I'm creating a classic php blog and have a dilemma about single or two mysql tables approach.
In the first case actual blogs would be placed inside actual table (100 rows max), and archived posts inside archive table (20.000 rows max).
Both tables have the same structure.
Querying on actual table is very often and on archive is not so often.
But sometimes there are join and union queries - covering both tables.
Logically, performances are much better on a smaller table but - is that in my case enough reason to create two tables instead single one?
There is also third solution - single table with two partitions actual - 100 rowsand archive - 20.000 rows.
What to do?
You wrote:
Logically, performances are much better on a smaller table
With respect, your intuition about this is entirely incorrect for tables containing less than about ten million rows. A purpose of SQL is to allow rapid retrieval of a few items from among many. Thousands of years of programmer labor (not an exaggeration) have gone into making this kind of thing very fast. You won't be able to outsmart that collective effort.
Put your items in one table. If you need to distinguish between active and inactive items, make a column called active or some such thing, and retrieve them with WHERE active=1 or some such query term.
If you think you're having performance problems you can add indexes to your tables. Read this. https://use-the-index-luke.com/
While designing databases, don't only think about how you will store the data; but think about all the possible cases:
How are you going to retrieve and update information?
Will there be different views and different permissions for different
people?
In your case, archive seems like a subset of actual table. So a single table would be preferred with a row for keeping track of archived files.
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 am working on a webapplication, which main functionality will be to present some data to user. However, there are several types of these data and each of them have to be presented in a diffrent way.
For example I have to list 9 results - 3 books, 3 authors and 3 files.
Book is described with (char)TITLE, (text)DESCRIPTION.
Author is described with (char)TITLE, (char)DESCRIPTION.
File is described with (char)URL.
Moreover, every type has fields like ID, DATE, VIEWS etc.
Book and Author are presented with simple HTML code, File use external reader embed on the website.
Should I build three diffrent tables and use JOIN while getting these data or build one table and store all types in there? Which attitude is more efficient?
Additional info - there are going to be really huge amounts of records.
The logical way of doing this is keeping things separate, which is following the 3NF rules o the database design. This gives more flexibility while retrieving different kinds of results specially when there is huge amount of data. Putting everything in a single table is absolutely bad DB practice.
That depends on the structure of your data.
If you have 1:1 relationships, say one book has one author, you can put the records in one row. If one book has several authors or one author has several books you should set up seperate tables books and authors and link those with a table author_has_books where you have both foreign keys. This way you won't store duplicate data and avoid inconsistencies.
More information about db normalization here:
http://en.wikipedia.org/wiki/Database_normalization
Separate them and create a relationship. That way, when you start to get a lot of data, you'll notice a performance boost because you are only calling 3 fields at a time (IE when you are just looking at a book) instead of 7.
I've been thinking about creating a forum in PHP so I did a little research to see what the standard is for the tables that people create in the database. On most websites I've looked up, they always choose to have one table for the threads and a second for the posts on the threads.
Having a table for the threads seems perfectly rational to me, but one table to hold all the posts on all the threads seems like a little too much. Would it be better to create a table for each thread that will hold that thread's posts instead sticking a few hundred thousand posts in one table?
The tables should represent the structure of the data in your database. If you have 2 objects, which in this case are your threads and your posts, you should put them in 2 tables.
Trust me, it will be a nightmare trying to figure out the right table to show for each post if you do it the way you're thinking. What would the SQL look like? Something like
SELECT *
FROM PostTable17256
and you would have to dynamically construct this query on each request.
However, by using 1 table, you can simply get a ThreadID and pass it as a variable to your query.
SELECT *
FROM Posts
WHERE ThreadID = $ThreadID
Relational databases are designed to have tables which hold lots of rows. You would probably be surprised what DBAs consider to be a "lot" by the way. A table with 1,000,000 rows is considered small to medium in most places.
Nope nope nope. Database love huge tables. Splitting posts into multiple tables will cause many many headaches.
Storing posts in one table is best solution.
MySQL can easily hold millions of rows in a table.
Creating multiple tables may cause few problems.
For example you will not be able to use JOIN with posts from different threads.
