Is Except operator computational expensive - sql

I have a table which includes 30 records, and a smaller table has 10 records, both tables have the same schema. All I want to do is to return a table, whose records are in the big table, but not in the small table. The solution I found is to use Except operator. However, when I run the query, it took me about 30 mins. so I am just wondering that if Except is computational expensive and it took a lot of resources?
Is there any functions can replace Except? Thanks for any help !


EXCEPT is a set operator and it should be reasonably optimized. It does remove duplicate values, so there is a bit more overhead than one might expect.
It is not so unoptimized that it would take 30 seconds on such small tables, unless you have columns whose size measures in many megabytes. Something else might be going on -- such as network or server contention.
EXCEPT is a very reasonable approach. NOT IN has a problem with NULL values and only works with one column. NOT EXISTS is going to work best when you have an appropriate index. Under some circumstances, EXCEPT is faster than NOT EXISTS.


In this case, you should be using EXISTS. It is one of the most performant operations in SQL Server
SELECT *
FROM big_table b
WHERE NOT EXISTS (
SELECT 1
FROM small_table s
WHERE s.id = b.id)


There is no need to make things complicated for something so simple.
SELECT * FROM Table1 WHERE ID NOT IN (SELECT ID FROM Table2)

Related

Can I alias and reuse my subqueries?

I'm working with a data warehouse doing report generation. As the name would suggest, I have a LOT of data. One of the queries that pulls a LOT of data is getting to take longer than I like (these aren't performed ad-hoc, these queries run every night and rebuild tables to cache the reports).
I'm looking at optimizing it, but I'm a little limited on what I can do. I have one query that's written along the lines of...
SELECT column1, column2,... columnN, (subQuery1), (subquery2)... and so on.
The problem is, the sub queries are repeated a fair amount because each statement has a case around them such as...
SELECT
column1
, column2
, columnN
, (SELECT
CASE
WHEN (subQuery1) > 0 AND (subquery2) > 0
THEN CAST((subQuery1)/(subquery2) AS decimal)*100
ELSE 0
END) AS "longWastefulQueryResults"
Our data comes from multiple sources and there are occasional data entry errors, so this prevents potential errors when dividing by a zero. The problem is, the sub-queries can repeat multiple times even though the values won't change. I'm sure there's a better way to do it...
I'd love something like what you see below, but I get errors about needing sq1 and sq2 in my group by clause. I'd provide an exact sample, but it'd be painfully tedious to go over.
SELECT
column1
, column2
, columnN
, (subQuery1) as sq1
, (subquery2) as sq2
, (SELECT
CASE
WHEN (sq1) > 0 AND (sq2) > 0
THEN CAST((sq1)/(sq2) AS decimal)*100
ELSE 0
END) AS "lessWastefulQueryResults"
I'm using Postgres 9.3 but haven't been able to get a successful test yet. Is there anything I can do to optimize my query?

Yup, you can create a Temp Table to store your results and query them again in the same session

I'm not sure how good the Postgres optimizer is, so I'm not sure whether optimizing in this way will do any good. (In my opinion, it shouldn't because the DBMS should be taking care of this kind of thing; but it's not at all surprising if it isn't.) OTOH if your current form has you repeating query logic, then you can benefit from doing something different whether or not it helps performance...
You could put the subqueries in with clauses up front, and that might help.
with subauery1 as (select ...)
, subquery2 as (select ...)
select ...
This is similar to putting the subqueries in the FROM clause as Allen suggests, but may offer more flexibility if your queries are complex.
If you have the freedom to create a temp table as Andrew suggests, that too might work but could be a double-edged sword. At this point you're limiting the optimizer's options by insisting that the temp tables be populated first and then used in the way that makes sense to you, which may not always be the way that actually gets the most efficiency. (Again, this comes down to how good the optimizer is... it's often folly to try to outsmart a really good one.) On the other hand, if you do create temp or working tables, you might be able to apply useful indexes or stats (if they contain large datasets) that would further improve downstream steps' performance.
It looks like many of your subqueries might return single values. You could put the queries into a procedure and capture those individual values as variables. This is similar to the temp table approach, but doesn't require creation of objects (as you may not be able to do that) and will have less risk of confusing the optimizer by making it worry about a table where there's really just one value.

