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Is it reasonable for an application to create database tables dynamically as a means of partitioning?
For example, say I have a large table "widgets" with a "userID" column identifying the owner of each row. If this table tended to grow extremely large, would it make sense to instead have the application create a new table called "widgets_{username}" for each new user? Assume that the application will only ever have to query for widgets belonging to a single user at a time (i.e. no need to try and join any of these user widget tables together).
Doing this would break up the one large table into more easily-managed chunks, but this doesn't seem like an elegant solution. In my mind, the database schema should be defined when the application is written, and any runtime data is stored as rows, not as additional tables.
As a more general question, is modifying the database schema at runtime ever ok?
Edit: This question is mostly hypothetical; I had a pretty good feeling that creating tables at runtime didn't make sense. That being said, we do have a table with millions of rows in our application. SELECTs perform fine, but things like deleting all rows owned by a particular user can take a while. Basically I'm looking for some solid reasoning why just dynamically creating a table for each user doesn't make sense for when I'm asked.
NO, NO, NO!! Now repeat after me, I will not do this because it will create many headaches and problems in the future! Databases are made to handle large amounts of information. they use indexes to quickly find what you are after. think phone book how effective is the index? would it be better to have a different book for each last name?
This will not give you anything performance wise. Keep a single table, but be sure to index on UserID and you'll be able to get the data fast. however if you split the table up, it becomes impossible/really really hard to get any info that spans multiple users, like search all users for a certain widget, count of all widgets of a certain type, etc. you need to have every query be built dynamically.
If deleting rows is slow, look into that. How many rows at one time are we talking about 10, 1000, 100000? What is your clustered index on this table? Could you use a "soft delete", where you have a status column that you UPDATE to "D" to mark the row as deleted. Can you delete the rows at a later time, with less database activity. is the delete slow because it is being blocked by other activity. look into those before you break up the table.
No, that would be a bad idea. However some DBMSs (e.g. Oracle) allow a single table to be partitioned on values of a column, which would achieve the objective without creating new tables at run time. Having said that, it is not "the norm" to partition tables like this: it is only usually done in very large databases.
Using an index on userID should result nearly in the same performance.
In my opinion, changing the database schema at runtime is bad practice.
Consider, for example, security issues...
Is it reasonable for an application to create database tables
dynamically as a means of partitioning?
No. (smile)
To preface, I'm aware (as should you!) that using SELECT * in production is bad, but I was maintaining a script written by someone else. And, I'm also aware that this question is low on specifics... But hypothetical scenario.
Let's say I have a script that selects everything from a table of 20 fields. Let's say typical customer information.
Then let's say being the good developer I am, I shorten the SELECT * to a SELECT of the 13 specific fields I'm actually using on the display end.
What type of performance benefit, if any, could I expect by explicitly listing the fields versus SELECT *?
I will say this, both queries take advantage of the same exact indexes. The more specific query does not have access to a covering index that the other query could not use, in case you were wondering.
I'm not expecting miracles, like adding an index that targets the more specific query. I'm just wondering.
It depends on three things: the underlying storage and retrieval mechanism used by your database, the nature of the 7 columns you're leaving out, and the number of rows returned in the result set.
If the 7 (or whatever number) columns you're leaving out are "cheap to retrieve" columns, and the number of rows returned is low, I would expect very little benefit. If the columns are "expensive" (for instance, they're large, or they're BLOBs requiring reference to another file that is never cached) and / or you're retrieving a lot of rows then you could expect a significant improvement. Just how much depends on how expensive it is in your particular database to retrieve that information and assemble in memory.
There are other reasons besides speed, incidentally, to use named columns when retrieving information having to do with knowing absolutely that certain columns are contained in the result set and that the columns are in the desired order that you want to use them in.
The main difference I would expect to see is reduced network traffic. If any of the columns are large, they could take time to transfer, which is of course a complete waste if you're not displaying them.
