We have a replication with goldengate from a prod environment.
The tables got initial dumped from the prod and afterwards we started the replication with goldengate. Now we want to migrate the data to another database. But the query plans are different from the prod environment. We think it is because all statistics from the database of the replication are broken/wrong.
The number of rows stated in dba_tables are null, 0 or differs 50-80%.
We tried to do dbms_stats.gather_table_stats on all relevant tables.
It's still broken. We run that querie for all tables that had wrong statistics:
dbms_stats.GATHER_TABLE_STATS(OWNNAME => 'SCHEMA', TABNAME => 'TABLE_NAME', CASCADE => true);
We can't migrate with the bad queryplans.
We are using Oracle Release 12.2.0.1.0 - Production
EDIT: After the answer of #Jon Heller we saw that some indices are partitioned in the prod environment not in the replication. Additionally the global preference DEGREE is 32768 on the replication and NULL on prod.
Are the tables built exactly the same way? Maybe a different table structure is causing the statistics to break, like if one table is partitioned and another is not. Try comparing the DDL:
select dbms_metadata.get_ddl('TABLE', 'TABLE1') from dual;
I'm surprised to hear that statistics are wrong even after gathering stats. Especially the number of rows - since 10g, that number should always be 100% accurate with the default settings.
Can you list the exact commands you are using to gather stats? Also, this is a stretch, but possibly the global preference were changed on one database. It would be pretty evil, but you could set a database default to only look at 0.00001% of the data, which would create terrible statistics. Check your global preferences between both databases.
--Thanks to Tim Hall for this query: https://oracle-base.com/dba/script?category=monitoring&file=statistics_prefs.sql
SELECT DBMS_STATS.GET_PREFS('AUTOSTATS_TARGET') AS autostats_target,
DBMS_STATS.GET_PREFS('CASCADE') AS cascade,
DBMS_STATS.GET_PREFS('DEGREE') AS degree,
DBMS_STATS.GET_PREFS('ESTIMATE_PERCENT') AS estimate_percent,
DBMS_STATS.GET_PREFS('METHOD_OPT') AS method_opt,
DBMS_STATS.GET_PREFS('NO_INVALIDATE') AS no_invalidate,
DBMS_STATS.GET_PREFS('GRANULARITY') AS granularity,
DBMS_STATS.GET_PREFS('PUBLISH') AS publish,
DBMS_STATS.GET_PREFS('INCREMENTAL') AS incremental,
DBMS_STATS.GET_PREFS('STALE_PERCENT') AS stale_percent
FROM dual;
If gathering statistics still leads to different results, the only thing I can think of is corruption. It may be time to create an Oracle service request.
(This is more of an extended comment than an answer, but it might take a lot of code to diagnose this problem. Please update the original question with more information as you find it.)
I’m a longtime MSSQL developer who finds himself back in PL/SQL for the first time since Oracle 7. I’m looking for some tuning advice re a large export stored procedure, which is sporadically and not very reproducably running slow at certain points. This happens around some static working tables which it truncates, fills and uses as part of the export. The code in outline typically looks like this:
create or replace Procedure BigMultiPurposeExport as (
-- about 2000 lines of other code
INSERT WORK_TABLE_5 SELECT WHATEVER1 FROM WHEREVER1;
INSERT WORK_TABLE_5 SELECT WHATEVER2 FROM WHEREVER2;
INSERT WORK_TABLE_5 SELECT WHATEVER3 FROM WHEREVER3;
INSERT WORK_TABLE_5 SELECT WHATEVER4 FROM WHEREVER4;
-- WORK_TABLE_5 now has 0 to ~500k rows whose content can vary drastically from run to run
-- e.g. one hourly run exports 3 whale sightings, next exports all tourist visits to Kenya this decade
-- about 1000 lines of other code
INSERT OUTPUT_TABLE_3
SELECT THIS, THAT, THE_OTHER
FROM BUSINESS_TABLE_1 BT1
INNER JOIN BUSINESS_TABLE_2 ON etc -- typical join on indexed columns
INNER JOIN BUSINESS_TABLE_3 ON etc -- typical join on indexed columns
INNER JOIN BUSINESS_TABLE_4 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_1 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_2 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_3 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_4 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_5 WT5 ON BT1.ID = WT5.BT1_ID AND WT5.RECORD_TYPE = 21
-- join above is now supported by indexes on BUSINESS_TABLE_1 (ID) and WORK_TABLE_5 (BT1_ID, RECORD_TYPE), originally wasn't
LEFT OUTER JOIN WORK_TABLE_6 ON etc -- typical join on indexed columns
LEFT OUTER JOIN WORK_TABLE_7 ON etc -- typical join on indexed columns
-- about 4000 lines of other code
)
That final insert into OUTPUT_TABLE_3 usually runs in under 10 seconds, but once in a while on certain customer servers it times out at our default 99 minutes. Then we have them take the tiemout off and run it on Friday night, and it finishes but takes 16 hours.
