common for versioning systems to have the infrequently used data separated out. In your case you'd only want everything >1 year to move out of the main table. And this is strictly an database administration task, not an application behavior. The application would only join the current list and the _archive list, if at all. Again, this highly depends on the use case. Are the old entries generally needed? Is there too much data to process regularily?