How to match a Set with a Big Collection of Sets stored in database.
[The collection may have millions of Sets].
Detailed Statement
[Prerequisite] A cluster has special property which is a set of attribute.
I will get an entity having a set of attribute.
If i have any existing cluster with exact same set of attribute (neither more nor less) then i will add the entity to that cluster. Else i will create a cluster having property as attribute set of new entity.
Above is the process of the clustering.
The problem is how i should store the data so that the system can run smoothly on very large dataset without performance issue.
What kind of database should i use for this? in SQL or NoSQL
What Possible Solution i thought of:
[MySQL]Store the attributes with cluster in a table so that clusterId to attributeId has m:n relation.[table cluster_attribute].
whenever an entity comes.
we run.
select clusterId,count(1) from cluster_attribute where attributeId in("comma separated IDs of attributes");
But this will not be good since we may find a long list of clusterId's which fullfills the above query.
In the same above table we perform query like.
select clusterId,count(1) cnt from cluster_attributes a
inner join cluster_attributes b on a.cluesterId=b.cluesterId
where b.attributeId in("comma separated IDs of attributes")
group by clusterId
having cnt = #sizeOfEntityAttributeSet;
This will scan much rows resulting slow query.
We store attribute as sorted Concatenation of attribute by any character | and make this column indexed.This way we will be able to query faster.But when ever i need to know which clusters have a certain attribute (A1), my query will go slow since i will need to use regexp search in mysql.
Items in set is non-duplicate.that is [a1,b1,c1] is valid while [a1,b1,a1,c1] is not.
millions of sets, each will hundreds of items.
Have 2 columns in the table for searching. One is the exact, complete, list of the values, sorted. It's a long string, probably TEXT. The other is a hash of that string. I might suggest MD5, then chop to 32 bits and put into INT UNSIGNED (or BINARY(4)). INDEX this column, but not UNIQUE.
Now, to check for existence, do likewise with the incoming 'set' -- build the string, and compute the hash. Look up the hashed value in the table. It will give you only a few rows, including some duds. Double check with the long string.
WHERE hash = $hash
AND str = '$str'
The lookup will be quite fast. The prep work (building the sorted string and computing the hash) will not be too difficult. It will be quite easy to code in, say, PHP.
Caveats:
This works only for an exact match of the set.
It scales quite well. If you have more than, say, a billion sets, then a 32-bit hash won't be big adequate. (But BIGINT and a longer BINARY would work.)
Related
I was just wondering about the efficiency of storing a large amount of boolean values inside of a CHAR or VARCHAR
data
"TFTFTTF"
vs
isFoo isBar isText
false true false
Would it be worth the worse performance by switching storing these values in this manner? I figured it would just be easier just to set a single value rather than having all of those other fields
thanks
Don't do it. MySQL offers types such as char(1) and tinyint that occupy the same space as a single character. In addition, MySQL offers enumerated types, if you want your flags to have more than one value -- and for the values to be recognizable.
That last point is the critical point. You want your code to make sense. The string 'FTF' does not make sense. The columns isFoo, isBar, and isText do make sense.
There is no need to obfuscate your data model.
This would be a bad idea, not only does it have no advantage in terms of the space used, it also has a bad influence on query performance and the comprehensibility of your data model.
Disk Space
In terms of storage usage, it makes no real difference whether the data is stored in a single varchar(n) or char(n) column or in multiple tinynt, char(1)or bit(1) columns. Only when using varchar you would need 1 to 2 bytes more disk space per entry.
For more information about the storage requirements of the different data types, see the MySql documentation.
Query Performance
If boolean values were stored in a VarChar, the search for all entries where a specific value is True would take much longer, since string operations would be necessary to find the correct entries. Even when searching for a combination of Boolean values such as "TFTFTFTFTT", the query would still take longer than if the boolean values were stored in individual columns. Furthermore you can assign indexes to single columns like isFoo or isBar, which has a great positive effect on query performance.
Data Model
A data model should be as comprehensible as possible and if possible independent of any kind of implementation considerations.
Realistically, a database field should only contain one atomic value, that is to say: a value that can't be subdivided into separate parts.