I narrowed the problem down to the join to WORK_TABLE_5, which had no index support, and put an index on the join terms. The next run took 4 seconds. But success has been intermittent, the customer occasionally gets some slow runs when they drastically change their export selection (i.e. drastically change the data in WORK_TABLE_5). And if we update statistics and rebuild indexes after a timed out export, it runs fine at the next attempt.
So, I am wondering about how best to handle truncating/filling static work tables with static indexes, statistics updated overnight, and a stored procedure compiled when the statistics are nothing like runtime.
I have a few general questions about things I'd like to understand better:
Is the nature of the data in the work table going to substantially effect the query plan? Does Oracle form its query plan when you compile the stored procedure? Could we get a highly inappropriate query plan if we compile the stored procedure with the table empty then use a table with 500k rows at runtime?
I expect that if this were an ad-hoc script then updating statistics on the problem table just before selecting from it would eliminate the sporadic slowdowns. But what if I were to update statistics inside the stored procedure, which is compiled with different statistics from runtime?
Anything else you'd like to add...
Thanks for any advice. I hope my MSSQL preconceptions haven't made me too far off base.
This is happening in Oracle 11g, but the code is deployed to assorted customers using Oracle 10 through 12 and I'd like to cater to all of those if possible.
-- Joel
Huge differences in table or index sizes can most definitely cause performance problems. The solution is to add statistics gathering to the procedure instead of relying on the default statistics jobs.
If you've been away from Oracle since version 7, the most important new feature is the Cost Based Optimizer. Oracle now builds query execution plans based on the optimizer statistics of tables, indexes, columns, expressions, system statistics, outlines, directives, dynamic sampling, etc. If you're a full time Oracle developer you should probably spend a day reading about optimizer statistics. Start with Managing Optimizer Statistics and DBMS_STATS in the official documentation.
Eventually the stored procedure should look like this:
--1: Insert into working tables.
insert into work_table...
--2: Gather statistics on working tables.
dbms_stats.gather_table_stats('SCHEMA_NAME', 'WORK_TABLE', ...);
--3: Use working tables.
insert into other_table select * from work_table...
There are so many statistics features it's hard to know exactly what parameters to use in that second step above. Here are some guesses about some features you might find useful:
DEGREE - One reason people avoid gathering statistics inside a process is the time is takes. You can significantly improve the run time by setting the degree. Although this also uses significantly more resources.
NO_INVALIDATE - It can be tricky to know when exactly are the statistics "set" for a query. Gathering statistics usually quickly invalidates execution plans that were based on old statistics. But not always. If you want to be 100% sure that the next query is using the latest statistics you want to set NO_INVALIDATE=>FALSE.
ESTIMATE_PERCENT In 11g and above you definitely want to use the default, which uses a faster algorithm. In 10g and below you may need to set the value to something low to make the gathering fast enough.
Although Oracle 10g and above comes with default statistics gathering jobs you cannot rely on them for a few reasons:
They are scheduled and may not run at the right time. If a process significantly changes the data then new stats are needed right away, not at 10 PM. If there are a lot of tables that need to be analyzed the job may not get to them all in one day.
