What are the performance implications of Oracle IN Clause with no joins? - sql

I have a query in this form that will on average take ~100 in clause elements, and at some rare times > 1000 elements. If greater than 1000 elements, we will chunk the in clause down to 1000 (an Oracle maximum).
The SQL is in the form of
SELECT * FROM tab WHERE PrimaryKeyID IN (1,2,3,4,5,...)
The tables I am selecting from are huge and will contain millions more rows than what is in my in clause. My concern is that the optimizer may elect to do a table scan (our database does not have up to date statistics - yeah - I know ...)
Is there a hint I can pass to force the use of the primary key - WITHOUT knowing the index name of the primary Key, perhaps something like ... /*+ DO_NOT_TABLE_SCAN */?
Are there any creative approaches to pulling back the data such that
We perform the least number of round-trips
We we read the least number of blocks (at the logical IO level?)
Will this be faster ..
SELECT * FROM tab WHERE PrimaryKeyID = 1
UNION
SELECT * FROM tab WHERE PrimaryKeyID = 2
UNION
SELECT * FROM tab WHERE PrimaryKeyID = 2
UNION ....

If the statistics on your table are accurate, it should be very unlikely that the optimizer would choose to do a table scan rather than using the primary key index when you only have 1000 hard-coded elements in the WHERE clause. The best approach would be to gather (or set) accurate statistics on your objects since that should cause good things to happen automatically rather than trying to do a lot of gymnastics in order to work around incorrect statistics.
If we assume that the statistics are inaccurate to the degree that the optimizer would be lead to believe that a table scan would be more efficient than using the primary key index, you could potentially add in a DYNAMIC_SAMPLING hint that would force the optimizer to gather more accurate statistics before optimizing the statement or a CARDINALITY hint to override the optimizer's default cardinality estimate. Neither of those would require knowing anything about the available indexes, it would just require knowing the table alias (or name if there is no alias). DYNAMIC_SAMPLING would be the safer, more robust approach but it would add time to the parsing step.
If you are building up a SQL statement with a variable number of hard-coded parameters in an IN clause, you're likely going to be creating performance problems for yourself by flooding your shared pool with non-sharable SQL and forcing the database to spend a lot of time hard parsing each variant separately. It would be much more efficient if you created a single sharable SQL statement that could be parsed once. Depending on where your IN clause values are coming from, that might look something like
SELECT *
FROM table_name
WHERE primary_key IN (SELECT primary_key
FROM global_temporary_table);
or
SELECT *
FROM table_name
WHERE primary_key IN (SELECT primary_key
FROM TABLE( nested_table ));
or
SELECT *
FROM table_name
WHERE primary_key IN (SELECT primary_key
FROM some_other_source);
If you got yourself down to a single sharable SQL statement, then in addition to avoiding the cost of constantly re-parsing the statement, you'd have a number of options for forcing a particular plan that don't involve modifying the SQL statement. Different versions of Oracle have different options for plan stability-- there are stored outlines, SQL plan management, and SQL profiles among other technologies depending on your release. You can use these to force particular plans for particular SQL statements. If you keep generating new SQL statements that have to be re-parsed, however, it becomes very difficult to use these technologies.

Related

Getting RID Lookup instead of Table Scan?