It's also fairly critical if your database library references columns by index (instead of name), because if the column order changes in the database, it'll break the code.
Coding-style wise, it allows you to see which columns the rest of the code will be using, without having to read it.
Hmm, in one simple experiment, I was surprised at how much difference it made.
I just did a simple query with three variations:
select *
select the field that is the primary key. (It might pull get this directly from the index without actually reading the record)
select a non-key field.
I used a table with a pretty large number of fields -- 72 of them -- including one CLOB. The query was just a select with one condition in the where clause.
Results:
Run * Key Non-key
1 .647 .020 .028
2 .599 .041 .014
3 .321 .019 .027
avg .522 .027 .023
Key vs non-key didn't seem to matter. (Which surprises me.) But retrieving just one field versus select * saved 95% of the runtime!
Of course this is one tiny experiment with one table. There could be many many relevant factors. I'm certainly not claiming that you will always reduce runtime by 95% by not using select *! But it's far more impressive than I expected.
When comparing 13 vs 20 fields, if the 7 fields that are left out are not fields such as CLOB/BLOBs or such, I would expect to see no noticable performance gain.
I/O is main DB bottleneck (most DB systems are I/O bound), so you might think that you would bring execution time to 13/20 of the original query execution time (since you need that much less data), but since normal fields are stored within the same physical structure (usually fields are arranged consecutively) and the file system reads whole blocks, your disk heads will read the same amount of data (assuming all 20 fields are less then block size; situation can change if the size of a record is bigger than a block of your filesystem).
The principle that SELECT * is bad has a different cause - stability of the system.
If you use SELECT * at wrong places then changes to underlying table(s) might break your system unexpectedly (mostly later, and if things break it is usually better if they break sooner). This can especially be intresting if normalize data (move columns from one table to another, while keeping the same name). In such case if you chain SELECT * in views and if you chain your views then you might actually not get any errors, but have (essentially) different end results.
Why don't you try it yourself and let us know?
It's all going to be dependent on how many columns and how wide they are.
Better still, do you have an actual performance problem? Tell us what your actual problem is and show us the code, and then we can suggest potential improvements. Chances are there are other improvements to be made that are much better than worrying about SELECT * vs. SELECT field list.
Select * means the database has to take time to lookup the fields. If you don't need all those fields (and anytime you have have an inner join you don't as the join field is repeated!) then you are wasting but server resources to get the data and network resources to transport the data. You may also be wasting memory to hold the recordset to work with it. And while the performance improvement may be tiny for one query, how many times is that query run? And people who use this abysmally poor technique tend to use it everywhere, so fixing all of them can be a major imporvement for not that much effort. And how hard is it to specify the fields? I don't know about every database, but in SQL Server I can drag and drop what I want from the object browser in seconds. So using select * is trading less than a minute of development time for a worse performance every single time the query is run and creating code that is fragile and subject to very bad problems as the schema changes. I see no reason to ever use select * in production code.
Why is SELECT * bad practice? Wouldn't it mean less code to change if you added a new column you wanted?
I understand that SELECT COUNT(*) is a performance problem on some DBs, but what if you really wanted every column?
There are really three major reasons:
Inefficiency in moving data to the consumer. When you SELECT *, you're often retrieving more columns from the database than your application really needs to function. This causes more data to move from the database server to the client, slowing access and increasing load on your machines, as well as taking more time to travel across the network. This is especially true when someone adds new columns to underlying tables that didn't exist and weren't needed when the original consumers coded their data access.
Indexing issues. Consider a scenario where you want to tune a query to a high level of performance. If you were to use *, and it returned more columns than you actually needed, the server would often have to perform more expensive methods to retrieve your data than it otherwise might. For example, you wouldn't be able to create an index which simply covered the columns in your SELECT list, and even if you did (including all columns [shudder]), the next guy who came around and added a column to the underlying table would cause the optimizer to ignore your optimized covering index, and you'd likely find that the performance of your query would drop substantially for no readily apparent reason.