Many DBAs disable the jobs. This is ridiculous and almost always a mistake. But you'll find many DBAs that disabled the job because they think they can do it better. Instead of working with the auto tasks, and settings preferences, many DBAs like to throw the whole thing out and replace it with a custom procedure that rots over time.
I have the following query which monitors if anyone tried to logon with a technical users on database:
SELECT COUNT (OS_USERNAME)
FROM DBA_AUDIT_SESSION
WHERE USERNAME IN ('USER1','USER2','USER3')
AND TIMESTAMP>=SYSDATE - 10/(24*60)
AND RETURNCODE !='0'
Unfortunately the performance of this SQL is quite poor since it does TABLE ACCESS FULL on sys.aud$. I tried to narrow it with:
SELECT COUNT (sessionid)
FROM sys.aud$
WHERE userid IN ('USER1','USER2','USER3')
AND ntimestamp# >=SYSDATE - 10/(24*60)
AND RETURNCODE !='0'
and action# between 100 and 102;
And it is even worse. Is it possible at all to optimize that query by forcing oracle to use indexes here? I would be grateful for any help&tips.
SYS.AUD$ does not have any default indexes but it is possible to create one on ntimestamp#.
But proceed with caution. The support document "The Effect Of Creating Index On Table Sys.Aud$ (Doc ID 1329731.1)" includes this warning:
Creating additional indexes on SYS objects including table AUD$ is not supported.
Normally that would be the end of the conversation and you'd want to try another approach. But in this case there are a few reasons why it's worth a shot:
The document goes on to say that an index may be helpful, and to test it first.
It's just an index. The SYS schema is special, but we're still just talking about an index on a table. It could slow things down, or maybe cause space errors, like any index would. But I doubt there's any chance it could do something crazy like cause wrong results bugs.
It's somewhat common to change the tablespace of the audit trail, so that table isn't sacred.
I've seen indexes on it before. 2 of the 400 databases I manage have an index on the columns SESSIONID,SES$TID (although I don't know why). Those indexes have been there for years, have been through an upgrade and patches, and haven't caused problems as far as I know.
Creating an "unsupported" index may be a good option for you, if you're willing to test it and accept a small amount of risk.
Oracle 10g onwards optimizer would choose the best plan for your query, provided you write proper joins. Not sure how many recocds exists in your DBA_AUDIT_SESSION , but you can always use PARALLEL hints to somewhat speed up the execution.
SELECT /*+Parallel*/ COUNT (OS_USERNAME)
--select COUNT (OS_USERNAME)
FROM DBA_AUDIT_SESSION
WHERE USERNAME IN ('USER1','USER2','USER3')
AND TIMESTAMP>=SYSDATE - 10/(24*60)
AND RETURNCODE !='0'
Query Cost reduces to 3 than earlier.
NumRows: 8080019
So it is pretty large due to company regulations. Unfortunately using /*+Parallel*/ here makes it run longer, so the performance is still worse.
Any other suggestions?
I have the same database on 2 different Oracle servers, one is 11.2.0.1.0 and the other is 11.2.0.4.0.
I have the same 2 geometry tables in both databases and run the following query on both servers. When run on an 11.2.0.1.0 version of Oracle, the query runs for a few minutes and I get results, the same query when run on 11.2.0.4.0 runs for about 3 seconds and returns no results.
The BLPUs table holds 36 million points and the PD_B2 table holds a polygon. I am trying to find all the points that fall in the polygon.
Other spatial queries do return rows but it takes hours and hours whereas the table join suggested in the Oracle Spatial documentation, takes 15 minutes to return all the points.
SELECT /*+ ordered */ a.uprn
FROM TABLE(SDO_JOIN('BLPUS', 'GEOLOC', 'PD_B2', 'GEOLOC','mask=ANYINTERACT')) c, blpus a, PD_B2 b
WHERE c.rowid1 = a.rowid
AND c.rowid2 = b.rowid;
The spatial queryies below return SDO_ROWIDSET() when run on the 11.2.0.4 server
select SDO_JOIN('BLPUS', 'GEOLOC', 'PD_B2', 'GEOLOC','mask=ANYINTERACT')
from dual;
select SDO_JOIN('BLPUS', 'GEOLOC', 'PD_B2', 'GEOLOC')
from dual;
On the 11.2.0.1 server they return results.