I'm using PHP and MySQL. I have records for:
events with various "event types" that are hierarchical (events can have multiple categories and subcategories, but there are a fixed amount of such categories and subcategories) (timestamped)
What is the best way to set up the table? Should I have a bunch of columns (30 or so) with enums for yes or no indicating membership in that category? or should I use MySQL SET datatype?
http://dev.mysql.com/tech-resources/articles/mysql-set-datatype.html
Basically I have performance in mind and I want to be able to retrieve all of the ids of the events for a given category. Just looking for some insight on the most efficient way to do this.
It sounds like you're chiefly concerned with performance.
A couple people have suggested splitting into 3 tables (category table plus either simple cross-reference table or a more sophisticated way of modeling the tree hierarchy, like nested set or materialized path), which is the first thing I thought when I read your question.
With indexes, a fully normalized approach like that (which adds two JOINs) will still have "pretty good" read performance. One issue is that an INSERT or UPDATE to an event now may also include one or more INSERT/UPDATE/DELETEs to the cross-reference table, which on MyISAM means the cross-reference table is locked and on InnoDB means the rows are locked, so if your database is busy with a significant number of writes you're going to have a larger contention problems than if just the event rows were locked.
Personally, I would try out this fully normalized approach before optimizing. But, I'll assume you know what you're doing, that your assumptions are correct (categories never change) and you have a usage pattern (lots of writes) that calls for a less-normalized, flat structure. That's totally fine and is part of what NoSQL is about.
SET vs. "lots of columns"
So, as to your actual question "SET vs. lots of columns", I can say that I've worked with two companies with smart engineers (whose products were CRM web applications ... one was actually events management), and they both used the "lots of columns" approach for this kind of static set data.
My advice would be to think about all of the queries you will be doing on this table (weighted by their frequency) and how the indexes would work.
First, with the "lots of columns" approach you are going to need indexes on each of these columns so that you can do SELECT FROM events WHERE CategoryX = TRUE. With the indexes, that is a super-fast query.
Versus with SET, you must use bitwise AND (&), LIKE, or FIND_IN_SET() to do this query. That means the query can't use an index and must do a linear search of all rows (you can use EXPLAIN to verify this). Slow query!
That's the main reason SET is a bad idea -- its index is only useful if you're selecting by exact groups of categories. SET works great if you'd be selecting categories by event, but not the other way around.
The primary problem with the less-normalized "lots of columns" approach (versus fully normalized) is that it doesn't scale. If you have 5 categories and they never change, fine, but if you have 500 and are changing them, it's a big problem. In your scenario, with around 30 that never change, the primary issue is that there's an index on every column, so if you're doing frequent writes, those queries become slower because of the number of indexes that have to updated. If you choose this approach, you might want to check the MySQL slow query log to make sure there aren't outlier slow queries because of contention at busy times of day.
In your case, if yours is a typical read-heavy web app, I think going with the "lots of columns" approach (as the two CRM products did, for the same reason) is probably sane. It is definitely faster than SET for that SELECT query.
TL;DR Don't use SET because the "select events by category" query will be slow.
It's good that the number of categories is fixed. If it wasn't you couldn't use either approach.
Check the Why You Shouldn't Use SET on the page you linked. I think that should give you a comprehensive guide.
I think the most important one is about indexes. Also, modifying a SET is slightly more complex.
The relationship between events and event types/categories is a many to many relationship, as echo says, but a simple xref table will leave you with a problem: If you want to query for all descendants of any given node, then you must make multiple recursive queries. On a deep tree, that will be very inefficient.
So when you say "retrieve all ids for a given category", if you do mean all descendants, then you want to use a Nested Set Model:
http://mikehillyer.com/articles/managing-hierarchical-data-in-mysql/
The Nested Set model makes writes updates a bit slower, but makes it very easy to retrieve subtrees:
To get the Televisions sub tree, you query for all categories left >= 2 and right <= 9.
Leaf nodes always have left = right - 1
You can find the count of descendants without pulling those rows: (right - left - 1)/2
Finding inheritance paths and depth is also very easy (single query stuff). See the article for full details.
You might try using a cross-reference (Xref) table, to create a many-to-many relationship between your events and their types.
create table event_category_event_xref
(
event_id int,
event_category_id int,
foreign key(event_id) references event(id),
foreign key (event_category_id) references event_category(id)
);
Event / category membership is defined by records in this table. So if you have a record with {event_id = 3, event_category_id = 52}, it means event #3 is in category #52. Similarly you can have records for {event_id = 3, event_category_id = 27}, and so on.
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?