Optimizing Oracle stored procedures - sql

I was recently tasked with optimizing some existing Oracle stored procedures. Each of the stored procedures query the database and generate an XML file output. One in particular was taking about 20 minutes to finish execution. Taking a look at it there were several nested loops and unnecessary queries. For example, rather than doing a
SELECT * from Employee e, Department d WHERE e.DEPT_ID = d.ID
--write data from query to XML
it was more like
FOR emp_rec in ( SELECT * from employee )
LOOP
SELECT * from Department WHERE id = emp_rec.DEPT_ID;
--write data from query to XML
END LOOP;
Changing all these cases to look more like the first option sped up the procedures immensely. My question is why? Why is doing a join in the select query quicker than manually combining the tables? What are the underlying processes?

Let's look at how the original version is likely to be processed.
FOR emp_rec in ( SELECT * from employee )
LOOP
SELECT * from Department WHERE id = emp_rec.DEPT_ID;
--write data from query to XML
END LOOP;
The loop query is likely to do a full table scan on employee. Then, for each row returned, it will execute the inner query. Assuming that id is the primary key of department, each execution of the query is likely to do a unique lookup using the primary key index.
Sounds great, right? Unique index lookups are usually the fastest way to get a single row (except for explicit lookup by ROWID). But think about what this is doing over multiple iterations of the loop. Presumably, every employee belongs to a department; every department has employees; and most or all departments have multiple employees.
So on multiple iterations of the loop, you're repeating the exact same work for the inner query multiple times. Yes, the data blocks may be cached so you don't have do repeat physical reads, but accessing data in the cache does have some CPU overhead, which can become very significant when the same blocks are accessed over and over again.
Furthermore, ultimately you will likely want every row in department at least once, and probably more than once. Since every single block in the table will need to be read, you're not really saving work by doing an index lookup -- you're adding work.
When you rewrite the loop as a single query, the optimizer is able to take this into account. One option it has would be to do a nested loop join driven by employee, which would be essentially the same as the explicit loop in PL/SQL (minus the context switching as pointed out by Mark). However, given the relationships between the two tables, and the lack of any filtering predicate, the optimizer will be able to tell that it's more efficient to simply full-scan both tables and do a merge or hash join. This actually results in fewer physical IOs (assuming a clean cache at the start of each execution) and much fewer logical IOs.

The "underlying processes" requires a big answer. I'll leave Tom Kyte to answer this one ;)

Related

Index to get row count of read-only (immutable) PostgreSQL table?

I have a script that runs several times a day, which records the row counts of several PostgreSQL tables.
Some of the tables though are read-only and never change. (No rows are added or removed, nor are any values changed.)
Is there a way I could quickly get the row count from PostgreSQL? Eg. Could I create an index on select count(*) from some_table;?
I'd prefer not to cache this in the script. If I were to cache in the script, I haven't found a reliably way to determine if a table has been changed since the last time the script has run.
Unfortunately, in postgresql SELECT COUNT(*) is often slower than mysql to which it often get's compared to.
You can use the following query as an alternative to SELECT COUNT(*).
SELECT reltuples FROM pg_class WHERE relname = 'mytable';
This is not always 100% upto date but for immutable tables it will be accurate every time. And instant. For very large tables the percentage error will be very small and thus well worth the massive saving in time.
If it does matter and the table does not contain nulls, you can use
SELECT COUNT(primary_key_column) FROM table
and this will be significantly faster than SELECT COUNT(*)

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.

minus vs delete where exist in oracle

I have a CREATE TABLE query which can be done using two methods (create as select statement for thousands/million records):
First method:
create table as select some data minus (select data from other table)
OR
first i should create the table as
create table as select .....
and then
delete from ..where exist.
I guess the second method is better.For which query the cost is less?Why is minus query not as fast as the second method?
EDIT:
I forgot to mention that the create statement has join from two tables as well.
The minus is slow probably because it needs to sort the tables on disk in order to compare them.
Try to rewrite the first query with NOT EXISTS instead of MINUS, it should be faster and will generate less REDO and UNDO (as a_horse_with_no_name mentioned). Of course, make sure that all the fields involved in the WHERE clauses are indexed!
The second one will write lots of records to disk and then remove them. This will in 9 of 10 cases take way longer then filtering what you write in to begin with.
So if the first one actually isn't faster we need more information about the tables and statements involved.

Does the order of fields in a WHERE clause affect performance in MySQL?