Sub-queries in the column list tend to be a questionable design. The first approach I'd take to solving this is to see if you can move them down to the from clause.
In addition to allowing you to use the result of those queries in multiple columns, doing this often helps the optimizer to come up with a better plan for your query. This is because the queries in the column list have to be executed for every row, rather than merged into the rest of the result set.
Since you only included a portion of the query in your question, I can't demonstrate this particularly well, but what you should be looking for would look more like:
SELECT column1,
column2,
columnn,
subquery1.sq1,
subquery2.sq2,
(SELECT CASE
WHEN (subquery1.sq1) > 0 AND (subquery2.sq2) > 0 THEN
CAST ( (subquery1.sq1) / (subquery2.sq2) AS DECIMAL) * 100
ELSE
0
END)
AS "lessWastefulQueryResults"
FROM some_table
JOIN (SELECT *
FROM other_table
GROUP BY some_columns) subquery1
ON some_table.some_columns = subquery1.some_columns
JOIN (SELECT *
FROM yet_another_table
GROUP BY more_columns) subquery1
ON some_table.more_columns = subquery1.more_columns

JOIN whole tables vs JOIN tables with less columns (fetched via subquery)

If I were to do a join between two tables, would there be any difference with respect to the fact I joined them as a whole, or I joined them after having extracted only the required columns (making the assumption that each table potentially has many columns)?
As an example, is
SELECT tableA.foreignKey, tableB.someValue
FROM tableA JOIN tableB ON tableA.foreignKey=tableB.key
any different from
SELECT tableA.foreignKey, tableB.someValue
FROM (SELECT foreignKey FROM tableA) tableA_filtered
JOIN (SELECT key, someValue FROM tableB) tableB_filtered
ON tableA_filtered.foreignKey=tableB_filtered.key
performace-wise?

Use the first one, since the second one uses subquery which creates a temporary table for the result. And actually (SELECT valueA FROM tableA) makes no sense at all because you are not aggregating some columns on the table.
Subquery are sometimes evil, not at all times. Tt depends upon the RDBMS you are using.

The general rule is that a subquery will always be slow.
Depending on the amount of data you are processing it could have a big impact.
Reciently i removed a subquery from a large select with alot of joins.
The SQL was processing about 100,000 rows if not more.
Removing the very simple sub select improved performance by 50 seconds.
Overall the sql was taking two minutes. So it had a big impact.

I think in the case that the tables have many columns, the second query could be faster. But its important to note that this two querys are not equivalent. The first one displays you all values from A and B, the second one only valueA from A and valueB from B! Anyway its more a theoretical question and hard to answer generally.
In reality I would leave this decisions to the database optimizer. But if you want to really know if there is a way to make it even faster, the only safe way is to measure and compare the runtimes of both querys.
And as a sidenote, the second query will very probable be flattened by the rewrite engine of your DBMS, so its the same as when you would write:
SELECT valueA, valueB FROM A, B WHERE A.valueA = B.valueB;

order of tables in FROM clause

For an sql query like this.
Select * from TABLE_A a
JOIN TABLE_B b
ON a.propertyA = b.propertyA
JOIN TABLE_C
ON b.propertyB = c.propertyB
Does the sequence of the tables matter. It wont matter in results, but do they affect the performance?
One can assume that the data in table C is much larger that a or b.

For each sql statement, the engine will create a query plan. So no matter how you put them, the engine will chose a correct path to build the query.
More on plans you have http://en.wikipedia.org/wiki/Query_plan
There are ways, considering what RDBMS you are using to enforce the query order and plan, using hints, however, if you feel that the engine does no chose the correct path.

Sometimes Order of table creates a difference here,(when you are using different joins)
Actually our Joins working on Cross Product Concept
If you are using query like this A join B join C
It will be treated like this (A*B)*C)
Means first result comes after joining A and B table then it will make join with C table
So if after inner joining A (100 record) and B (200 record) if it will give (100 record)
And then these ( 100 record ) will compare with (1000 record of C)

No.
Well, there is a very, very tiny chance of this happening, see this article by Jonathan Lewis. Basically, the number of possible join orders grows very quickly, and there's not enough time for the Optimizer to check them all. The sequence of the tables may be used as a tie-breaker in some very rare cases. But I've never seen this happen, or even heard about it happening, to anybody in real life. You don't need to worry about it.

Does SQL performance degrade as the number elements in an "IN" clause increases?

I have a query like this,
SELECT Name FROM Customers WHERE Id IN (1,4,3,6,7)
There might be millions of customers in the DataBase. Will there be an efficiency problem with this query ? When the number of Ids inside IN statement are more ? If so, Why and Any workaround ?
I Use SQLServer. Below is my table Structure
Id|Name|Designation
Id is the primary key -non clustered index.