SQL Fiddle: http://sqlfiddle.com/#!3/23cf8
In this query, when I have an In clause on an Id, and then also select other columns, the In is evaluated first, and then the Details column and other columns are pulled in via a RID Lookup:
--In production and in SQL Fiddle, Details is grabbed via a RID Lookup after the In clause is evaluated
SELECT [Id]
,[ForeignId]
,Details
--Generate a numbering(starting at 1)
--,Row_Number() Over(Partition By ForeignId Order By Id Desc) as ContactNumber --Desc because older posts should be numbered last
FROM SupportContacts
Where foreignId In (1,2,3,5)
With this query, the Details are being pulled in via a Table Scan.
With NumberedContacts AS
(
SELECT [Id]
,[ForeignId]
--Generate a numbering(starting at 1)
,Row_Number() Over(Partition By ForeignId Order By Id Desc) as ContactNumber --Desc because older posts should be numbered last
FROM SupportContacts
Where ForeignId In (1,2,3,5)
)
Select nc.[Id]
,nc.[ForeignId]
,sc.[Details]
From NumberedContacts nc
Inner Join SupportContacts sc on nc.Id = sc.Id
Where nc.ContactNumber <= 2 --Only grab the last 2 contacts per ForeignId
;
In SqlFiddle, the second query actually gets a RID Lookup, whereas in production with a million records it produces a Table Scan (the IN clause eliminates 99% of the rows)
Otherwise the query plan shown in SQL Fiddle is identical, the only difference being that for the second query the RID Lookup in SQL Fiddle, is a Table Scan in production :(
I would like to understand possibilities that would cause this behavior? What kinds of things would you look at to help determine the cause of it using a table scan here?
How can I influence it to use a RID Lookup there?
From looking at operation costs in the actual execution plan, I believe I can get the second query very close in performance to the first query if I can get it to use a RID Lookup. If I don't select the Detail column, then the performance of both queries is very close in production. It is only after adding other columns like Detail that performance degrades significantly for the second query. When I put it in SQL Fiddle and saw that the execution plan used an RID Lookup, I was surprised but slightly confused...
It doesn't have a clustered index because in testing with different clustered indexes, there was slightly worse performance for this and other queries. That was before I began adding other columns like Details though, and I can experiment with that more, but would like to have a understanding of what is going on now before I start shooting in the dark with random indexes.
What if you would change your main index to include the Details column?
If you use:
CREATE NONCLUSTERED INDEX [IX_SupportContacts_ForeignIdAsc_IdDesc]
ON SupportContacts ([ForeignId] ASC, [Id] DESC)
INCLUDE (Details);
then neither a RID lookup nor a table scan would be needed, since your query could be satisfied from just the index itself....
The differences in the query plans will be dependent on the types of indexes that exist and the statistics of the data for those tables in the different environments.
The optimiser uses the statistics (histograms of data frequency, mostly) and the available indexes to decide which execution plan is going to be the quickest.
So, for example, you have noticed that the performance degrades when the 'Details' column is included. This is an almost sure sign that either the 'Details' column is not part of an index, or if it is part of an index, the data in that column is mostly unique such that the index accesses would be equivalent (or almost equivalent) to a table scan.
Often when this situation arises, the optimiser will choose a table scan over the index access, as it can take advantage of things like block reads to access the table records faster than perhaps a fragmented read of an index.
To influence the path that will be chose by the optimiser, you would need to look at possible indexes that could be added/modified to make an index access more efficient, but this should be done with care as it can adversely affect other queries as well as possibly degrading insert performance.
The other important activity you can do to help the optimiser is to make sure the table statistics are kept up to date and refreshed at a frequency that is appropriate to the rate of change of the frequency distribution in the table data
If it's true that 99% of the rows would be omitted if it performed the query using the relevant index + RID then the likeliest problem in your production environment is that your statistics are out of date and the optimiser doesn't realise that ForeignID in (1,2,3,5) would limit the result set to 1% of the total data.
Here's a good link for discovering more about statistics from Pinal Dave: http://blog.sqlauthority.com/2010/01/25/sql-server-find-statistics-update-date-update-statistics/
As for forcing the optimiser to follow the correct path WITHOUT updating the statistics, you could use a table hint - if you know the index that your plan should be using which contains the ID and ForeignID columns then stick that in your query as a hint and force SQL optimiser to use the index:
http://msdn.microsoft.com/en-us/library/ms187373.aspx
FYI, if you want the best performance from your second query, use this index and avoid the headache you're experiencing altogether:
create index ix1 on SupportContacts(ForeignID, Id DESC) include (Details);