Columns that do not contain atomic values:
cannot be sorted
cannot be grouped
cannot be indexed
So let's say you want to find all rows where isFoo is true you wouldn't be able to do it unless you were to do string operations like "find the third characters in this string and see if it's equal to "F". This would imply a full table scan with every query which would degrade performance quite dramatically.
it depends on what you want to do after storing the data in this format.
after retrieving this record you will have to do further processing on the server side which worsen the performance if you want to load the data by checking specific conditions. the logic in the server would become complex.
The columns isFoo, isBar, and isText would help you to write queries better.
I need to set values to a "Yes or No" column name STATUS. And I'm thinking about 2 methods.
method 1 (use letter): set value Y/N then find all rows that have value Y in field STATUS by a query like:
SELECT * FROM post WHERE status="Y"
method 2 (use number): set value 1/0 then find all rows that have value 1 in field STATUS by a query like:
SELECT * FROM post WHERE status=1
Should I use method 1 or method 2? Which one is faster? Which one is better?
The two are essentially equivalent, so this becomes a question of which is better for your application.
If you are concerned about space, then the smallest space for one character is char(1), using 8 bits. With a number, you can use bit or set types for pack multiple flags. But, this only makes a difference if you have lots of flags.
The store-it-as-a-number approach has a slight advantage, where you can count the "Yes" values by doing:
select sum(status)
(Of course, in MySQL, this is only a marginal improvement on sum(status = 'Y').
The store-it-as-a-letter approach has a slight advantage if you decide to include "Maybe" or other values at some point in the future.
Finally, any difference in performance in different ways of representing these values is going to be very, very minimal. You would need a table with millions and millions of rows to start to notice a problem. So, use the mechanism that works best for your application and way of representing the value.
Second one is definitely faster primarily because whenever you involve something within quotes , it is meaningless to SQL. It would be better to use types that are non string in order to get better performance. I would suggest using METHOD 2.
Fastest way would be ;
SELECT * FROM post WHERE `status` = FIND_IN_SET(`status`,'y');
I think you should create column with ENUM('n','y'). Mysql stores this type in optimal way. It also will help you to store only allowed values in the field.
You can also make it more human friendly ENUM('no','yes') without affect to performance. Because strings 'no' and 'yes' are stored only once per ENUM definition. Mysql stores only index of the value per row.
I think the method 1 is better if you are concerned with the storage prospective .
As storing an integer i.e 1/2 takes 4 bytes of memory where as a character takes only 1 byte of memory. So its better to use method 1.
This may increase some performance .
I am curious about the disadvantage of quoting integers in MYSQL queries
For example
SELECT col1,col2,col3 FROM table WHERE col1='3';
VS
SELECT col1,col2,col3 FROM table WHERE col1= 3;
If there is a performance cost, what is the size of it and why does it occur? Are there any other disavantages other that performance?
Thanks
Andrew
Edit: The reason for this question
1. Because I want to learn the difference because I am curious
2. I am experimenting with a way of passing composite keys from my database around in my php code as psudo-Id-keys(PIK). These PIK's are the used to target the record.
For example, given a primary key (AreaCode,Category,RecordDtm)
My PIK in the url would look like this:
index.php?action=hello&Id=20001,trvl,2010:10:10 17:10:45
And I would select this record like this:
$Id = $_POST['Id'];//equals 20001,trvl,2010:10:10 17:10:45
$sql = "SELECT AreaCode,Category,RecordDtm,OtherColumns.... FROM table WHERE (AreaCode,Category,RecordDtm) = ({$Id});
$mysqli->query($sql):
......and so on.
At this point the query won't work because of the datetime(which must be quoted) and it is open to sql injection because I haven't escaped those values. Given the fact that I won't always know how my PIK's are constructed I would write a function splits the Id PIK at the commas, cleans each part with real_escape_string and puts It back together with the values quoted. For Example:
$Id = "'20001','trvl','2010:10:10 17:10:45'"
Of course, in this function that is breaking apart and cleaning the Id I could check if the value is a number or not. If it is a number, don't quote it. If it is anything but a string then quote it.
The performance cost is that whenever mysql needs to do a type conversion from whatever you give it to datatype of the column. So with your query
SELECT col1,col2,col3 FROM table WHERE col1='3';
If col1 is not a string type, MySQL needs to convert '3' to that type. This type of query isn't really a big deal, as the performance overhead of that conversion is negligible.
