I'm working in SQL Workbench in Redshift. We have daily event tables for customer accounts, the same format each day just with updated info. There are currently 300+ tables. For a simple example, I would like to extract the top 10 rows from each table and place them in 1 table.
Table name format is Events_001, Events_002, etc. Typical values are Customer_ID and Balance.
Redshift does not appear to support declare variables, so I'm a bit stuck.
You've effectively invented a kind of pseudo-partitioning; where you manually partition the data by day.
To manually recombine the tables create a view to union everything together...
CREATE VIEW
events_combined
AS
SELECT 1 AS partition_id, * FROM events_001
UNION ALL
SELECT 2 AS partition_id, * FROM events_002
UNION ALL
SELECT 3 AS partition_id, * FROM events_003
etc, etc
That's a hassle, you need to recreate the view every time you add a new table.
That's why most modern databases have partitioning schemes built in to them, so all the boiler-plate is taken care of for you.
But RedShift doesn't do that. So, why not?
In general because RedShift has many alternative mechanisms for dividing and conquering data. It's columnar, so you can avoid reading columns you don't use. It's horizontally partitioned across multiple nodes (sharded), to share the load with large volumes of data. It's sorted and compressed in pages to avoid loading rows you don't want or need. It has dirty pages for newly arriving data, which can then be cleaned up with a VACUUM.
So, I would agree with others that it's not normal practice. Yet, Amazon themselves do have a help page (briefly) describing your use case.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-time-series-tables.html
So, I'd disagree with "never do this". Still, it is a strong indication that you've accidentally walked in to an anti-pattern and should seriously re-consider your design.
As others have pointed out many small tables in Redshift is really inefficient, like terrible if taken to the extreme. But that is not your question.
You want to know how to perform the same query on multiple tables from SQL Workbench. I'm assuming you are referring to SQLWorkbench/J. If so you can define variables in the bench and use these variable in queries. Then you just need to update the variable and rerun the query. Now SQLWorkbench/J doesn't offer any looping or scripting capabilities. If you want to loop you will need to wrap the bench in a script (like a BAT file or a bash script).
My preference is to write a jinja template with the SQL in it along with any looping and variable substitution. Then apply a json with the table names and presto you have all the SQL for all the tables in one file. I just need to run this - usually with the psql cli but at times I'm import it into my bench.
My advice is to treat Redshift as a query execution engine and use an external environment (Lambda, EC2, etc) for the orchestration of what queries to run and when. Many other databases (try to) provide a full operating environment inside the database functionality. Applying this pattern to Redshift often leads to problems. Use Redshift for what it is great at and perform the other actions elsewhere. In the end you will find that the large AWS ecosystem provides extended capabilities as compared to other databases, it's just that these aren't all done inside of Redshift.
I have an aggregate data set that spans multiple years. The data for each respective year is stored in a separate table named Data. The data is currently sitting in MS ACCESS tables, and I will be migrating it to SQL Server.
I would prefer that data for each year is kept in separate tables, to be merged and queried at runtime. I do not want to do this at the expense of efficiency, however, as each year is approx. 1.5M records of 40ish fields.
I am trying to avoid having to do an excessive number of UNIONS in the query. I would also like to avoid having to edit the query as each new year is added, leading to an ever-expanding number of UNIONs.
Is there an easy way to do these UNIONs at runtime without an extensive SQL query and high system utility? Or, if all the data should be managed in one large table, is there a quick and easy way to append all the tables together in a single query?
If you really want to store them in separate tables, then I would create a view that does that unioning for you.
create view AllData
as
(
select * from Data2001
union all
select * from Data2002
union all
select * from Data2003
)
But to be honest, if you use this, why not put all the data into 1 table. Then if you wanted you could create the views the other way.
create view Data2001
as
(
select * from AllData
where CreateDate >= '1/1/2001'
and CreateDate < '1/1/2002'
)
A single table is likely the best choice for this type of query. HOwever you have to balance that gainst the other work the db is doing.
One choice you did not mention is creating a view that contains the unions and then querying on theview. That way at least you only have to add the union statement to the view each year and all queries using the view will be correct. Personally if I did that I would write a createion query that creates the table and then adjusts the view to add the union for that table. Once it was tested and I knew it would run, I woudl schedule it as a job to run on the last day of the year.