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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,5321,5322,5323,5324,5325,5326,5327,5328,5329,5330,5331,5332,5333,5334,5335,5336,5337,5338,5339,5340,5341,5342,5343,5344,5345,5346,5347,5348,5349,5350,5351,5352,5353,5354,5355,5356,5357,5358,5359,5360,5361,5362,5363,5364,5365,5366,5367,5368,5369,5370,5371,5372,5373,5374,5375,5376,5377,5378,5379,5380,5381,5382,5383,5384,5385,5386,5387,5388,5389,5390,5391,5392,5393,5394,5395,5396,5397,5398,5399,5400,5401,5402,5403,5404,5405,5406,5407,5408,5409,5410,5411,5412,5413,5414,5415,5416,5417,5418,5419,5420,5421,5422,5423,5424,5425,5426,5427,5428,5429,5430,5431,5432,5433,5434,5435,5436,5437,5438,5439,5440,5441,5442,5443,5444,5445,5446,5447,5448,5449,5450,5451,5452,5453,5454,5455,5456,5457,5458,5459,5460,5461,5462,5463,5464,5465,5466,5467,5468,5469,5470,5471,5472,5473,5474,5475,5476,5477,5478,5479,5480,5481,5482,5483,5484,5485,5486,5487,5488,5489,5490,5491,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,5502,5503,5504,5505,5506,5507,5508,5509,5510,5511,5512,5513,5514,5515,5516,5517,5518,5519,5520,5521,5522,5523,5524,5525,5526,5527,5528,5529,5530,5531,5532,5533,5534,5535,5536,5537,5538,5539,5540,5541,5542,5543,5544,5545,5546,5547,5548,5549,5550,5551,5552,5553,5554,5555,5556,5557,5558,5559,5560,5561,5562,5563,5564,5565,5566,5567,5568,5569,5570,5571,5572,5573,5574,5575,5576,5577,5578,5579,5580,5581,5582,5583,5584,5585,5586,5587,5588,5589,5590,5591,5592,5593,5594,5595,5596,5597,5598,5599,5600,5601,5602,5603,5604,5605,5606,5607,5608,5609,5610,5611,5612,5613,5614,5615,5616,5617,5618,5619,5620,5621,5622,5623,5624,5625,5626,5627,5628,5629,5630,5631,5632,5633,5634,5635,5636,5637,5638,5639,5640,5641,5642,5643,5644,5645,5646,5647,5648,5649,5650,5651,5652,5653,5654,5655,5656,5657,5658,5659,5660,5661,5662,5663,5664,5665,5666,5667,5668,5669,5670,5671,5672,5673,5674,5675,5676,5677,5678,5679,5680,5681,5682,5683,5684,5685,5686,5687,5688,5689,5690,5691,5692,5693,5694,5695,5696,5697,5698,5699,5700,5701,5702,5703,5704,5705,5706,5707,5708,5709,5710,5711,5712,5713,5714,5715,5716,5717,5718,5719,5720,5721,5722,5723,5724,5725,5726,5727,5728,5729,5730,5731,5732,5733,5734,5735,5736,5737,5738,5739,5740,5741,5742,5743,5744,5745,5746,5747,5748,5749,5750,5751,5752,5753,5754,5755,5756,5757,5758,5759,5760,
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.

Oracle: How can I find tablespace fragmentation?

