We are running a query every 5 minutes with a JOIN. On one side of the JOIN is table1#time1-time2 (as we only look at the incremental part), another side of the JOIN is table2, which keeps changing as we are stream data into it. The JOIN is now like
[table1#time1-time2] AS T1 INNER JOIN EACH table2 AS T2 ON T1.id = T2.id
Since every time this query involves the whole T2, is there any possible optimization I can do, such as using cache or else, in order to minimize the money cost?
EDIT
The query:
Copy pasting text would be better, hard to read the query on that screenshot.
That said, I see a SELECT * for the second table. Selecting only the needed columns would only query a fraction of the table, instead of all of it.
Also, why are you generating a row_in and joining on a different one?
Related
I joined a new company that uses SAS Enterprise Guide.
I have 2 tables, table A has 100 row, and table B has over 30M rows (50-60 columns).
I tried to do a right join from A (100) to B (30M), it took over 2 hours and no result come back. I want to ask, will it help if I do a left join? I used the GUI and created the following query.
30M Record <- 100 Record ?
or
100 Record -> 30M Record ?
PROC SQL;
CREATE TABLE WORK.QUERY_FOR_CASE_NUMBER AS
SELECT t2.EMPGRPCOM,
t2.SEQINVNUM,
t2.SBSID,
t2.SBSLASTNAME,
t2.SBSFIRSTNAME,
t2.PMTDUEDATE,
t2.PREMAMT,
t2.ITEMDESC,
t2.EFFDATE,
t2.PAYAMT,
t2.MCAIDRATECD,
t2.REBILLIND,
t2.BILLTYPE
FROM WORK.'CASE NUMBER'n t1
LEFT JOIN DW.BILLING t2 ON (t1.CaseNumber = t2.SBSID)
WHERE t2.LOB = 'MD' AND t2.PMTDUEDATE BETWEEN '1Jan2015:0:0:0'dt AND '31Dec2017:0:0:0'dt AND t2.SITEID = '0001';
QUIT;
Left join and Right join, all other things aside, are equivalent - if you implement them the same way, anyway. I.E.,
select a.*
from a
left join
b
on a.id=b.id
;
vs
select a.*
from b
right join
a
on b.id=a.id
;
Same exact query, no difference, same time used. SQL is an interpreted language, meaning the SQL interpreter looks at what you send it and figures out what the best way to do it is - so it sees both queries and knows in both cases to do the same thing.
You can read about this in all sorts of articles, this one is a good starting point, or if that link ages just search for "right join vs left join".
Now, what you might want to consider is writing this in a different way, namely not using SQL; this kind of query SQL should be good at but sometimes isn't for some reason. I would write it as a hash table search, where the smaller case_number dataset is loaded to memory, then data step iterate over the larger table and check if it's found in the smaller dataset - if so, then great, return it.
I'd also think about whether left/right join is what you want, vs. inner join. Seems to me that if you're returning solely t2 values, right/left join isn't correct (when t1 is the "primary"): you'll just get empty rows for the non-matches. Either return a t1 variable, or use inner join.
I have a SQL statement:
select
t3.item1,
t3.item2,
sum(t1.moneys)
from
table t1
inner join table t2 on t1.key = t2.key
inner join table t3 on t1.key2 = t3.key2
where
t2.type = 'thistype'
and t3.type2 = 'thistype'
group by
t3.item1, t3.item2
If I remove the group by, sum, or where clause it runs fine - but if I add back any of those it hangs forever... any ideas... this is on SQL Server Management Studio 2008 R2
Thanks
Further Testing
so I created a view:
select
t3.item1,
t3.item2,
t1.moneys,
t2.type,
t3.type2
from
table t1
inner join table t2 on t1.key = t2.key
inner join table t3 on t1.key2 = t3.key2
and I can select top 1000 from the view fine and see the type I want to specifically look at in the data, but when I add the 'where type2 = 'thistype'' it hangs again...
Your joining three tables together with millions of records, this is normal for it take a bit to run.
To answer your question about statistics, they are what the indices utilize to retrieve records faster from your tables. Without accurate or up to date statistics, indices can actually slow your queries down.
http://blogs.technet.com/b/rob/archive/2008/05/16/sql-server-statistics.aspx
I think we'd need to see some table structure and know some more things about your DB before we can give a solid answer. First thing, though, is to run a trace on it and see what that tells you.