I have discovered that a much smaller table of points will work on 11.2.0.4 so it seems that there is a size limit on 11.2.0.4 when using SDO_JOIN where as 11.2.0.1 seems to cope with the large table.
Does anyone know why this is or if there is an actual limit on table size when using SDO_JOIN?
This is strange. I see no reason why SDO_JOIN will not work the same way in 11.2.0.4. At least I have not seen that sort of behavior before. It looks like a bug to me and I suggest you file a service request with Oracle Support so we can take a look. You may need to provide a dump of the tables - or at least of a small enough subset that demonstrates the problem.
That said there are a few things to check: did you apply the 11.2.0.4 patch on the same database ? I.e. nothing changed in terms of table structures or content, grants, etc ?
When you say that the query returns no rows, does it do so immediately ? Or does it perform some processing before completing without anything being returned ?
How large is the PD_B2 table ?
What happens when you do:
select SDO_JOIN('BLPUS', 'GEOLOC', 'PD_B2', 'GEOLOC','mask=ANYINTERACT')
from dual;
Does this also return nothing ?
What happens if you do
select SDO_JOIN('BLPUS', 'GEOLOC', 'PD_B2', 'GEOLOC')
from dual;
Both should return something that looks like this:
SDO_ROWIDSET(SDO_ROWIDPAIR('AAAW3LAAGAAAADmAAu', 'AAAW3TAAGAAAAg7AAC'), SDO_ROWIDPAIR('AAAW3LAAGAAAADmABE', 'AAAW3TAAGAAAAgrAAA'),...)
You will see this if you run the query in sqlplus. [If you use a GUI (like TOAD or SQLDeveloper) then you may not see that. All those GUIs have problems dealing with objects or arrays.]
But the fact that your 11.2.0.4 tests complete very quickly probably means that you get an empty result back, maybe like this:
SDO_ROWIDSET()
and that confirms that something did not work.
Now, from what you say PD_B2 only contains one row ? If so then there is no reason whatsoever to go via SDO_JOIN. A straightforward spatial query is easier to write. SDO_JOIN only makes sense when both tables being joined contain multiple rows. Then again, if one of the tables is very small (like the PD_B2 table in your case), then it anyway falls back to a simple query.
A simple query would look like this:
SELECT a.uprn
FROM blpus a, PD_B2 b
WHERE sdo_anyinteract (a.geoloc, b.geoloc) = 'TRUE';
Does this return what you expect in 11.2.0.4 ?
You may also want to examine the cost of the query and the query plan used. In sqlplus, do this:
set timing on
set autotrace traceonly
then run the above query. The effect is that sqlplus will not display any results - but it will still fetch and format the output: it will just not print them. At the end you will get a printout of the query plan used as well as some execution statistics and the elapsed time. Please add those results to your question.
Running the query from within your application should have a similar profile: similar response time and database-side costs - assuming you do fetch all the 1.3 million rows.
BTW, what do you do with those results ? Do you show them out as a report ? Do you save them into a table for later analysis ? Surely you do not want to show them all on a map ?
To clarify: SDO_JOIN is only for matching many to many geometries. For matching one to many, the simple SDO_ANYINTERACT() is what you need. As a matter of fact, SDO_JOIN will automatically fall back to a simple SDO_ANYINTERACT when one of the sets is very much smaller than the other. I can't see how SDO_JOIN could be faster in your circumstances (i.e. when searching for all objects that match one object) since it will perform the same as an SDO_ANYINTERACT and will require extra joins to the two tables.
Going back to the original issue - that of a difference in behavior in SDO_JOIN between 11.2.0.1 and 11.2.0.4 that IMO is definitely a bug that you need to report to Oracle Support.
Just wondering if any of you people use Count(1) over Count(*) and if there is a noticeable difference in performance or if this is just a legacy habit that has been brought forward from days gone past?
The specific database is SQL Server 2005.
There is no difference.
Reason:
Books on-line says "COUNT ( { [ [ ALL | DISTINCT ] expression ] | * } )"
"1" is a non-null expression: so it's the same as COUNT(*).