This query is as basic as it can get.
If you need to find the name of 5 customers, there is simply no other sane way of writing it.
It will perform well if you have an index on ID. The performance is almost instantaneous, directly related to the number of items in the IN clause.
If you don't it will scan the table, and the performance becomes directly related to the number of records in the table.

Assuming you have properly indexed the Id column, there should be no problem. That is the correct method, and if it does not work, you need a new database. (Millions shouldn't be an issue with most regular pieces of software; if you make it to multiple billions you might need to investigate clustered databases).

If you execute the following query:
select * from sys.objects where object_id in (
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,
31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,
58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,
85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,
109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,
130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,
151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320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5761,5762,5763,5764,5765,5766,5767,5768,5769,5770,5771,5772,5773,5774,5775,5776)
(I'm not going to break up all the lines).
In the resulting query, approximately 5% of the cost of the query is taken up with a constant scan (which is effectively turning all of those numbers into a temp table internally and that table is then passed to a join operator).
But, this is a remarkably simple query overall. For any more complex query, I'd expect that the cost, as a percentage, will go down (since I expect the absolute cost to remain the same)

I know this isn't the question that was asked, but, say your list of IDs came from another query:
SELECT Name FROM Customers WHERE Id IN (SELECT ID FROM X WHERE X.FIELD = COND)
Then this is cause to rewrite your query using EXISTS:
SELECT Name
FROM CUSTOMERS
WHERE EXISTS
(
SELECT *
FROM X
WHERE X.FIELD = COND
AND X.ID = CUSTOMERS.ID
);
This is efficient because EXISTS gives more opportunity for the optimizer to determine an efficient execution path, whereas IN forces the subquery to be fully evaluated.
The query you specified didn't have a subsquery. It just has a list of constants which has little opportunity to be further optimized. As is, you have to do with the best you got, i.e. index the ID column as recommended by #zebediah49.

SQL Distinct keyword bogs down performance?

I have received a SQL query that makes use of the distinct keyword. When I tried running the query it took at least a minute to join two tables with hundreds of thousands of records and actually return something.
I then took out the distinction and it came back in 0.2 seconds. Does the distinct keyword really make things that bad?
Here's the query:
SELECT DISTINCT
c.username, o.orderno, o.totalcredits, o.totalrefunds,
o.recstatus, o.reason
FROM management.contacts c
JOIN management.orders o ON (c.custID = o.custID)
WHERE o.recDate > to_date('2010-01-01', 'YYYY/MM/DD')

Yes, as using DISTINCT will (sometimes according to a comment) cause results to be ordered. Sorting hundreds of records takes time.
Try GROUP BY all your columns, it can sometimes lead the query optimiser to choose a more efficient algorithm (at least with Oracle I noticed significant performance gain).

Distinct always sets off alarm bells to me - it usually signifies a bad table design or a developer who's unsure of themselves. It is used to remove duplicate rows, but if the joins are correct, it should rarely be needed. And yes there is a large cost to using it.
What's the primary key of the orders table? Assuming it's orderno then that should be sufficient to guarantee no duplicates. If it's something else, then you may need to do a bit more with the query, but you should make it a goal to remove those distincts! ;-)
Also you mentioned the query was taking a while to run when you were checking the number of rows - it can often be quicker to wrap the entire query in "select count(*) from ( )" especially if you're getting large quantities of rows returned. Just while you're testing obviously. ;-)
Finally, make sure you have indexed the custID on the orders table (and maybe recDate too).

Purpose of DISTINCT is to prune duplicate records from the result set for all the selected columns.
If any of the selected columns is unique after join you can drop DISTINCT.
If you don't know that, but you know that the combination of the values of selected column is unique, you can drop DISTINCT.
Actually, normally, with properly designed databases you rarely need DISTINCT and in those cases that you do it is (?) obvious that you need it. RDBMS however can not leave it to chance and must actually build an indexing structure to establish it.
Normally you find DISTINCT all over the place when people are not sure about JOINs and relationships between tables.
Also, in classes when talking about pure relational databases where the result should be a proper set (with no repeating elements = records) you can find it quite common for people to stick DISTINCT in to guarantee this property for purposes of theoretical correctness. Sometimes this creeps in into production systems.

You can try to make a group by like this:
SELECT c.username,
o.orderno,
o.totalcredits,
o.totalrefunds,
o.recstatus,
o.reason
FROM management.contacts c,
management.orders o
WHERE c.custID = o.custID
AND o.recDate > to_date('2010-01-01', 'YYYY-MM-DD')
GROUP BY c.username,
o.orderno,
o.totalcredits,
o.totalrefunds,
o.recstatus,
o.reason
Also verify if you have index on o.recDate