I have two lists of words and I need to find matches (intersection of the two sets.)
Should I store each list as a string and find matches through string functions (like a regular expression) or store the words in a table, and have SQL find matches by joining?
It is almost impossible to say without more information about the problem. Here are some things to consider:
How many different distinct items do you have?
How many different combinations would be on a typical row?
Do your searches require looking for wildcards?
How long are the individual items?
Specifics on the database engine and hardware you are running on.
I want to emphasize that in almost all situations, you want to store the values in another table. Performance is not necessarily the primary reason. More important are ease of updating and deleting individual values, and the ability to support many more types of queries (such as a list of all available values).
But, we can still think about the performance issues. Storing values in a single string simply requires fetching the page with the record on it, and then applying a function that goes through the string. For simple patterns (such as identifying the presence of a fixed substring), this should go quite fast. There are few things that computers do faster than looping through strings and comparing values (assuming a reasonable implementation).
In the fastest possible join, both tables need to be read in, and the keys need to be matched. This requires additional effort. The situation is even worse, because you really want two additional tables, one for the individual string items and the other for the relationship between the original records and the items.
At this point, you may think "gosh, strings seem like a better idea". This is wrong. One of the big differences is in average size. If you items are, on average, longer than, say, 4 characters, then you save space by using a reference table. This saved space immediately translates into improved performance, because there is less I/O. With indexes, the additional tables would be in memory anyway, so the matching would be quite fast.
And, there is the issue of querying. You can use standard SQL functions for queries such as records that have A and B (many string functions are database specific). You can easily find out exactly which items are in the database, and relatively easily find what pairs exist on records. You can keep track of when an item is added to a record, and the first time it appears in the database. Generally, this flexible functionality -- which is just basic SQL functionality -- is what you need when managing this type of data.
Storing in a table will be much faster than a SQL string manipulation function in most circumstances especially if you can index the words.
I think you're asking if this:
SELECT word FROM table_one WHERE word in (SELECT word FROM table_two)
is faster than this:
SELECT table_one.word FROM table_one
INNER JOIN table_two ON table_one.word = table_two.word
The first answer should be faster, because the second creates a (potentially large) temporary object (the joined table).
Note that I assume you have an index on word. Also: if the strings are very long (URLs, for example), this will be very slow, and you should match on a hash instead.
This question seems to have been asked a lot and the answer seems to be "it depends on the details". so I am asking for my specific case: Is it better for me to have multiple queries or use joins?
The details are as follows:
"products" table -- probably around 2000 rows, 15 or so columns
"tags" table -- probably around 10 rows, 3 columns
"types" table -- probably around 10 rows, 3 columns
I need the "tags" and "types" table to get the tag/type-id that is in the product table.
My gut says that if i join the tables i end up searching a much much larger set so its better to do multiple queries, but i am not really sure...
Thoughts?
No, join will probably outperform multiple queries. Your tables are extremely small.
Ask yourself what extra work would be involved in doing multiple queries... I don't know what you need this data for, but I assume you would, at some point, need to correlate the results - match Tags and Types to Products, wouldn't you? If you don't do that with a join, you just have to do it elsewhere with some other mechanism.
Further, your conception of this overlooks the fact that databases are designed for join scenarios. If you perform three isolated queries, the database has no opportunity to optimize its querying behavior across the results you're looking for. If you do it in one query with a join, it does have that opportunity.
Leave the problem of producing a resultset of ~2000 * 10 * 10 records and then filtering it up to the database, in my opinion - that's what it's good at. :)
The amount of data is too small to demonstrate one over the other, but multiple separate queries will use more with respect to transferring over the wire than a single query. There is packet overhead, and separate data sets risks difference if the data set changes between queries if not in the same transaction.
JOINs specifically might not be necessary, EXISTS or IN can be used if the supporting tables don't expose columns in the resultset. A JOIN between tables that are parent & child, and there can be more than one child to a parent will inflate the rows searched -- not necessarily the rows returned.
Assuming that everything has indexes on the primary keys (you should be doing that), then joins will be very efficient. The only case where joins would be worse is if you had some kind of external caching of query results (as some ORMs will do for you), your products table was much bigger, and you were querying at a sufficient rate to keep the results of the two smaller queries (but not the third) in cache. In that scenario multiple queries becomes faster because you're only making one of the three queries. But the difference is going to be hard to measure.
If the database is not on localhost but accessed over a network it's better to send one request, let the database do the work and retrieve the data at once. This will give you less network delay. So joins are preferred.