The optimizer recognizes it for what it is: trivial.
The same as EXISTS (SELECT * ... or EXISTS (SELECT 1 ...
Example:
SELECT COUNT(1) FROM dbo.tab800krows
SELECT COUNT(1),FKID FROM dbo.tab800krows GROUP BY FKID
SELECT COUNT(*) FROM dbo.tab800krows
SELECT COUNT(*),FKID FROM dbo.tab800krows GROUP BY FKID
Same IO, same plan, the works
Edit, Aug 2011
Similar question on DBA.SE.
Edit, Dec 2011
COUNT(*) is mentioned specifically in ANSI-92 (look for "Scalar expressions 125")
Case:
a) If COUNT(*) is specified, then the result is the cardinality of T.
That is, the ANSI standard recognizes it as bleeding obvious what you mean. COUNT(1) has been optimized out by RDBMS vendors because of this superstition. Otherwise it would be evaluated as per ANSI
b) Otherwise, let TX be the single-column table that is the
result of applying the <value expression> to each row of T
and eliminating null values. If one or more null values are
eliminated, then a completion condition is raised: warning-
In SQL Server, these statements yield the same plans.
Contrary to the popular opinion, in Oracle they do too.
SYS_GUID() in Oracle is quite computation intensive function.
In my test database, t_even is a table with 1,000,000 rows
This query:
SELECT COUNT(SYS_GUID())
FROM t_even
runs for 48 seconds, since the function needs to evaluate each SYS_GUID() returned to make sure it's not a NULL.
However, this query:
SELECT COUNT(*)
FROM (
SELECT SYS_GUID()
FROM t_even
)
runs for but 2 seconds, since it doen't even try to evaluate SYS_GUID() (despite * being argument to COUNT(*))
I work on the SQL Server team and I can hopefully clarify a few points in this thread (I had not seen it previously, so I am sorry the engineering team has not done so previously).
First, there is no semantic difference between select count(1) from table vs. select count(*) from table. They return the same results in all cases (and it is a bug if not). As noted in the other answers, select count(column) from table is semantically different and does not always return the same results as count(*).
Second, with respect to performance, there are two aspects that would matter in SQL Server (and SQL Azure): compilation-time work and execution-time work. The Compilation time work is a trivially small amount of extra work in the current implementation. There is an expansion of the * to all columns in some cases followed by a reduction back to 1 column being output due to how some of the internal operations work in binding and optimization. I doubt it would show up in any measurable test, and it would likely get lost in the noise of all the other things that happen under the covers (such as auto-stats, xevent sessions, query store overhead, triggers, etc.). It is maybe a few thousand extra CPU instructions. So, count(1) does a tiny bit less work during compilation (which will usually happen once and the plan is cached across multiple subsequent executions). For execution time, assuming the plans are the same there should be no measurable difference. (One of the earlier examples shows a difference - it is most likely due to other factors on the machine if the plan is the same).
As to how the plan can potentially be different. These are extremely unlikely to happen, but it is potentially possible in the architecture of the current optimizer. SQL Server's optimizer works as a search program (think: computer program playing chess searching through various alternatives for different parts of the query and costing out the alternatives to find the cheapest plan in reasonable time). This search has a few limits on how it operates to keep query compilation finishing in reasonable time. For queries beyond the most trivial, there are phases of the search and they deal with tranches of queries based on how costly the optimizer thinks the query is to potentially execute. There are 3 main search phases, and each phase can run more aggressive(expensive) heuristics trying to find a cheaper plan than any prior solution. Ultimately, there is a decision process at the end of each phase that tries to determine whether it should return the plan it found so far or should it keep searching. This process uses the total time taken so far vs. the estimated cost of the best plan found so far. So, on different machines with different speeds of CPUs it is possible (albeit rare) to get different plans due to timing out in an earlier phase with a plan vs. continuing into the next search phase. There are also a few similar scenarios related to timing out of the last phase and potentially running out of memory on very, very expensive queries that consume all the memory on the machine (not usually a problem on 64-bit but it was a larger concern back on 32-bit servers). Ultimately, if you get a different plan the performance at runtime would differ. I don't think it is remotely likely that the difference in compilation time would EVER lead to any of these conditions happening.