I've a JOIN beween two tables. It's really really slow and I can't find why.
The query takes hours in a PRODUCTION environment on a very big Client.
Can you ask me what you need to understand why it doesn't work well?
I can add indexes, partition the table, etc. It's Oracle 10g.
I expect a few thousand record. Because of the following condition:
f.eif_campo1 != c.fornitura AND and f.field29 = 'New'
Infact it should be always verified for all 18 million records
SELECT c.id_messaggio
,f.campo1
,c.f
FROM
flows c,
tab f
WHERE
f.field198 = c.id_messaggio
AND f.extra_id = c.extra_id
and f.field1 != c.ExampleF
and f.field29 = 'New'
and c.processtype in ('Example1')
and c.flag_ann = 'N';
Selectivity for the following record expressed as number of distinct values:
COUNT (DISTINCT extra_id) =>17*10^6,
COUNT (DISTINCT (extra_id || field20)) =>17*10^6,
COUNT (DISTINCT field198) =>36*10^6,
COUNT (DISTINCT (field19 || field20)) =>45*10^6,
COUNT (DISTINCT (field1)) =>18*10^6,
COUNT (DISTINCT (field20)) =>47
This is the execution plan [See large image][1]
![enter image description here][2]
Extra details:
I have relaxed one contition to see how many records are taken. 300 thousand.
![enter image description here][7]
--03:57 mins with parallel execution /*+ parallel(c 8) parallel(f 24) */
--395.358 rows
SELECT count(1)
FROM
flows c,
flet f
WHERE
f.field19 = c.id_messaggio
AND f.extra_id = c.extra_id
and f.field20 = 'ExampleF'
and c.process_type in ('ExampleP')
and c.flag_ann = 'N';
Your explain plan shows the following.
The database uses an index to retrieve rows from ENI_FLUSSI_HUB where
flh_tipo_processo_cod in ('VT','VOLTURA_ENI','CC')
It then winnows the rows
where flh_flag_ann = 'N'
This produces a result set which is used to access
rows from ETL_ELAB_INTERF_FLAT on the basis of f.idde_identif_dati_ext_id =
c.idde_identif_dati_ext_id
Finally those rows are filtered on the basis of the
remaining parts of the WHERE clause.
Now, the starting point is a good one if flh_tipo_processo_cod is a selective
column: that is, if it contains hundreds of different values, or if the values in
your list are relatively rare. It might even be a good path of the flag column
identifies relatively few columns with a value of 'N'. So you need to understand
both the distribution of your data - how many distinct values you have - and its
skew - which values appear very often or hardly at all. The overall
performance suggests that the distribution and/or skew of the
flh_tipo_processo_cod and flh_flag_ann columns is not good.
So what can you do? One approach is to follow Ben's suggestion, and use full
table scans. If you have an Enterprise Edition licence and plenty of CPU capacity
you could try parallel query to improve things. That might still be too slow, or it might be too disruptive for other users.
An alternative approach would be to use better indexes. A composite index on
eni_flussi_hub(flh_tipo_processo_cod,flh_flag_ann,idde_identif_dati_ext_id,
flh_fornitura,flh_id_messaggio) would avoid the need to read that table. Whether
this would be a new index or a replacement for ENI_FLK_IDX3 depends on the other
activity against the table. You might be able to benefit from index compression.
All the columns in the query projection are referenced in the WHERE clause. So
you could also use a composite index on the other table to avoid table reads. Agsin you need to understand the distribution and skew of the data. But you should probably lead with the least selective columns. Something like etl_elab_interf_flat(etl_elab_interf_flat,eif_campo200,dde_identif_dati_ext_id,eif_campo1,eif_campo198). Probably this is a new index. It's unlikely you would want to replace ETL_EIF_FK_IDX4 with this (especially if that really is an index on a foreign key constraint)..
Of course these are just guesses on my part. Tuning is a science and to do it properly requires lots of data. Use the Wait Interface to investigate where the database is spending its time. Use the 10053 event to understand why the Optimizer makes the choices it does. But above all, don't implement partitioning unless you really know the ramifications.
The simple answer seems to be your explain plan. You're accessing both tables by index rowid. Whilst to select a single row you cannot - to my knowledge - get faster, in your case you're selecting a lot more than a single row.
This means that for every single row you, you're going into both tables one row at a time, which when you're looking a significant proportion of a table or index is not what you want to do.
My suggestion would be to force a full scan of one or both of your tables. Try to use the smaller as a driver first:
SELECT /*+ full(c) */ c.flh_id_messaggio
, f.eif_campo1
, c.f
FROM flows c,
JOIN flet f
ON f.field19 = c.flh_id_messaggio
AND f.extra_id = c.extra_id
AND f.field1 <> c.f
WHERE ...
But you may have to change /*+ full(c) */ to /*+ full(c) full(f) */.
Your indexes seem to be separate column indexes as well. For this, and if possible, I would have indexes on:
flows of id_messaggio, extra_id, f
and on flet of field19, extra_id, field1.
This will only really matter if you do not use as full scan. Or, if you have everything you're returning and selecting is in one index.

SQL "WITH" Performance and Temp Table (possible "Query Hint" to simplify)