Say tableA has 1 row to be returned but will have 100 columns returned while tableB has 100 rows to be returned but only one column from each. TableB has a foreign key for table A.
Will a left join of tableA to tableB return 100*100 cells of data while 2 separate queries return 100 + 100 cells of data or 50 times less data or is that a misunderstanding of how it works?
Is it ever more efficient to use many simple queries rather than fewer more complex ones?
First and foremost, I would question a table with 100 columns, and suggest that there is a possibly a better design for your schema. In the real world, this number of columns is less common, so typically the difference in the amount of data returned with one query vs. two becomes less significant. 100 columns in a table is not necessarily bad, just a flag that it shold be considered.
However, assuming your numbers are what they are to make clear the question, there are a few important variables to consider:
1 - What is the speed of the link between the db server and the application server? If it is very slow, then you are probably better off minimizing the amount data returned vs. the number of queries you run. If it is not slow, then you will likely expend more time in the execution of two queries than you would returning the increased payload. Which is better can only be determined by testing in your own environment.
2 - How efficient is the transport protocol itself? Perhaps there is some kind of compression of the data, or an even more clever algorithm that knows column 2 through 101 are duplicate for every row, so it only passes them once. Strategies like this in the transport protocol would mitigate any of your concerns. Again, this is why you need to test in your own envionment to know for sure.
As others have pointed out, you also need to consider what will be done with the data once you get it (e.g., JOINs, GROUPing, etc), but I am limiting my response to the specifics of your question around query count vs. payload size.
What is best at joining? A database engine or client code? Saying that, I use both techniques: it depends on the client and how data will be used.
Where the data requires some processing to, say, render on a web page I'd probably split header and details recordsets. We do use this because we have some business logic between DB and HTML
Where it's consumed simply and linearly, I'd join in the database to avoid unnecessary processing. For example, simple reports or exports
It depends, if you only take into account the SQL efficiency obviusly several simpler and smaller result queries will be more efficient.
But you need to take into account the whole process if the join will be made otherwise on the client or you need to filter results after the join, then probably the DBM will be more efficient that doing it on your code.
Coding is always a tradeoff between diferent systems, DB vs Client, RAM vs CPU... you need to be conscious about this and try to find the perfect solution.
In this case probably 2 queries outperform 1 but that is not a general solution.
I think that your question basically is about database normalization. In general, it is advisable to normalize a database into multiple tables (using primary and foreign keys) and to join them as needed upon queries. This is better for insert/update performance and for keeping the data consistent, and usually results in smaller database sizes as well.
As for the row numbers returned, only a cross join would actually return 100*100 rows; any inner or outer join will not create all combinations, but rather tie together rows on the given conditions, and for outer joins preserve rows which could not be matched. Wikipedia has some samples in its JOIN article.
For very query-intense applications, the performance may be better when using less normlized tables. However, as always with optimizations, I'd only consider going into that direction after seeing real measurable problems (e.g. with a profiling tool).
In general, try to keep the number of roundtrips to the database low; a large number of single simple queries will suffer from the overhead of talking to the DB engine (network etc.). If you need to execute complex series of statements, consider using stored procedures.
Generally fewer queries makes for better performance, as long as the queries return data that is actually related. There is no point in trying to put unrelated data into the same query just to reduce the number or queries.
There are of course exceptions, and your example may be one of them. However, it depends on more than the number of fields returnes, like what the fields actually return, i.e. the actual amount of data.
As an example of how the number of queries affects performance, I can mention a solution that I have (sadly enough) seen many times. In that solution the programmer would first get a number of records from one table, then loop through the records and run another query for each record to get the related records from another table. This clearly results in a lot of queries, and a solution having either one or two queries would be much more efficient.
“Is it ever more efficient to use many simple queries rather than fewer more complex ones?”
The query that requires the least amount of data to traverse, and gives you no more than what you need is the more efficient one. Beyond this, there can be RDBMS specific conditions that can be more efficient on one RDBMS system than another. At the very low level, when you deal with less data, then your results can be retrieved much quicker, so efficient queries are queries that only work with the least amount of data needed to get you the result you are looking for.
I've heard that SELECT * is generally bad practice to use when writing SQL commands because it is more efficient to SELECT columns you specifically need.
If I need to SELECT every column in a table, should I use
SELECT * FROM TABLE
or
SELECT column1, colum2, column3, etc. FROM TABLE
Does the efficiency really matter in this case? I'd think SELECT * would be more optimal internally if you really need all of the data, but I'm saying this with no real understanding of database.