I have two indexed fields in a table - type and userid (individual indexes, not a composite).
types field values are very limited (let's say it is only 0 or 1), so 50% of table records have the same type. userid values, on the other hand, come from a much larger set, so the amount of records with the same userid is small.
Will any of these queries run faster than the other:
select * from table where type=1 and userid=5
select * from table where userid=5 and type=1
Also if both fields were not indexed, would it change the behavior?
SQL was designed to be a declarative language, not a procedural one. So the query optimizer should not consider the order of the where clause predicates in determining how to apply them.
I'm probably going to waaaay over-simplify the following discussion of an SQL query optimizer. I wrote one years ago, along these lines (it was tons of fun!). If you really want to dig into modern query optimization, see Dan Tow's SQL Tuning, from O'Reilly.
In a simple SQL query optimizer, the SQL statement first gets compiled into a tree of relational algebra operations. These operations each take one or more tables as input and produce another table as output. Scan is a sequential scan that reads a table in from the database. Sort produces a sorted table. Select produces a table whose rows are selected from another table according to some selection condition. Project produces a table with only certain columns of another table. Cross Product takes two tables and produces an output table composed of every conceivable pairing of their rows.
Confusingly, the SQL SELECT clause is compiled into a relational algebra Project, while the WHERE clause turns into a relational algebra Select. The FROM clause turns into one or more Joins, each taking two tables in and producing one table out. There are other relational algebra operations involving set union, intersection, difference, and membership, but let's keep this simple.
This tree really needs to be optimized. For example, if you have:
select E.name, D.name
from Employee E, Department D
where E.id = 123456 and E.dept_id = D.dept_id
with 5,000 employees in 500 departments, executing an unoptimized tree will blindly produce all possible combinations of one Employee and one Department (a Cross Product) and then Select out just the one combination that was needed. The Scan of Employee will produce a 5,000 record table, the Scan of Department will produce a 500 record table, the Cross Product of those two tables will produce a 2,500,000 record table, and the Select on E.id will take that 2,500,000 record table and discard all but one, the record that was wanted.
[Real query processors will try not to materialize all of these intermediate tables in memory of course.]
So the query optimizer walks the tree and applies various optimizations. One is to break up each Select into a chain of Selects, one for each of the original Select's top level conditions, the ones and-ed together. (This is called "conjunctive normal form".) Then the individual smaller Selects are moved around in the tree and merged with other relational algebra operations to form more efficient ones.
In the above example, the optimizer first pushes the Select on E.id = 123456 down below the expensive Cross Product operation. This means the Cross Product just produces 500 rows (one for each combination of that employee and one department). Then the top level Select for E.dept_id = D.dept_id filters out the 499 unwanted rows. Not bad.
If there's an an index on Employee's id field, then the optimizer can combine the Scan of Employee with the Select on E.id = 123456 to form a fast index Lookup. This means that only one Employee row is read into memory from disk instead of 5,000. Things are looking up.
The final major optimization is to take the Select on E.dept_id = D.dept_id and combine it with the Cross Product. This turns it into a relational algebra Equijoin operation. This doesn't do much by itself. But if there's an index on Department.dept_id, then the lower level sequential Scan of Department feeding the Equijoin can be turned into a very fast index Lookup of our one employee's Department record.
Lesser optimizations involve pushing Project operations down. If the top level of your query just needs E.name and D.name, and the conditions need E.id, E.dept_id, and D.dept_id, then the Scan operations don't have to build intermediate tables with all the other columns, saving space during the query execution. We've turned a horribly slow query into two index lookups and not much else.
Getting more towards the original question, let's say you've got:
select E.name
from Employee E
where E.age > 21 and E.state = 'Delaware'
The unoptimized relational algebra tree, when executed, would Scan in the 5,000 employees and produce, say, the 126 ones in Delaware who are older than 21. The query optimizer also has some rough idea of the values in the database. It might know that the E.state column has the 14 states that the company has locations in, and something about the E.age distributions. So first it sees if either field is indexed. If E.state is, it makes sense to use that index to just pick out the small number of employees the query processor suspects are in Delaware based on its last computed statistics. If only E.age is, the query processor likely decides that it's not worth it, since 96% of all employees are 22 and older. So if E.state is indexed, our query processor breaks the Select and merges the E.state = 'Delaware' with the Scan to turn it into a much more efficient Index Scan.
Let's say in this example that there are no indexes on E.state and E.age. The combined Select operation takes place after the sequential "Scan" of Employee. Does it make a difference which condition in the Select is done first? Probably not a great deal. The query processor might leave them in the original order in the SQL statement, or it might be a bit more sophisticated and look at the expected expense. From the statistics, it would again find that the E.state = 'Delaware' condition should be more highly selective, so it would reverse the conditions and do that first, so that there are only 126 E.age > 21 comparisons instead of 5,000. Or it might realize that string equality comparisons are much more expensive than integer compares and leave the order alone.
At any rate, all this is very complex and your syntactic condition order is very unlikely to make a difference. I wouldn't worry about it unless you have a real performance problem and your database vendor uses the condition order as a hint.
Most query optimizers use the order in which conditions appear as a hint. If everything else is equal, they will follow that order.
However, many things can override that:
the second field has an index and the first has not
there are statistics to suggest that field 2 is more selective
the second field is easier to search (varchar(max) vs int)
So (and this is true for all SQL optimization questions) unless you observe a performance issue, it's better to optimize for clarity, not for (imagined) performance.
It shouldn't in your small example. The query optimizer should do the right thing. You can check for sure by adding explain to the front of the query. MySQL will tell you how it's joining things together and how many rows it needs to search in order to do the join. For example:
explain select * from table where type=1 and userid=5
If they were not indexed it would probably change behavior.

