how to improve the performance on this type of Query? - sql

I have query that I am searching for a range of user accounts but every time I pass query I will be using multiple id number's first 5 digits and based on that I will searching. I wanted to know is there any other way to re-write this query for user Id range when we use more than 10 userIDs to search? Is there going to be huge performance hit with this kind of search in query?
example:
select A.col1,B.col2,B.col3
from table1 A,table2 B
where A.col2=B.col2
and (B.col_id like '12345%'
OR B.col_id like '47474%'
OR B.col_id like '59598%');
I am using Oracle11g.

Actually it is not important how many UserIDs you will pass to the query. The most considerable part is what is selectivity of your query. In other words: how many rows will return your query and how many rows are there in your tables. If the number of returned rows is relatively small then it is good idea to create an index on column B.col_id. There is also nothing bad in using OR condition. Basically each OR will add one more INDEX RANGE SCAN to the execution plan with final CONCATENATION (but you'd rather check your actual plan to be sure). If the total cost of all that operations are lower than full table scan then Oracle CBO will use your index. In other case if you select >=20-30% of data at once then full table scan is most likely to happen and you should even less be worried about OR because all data will be read and comparing each value with your multiple conditions won't add much overhead.

Generally the use of LIKE makes it impossible for Oracle to use indexes.
If the query is going to be reused, consider creating a synthetic column with the first 5 characters of COL_ID. Put a non-unique index on it. Put your search keys in a table and join that to that column.
There may be a way to do it with a functional index on the first 5 characters.

I don't know if the performance will be better or not, but another way to write this is with a union:
select A.col1,B.col2,B.col3
from table1 A,table2 B
where A.col2=B.col2
and (A.col_id like '12345%')
union all
select A.col1,B.col2,B.col3
from table1 A,table2 B
where A.col2=B.col2
and (A.col_id like '47474%') -- you did mean A.col_id again, right?
union all
select A.col1,B.col2,B.col3
from table1 A,table2 B
where A.col2=B.col2
and (A.col_id like '59598%'); -- and A.col_id here too?

Related

Does index still exists in these situations?

I have some questions about index.
First, if I use a index column in WITH clause, dose this column still works as index column in main query?
For example,
WITH TEST AS (
SELECT EMP_ID
FROM EMP_MASTER
)
SELECT *
FROM TEST
WHERE EMP_ID >= '2000'
'EMP_ID' in 'EMP_MASTER' table is PK and index for EMP_MASTER consists of EMP_ID.
In this situation, Does 'Index Scan' happen in main query?
Second, if I join two tables and then use two index columns from each table in WHERE, does 'Index Scan' happen?
For example,
SELECT *
FROM A, B
WHERE A.COL1 = B.COL1 AND
A.COL1 > 200 AND
B.COL1 > 100
Index for table A consists of 'COL1' and index for table B consists of 'COL1'.
In this situation, does 'Index Scan' happen in each table before table join?
If you give me some proper advice, I really appreciate that.
First, SQL is a declarative language, not a procedural language. That is, a SQL query describes the result set, not the specific processing. The SQL engine uses the optimizer to determine the best execution plan to generate the result set.
Second, Oracle has a reasonable optimizer.
Hence, Oracle will probably use the indexes in these situations. However, you should look at the execution plan to see what Oracle really does.
First, if I use a index column in WITH clause, dose this column still works as index column in main query?
Yes. A CTE (the WITH part) is a query just like any other - and if a query references a physical table column used by an index then the engine will use the index if it thinks it's a good idea.
In this situation, Does 'Index Scan' happen in main query?
We can't tell from the limitated information you've provided. An engine will scan or seek an index based on its heuristics about the distribution of data in the index (e.g. STATISTICS objects) and other information it has, such as cached query execution plans.
In this situation, does 'Index Scan' happen in each table before table join?
As it's a range query, it probably would make sense for an engine to use an index scan rather than an index seek - but it also could do a table-scan and ignore the index if the index isn't selective and specific enough. Also factor in query flags to force reading non-committed data (e.g. for performance and to avoid locking).

Is this index defined correctly for this join usage? (Postgres)

select
*
from
tbl1 as a
inner join
tbl2 as b on
tbl1.id=b.id
left join
tbl3 as c on
tbl2.id=tb3.parent_id and
tb3.some_col=2 and
tb3.attribute_id=3
In the example above:
If I want optimal performance on the join, should I set the index on tbl3 as so?
parent_id,
some_col,
attribute_id
The answer depends on the chosen join type.
If PostgreSQL chooses a nested loop or a merge outer join, your index is perfect.
If PostgreSQL chooses a hash outer join, the index won't help at all. In that case you need an index on (some_col, attribute_id).
Work with EXPLAIN to make the best choice for your case.
Note: If one of the conditions on some_col and attribute_id is not selective (doesn't filter out a significant number of rows), it is often better to omit that column in the index. In that case, it is better to get the benefit of a smaller index and more HOT updates.
My answer is "Maybe". I am speaking from experience with SQL Server, so someone please correct me if I am wrong and it is different in Postgres.
Your index looks fine for the most part. An issue that may arise is using the SELECT *. If tbl3 has more columns than what is defined in your index and you are querying those fields, they won't be in your index and the engine will have to do additional lookups outside that index.
Another thing would be based on the cardinality of your fields, meaning which are the most selective. If parent_id has a high cardinality, meaning very few duplicates, it could cause more reads against the index. However, if your lowest cardinality field is first and the db can quickly filter out huge chunks of data, that might be more efficient.
I have seen both work very well in SQL Server. SQL Server has even recommended indexes, I apply them, and then it recommends a different one based on field cardinality. Again, I am not familiar with the Postgres engine and I am just assuming these topics apply across both. If all else fails, create 3 indexes with different column order and see which one the engine likes the best.