I'm curious to know what the best practice is in this case.
UPDATE: I probably should specify that the only situation where I would really want to do a SELECT * is when I'm selecting data from one table where I know all columns will always need to be retrieved, even when new columns are added.
Given the responses I've seen however, this still seems like a bad idea and SELECT * should never be used for a lot more technical reasons that I ever though about.
One reason that selecting specific columns is better is that it raises the probability that SQL Server can access the data from indexes rather than querying the table data.
Here's a post I wrote about it: The real reason select queries are bad index coverage
It's also less fragile to change, since any code that consumes the data will be getting the same data structure regardless of changes you make to the table schema in the future.
Given your specification that you are selecting all columns, there is little difference at this time. Realize, however, that database schemas do change. If you use SELECT * you are going to get any new columns added to the table, even though in all likelihood, your code is not prepared to use or present that new data. This means that you are exposing your system to unexpected performance and functionality changes.
You may be willing to dismiss this as a minor cost, but realize that columns that you don't need still must be:
Read from database
Sent across the network
Marshalled into your process
(for ADO-type technologies) Saved in a data-table in-memory
Ignored and discarded / garbage-collected
Item #1 has many hidden costs including eliminating some potential covering index, causing data-page loads (and server cache thrashing), incurring row / page / table locks that might be otherwise avoided.
Balance this against the potential savings of specifying the columns versus an * and the only potential savings are:
Programmer doesn't need to revisit the SQL to add columns
The network-transport of the SQL is smaller / faster
SQL Server query parse / validation time
SQL Server query plan cache
For item 1, the reality is that you're going to add / change code to use any new column you might add anyway, so it is a wash.
For item 2, the difference is rarely enough to push you into a different packet-size or number of network packets. If you get to the point where SQL statement transmission time is the predominant issue, you probably need to reduce the rate of statements first.
For item 3, there is NO savings as the expansion of the * has to happen anyway, which means consulting the table(s) schema anyway. Realistically, listing the columns will incur the same cost because they have to be validated against the schema. In other words this is a complete wash.
For item 4, when you specify specific columns, your query plan cache could get larger but only if you are dealing with different sets of columns (which is not what you've specified). In this case, you do want different cache entries because you want different plans as needed.
So, this all comes down, because of the way you specified the question, to the issue resiliency in the face of eventual schema modifications. If you're burning this schema into ROM (it happens), then an * is perfectly acceptable.
However, my general guideline is that you should only select the columns you need, which means that sometimes it will look like you are asking for all of them, but DBAs and schema evolution mean that some new columns might appear that could greatly affect the query.
My advice is that you should ALWAYS SELECT specific columns. Remember that you get good at what you do over and over, so just get in the habit of doing it right.
If you are wondering why a schema might change without code changing, think in terms of audit logging, effective/expiration dates and other similar things that get added by DBAs for systemically for compliance issues. Another source of underhanded changes is denormalizations for performance elsewhere in the system or user-defined fields.
You should only select the columns that you need. Even if you need all columns it's still better to list column names so that the sql server does not have to query system table for columns.
Also, your application might break if someone adds columns to the table. Your program will get columns it didn't expect too and it might not know how to process them.
Apart from this if the table has a binary column then the query will be much more slower and use more network resources.
There are four big reasons that select * is a bad thing:
The most significant practical reason is that it forces the user to magically know the order in which columns will be returned. It's better to be explicit, which also protects you against the table changing, which segues nicely into...
If a column name you're using changes, it's better to catch it early (at the point of the SQL call) rather than when you're trying to use the column that no longer exists (or has had its name changed, etc.)
Listing the column names makes your code far more self-documented, and so probably more readable.
If you're transferring over a network (or even if you aren't), columns you don't need are just waste.
Specifying the column list is usually the best option because your application won't be affected if someone adds/inserts a column to the table.
Specifying column names is definitely faster - for the server. But if
performance is not a big issue (for example, this is a website content database with hundreds, maybe thousands - but not millions - of rows in each table); AND
your job is to create many small, similar applications (e.g. public-facing content-managed websites) using a common framework, rather than creating a complex one-off application; AND
flexibility is important (lots of customization of the db schema for each site);
then you're better off sticking with SELECT *. In our framework, heavy use of SELECT * allows us to introduce a new website managed content field to a table, giving it all of the benefits of the CMS (versioning, workflow/approvals, etc.), while only touching the code at a couple of points, instead of a couple dozen points.