Net-net: Please use whichever of the two you want as none of this matters in any practical form. (There are far, far larger factors that impact performance in SQL beyond this topic, honestly).
I hope this helps. I did write a book chapter about how the optimizer works but I don't know if its appropriate to post it here (as I get tiny royalties from it still I believe). So, instead of posting that I'll post a link to a talk I gave at SQLBits in the UK about how the optimizer works at a high level so you can see the different main phases of the search in a bit more detail if you want to learn about that. Here's the video link: https://sqlbits.com/Sessions/Event6/inside_the_sql_server_query_optimizer
Clearly, COUNT(*) and COUNT(1) will always return the same result. Therefore, if one were slower than the other it would effectively be due to an optimiser bug. Since both forms are used very frequently in queries, it would make no sense for a DBMS to allow such a bug to remain unfixed. Hence you will find that the performance of both forms is (probably) identical in all major SQL DBMSs.
In the SQL-92 Standard, COUNT(*) specifically means "the cardinality of the table expression" (could be a base table, `VIEW, derived table, CTE, etc).
I guess the idea was that COUNT(*) is easy to parse. Using any other expression requires the parser to ensure it doesn't reference any columns (COUNT('a') where a is a literal and COUNT(a) where a is a column can yield different results).
In the same vein, COUNT(*) can be easily picked out by a human coder familiar with the SQL Standards, a useful skill when working with more than one vendor's SQL offering.
Also, in the special case SELECT COUNT(*) FROM MyPersistedTable;, the thinking is the DBMS is likely to hold statistics for the cardinality of the table.
Therefore, because COUNT(1) and COUNT(*) are semantically equivalent, I use COUNT(*).
COUNT(*) and COUNT(1) are same in case of result and performance.
I would expect the optimiser to ensure there is no real difference outside weird edge cases.
As with anything, the only real way to tell is to measure your specific cases.
That said, I've always used COUNT(*).
As this question comes up again and again, here is one more answer. I hope to add something for beginners wondering about "best practice" here.
SELECT COUNT(*) FROM something counts records which is an easy task.
SELECT COUNT(1) FROM something retrieves a 1 per record and than counts the 1s that are not null, which is essentially counting records, only more complicated.
Having said this: Good dbms notice that the second statement will result in the same count as the first statement and re-interprete it accordingly, as not to do unnecessary work. So usually both statements will result in the same execution plan and take the same amount of time.
However from the point of readability you should use the first statement. You want to count records, so count records, not expressions. Use COUNT(expression) only when you want to count non-null occurences of something.
I ran a quick test on SQL Server 2012 on an 8 GB RAM hyper-v box. You can see the results for yourself. I was not running any other windowed application apart from SQL Server Management Studio while running these tests.
My table schema:
CREATE TABLE [dbo].[employee](
[Id] [bigint] IDENTITY(1,1) NOT NULL,
[Name] [nvarchar](50) NOT NULL,
CONSTRAINT [PK_employee] PRIMARY KEY CLUSTERED
(
[Id] ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY]
GO
Total number of records in Employee table: 178090131 (~ 178 million rows)
First Query:
Set Statistics Time On
Go
Select Count(*) From Employee
Go
Set Statistics Time Off
Go
Result of First Query:
SQL Server parse and compile time:
CPU time = 0 ms, elapsed time = 35 ms.
(1 row(s) affected)
SQL Server Execution Times:
CPU time = 10766 ms, elapsed time = 70265 ms.
SQL Server parse and compile time:
CPU time = 0 ms, elapsed time = 0 ms.
Second Query:
Set Statistics Time On
Go
Select Count(1) From Employee
Go
Set Statistics Time Off
Go
Result of Second Query:
SQL Server parse and compile time:
CPU time = 14 ms, elapsed time = 14 ms.
(1 row(s) affected)
SQL Server Execution Times:
CPU time = 11031 ms, elapsed time = 70182 ms.