Binding Problems. When you SELECT *, it's possible to retrieve two columns of the same name from two different tables. This can often crash your data consumer. Imagine a query that joins two tables, both of which contain a column called "ID". How would a consumer know which was which? SELECT * can also confuse views (at least in some versions SQL Server) when underlying table structures change -- the view is not rebuilt, and the data which comes back can be nonsense. And the worst part of it is that you can take care to name your columns whatever you want, but the next guy who comes along might have no way of knowing that he has to worry about adding a column which will collide with your already-developed names.
But it's not all bad for SELECT *. I use it liberally for these use cases:
Ad-hoc queries. When trying to debug something, especially off a narrow table I might not be familiar with, SELECT * is often my best friend. It helps me just see what's going on without having to do a boatload of research as to what the underlying column names are. This gets to be a bigger "plus" the longer the column names get.
When * means "a row". In the following use cases, SELECT * is just fine, and rumors that it's a performance killer are just urban legends which may have had some validity many years ago, but don't now:
SELECT COUNT(*) FROM table;
in this case, * means "count the rows". If you were to use a column name instead of * , it would count the rows where that column's value was not null. COUNT(*), to me, really drives home the concept that you're counting rows, and you avoid strange edge-cases caused by NULLs being eliminated from your aggregates.
Same goes with this type of query:
SELECT a.ID FROM TableA a
WHERE EXISTS (
SELECT *
FROM TableB b
WHERE b.ID = a.B_ID);
in any database worth its salt, * just means "a row". It doesn't matter what you put in the subquery. Some people use b's ID in the SELECT list, or they'll use the number 1, but IMO those conventions are pretty much nonsensical. What you mean is "count the row", and that's what * signifies. Most query optimizers out there are smart enough to know this. (Though to be honest, I only know this to be true with SQL Server and Oracle.)
The asterisk character, "*", in the SELECT statement is shorthand for all the columns in the table(s) involved in the query.
Performance
The * shorthand can be slower because:
Not all the fields are indexed, forcing a full table scan - less efficient
What you save to send SELECT * over the wire risks a full table scan
Returning more data than is needed
Returning trailing columns using variable length data type can result in search overhead
Maintenance
When using SELECT *:
Someone unfamiliar with the codebase would be forced to consult documentation to know what columns are being returned before being able to make competent changes. Making code more readable, minimizing the ambiguity and work necessary for people unfamiliar with the code saves more time and effort in the long run.
If code depends on column order, SELECT * will hide an error waiting to happen if a table had its column order changed.
Even if you need every column at the time the query is written, that might not be the case in the future
the usage complicates profiling
Design
SELECT * is an anti-pattern:
The purpose of the query is less obvious; the columns used by the application is opaque
It breaks the modularity rule about using strict typing whenever possible. Explicit is almost universally better.
When Should "SELECT *" Be Used?
It's acceptable to use SELECT * when there's the explicit need for every column in the table(s) involved, as opposed to every column that existed when the query was written. The database will internally expand the * into the complete list of columns - there's no performance difference.
Otherwise, explicitly list every column that is to be used in the query - preferably while using a table alias.
Even if you wanted to select every column now, you might not want to select every column after someone adds one or more new columns. If you write the query with SELECT * you are taking the risk that at some point someone might add a column of text which makes your query run more slowly even though you don't actually need that column.
Wouldn't it mean less code to change if you added a new column you wanted?
The chances are that if you actually want to use the new column then you will have to make quite a lot other changes to your code anyway. You're only saving , new_column - just a few characters of typing.
If you really want every column, I haven't seen a performance difference between select (*) and naming the columns. The driver to name the columns might be simply to be explicit about what columns you expect to see in your code.
Often though, you don't want every column and the select(*) can result in unnecessary work for the database server and unnecessary information having to be passed over the network. It's unlikely to cause a noticeable problem unless the system is heavily utilised or the network connectivity is slow.