I know the DB gurus are going to hate me for this - go ahead, vote me down - but in my world, developer time is scarce and CPU cycles are abundant, so I adjust accordingly what I conserve and what I waste.
SELECT * is a bad practice even if the query is not sent over a network.
Selecting more data than you need makes the query less efficient - the server has to read and transfer extra data, so it takes time and creates unnecessary load on the system (not only the network, as others mentioned, but also disk, CPU etc.). Additionally, the server is unable to optimize the query as well as it might (for example, use covering index for the query).
After some time your table structure might change, so SELECT * will return a different set of columns. So, your application might get a dataset of unexpected structure and break somewhere downstream. Explicitly stating the columns guarantees that you either get a dataset of known structure, or get a clear error on the database level (like 'column not found').
Of course, all this doesn't matter much for a small and simple system.
Lots of good reasons answered here so far, here's another one that hasn't been mentioned.
Explicitly naming the columns will help you with maintenance down the road. At some point you're going to be making changes or troubleshooting, and find yourself asking "where the heck is that column used".
If you've got the names listed explicitly, then finding every reference to that column -- through all your stored procedures, views, etc -- is simple. Just dump a CREATE script for your DB schema, and text search through it.
Performance wise, SELECT with specific columns can be faster (no need to read in all the data). If your query really does use ALL the columns, SELECT with explicit parameters is still preferred. Any speed difference will be basically unnoticeable and near constant-time. One day your schema will change, and this is good insurance to prevent problems due to this.
definitely defining the columns, because SQL Server will not have to do a lookup on the columns to pull them. If you define the columns, then SQL can skip that step.
It's always better to specify the columns you need, if you think about it one time, SQL doesn't have to think "wtf is *" every time you query. On top of that, someone later may add columns to the table that you actually do not need in your query and you'll be better off in that case by specifying all of your columns.
The problem with "select *" is the possibility of bringing data you don't really need. During the actual database query, the selected columns don't really add to the computation. What's really "heavy" is the data transport back to your client, and any column that you don't really need is just wasting network bandwidth and adding to the time you're waiting for you query to return.
Even if you do use all the columns brought from a "select *...", that's just for now. If in the future you change the table/view layout and add more columns, you'll start bring those in your selects even if you don't need them.
Another point in which a "select *" statement is bad is on view creation. If you create a view using "select *" and later add columns to your table, the view definition and the data returned won't match, and you'll need to recompile your views in order for them to work again.
I know that writing a "select *" is tempting, 'cause I really don't like to manually specify all the fields on my queries, but when your system start to evolve, you'll see that it's worth to spend this extra time/effort in specifying the fields rather than spending much more time and effort removing bugs on your views or optimizing your app.
While explicitly listing columns is good for performance, don't get crazy.
So if you use all the data, try SELECT * for simplicity (imagine having many columns and doing a JOIN... query may get awful). Then - measure. Compare with query with column names listed explicitly.
Don't speculate about performance, measure it!
Explicit listing helps most when you have some column containing big data (like body of a post or article), and don't need it in given query. Then by not returning it in your answer DB server can save time, bandwidth, and disk throughput. Your query result will also be smaller, which is good for any query cache.
You should really be selecting only the fields you need, and only the required number, i.e.
SELECT Field1, Field2 FROM SomeTable WHERE --(constraints)
Outside of the database, dynamic queries run the risk of injection attacks and malformed data. Typically you get round this using stored procedures or parameterised queries. Also (although not really that much of a problem) the server has to generate an execution plan each time a dynamic query is executed.
It is NOT faster to use explicit field names versus *, if and only if, you need to get the data for all fields.
Your client software shouldn't depend on the order of the fields returned, so that's a nonsense too.
And it's possible (though unlikely) that you need to get all fields using * because you don't yet know what fields exist (think very dynamic database structure).
Another disadvantage of using explicit field names is that if there are many of them and they're long then it makes reading the code and/or the query log more difficult.
So the rule should be: if you need all the fields, use *, if you need only a subset, name them explicitly.
The result is too huge. It is slow to generate and send the result from the SQL engine to the client.
The client side, being a generic programming environment, is not and should not be designed to filter and process the results (e.g. the WHERE clause, ORDER clause), as the number of rows can be huge (e.g. tens of millions of rows).