Given the example queries below (Simplified examples only)
DECLARE #DT int; SET #DT=20110717; -- yes this is an INT
WITH LargeData AS (
SELECT * -- This is a MASSIVE table indexed on dt field
FROM mydata
WHERE dt=#DT
), Ordered AS (
SELECT TOP 10 *
, ROW_NUMBER() OVER (ORDER BY valuefield DESC) AS Rank_Number
FROM LargeData
)
SELECT * FROM Ordered
and ...
DECLARE #DT int; SET #DT=20110717;
BEGIN TRY DROP TABLE #LargeData END TRY BEGIN CATCH END CATCH; -- dump any possible table.
SELECT * -- This is a MASSIVE table indexed on dt field
INTO #LargeData -- put smaller results into temp
FROM mydata
WHERE dt=#DT;
WITH Ordered AS (
SELECT TOP 10 *
, ROW_NUMBER() OVER (ORDER BY valuefield DESC) AS Rank_Number
FROM #LargeData
)
SELECT * FROM Ordered
Both produce the same results, which is a limited and ranked list of values from a list based on a fields data.
When these queries get considerably more complicated (many more tables, lots of criteria, multiple levels of "with" table alaises, etc...) the bottom query executes MUCH faster then the top one. Sometimes in the order of 20x-100x faster.
The Question is...
Is there some kind of query HINT or other SQL option that would tell the SQL Server to perform the same kind of optimization automatically, or other formats of this that would involve a cleaner aproach (trying to keep the format as much like query 1 as possible) ?
Note that the "Ranking" or secondary queries is just fluff for this example, the actual operations performed really don't matter too much.
This is sort of what I was hoping for (or similar but the idea is clear I hope). Remember this query below does not actually work.
DECLARE #DT int; SET #DT=20110717;
WITH LargeData AS (
SELECT * -- This is a MASSIVE table indexed on dt field
FROM mydata
WHERE dt=#DT
**OPTION (USE_TEMP_OR_HARDENED_OR_SOMETHING) -- EXAMPLE ONLY**
), Ordered AS (
SELECT TOP 10 *
, ROW_NUMBER() OVER (ORDER BY valuefield DESC) AS Rank_Number
FROM LargeData
)
SELECT * FROM Ordered
EDIT: Important follow up information!
If in your sub query you add
TOP 999999999 -- improves speed dramatically
Your query will behave in a similar fashion to using a temp table in a previous query. I found the execution times improved in almost the exact same fashion. WHICH IS FAR SIMPLIER then using a temp table and is basically what I was looking for.
However
TOP 100 PERCENT -- does NOT improve speed
Does NOT perform in the same fashion (you must use the static Number style TOP 999999999 )
Explanation:
From what I can tell from the actual execution plan of the query in both formats (original one with normal CTE's and one with each sub query having TOP 99999999)
The normal query joins everything together as if all the tables are in one massive query, which is what is expected. The filtering criteria is applied almost at the join points in the plan, which means many more rows are being evaluated and joined together all at once.
In the version with TOP 999999999, the actual execution plan clearly separates the sub querys from the main query in order to apply the TOP statements action, thus forcing creation of an in memory "Bitmap" of the sub query that is then joined to the main query. This appears to actually do exactly what I wanted, and in fact it may even be more efficient since servers with large ammounts of RAM will be able to do the query execution entirely in MEMORY without any disk IO. In my case we have 280 GB of RAM so well more then could ever really be used.
Not only can you use indexes on temp tables but they allow the use of statistics and the use of hints. I can find no refernce to being able to use the statistics in the documentation on CTEs and it says specifically you cann't use hints.
Temp tables are often the most performant way to go when you have a large data set when the choice is between temp tables and table variables even when you don't use indexes (possobly because it will use statistics to develop the plan) and I might suspect the implementation of the CTE is more like the table varaible than the temp table.
I think the best thing to do though is see how the excutionplans are different to determine if it is something that can be fixed.
What exactly is your objection to using the temp table when you know it performs better?
The problem is that in the first query SQL Server query optimizer is able to generate a query plan. In the second query a good query plan isn't able to be generated because you're inserting the values into a new temporary table. My guess is there is a full table scan going on somewhere that you're not seeing.
What you may want to do in the second query is insert the values into the #LargeData temporary table like you already do and then create a non-clustered index on the "valuefield" column. This might help to improve your performance.
It is quite possible that SQL is optimizing for the wrong value of the parameters.
There are a couple of options
Try using option(RECOMPILE). There is a cost to this as it recompiles the query every time but if different plans are needed it might be worth it.
You could also try using OPTION(OPTIMIZE FOR #DT=SomeRepresentatvieValue) The problem with this is you pick the wrong value.
See I Smell a Parameter! from The SQL Server Query Optimization Team blog