If you name the columns in a SELECT statement, they will be returned in the order specified, and may thus safely be referenced by numerical index. If you use "SELECT *", you may end up receiving the columns in arbitrary sequence, and thus can only safely use the columns by name. Unless you know in advance what you'll be wanting to do with any new column that gets added to the database, the most probable correct action is to ignore it. If you're going to be ignoring any new columns that get added to the database, there is no benefit whatsoever to retrieving them.
In a lot of situations, SELECT * will cause errors at run time in your application, rather than at design time. It hides the knowledge of column changes, or bad references in your applications.
Think of it as reducing the coupling between the app and the database.
To summarize the 'code smell' aspect:
SELECT * creates a dynamic dependency between the app and the schema. Restricting its use is one way of making the dependency more defined, otherwise a change to the database has a greater likelihood of crashing your application.
If you add fields to the table, they will automatically be included in all your queries where you use select *. This may seem convenient, but it will make your application slower as you are fetching more data than you need, and it will actually crash your application at some point.
There is a limit for how much data you can fetch in each row of a result. If you add fields to your tables so that a result ends up being over that limit, you get an error message when you try to run the query.
This is the kind of errors that are hard to find. You make a change in one place, and it blows up in some other place that doesn't actually use the new data at all. It may even be a less frequently used query so that it takes a while before someone uses it, which makes it even harder to connect the error to the change.
If you specify which fields you want in the result, you are safe from this kind of overhead overflow.
I don't think that there can really be a blanket rule for this. In many cases, I have avoided SELECT *, but I have also worked with data frameworks where SELECT * was very beneficial.
As with all things, there are benefits and costs. I think that part of the benefit vs. cost equation is just how much control you have over the datastructures. In cases where the SELECT * worked well, the data structures were tightly controlled (it was retail software), so there wasn't much risk that someone was going to sneek a huge BLOB field into a table.
Reference taken from this article.
Never go with "SELECT *",
I have found only one reason to use "SELECT *"
If you have special requirements and created dynamic environment when add or delete column automatically handle by application code. In this special case you don’t require to change application and database code and this will automatically affect on production environment. In this case you can use “SELECT *”.
Generally you have to fit the results of your SELECT * ... into data structures of various types. Without specifying which order the results are arriving in, it can be tricky to line everything up properly (and more obscure fields are much easier to miss).
This way you can add fields to your tables (even in the middle of them) for various reasons without breaking sql access code all over the application.
Using SELECT * when you only need a couple of columns means a lot more data transferred than you need. This adds processing on the database, and increase latency on getting the data to the client. Add on to this that it will use more memory when loaded, in some cases significantly more, such as large BLOB files, it's mostly about efficiency.
In addition to this, however, it's easier to see when looking at the query what columns are being loaded, without having to look up what's in the table.
Yes, if you do add an extra column, it would be faster, but in most cases, you'd want/need to change your code using the query to accept the new columns anyways, and there's the potential that getting ones you don't want/expect can cause issues. For example, if you grab all the columns, then rely on the order in a loop to assign variables, then adding one in, or if the column orders change (seen it happen when restoring from a backup) it can throw everything off.
This is also the same sort of reasoning why if you're doing an INSERT you should always specify the columns.
Selecting with column name raises the probability that database engine can access the data from indexes rather than querying the table data.
SELECT * exposes your system to unexpected performance and functionality changes in the case when your database schema changes because you are going to get any new columns added to the table, even though, your code is not prepared to use or present that new data.
There is also more pragmatic reason: money. When you use cloud database and you have to pay for data processed there is no explanation to read data that you will immediately discard.
For example: BigQuery:
Query pricing
Query pricing refers to the cost of running your SQL commands and user-defined functions. BigQuery charges for queries by using one metric: the number of bytes processed.
and Control projection - Avoid SELECT *:
Best practice: Control projection - Query only the columns that you need.