SQL Server parse and compile time:
CPU time = 0 ms, elapsed time = 0 ms.
You can notice there is a difference of 83 (= 70265 - 70182) milliseconds which can easily be attributed to exact system condition at the time queries are run. Also I did a single run, so this difference will become more accurate if I do several runs and do some averaging. If for such a huge data-set the difference is coming less than 100 milliseconds, then we can easily conclude that the two queries do not have any performance difference exhibited by the SQL Server Engine.
Note : RAM hits close to 100% usage in both the runs. I restarted SQL Server service before starting both the runs.
SET STATISTICS TIME ON
select count(1) from MyTable (nolock) -- table containing 1 million records.
SQL Server Execution Times:
CPU time = 31 ms, elapsed time = 36 ms.
select count(*) from MyTable (nolock) -- table containing 1 million records.
SQL Server Execution Times:
CPU time = 46 ms, elapsed time = 37 ms.
I've ran this hundreds of times, clearing cache every time.. The results vary from time to time as server load varies, but almost always count(*) has higher cpu time.
There is an article showing that the COUNT(1) on Oracle is just an alias to COUNT(*), with a proof about that.
I will quote some parts:
There is a part of the database software that is called “The
Optimizer”, which is defined in the official documentation as
“Built-in database software that determines the most efficient way to
execute a SQL statement“.
One of the components of the optimizer is called “the transformer”,
whose role is to determine whether it is advantageous to rewrite the
original SQL statement into a semantically equivalent SQL statement
that could be more efficient.
Would you like to see what the optimizer does when you write a query
using COUNT(1)?
With a user with ALTER SESSION privilege, you can put a tracefile_identifier, enable the optimizer tracing and run the COUNT(1) select, like: SELECT /* test-1 */ COUNT(1) FROM employees;.
After that, you need to localize the trace files, what can be done with SELECT VALUE FROM V$DIAG_INFO WHERE NAME = 'Diag Trace';. Later on the file, you will find:
SELECT COUNT(*) “COUNT(1)” FROM “COURSE”.”EMPLOYEES” “EMPLOYEES”
As you can see, it's just an alias for COUNT(*).
Another important comment: the COUNT(*) was really faster two decades ago on Oracle, before Oracle 7.3:
Count(1) has been rewritten in count(*) since 7.3 because Oracle like
to Auto-tune mythic statements. In earlier Oracle7, oracle had to
evaluate (1) for each row, as a function, before DETERMINISTIC and
NON-DETERMINISTIC exist.
So two decades ago, count(*) was faster
For another databases as Sql Server, it should be researched individually for each one.
I know that this question is specific for SQL Server, but the other questions on SO about the same subject (without mention a specific database) were closed and marked as duplicated from this answer.
In all RDBMS, the two ways of counting are equivalent in terms of what result they produce. Regarding performance, I have not observed any performance difference in SQL Server, but it may be worth pointing out that some RDBMS, e.g. PostgreSQL 11, have less optimal implementations for COUNT(1) as they check for the argument expression's nullability as can be seen in this post.
I've found a 10% performance difference for 1M rows when running:
-- Faster
SELECT COUNT(*) FROM t;
-- 10% slower
SELECT COUNT(1) FROM t;
COUNT(1) is not substantially different from COUNT(*), if at all. As to the question of COUNTing NULLable COLUMNs, this can be straightforward to demo the differences between COUNT(*) and COUNT(<some col>)--
USE tempdb;
GO
IF OBJECT_ID( N'dbo.Blitzen', N'U') IS NOT NULL DROP TABLE dbo.Blitzen;
GO
CREATE TABLE dbo.Blitzen (ID INT NULL, Somelala CHAR(1) NULL);
INSERT dbo.Blitzen SELECT 1, 'A';
INSERT dbo.Blitzen SELECT NULL, NULL;
INSERT dbo.Blitzen SELECT NULL, 'A';
INSERT dbo.Blitzen SELECT 1, NULL;
SELECT COUNT(*), COUNT(1), COUNT(ID), COUNT(Somelala) FROM dbo.Blitzen;
GO
DROP TABLE dbo.Blitzen;
GO