At first blush, I have found that issues with aggregate functions (sum, group by, etc) tend to stem from a) overly large data sets (that is: you're just trying to pull back too much data) or b) from overly-complicated structure or relationships on the joined tables.
However, those are just my general rules-of-thumb, and may not apply in a specific situation: run a trace and any other form of profiling you can and see what that tells you.
Has you looked at the execution plan you're getting? That will tell you where the problem is. Do you have covering indices on the columns on which you're joining and grouping?
Is it possible that the execution plan is corrupted?
http://msdn.microsoft.com/en-us/library/aa175244(v=sql.80).aspx
Try recompiling the plan using sp_recompile
I have 3 tables Table1 (with 1020690 records), Table2(with 289425 records), Table 3(with 83692 records).I have something like this
SELECT * FROM Table1 T1 /* OK fine select * is bad when not all columns are needed, this is just an example*/
LEFT JOIN Table2 T2 ON T1.id=T2.id
LEFT JOIN Table3 T3 ON T1.id=T3.id
and a query like this
SELECT * FROM Table1 T1
LEFT JOIN Table3 T3 ON T1.id=T3.id
LEFT JOIN Table2 T2 ON T1.id=T2.id
The query plan shows me that it uses 2 Merge Join for both the joins. For the first query, the first merge is with T1 and T2 and then with T3. For the second query, the first merge is with T1 and T3 and then with T2.
Both these queries take about the same time(40 seconds approx.) or sometimes Query1 takes couple of seconds longer.
So my question is, does the join order matter ?
The join order for a simple query like this should not matter. If there's a way to reorder the joins to improve performance, that's the job of the query optimizer.
In theory, you shouldn't worry about it -- that's the point of SQL. Trying to outthink the query optimizer is generally not going to give better results. Especially in MS SQL Server, which has a very good query optimizer.
I wouldn't expect this query to take 40 seconds. You might not have the right indexes defined. You should use tools like SQL Server Profiler or SQL Server Database Engine Tuning Advisor to see if it can recommend any new indexes.
The query optimizer will use a combination of the constraints, indexes, and statistics collected on the table to build an execution plan. In most cases this works well. However, I do occasionally encounter scenarios where the execution plan is chosen poorly. Often times tweaking the query can effectively coerce the optimizer into a choosing a better plan. I can offer no general rules for doing this though. When all else fails you could resort to the FORCE ORDER query hint.
And yes, the join order can have a significant impact on execution time of your query. The idea is that by joining the tables that yield the smallest results first will cause the next join to be computed more quickly. Edit: It is important to note, however, that in the abscense of FORCE ORDER and in all other things being equal the order you specify in the query may have no correlation with the way the optimizer builds the execution plan.
In general, SQL Server is smart enough to pick out the best way to join and it will not only use the order you wrote in the query. That said, I find it easier to understand a complex query if all the inner joins are first and then the left joins.
Provided that the tables could essentially be inner joined, since the where clause excludes all records that don't match, just exactly how bad is it to use the first of the following 2 query statement syntax styles:
SELECT {COLUMN LIST}
FROM TABLE1 t1, TABLE2 t2, TABLE3 t3, TABLE4 t4 (etc)
WHERE t1.uid = t2.foreignid
AND t2.uid = t3.foreignid
AND t3.uid = t4.foreignid
etc
instead of
SELECT {COLUMN LIST}
FROM TABLE1 t1
INNER JOIN TABLE2 t2 ON t1.uid = t2.foreignid
INNER JOIN TABLE3 t3 ON t2.uid = t3.foreignid
INNER JOIN TABLE4 t4 ON t3.uid = t4.foreignid
I'm not sure if this is limited to microsoft SQL, or even a particular version, but my understanding is that the first scenario does a full outer join to make all possible correlations accessible.
I've used the first approach in the past to optimise queries that access two significantly large stores of data that each have peripheral table joined to them, with the product of those joins coming together late in the query. By allowing each of the "larger" table to join to their respective lookup tables, and only combining a specific subset of each of the larger tables, I found that there were notable speed improvements over introducing the large tables to each other prior to specific filtering.
Under normal (simple joins) circumstance, would it not be far better to use the second scenario? I find it to be more easily readable and it seems like it'll be much faster.
