I am working on a project that uses PHP/MYSQL as the backend for an IOS app that makes a lot of use of dictionaries and arrays containing text or strings.
I need to store this text in MYSQL (coming from Arrays of srtrings on phone) and then query to see the text contains (case insensitive) a word or phrase in question.
For example, if the array consists of {Ford, Chevy, Toyota, BMW, Buick}, I might want to query it to see it contains Saab.
I know storing arrays in a field is not MYSQL friendly as it prevents optimization. However, it would be way too complicated to create individual tables for these collections of words which are created by users.
So I'm looking for a reasonable way to store them, perhaps delimited with spaces or with commas that makes possible reasonably efficient searches.
If they are stored separated by spaces, I gather you can do something with regex like:
SELECT
*
FROM
`wordgroups`
WHERE
wordgroup regexp '(^|[[:space:]])BLA([[:space:]]|$)';
But this seems funky.
Is there a better way to do this? Thanks for any insights
Consider using a FULLTEXT index. And use MATCH(...) AGAINST(... IN NATURAL LANGUAGE MODE).
FULLTEXT is very fast for "words", and IN NATURAL MODE may solve your Saab example.
Using regexp can achieve what you want, however, your query will be inefficient, since it cannot rely on any indexes.
If you want to store a list of words and their position within the array does not matter, then you may consider storing them in a single field, space delimited. But instead of using a regexp, use fulltext indexing and searching. This method has a clear advantage over searching with regexp: it uses an index. It has some drawbacks as well: there is a stopword list (these are excluded from searching) and there is a minimum word length as well. The good news is that these parameters are configurable. Also, you get all the drawbacks of storing data in a delimited field, as detailed in Is storing a delimited list in a database column really that bad? question here on SO.
However, if you want to use dictionaries (key - value pairs) or the position within the list may be important, then the above data structure will not do.
In this case, I would consider if mysql is the right choice for storing my data in the first place. If you have multi-dimensional lists, or lists containing lists, then I would definitely choose a different nosql solution.
If you only need simple, two-dimensional lists / dictionaries, then you can store all of them in a single table with a similar structure as below:
list_id - unique identifier of the list, primary key
user_id - id of the user the list belongs to
key - for dictionaries this is the lookup field (indexed), for other lists it may store the position of the element. String data type.
value - the field holding the value (indexed). Data type should be string, so that it could hold different data types as well.
A search to determine if a list holds a certain value would be fast and efficient lookup using the index on either the key or value fields.
Related
I need to store a dynamic number of integer attributes (1-8). I'm storing them in individual columns in the database table, like:
attribute_1, attribute_2, ..., attribute_8
This makes for a fairly messy model when methods need to reference these, as well as an unwieldy database table and schema.
These are assigned default values (but are overridable on a form), and represent unique identifiers for the user.
For instance, a Brew is composed of up to eight batches before they are mixed together in a fermenter. The brewer might want to go back and refer to any one of these by its unique identifying number. I'm assigning these unique values based on the last highest value when creating a new Brew. However, the user may want to override these default values to some other numbers.
In most cases (smaller breweries), they'll probably only use the first two, but some larger breweries would use all eight.
There must be a better way to store these than having eight different attributes with the same name and a number at the end.
I'm using MySQL. Is there an easy/concise way to store an array or a JSON hash but still be able to edit these values on a form?
I would not store attributes like that. It will limit you in the future. Let say you want to know which brews have used attribute_4? You will have to scan the entire brews table, open the attributes field and deconstruct it to see if 4 is in there.
Much better to separate Brew and Attributes in two tables, and link them, like so:
Another benefit, is you can add attributes easily.
Storing JSON is ok, like #max pointed out. I just propose the normalized database way of doing it.
In database table design, which of the following is better design for event-log type of data growth
Design 1) Numeric columns(Long) and character columns (Varchar2) with
Index:
..(pkey)|..|..|StockNumber Long | StockDomain Varchar2 |...
.. |..|..|11111 | Finance
.. |..|..|23458 | Medical
Design 2) Character column Varchar2 with Index:
..(pkey)|..|..|StockDetails Varchar2(1000) |..|..
.. |..|..|11111;Finance |..|..
.. |..|..|23458;Medical |..|..
Design advantages: First design is very specific and Second design is more general which can accommodate more general data.In both the cases, columns indexed.
Storage: First design indexes require less storage than second
Performance: Same?
I am having a question about performance vs flexibility. Obviously, first design is better. But second design is the more general purpose. Let me know your insights
Note: Edited the question for more clarity.
In general, having discrete columns is the better way to go for a few reasons:
Datatypes - You have guarantees that the data you have saved is in the right formats, at least as far as non string columns go, your stockNumber will always be a number if it's a bigint/long, trying to set it to anything else will cause your insert/update to error. As part of a colon separated value (CSV) string there is a chance of bad data when it's part of a string.
Querying - Querying a single column has to be done using LIKE since you are looking for a substring of the single column string. If I look for WHERE StockDetails LIKE '%11111%' I will find the first line, but I may find another line where a dollar value inside that column, in a different field is $11111. With discrete columns your query would be WHERE StockNumber = 11111 guaranteeing it finds the data only in that column.
Using the data - Once you have found the row you're wanting, you then have to read the data. This means parsing out your CSV into separate fields. If one of those fields had a colon in it, and it is improperly escaped, the rest of the data is going to be parsed wrong, and you still need your values in a guaranteed same order, leaving blank sections ;; where you would have had a null value in a column.