Several layers of nested subqueries with Exists/In, best performance?

I'm working on some rather large queries for a search function. There are a number of different inputs and the queries are pretty big as a result. It's grown to where there are nested subqueries 2 layers deep. Performance has become an issue on the ones that will return a large dataset and likely have to sift through a massive load of records to do so. The ones that have less comparing to do perform fine, but some of these are getting pretty bad. The database is DB2 and has all of the necessary indexes, so that shouldn't be an issue. I'm wondering how to best write/rewrite these queries to perform as I'm not quite sure how the optimizer is going to handle it. I obviously can't dump the whole thing here, but here's an example:
Select A, B
from TableA
--A series of joins--
WHERE TableA.A IN (
Select C
from TableB
--A few joins--
WHERE TableB.C IN (
Select D from TableC
--More joins and conditionals--
)
)
There are also plenty of conditionals sprinkled throughout, the vast majority of which are simple equality. You get the idea. The subqueries do not provide any data to the initial query. They exist only to filter the results. A problem I ran into early on is that the backend is written to contain a number of partial query strings that get assembled into the final query (with 100+ possible combinations due to the search options, it simply isn't feasible to write a query for each), which has complicated the overall method a bit. I'm wondering if EXISTS instead of IN might help at one or both levels, or another bunch of joins instead of subqueries, or perhaps using WITH above the initial query for TableC, etc. I'm definitely looking to remove bottlenecks and would appreciate any feedback that folks might have on how to handle this.
I should probably also add that there are potential unions within both subqueries.
It would probably help to use inner joins instead.
Select A, B
from TableA
inner join TableB on TableA.A = TableB.C
inner join TableC on TableB.C = TableC.D
Databases were designed for joins, but the optimizer might not figure out that it can use an index for a sub-query. Instead it will probably try to run the sub-query, hold the results in memory, and then do a linear search to evaluate the IN operator for every record.
Now, you say that you have all of the necessary indexes. Consider this for a moment.
If one optional condition is TableC.E = 'E' and another optional condition is TableC.F = 'F',
then a query with both would need an index on fields TableC.E AND TableC.F. Many young programmers today think they can have one index on TableC.E and one index on TableC.F, and that's all they need. In fact, if you have both fields in the query, you need an index on both fields.
So, for 100+ combinations, "all of the necessary indexes" could require 100+ indexes.
Now an index on TableC.E, TableC.F could be use in a query with a TableC.E condition and no TableC.F condition, but could not be use when there is a TableC.F condition and no TableC.E condition.
Hundreds of indexes? What am I going to do?
In practice it's not that bad. Let's say you have N optional conditions which are either in the where clause or not. The number of combinations is 2 to the nth, or for hundreds of combinations, N is log2 of the number of combinations, which is between 6 and 10. Also, those log2 conditions are spread across three tables. Some databases support multiple table indexes, but I'm not sure DB2 does, so I'd stick with single table indexes.
So, what I am saying is, say for the TableC.E, and TableC.F example, it's not enough to have just the following indexes:
TableB ON C
TableC ON D
TableC ON E
TableC ON F
For one thing, the optimizer has to pick among which one of the last three indexes to use. Better would be to include the D field in the last two indexes, which gives us
TableB ON C
TableC ON D, E
TableC ON D, F
Here, if neither field E nor F is in the query, it can still index on D, but if either one is in the query, it can index on both D and one other field.
Now suppose you have an index for 10 fields which may or may not be in the query. Why ever have just one field in the index? Why not add other fields in descending order of likelihood of being in the query?
Consider that when planning your indexes.
I found out "IN" predicate is good for small subqueries and "EXISTS" for large subqueries.
Try to execute query with "EXISTS" predicate for large ones.
SELECT A, B
FROM TableA
WHERE EXISTS (
Select C
FROM TableB
WHERE TableB.C = TableA.A)

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.

SQLite - select expression is very slow

I'm experiencing some heavy performance-issues with a query in SQLite. Currently there are around 20000 entries in the table activity_tbl and about 40 in the table activity_data_tbl. I have an index for both of the columns used in the query below, but it doesn't seem to have any effect on the performance at all.
SELECT a._id, a.start_time + b.length AS time
FROM activity_tbl a INNER JOIN activity_data_tbl b
ON a.activity_data_id = b._data_id
WHERE time > ?
ORDER BY 2
LIMIT 1
As you can see, I select one column and a value created from adding two columns together. I guess this is what's causing the low performance, since the query is very fast if I just select a.start_time or b.length.
Do you guys have any suggestion for how I could optimize this?
Try putting an index on the time column. This should speed up the query
This query is not optimizable using indexes for the filter part since you are filtering and ordering on a calculated value. To optimize the query you will either need to filter on one of the actual table columns (starttime or length) or pre-compute the time values before querying.
The only place an index will help, and I assume you have one, is on b.data_id.
A compound index may help. According to its docs, SQLite tries to avoid to access the table, if the index has enough information. So if the engine did its homework it will recognize that the index is enough to compute the where clause value and spare some time. If it does not work, only the pre-computation will do.
If you are more often confronted with similar tasks, please read this: http://www.sqlite.org/rtree.html