Selecting from a Large Table SQL 2005

I have a SQL table it has more than 1000000 rows, and I need to select with the query as you can see below:
SELECT DISTINCT TOP (200) COUNT(1) AS COUNT, KEYWORD
FROM QUERIES WITH(NOLOCK)
WHERE KEYWORD LIKE '%Something%'
GROUP BY KEYWORD ORDER BY 'COUNT' DESC
Could you please tell me how can I optimize it to speed up the execution process? Thank you for useful answers.
I'd first look at the execution plan to see how sql server is trying to access your data. Here is a link to just one of many articles on execution plan analysis.
Asking a question about SQL Server performance without providing a schema is a complete waste of everybody time. I'm going to answer a different question, which is one you should had been ask in the first place:
What schema should I use to
efficiently satisfy a query like
SELECT DISTINCT TOP (200) COUNT(1) AS
COUNT, KEYWORD FROM QUERIES WHERE
KEYWORD LIKE '%Something%'GROUP BY
KEYWORD ORDER BY 'COUNT' DESC when QUERIES table has over 1M rows?
The proper schema depend on the selectivity of KEYWORD. One possible design would be to normalize KEYWORD into a lookup table and have a narrow non-clustered index on the lookup id:
CREATE TABLE KEYWORDS (KeywordId INT NOT NULL IDENTITY(1,1) PRIMARY KEY,
Keyword VARCHAR(...) UNIQUE);
CREATE TABLE QUERIES (...,
KeywordId INT NOT NULL,
CONSTRAINT FK_KEYWORD
FOREIGN KEY KeywordId
REFERENCES KEYWORDS (KeywordId),
...);
CREATE INDEX ndxQueriesKeyword ON Queries (KeywordId);
If the number of distinct keyword is relatively low, the original query can be satisfied quickly by a scan of the Keywqord table followed by a nexted loop range scan of the ndxQueriesKeyword index, which is very narrow and therefore generates low IO.
As the number of distinct keyword increases, this approach may start showing problems due to the high number of range scans on the Queries table, and possible even due to the full scan on the Keywords table.
You may consider using a different WHERE clause, namely one LIKE 'Something%, which is SARGable and can leverage an index on KEYWORK, benefiting from a range reduction and a narrower scan than a full table scan.
If you are on Enterprise Edition you can consider adding an indexes view with the pre-computed aggregates:
CREATE VIEW vwQueryKeywords
WITH SCHEMABINDING
AS SELECT KEYWORD, COUNT_BIG(*) as COUNT
FROM dbo.QUERIES
GROUP BY KEYWORD;
CREATE CLUSTERED INDEX cdxQueryKeywords ON vwQueryKeywords(KEYWORD);
On EE the optimizer will consider the indexed view for the original query. On non-EE you will have to change the query to run against the view with the NOEXPAND hint:
SELECT KEYWORD, COUNT
FROM vwQueryKeywords WITH(NOEXPAND)
WHERE KEYWORD LIKE '%Something%';
Another completely different approach is to ditch the LIKE '%Something%' condition altogether in favor of fullt-text search:
SELECT DISTINCT TOP (200) COUNT(1) AS
COUNT, KEYWORD FROM QUERIES WHERE
CONTAINS (Keyword, Something)
GROUP BY
KEYWORD ORDER BY 'COUNT' DESC
Because the FT search is a reverse-index lookup, it may prove optimal over a traditional WHERE. The only issue is that you'll only be able to search for full words, since FT won't let you search partial matches the way LIKE does. Again, the actual mileage will vary based on Keyword data profile (ie. its statistics and distribution).
As Jeremy stated, you need to look at the execution plan and client statistics to see what is faster. However, a couple of suggestions. First, do you really need a prefixing wildcard on your search? I.e., LIKE '%Something%' will not be able to use an index whereas LIKE 'Something%' will. Second, you might try a CTE to see if will be faster. So, something like:
;With NumberedItems As
(
Select Keyword, Count(*) As [Count]
, ROW_NUMBER() OVER ( ORDER BY Keyword, Count(*) DESC ) As ItemRank
From Queries WITH (NOLOCK)
Where Keyword LIKE '%Something%'
Group By Keyword
)
Select Keyword, [Count]
From NumberedItems
Where ItemRank <= 200
It's rather hard to guess what may be causing the performance issues with just a query and no schema or execution plan. You should definitely read-up on them as all performance tuning of SQL queries is ultimately driven by the execution plan.
If you really want to delve into it, you can also read up on the query optimizer which attempts to execute your query using the most optimal plan. Understanding the optimizer is important to ensure you are taking full advantage of the indexes, etc. you have on the database. Microsoft also has several helpful documents such as this on troubleshooting performance issue.
For your particular case, the bottleneck is most likely in the WHERE clause. LIKE comparisons tend to be inefficient, especially when surrounded by percent signs as the query tends to be unable to take advantage of indexes on the column, etc. Depending on how you've stored data, full-text indexing may be a useful option, as that can frequently outperform LIKE '%SOMEVALUE%'.
If you can't use a full-text search engine from a third party, create an inverted index from your text periodically and search that instead. A naive implementation would beat your current strategy.
http://en.wikipedia.org/wiki/Inverted_index
Your query is not optimizable (without implementing some form of full-text indexing, itself expensive) because you have a leading wildcard in your keyword match. You would need to split the keywords out into separate column values (probably in a separate, related table) and search on an exact match or, at least, a match with the wildcard not at the beginning of the text.
Additionally the results you're getting may not be accurate if you have some keywords that are nested in others (eg "cart" will match a keyword search on "car", which is not what you want).