However, when you try to do the same thing when, say, joining 2 table that have several million rows each. If the columns in the ON clause are not the same datatype, then MySQL will have to convert several million rows every single time you run your query, and that is where the performance overhead comes in.
Strings also have a different sort order from numbers.
Compare:
SELECT 312 < 41
(yields 0, because 312 numerically comes after 41)
to:
SELECT '312' < '41'
(yields 1, because '312' lexicographically comes before '41')
Depending on the way your query is built using quotes might give wrong results or none at all.
Numbers should be used as such, so never use quotes unless you have a special reason to do so.
According to me, I think there is no performance/size cost in the case you have mentioned. Even if there is, then it is very much negligible and wont affect your application as such.
It gives the wrong impression about the data type for the column. As an outsider, I assume the column in question is CHAR/VARCHAR & choose operations accordingly.
Otherwise MySQL, like most other databases, will implicitly convert the value to whatever the column data type is. There's no performance issue with this that I'm aware of but there's a risk that supplying a value that requires explicit conversion (using CAST or CONVERT) will trigger an error.
I have a table where one of the columns is a sort of id string used to group several rows from the table. Let's say the column name is "map" and one of the values for map is e.g. "walmart". The column has an index on it, because I use to it filter those rows which belong to a certain map.
I have lots of such maps and I don't know how much space the different map values take up from the table. Does MYSQL recognizes the same map value is stored for multiple rows and stores it only once internally and only references it with an internal numeric id?
Or do I have to replace the map string with a numeric id explicitly and use a different table to pair map strings to ids if I want to decrease the size of the table?
MySQL will store the whole data for every row, regardless of whether the data already exists in a different row.
If you have a limited set of options, you could use an ENUM field, else you could pull the names into another table and join on it.
I think MySQL will duplicate your content each time : it stores data row by row, unless you explicitly specify otherwise (putting the data in another table, like you suggested).
Using another table will mean you need to add a JOIN in some of your queries : you might want to think a bit about the size of your data (are they that big ?), compared to the (small ?) performance loss you may encounter because of that join.
Another solution would be using an ENUM datatype, at least if you know in advance which string you will have in your table, and there are only a few of those.
Finally, another solution might be to store an integer "code" corresponding to the strings, and have those code translated to strings by your application, totally outside of the database (or use some table to store the correspondances, but have that table cached by your application, instead of using joins in SQL queries).
It would not be as "clean", but might be better for performances -- still, this may be some kind of micro-optimization that is not necessary in your case...
If you are using the same values over and over again, then there is a good functional reason to move it to a separate table, totally aside from disk space considerations: To avoid problems with inconsistent data.
Suppose you have a table of Stores, which includes a column for StoreName. Among the values in StoreName "WalMart" occurs 300 times, and then there's a "BalMart". Is that just a typo for "WalMart", or is that a different store?
Also, if there's other data associated with a store that would be constant across the chain, you should store it just once and not repeatedly.
Of course, if you're just showing locations on a map and you really don't care what they are, it's just a name to display, then this would all be irrelevant.
And if that's the case, then buying a bigger disk is probably a simpler solution than redesigning your database just to save a few bytes per record. Because if we're talking arbitrary strings for place names here, then trying to find duplicates and have look-ups for them is probably a lot of work for very little gain.
Let's say, I have :
Key | Indexes | Key-values
----+---------+------------
001 | 100001 | Alex
002 | 100002 | Micheal
003 | 100003 | Daniel
Lets say, we want to search 001, how to do the fast searching process using hash table?
Isn't it the same as we use the "SELECT * from .. " in mysql? I read alot, they say, the "SELECT *" searching from beginning to end, but hash table is not? Why and how?
By using hash table, are we reducing the records we are searching? How?
Can anyone demonstrate how to insert and retrieve hash table process in mysql query code? e.g.,
SELECT * from table1 where hash_value="bla" ...
Another scenario:
If the indexes are like S0001, S0002, T0001, T0002, etc. In mysql i could use:
SELECT * from table WHERE value = S*
isn't it the same and faster?
A simple hash table works by keeping the items on several lists, instead of just one. It uses a very fast and repeatable (i.e. non-random) method to choose which list to keep each item on. So when it is time to find the item again, it repeats that method to discover which list to look in, and then does a normal (slow) linear search in that list.