INNER JOIN ON vs WHERE clause
Maybe the best way to answer this is to take a look at how the database handles the query internally. If you're on SQL Server, use Profiler to see how many reads etc. each query takes and the query plan to see what route is being taken through the data. Statistics, skewing etc. will also most likely play a role.
The first query doesn't produce a full OUTER join (which is the union of both LEFT and RIGHT joins). Essentially unless there are some [internal] SQL parser - specific optimizations, both queries are equal.
Personally I would never use the first syntax. It may be the same performancewise but it is harder to maintain and far more subject to accidental cross joins when things get complex. If you miss an ON condition, it will fail the syntax check , if you miss one of the WHERE conditions that is the equivalent of an ON condition, it will happily do a cross join. It is also a syntax that is 17 years out of date for goodness sakes!
Further, the left and right join syntax in the old syntax are broken in SQL Server and do NOT always return the correct results (it can sometimes interpet the results as a corss join instead of an outerjoin) and they have been deprecated and will not be useable at all in the next version. If you need to change one of the queries to use an outer join, then you can be looikng at a major rewrite as it is especially bad to try to mix the two kinds of syntax.
Given these two queries:
Select t1.id, t2.companyName
from table1 t1
INNER JOIN table2 t2 on t2.id = t1.fkId
WHERE t2.aField <> 'C'
OR:
Select t1.id, t2.companyName
from table1 t1
INNER JOIN table2 t2 on t2.id = t1.fkId and t2.aField <> 'C'
Is there a demonstrable difference between the two? Seems to me that the clause "t2.aField <> 'C'" will run on every row in t2 that meets the join criteria regardless. Am I incorrect?
Update: I did an "Include Actual Execution Plan" in SQL Server. The two queries were identical.
I prefer to use the Join criteria for explaining how the tables are joined together.
So I would place the additional clause in the where section.
I hope (although I have no stats), that SQL Server would be clever enough to find the optimal query plan regardless of the syntax you use.
HOWEVER, if you have indexes which also have id, and aField in them, I would suggest placing them together in the inner join criteria.
It would be interesting to see the query plan's in these 2 (or 3) scenarios, and see what happens. Nice question.
There is a difference. You should do an EXPLAIN PLAN for both of the selects and see it in detail.
As for a simplier explanation:
The WHERE clause gets executed only after the joining of the two tables, so it executes for each row returned from the join and not nececerally every one from table2.
Performance wise its best to eliminate unwanted results early on so there should be less rows for joins, where clauses or other operations to deal with later on.
In the second example, there are 2 columns that have to be same for the rows to be joined together so it usually will give different results than the first one.
It depends.
SELECT
t1.foo,
t2.bar
FROM
table1 t1
LEFT JOIN table2 t2 ON t1.SomeId = t2.SomeId
WHERE
t2.SomeValue IS NULL
is different from
SELECT
t1.foo,
t2.bar
FROM
table1 t1
LEFT JOIN table2 t2 ON t1.SomeId = t2.SomeId AND t2.SomeValue IS NULL
It is different because the former crosses out all records from t2 that have NULL in t2.SomeValue and those from t1 that are not referenced in t2. The latter crosses out only the t2 records that have NULL in t2.SomeValue.
Just use the ON clause for the join condition and the WHERE clause for the filter.
Unless moving the join condition to the where clause changes the meaning of the query (like in the left join example above), then it doesn't matter where you put them. SQL will re-arrange them, and as long as they are provably equivalent, you'll get the same query.
That being said, I think it's more of a logical / readability thing. I usually put anything that relates two tables in the join, and anything that filters in the where.
I'd prefer first query. SQL server will use the best join type for your query based on indexes you have, after that will apply WHERE clause. But you can run both queries at the same time, look at execution plans, compare and choose the fastest (optimize adding indexes also).
unless you are working on a single-user app or something similarly small that creates trivial load, the only considerations that mean anything is how the server will process your query.
The answers that mention query plans give good advice.
In addition, set io statistics on to get an idea of how many reads your query will generate (I especially love Azder's post).
Think of every DB server as a pump of data from disk to client. That pump goes faster if it performs only the IO needed to get the job done. If the data is in cache it will be even faster. But you don't want to be reading more than you need from disk - that will result in crowding out of your cache useful data for no good reason.