Naming each column you expect to get in your application also ensures your application won't break if someone alters the table, as long as your columns are still present (in any order).
Performance wise I have seen comments that both are equal. but usability aspect there are some +'s and -'s
When you use a (select *) in a query and if some one alter the table and add new fields which do not need for the previous query it is an unnecessary overhead. And what if the newly added field is a blob or an image field??? your query response time is going to be really slow then.
In other hand if you use a (select col1,col2,..) and if the table get altered and added new fields and if those fields are needed in the result set, you always need to edit your select query after table alteration.
But I suggest always to use select col1,col2,... in your queries and alter the query if the table get altered later...
This is an old post, but still valid. For reference, I have a very complicated query consisting of:
12 tables
6 Left joins
9 inner joins
108 total columns on all 12 tables
I only need 54 columns
A 4 column Order By clause
When I execute the query using Select *, it takes an average of 2869ms.
When I execute the query using Select , it takes an average of 1513ms.
Total rows returned is 13,949.
There is no doubt selecting column names means faster performance over Select *
Select is equally efficient (in terms of velocity) if you use * or columns.
The difference is about memory, not velocity. When you select several columns SQL Server must allocate memory space to serve you the query, including all data for all the columns that you've requested, even if you're only using one of them.
What does matter in terms of performance is the excecution plan which in turn depends heavily on your WHERE clause and the number of JOIN, OUTER JOIN, etc ...
For your question just use SELECT *. If you need all the columns there's no performance difference.
It depends on the version of your DB server, but modern versions of SQL can cache the plan either way. I'd say go with whatever is most maintainable with your data access code.
One reason it's better practice to spell out exactly which columns you want is because of possible future changes in the table structure.
If you are reading in data manually using an index based approach to populate a data structure with the results of your query, then in the future when you add/remove a column you will have headaches trying to figure out what went wrong.
As to what is faster, I'll defer to others for their expertise.
As with most problems, it depends on what you want to achieve. If you want to create a db grid that will allow all columns in any table, then "Select *" is the answer. However, if you will only need certain columns and adding or deleting columns from the query is done infrequently, then specify them individually.
It also depends on the amount of data you want to transfer from the server. If one of the columns is a defined as memo, graphic, blob, etc. and you don't need that column, you'd better not use "Select *" or you'll get a whole bunch of data you don't want and your performance could suffer.
To add on to what everyone else has said, if all of your columns that you are selecting are included in an index, your result set will be pulled from the index instead of looking up additional data from SQL.
SELECT * is necessary if one wants to obtain metadata such as the number of columns.
Gonna get slammed for this, but I do a select * because almost all my data is retrived from SQL Server Views that precombine needed values from multiple tables into a single easy to access View.
I do then want all the columns from the view which won't change when new fields are added to underlying tables. This has the added benefit of allowing me to change where data comes from. FieldA in the View may at one time be calculated and then I may change it to be static. Either way the View supplies FieldA to me.
The beauty of this is that it allows my data layer to get datasets. It then passes them to my BL which can then create objects from them. My main app only knows and interacts with the objects. I even allow my objects to self-create when passed a datarow.
Of course, I'm the only developer, so that helps too :)
What everyone above said, plus:
If you're striving for readable maintainable code, doing something like:
SELECT foo, bar FROM widgets;
is instantly readable and shows intent. If you make that call you know what you're getting back. If widgets only has foo and bar columns, then selecting * means you still have to think about what you're getting back, confirm the order is mapped correctly, etc. However, if widgets has more columns but you're only interested in foo and bar, then your code gets messy when you query for a wildcard and then only use some of what's returned.
And remember if you have an inner join by definition you do not need all the columns as the data in the join columns is repeated.
It's not like listing columns in SQl server is hard or even time-consuming. You just drag them over from the object browser (you can get all in one go by dragging from the word columns). To put a permanent performance hit on your system (becasue this can reduce the use of indexes and becasue sending unneeded data over the network is costly) and make it more likely that you will have unexpected problems as the database changes (sometimes columns get added that you do not want the user to see for instance) just to save less than a minute of development time is short-sighted and unprofessional.
Absolutely define the columns you want to SELECT every time. There is no reason not to and the performance improvement is well worth it.
They should never have given the option to "SELECT *"
If you need every column then just use SELECT * but remember that the order could potentially change so when you are consuming the results access them by name and not by index.
I would ignore comments about how * needs to go get the list - chances are parsing and validating named columns is equal to the processing time if not more. Don't prematurely optimize ;-)