Projection refers to the number of columns that are read by your query. Projecting excess columns incurs additional (wasted) I/O and materialization (writing results).
Using SELECT * is the most expensive way to query data. When you use SELECT *, BigQuery does a full scan of every column in the table.
Understand your requirements prior to designing the schema (if possible).
Learn about the data,
1)indexing
2)type of storage used,
3)vendor engine or features; ie...caching, in-memory capabilities
4)datatypes
5)size of table
6)frequency of query
7)related workloads if the resource is shared
8)Test
A) Requirements will vary. If the hardware can not support the expected workload, you should re-evaluate how to provide the requirements in the workload. Regarding the addition column to the table. If the database supports views, you can create an indexed(?) view of the specific data with the specific named columns (vs. select '*'). Periodically review your data and schema to ensure you never run into the "Garbage-in" -> "Garbage-out" syndrome.
Assuming there is no other solution; you can take the following into account. There are always multiple solutions to a problem.
1) Indexing: The select * will execute a tablescan. Depending on various factors, this may involve a disk seek and/or contention with other queries. If the table is multi-purpose, ensure all queries are performant and execute below you're target times. If there is a large amount of data, and your network or other resource isn't tuned; you need to take this into account. The database is a shared environment.
2) type of storage. Ie: if you're using SSD's, disk, or memory. I/O times and the load on the system/cpu will vary.
3) Can the DBA tune the database/tables for higher performance? Assumming for whatever reason, the teams have decided the select '*' is the best solution to the problem; can the DB or table be loaded into memory. (Or other method...maybe the response was designed to respond with a 2-3 second delay? --- while an advertisement plays to earn the company revenue...)
4) Start at the baseline. Understand your data types, and how results will be presented. Smaller datatypes, number of fields reduces the amount of data returned in the result set. This leaves resources available for other system needs. The system resources are usually have a limit; 'always' work below these limits to ensure stability, and predictable behaviour.
5) size of table/data. select '*' is common with tiny tables. They typically fit in memory, and response times are quick. Again....review your requirements. Plan for feature creep; always plan for the current and possible future needs.
6) Frequency of query / queries. Be aware of other workloads on the system. If this query fires off every second, and the table is tiny. The result set can be designed to stay in cache/memory. However, if the query is a frequent batch process with Gigabytes/Terabytes of data...you may be better off to dedicate additional resources to ensure other workloads aren't affected.
7) Related workloads. Understand how the resources are used. Is the network/system/database/table/application dedicated, or shared? Who are the stakeholders? Is this for production, development, or QA? Is this a temporary "quick fix". Have you tested the scenario? You'll be surprised how many problems can exist on current hardware today. (Yes, performance is fast...but the design/performance is still degraded.) Does the system need to performance 10K queries per second vs. 5-10 queries per second. Is the database server dedicated, or do other applications, monitoring execute on the shared resource. Some applications/languages; O/S's will consume 100% of the memory causing various symptoms/problems.
8) Test: Test out your theories, and understand as much as you can about. Your select '*' issue may be a big deal, or it may be something you don't even need to worry about.
There's an important distinction here that I think most answers are missing.
SELECT * isn't an issue. Returning the results of SELECT * is the issue.
An OK example, in my opinion:
WITH data_from_several_tables AS (
SELECT * FROM table1_2020
UNION ALL
SELECT * FROM table1_2021
...
)
SELECT id, name, ...
FROM data_from_several_tables
WHERE ...
GROUP BY ...
...
This avoids all the "problems" of using SELECT * mentioned in most answers:
Reading more data than expected? Optimisers in modern databases will be aware that you don't actually need all columns
Column ordering of the source tables affects output? We still select and
return data explicitly.
Consumers can't see what columns they receive from the SQL? The columns you're acting on are explicit in code.
Indexes may not be used? Again, modern optimisers should handle this the same as if we didn't SELECT *
There's a readability/refactorability win here - no need to duplicate long lists of columns or other common query clauses such as filters. I'd be surprised if there are any differences in the query plan when using SELECT * like this compared with SELECT <columns> (in the vast majority of cases - obviously always profile running code if it's critical).