One way to do this is by using horizontal partitioning.
You basically create a partitioning function that informs the DBMS to create separate tables for each period, each with a constraint informing the DBMS that there will only be data for a specific year in each.
At query execution time, the optimiser can decide whether it is possible to completely ignore one or more partitions to speed up execution time.
The setup overhead of such a schema is non-trivial, and it only really makes sense if you have a lot of data. Although 1.5 million rows per year might seem a lot, depending on your query plans, it shouldn't be any big deal (for a decently specced SQL server). Refer to documentation
I can't add comments due to low rep, but definitely agree with 1 table, and partitioning is helpful for large data sets, and is supported in SQL Server, where the data will be getting migrated to.
If the data is heavily used and frequently updated then monthly partitioning might be useful, but if not, given the size, partitioning probably isn't going to be very helpful.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
select * vs select column
I was just having a discussion with one of my colleague on the SQL Server performance on specifying the query command in the stored procedure.
So I want to know which one is preferred over another and whats the concrete reason behind that.
Suppose, We do have one table called
Employees(EmpName,EmpAddress)
And we want to select all the records from the table. So we can write the query in two ways,
Select * from Employees
Select EmpName, EmpAddress from Employees
So I would like to know is there any specific difference or performance issue in the above queries or are they just equal to the SQL Server Engine.
UPDATE:
Lets say the table schema won't change anymore. So no point for future maintenance.
Performance wise, lets say, the usage is very very high i.e. millions of hits per seconds on the database server. I want a clear and precise performance rating on both approaches.
No Indexing is done on the entire table.
The specific difference would show its ugly head if you add a column to the table.
Suddenly, the query you expected to return two columns now returns three. If you coded specifically for the two columns, the rest of your code is now broken.
Performance-wise, there shouldn't be a difference.
I always take the approach that being as specific as possible is the best when dealing with databases. If the table has two columns and you only need those two columns, be specific. Specify those two columns. It'll save you headaches in the future.
I am an avid avokat of the "be as specific as possible" rule, too. Not following it will hurt you in the long run. However, your question seems to be coming from a different background, so let me attempt to answer it.
When you submit a query to SQL Server it goes through several stages:
transmitting of query string over the network.
parsing of query string, producing a parse-tree
linking the referenced objects in the parse tree to existing objects
optimizing based on statistics and row count/size estimates
executing
transmitting of result data over the network
Let's look at each one:
The * query is a few bytes shorter, so step this will be faster
The * query contains fewer "tokens" so this should(!) be faster
During linking the list of columns need to be puled and compared to the query string. Here the "*" gets resolved to the actual column reference. Without access to the code it is impossible to say which version takes less cycles, however the amount of data accessed is about the same so this should be similar.
-6. In these stages there is no difference between the two example queries, as they will both get compiled to the same execution plan.
Taking all this into account, you will probably save a few nanoseconds when using the * notation. However, you example is very simplistic. In a more complex example it is possible that specifying as subset of columns of a table in a multi table join will lead to a different plan than using a *. If that happens we can be pretty certain that the explicit query will be faster.
The above comparison also assumes that the SQL Server process is running alone on a single processor and no other queries are submitted at the same time. If the process has to yield during the compilation those extra cycles will be far more than the ones we are trying to save.
So, the amont of saving we are talking about is very minute compared to the actual execution time and should not be used as an excuse for a "bad" coding practice.
I hope this answers your question.
You should always reference columns explicitly. This way, if the table structure changes (and such changes are made in an intelligent, backward-compatible way), your queries will continue to work and can be modified over time.
Also, unless you actually need all of the columns from the table (not typical), using SELECT * is bringing more data to your application than is necessary, and potentially forcing a clustered index scan instead of what might have been satisfied by a narrower covering index.
Bad habits to kick : using SELECT * / omitting the column list
Performance wise there are no difference between those 2 i think.But those 2 are used in different cases what may be the difference.
Consider a slightly larger table.If your table(Employees) contains 10 columns,then the 1st query will retain all of the information of the table.But for 2nd query,you may specify which columns information you need.So when you need all of the information of employees no.1 is the best one rather than specifying all of the column names.
Ofcourse,when you need to ALTER a table then those 2 would not be equal.
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 ;-)