There is a middle ground between storing CSVs and a separate columns. I have seen, and in fact am doing on one major project, data stored in a table as json. With json you have property names, so you don't care the order the fields appear in the string, because domain will still always be domain, any non standard fields you don't need in an entry (say a property that only exists for the medical domain) will just not be there rather than needing a blank double colon, and parsers for json exist in all languages I can think of that you would connect to your database, there's no need to manually code something to parse out your CSV string. For example your StockDetails given above would look like this:
+--------------------------------------+
| StockDetails |
+--------------------------------------+
| {"number":11111, "domain":"Finance"} |
| {"number":23458, "domain":"Medical"} |
+--------------------------------------+
This solves issues 2 and 3 above:
You now write your query as WHERE StockDetails LIKE '%"number":11111 including the json property name guarantees you don't find the data anywhere else in your string.
You don't need to worry about fields out of order, or missing in your string causing your data to be unusable, using json gives you the key/value pair, all you need to do is handle nulls where the key doesn't exist. This also lets you add fields easily, adding a new CSV field can break your code to parse it, the number of values will be off for your existing data, so you will need to update all rows potentially, however since in json you only store non null fields, a new field will be treated like any other null value on existing data.
In relational database design, you need discrete columns. One value per column per row.
This is the only way to use data types and constraints to implement some data integrity. In your second design, how would you implement a UNIQUE constraint on either StockNumber or StockDomain? How would you make sure StockNumber is actually a number?
This is the only way to create indexes on each column individually, or create a compound index that puts the StockDomain first.
As an analogy, look in the telephone book: can you find all people whose first name is "Bill" easily or efficiently? No, you have to search the whole book to find people with a specific first name. The order of columns in an index matters.
The second design is practically not a database at all — it's a file.
To respond to your comments, I'm reiterating what I wrote in a comment:
Sometimes denormalization is worthwhile, but I can't tell [if your second design is worthwhile], because you haven't described how you will query this data. You must take into account your query needs before you can decide on any optimization.
Stated another way: denormalization, like all other optimizations, benefits one query type, at the expense of other query types. Therefore you need to know which queries you need to be optimal, and which queries are less important, so it won't hurt your overall performance if the other queries are degraded.
If you can't predict the queries, default to designing a database with rules of normalization. Normalization is not designed for performance optimization, it's designed to prevent data anomalies, which is a good goal too.
You have posted several new comments, I guess in the hopes that I will suddenly understand and endorse your second design. But you still haven't described any specific query that will be optimized by using your second design.
My knowledge of relational databases is more limited, but is there a SQL command that can be used to create a column that contains a set in each row?
I am trying to create a table with 2 columns. 1 for specific IDs and a 2nd for sets that correspond to these IDs.
I read about
http://dev.mysql.com/doc/refman/5.1/en/set.html
However, the set data type requires that you know what items may be in your set. However, I just want there to be a variable-number list of items that don't repeat.
It would be much better to create that list of items as multiple rows in a second table. Then you could have as many items in the list you want, you could sort them, search for a specific item, make sure they're unique, etc.
See also my answer to Is storing a delimited list in a database column really that bad?
No, there's no MySQL data type for arbitrary sets. You can use a string containing a comma-delimited list; there are functions like FIND_IN_SET() that will operate on such values.
But this is poor database design. If you have an open-ended list, you should store it in a table with one row per value. This will allow them to be indexed, making searching faster.
MySQL doesn't support arrays, lists or other data structures like that. It does however support strings so use that and FIND_IN_SET() function:
http://dev.mysql.com/doc/refman/5.7/en/string-functions.html#function_find-in-set
"SET" data type won't be a good choice here.
You can use the "VARCHAR" and store the values in CSV format. You handle them at application level.
Example: INSERT into my_table(id, myset) values(1, "3,4,7");
I currently have a table in MySQL that stores values normally, but I want to add a field to that table that stores an array of values, such as cities. Should I simply store that array as a CSV? Each row will need it's own array, so I feel uneasy about making a new table and inserting 2-5 rows for each row inserted in the previous table.
I feel like this situation should have a name, I just can't think of it :)
Edit
number of elements - 2-5 (a selection from a dynamic list of cities, the array references the list, which is a table)
This field would not need to be searchable, simply retrieved alongside other data.
The "right" way would be to have another table that holds each value but since you don't want to go that route a delimited list should work. Just make sure that you pick a delimiter that won't show up in the data. You can also store the data as XML depending on how you plan on interacting with the data this may be a better route.
I would go with the idea of a field containing your comma (or other logical delimiter) separated values. Just make sure that your field is going to be big enough to hold your maximum array size. Then when you pull the field out, it should be easy to perform an explode() on the long string using your delimiter, which will then immediately populate your array in the code.
Maybe the word you're looking for is "normalize". As in, move the array to a separate table, linked to the first by means of a key. This offers several advantages:
The array size can grow almost indefinitely
Efficient storage
Ability to search for values in the array without having to use "like"
Of course, the decision of whether to normalize this data depends on many factors that you haven't mentioned, like the number of elements, whether or not the number is fixed, whether the elements need to be searchable, etc.
Is your application PHP? It might be worth investigating the functions serialize and unserialize.
These two functions allow you to easily store an array in the database, then recreate that array at a later time.
As others have mentioned, another table is the proper way to go.
But if you really don't want to do that(?), assuming you're using PHP with MySQL, why not use the serialize() and store a serialized value?
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.