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 ;-)
What are some of the methods/techniques experienced SQL developers use to determine if a particular SQL query will scale well as load increases, rows in associated tables increase etc.
Some rules that I follow that make the most difference.
Don't use per-row functions in your queries like if, case, coalesce and so on. Work around them by putting data in the database in the format you're going to need it, even if that involves duplicate data.
For example, if you need to lookup surnames fast, store them in the entered form and in their lowercase form, and index the lowercase form. Then you don't have to worry about things like select * from tbl where lowercase(surname) = 'smith';
Yes, I know that breaks 3NF but you can still guarantee data integrity by judicious use of triggers or pre-computed columns. For example, an insert/update trigger on the table can force the lower_surname column to be set to the lowercase version of surname.
This moves the cost of conversion to the insert/update (which happens infrequently) and away from the select (which happens quite a lot more). You basically amortise the cost of conversion.
Make sure that every column used in a where clause is indexed. Not necessarily on its own but at least as the primary part of a composite key.
Always start off in 3NF and only revert if you have performance problems (in production). 3NF is often the easiest to handle and reverting should only be done when absolutely necessary.
Profile, in production (or elsewhere, as long as you have production data and schemas). Database tuning is not a set-and-forget operation unless the data in your tables never changes (very rare). You should be monitoring, and possibly tuning, periodically to avoid the possibility that changing data will bring down performance.
Don't, unless absolutely necessary, allow naked queries to your database. Try to control what queries can be run. Your job as a DBA will be much harder if some manager can come along and just run:
select * from very_big_table order by column_without_index;
on your database.
If managers want to be able to run ad-hoc queries, give them a cloned DBMS (or replica) so that your real users (the ones that need performance) aren't affected.
Don't use union when union all will suffice. If you know that there can be no duplicates between two selects of a union, there's no point letting the DBMS try to remove them.
Similarly, don't use select distinct on a table if you're retrieving all the primary key columns (or all columns in a unique constraint). There is no possibility of duplicates in those cases so, again, you're asking the DBMS to do unnecessary work.
Example: we had a customer with a view using select distinct * on one of their tables. Querying the view took 50 seconds. When we replaced it with a view starting select *, the time came down to sub-second. Needless to say, I got a good bottle of red wine out of that :-)
Try to avoid select * as much as possible. In other words, only get the columns you need. This makes little difference when you're using MySQL on your local PC but, when you have an app in California querying a database in Inner Mongolia, you want to minimise the amount of traffic being sent across the wire as much as possible.
don't make tables wide, keep them narrow as well as the indexes. Make sure that queries are fully covered by indexes and that those queries are SARGable.
Test with a ton of data before going in production, take a look at this: Your testbed has to have the same volume of data as on production in order to simulate normal usage
Pull up the execution plan and look for any of the following:
Table Scan
[Clustered] Index Scan
RID Lookup
Bookmark Lookup
Key Lookup
Nested Loops
Any of those things (in descending order from most to least scalable) mean that the database/query likely won't scale to much larger tables. An ideal query will have mostly index seeks, hash or merge joins, the occasional sort, and other low-impact operations (spools and so on).
The only way to prove that it will scale, as other answers have pointed out, is to test it on data of the desired size. The above is just a rule of thumb.
In addition (and along the same lines) to Robert's suggestion, consider the execution plan. Is it utilizing indexes? Are there any scans or such? Can you simply for the query in any way? For example, Eliminate IN in favor of EXISTS and only join to tables you need to join to.
You don't mention the technology -- keep in mind that different technologies can affect the efficiency of more complex queries.
I strongly recommend reading some reference material on this. This (hyperlink below) is probably a pretty good book to look into. Make sure to look under "Selectivity", among other topics.
SQL Tuning - Dan Tow