Performance: Subquery or Joining

I got a little question about performance of a subquery / joining another table
INSERT
INTO Original.Person
(
PID, Name, Surname, SID
)
(
SELECT ma.PID_new , TBL.Name , ma.Surname, TBL.SID
FROM Copy.Person TBL , original.MATabelle MA
WHERE TBL.PID = p_PID_old
AND TBL.PID = MA.PID_old
);
This is my SQL, now this thing runs around 1 million times or more.
My question is what would be faster?
If I change TBL.SID to (Select new from helptable where old = tbl.sid)
OR
If I add the 'HelpTable' to the from and do the joining in the where?
edit1
Well, this script runs only as much as there r persons.
My program has 2 modules one that populates MaTabelle and one that transfers data. This program does merge 2 databases together and coz of this, sometimes the same Key is used.
Now I'm working on a solution that no duplicate Keys exists.
My solution is to make a 'HelpTable'. The owner of the key(SID) generates a new key and writes it into a 'HelpTable'. All other tables that use this key can read it from the 'HelpTable'.
edit2
Just got something in my mind:
if a table as a Key that can be null(foreignkey that is not linked)
then this won't work with the from or?
Modern RDBMs, including Oracle, optimize most joins and sub queries down to the same execution plan.
Therefore, I would go ahead and write your query in the way that is simplest for you and focus on ensuring that you've fully optimized your indexes.
If you provide your final query and your database schema, we might be able to offer detailed suggestions, including information regarding potential locking issues.
Edit
Here are some general tips that apply to your query:
For joins, ensure that you have an index on the columns that you are joining on. Be sure to apply an index to the joined columns in both tables. You might think you only need the index in one direction, but you should index both, since sometimes the database determines that it's better to join in the opposite direction.
For WHERE clauses, ensure that you have indexes on the columns mentioned in the WHERE.
For inserting many rows, it's best if you can insert them all in a single query.
For inserting on a table with a clustered index, it's best if you insert with incremental values for the clustered index so that the new rows are appended to the end of the data. This avoids rebuilding the index and often avoids locks on the existing records, which would slow down SELECT queries against existing rows. Basically, inserts become less painful to other users of the system.
Joining would be much faster than a subquery
The main difference betwen subquery and join is
subquery is faster when we have to retrieve data from large number of tables.Because it becomes tedious to join more tables.
join is faster to retrieve data from database when we have less number of tables.
Also, this joins vs subquery can give you some more info
Instead of focussing on whether to use join or subquery, I would focus on the necessity of doing 1,000,000 executions of that particular insert statement. Especially as Oracle's optimizer -as Marcus Adams already pointed out- will optimize and rewrite your statements under the covers to its most optimal form.
Are you populating MaTabelle 1,000,000 times with only a few rows and issue that statement? If yes, then the answer is to do it in one shot. Can you provide some more information on your process that is executing this statement so many times?
EDIT: You indicate that this insert statement is executed for every person. In that case the advice is to populate MATabelle first and then execute once:
INSERT
INTO Original.Person
(
PID, Name, Surname, SID
)
(
SELECT ma.PID_new , TBL.Name , ma.Surname, TBL.SID
FROM Copy.Person TBL , original.MATabelle MA
WHERE TBL.PID = MA.PID_old
);
Regards,
Rob.