By dividing the items up into 17 lists, the search becomes 17 times faster, which is a good improvement.
Although of course this is only true if the lists are roughly the same length, so it is important to choose a good method of distributing the items between the lists.
In your example table, the first column is the key, the thing we need to find the item. And lets suppose we will maintain 17 lists. To insert something, we perform an operation on the key called hashing. This just turns the key into a number. It doesn't return a random number, because it must always return the same number for the same key. But at the same time, the numbers must be "spread out" widely.
Then we take the resulting number and use modulus to shrink it down to the size of our list:
Hash(key) % 17
This all happens extremely fast. Our lists are in an array, so:
_lists[Hash(key % 17)].Add(record);
And then later, to find the item using that key:
Record found = _lists[Hash(key % 17)].Find(key);
Note that each list can just be any container type, or a linked list class that you write by hand. When we execute a Find in that list, it works the slow way (examine the key of each record).
Do not worry about what MySQL is doing internally to locate records quickly. The job of a database is to do that sort of thing for you. Just run a SELECT [columns] FROM table WHERE [condition]; query and let the database generate a query plan for you. Note that you don't want to use SELECT *, since if you ever add a column to the table that will break all your old queries that relied on there being a certain number of columns in a certain order.
If you really want to know what's going on under the hood (it's good to know, but do not implement it yourself: that is the purpose of a database!), you need to know what indexes are and how they work. If a table has no index on the columns involved in the WHERE clause, then, as you say, the database will have to search through every row in the table to find the ones matching your condition. But if there is an index, the database will search the index to find the exact location of the rows you want, and jump directly to them. Indexes are usually implemented as B+-trees, a type of search tree that uses very few comparisons to locate a specific element. Searching a B-tree for a specific key is very fast. MySQL is also capable of using hash indexes, but these tend to be slower for database uses. Hash indexes usually only perform well on long keys (character strings especially), since they reduce the size of the key to a fixed hash size. For data types like integers and real numbers, which have a well-defined ordering and fixed length, the easy searchability of a B-tree usually provides better performance.
You might like to look at the chapters in the MySQL manual and PostgreSQL manual on indexing.
http://en.wikipedia.org/wiki/Hash_table
Hash tables may be used as in-memory data structures. Hash tables may also be adopted for use with persistent data structures; database indices sometimes use disk-based data structures based on hash tables, although balanced trees are more popular.
I guess you could use a hash function to get the ID you want to select from. Like
SELECT * FROM table WHERE value = hash_fn(whatever_input_you_build_your_hash_value_from)
Then you don't need to know the id of the row you want to select and can do an exact query. Since you know that the row will always have the same id because of the input you build the hash value form and you can always recreate this id through the hash function.
However this isn't always true depending on the size of the table and the maximum number of hashvalues (you often have "X mod hash-table-size" somewhere in your hash). To take care of this you should have a deterministic strategy you use each time you get two values with the same id. You should check Wikipedia for more info on this strategy, its called collision handling and should be mentioned in the same article as hash-tables.
MySQL probably uses hashtables somewhere because of the O(1) feature norheim.se (up) mentioned.
Hash tables are great for locating entries at O(1) cost where the key (that is used for hashing) is already known. They are in widespread use both in collection libraries and in database engines. You should be able to find plenty of information about them on the internet. Why don't you start with Wikipedia or just do a Google search?
I don't know the details of mysql. If there is a structure in there called "hash table", that would probably be a kind of table that uses hashing for locating the keys. I'm sure someone else will tell you about that. =)
EDIT: (in response to comment)
Ok. I'll try to make a grossly simplified explanation: A hash table is a table where the entries are located based on a function of the key. For instance, say that you want to store info about a set of persons. If you store it in a plain unsorted array, you would need to iterate over the elements in sequence in order to find the entry you are looking for. On average, this will need N/2 comparisons.
If, instead, you put all entries at indexes based on the first character of the persons first name. (A=0, B=1, C=2 etc), you will immediately be able to find the correct entry as long as you know the first name. This is the basic idea. You probably realize that some special handling (rehashing, or allowing lists of entries) is required in order to support multiple entries having the same first letter. If you have a well-dimensioned hash table, you should be able to get straight to the item you are searching for. This means approx one comparison, with the disclaimer of the special handling I just mentioned.