Optimization by order of columns in select clause of SQL statement

Optimization by order of columns in select clause of SQL statement - sql

I was asked to optimize a SQL query in one of the interviews I attended. The table PRODUCTS structure is like this:
PRODUCT_NAME - Which has around unique 200 values repeated
STATE - Which has around 20 unique values repeated
COUNTRY - Which has around unique 5 values repeated
The table contains 1 million rows. I was given the below SQL statement and was asked to complete it. The SQL is to fetch all the products for a particular state.
SELECT _______
FROM PRODUCTS
WHERE STATE = 'CALIFORNIA'
My answer was below:
SELECT PRODUCT_NAME, STATE, COUNTRY
FROM PRODUCTS
WHERE STATE = 'CALIFORNIA'
The interviewer was not happy with the answer and later told me that the order of the columns in the select clause could have been used to optimize and I had failed to do it.
So does the order of the columns being used in the select statement have any significant improvement in efficiency of a select query. If so, how?

I cannot fathom what the interviewer is thinking or what type of database the interviewer is referring to.
Databases store data on data pages, which use a binary format and contain other information (such as null-flags and perhaps record ids and page ids and so on). Retrieving values for a record requires parsing the data page -- and this takes place regardless of the order of the columns being returned by the query.
Perhaps the confusion is with indexes. Some databases recommend ordering the columns in a multi-column index based on selectivity (i.e. the number of values). When all columns in the index are used for equality comparisons, then there might be some slight optimization. However, the ordering of the columns in indexes is usually influenced by other factors, based on the queries being optimized.
The only optimization I can readily think of is removing columns. If you know the state, there is no reason to return the state. And you probably intend for that state to be in the United States, so the country is irrelevant as well. There might be some optimization to using a constant ('California' as state), but it is hard to imagine anyone actually caring about such a nano improvement in performance on a query that reads much of a large table.

Related

Is it a good practice to store num_of_x column in SQL Tables?

Let's consider an example:
A database designed to store department information in a company:
The HR department has five employees.
There's two table: Department and Employees
It is a frequent use case that you wish to find out how many employees does department x have. There are two ways to get number of employees, one is keeping it in a column, the other is looking up the count in the employee table
My question is this:
Does it make sense to store a num_of_employees column in Department table?
Here's what my views are:
It'll be a bit of a headache to always keep the count value in the num_of_ columns synced to the actual number in the other table (requiring triggers on insertion, deletion)
In the case where only the number of employees is actually required: The query will be significantly faster (as it won't have to do a count(*) on the other table at all)
Now, the above was a rather simplified version of the real problem I have which is dozens of inter-connected table, with frequent querying use cases where I am asking, wherever X = (some value) in table 1, how many corresponding rows (via foreign key) does it have in table 2, table 3 and so on.
So, when is it a good practice to have num_of_x columns in a table? Does the above use-case count as a good situation where it should be used (or not)?

The number of employees in a department can easily be calculated using:
select count(*)
from employees e
where e.department_id = ?;
With an index on department_id, this should be pretty fast and always up-to-date.
Maintaining a count on a central table is faster for querying purposes. However, it requires having triggers for inserts, updates, and deletes on the `employees table -- triggers that slow down DML operations.
Under most circumstances, just use a query to calculate the value. If performance is an issue, consider more extreme solutions such as pre-calculating the value.

Does it make sense to store a num_of_employees column in Department table?
That's redundancy. Redundancy can have:
Benefits: Redundancy can be helpful when it's expensive to compute the value each time. This way you just get the value without any further delay.
Drawbacks: Redundancy keeps the same value repeated multiple times in the database. This could lead to discrepancies, or some values to be stale.
In your particular case I would think there benefits would be small (cheap to compute the value) with significant drawbacks (showing stale/wrong value). Considering this I would [personally] not use redundancy for this case.

What's an Efficient Alternative to LIMIT?

My question basically is: How can I tell my database to do a seq scan and STOP after the first match to my WHERE condition?
Assuming I want to find the first event of a certain type, I could write the following query:
select *
from installs
where country = 'China'
order by install_date
limit 1
The problem here is that according to order of operations the engine would scan all the table and generate a dataset that matches my filter, then sort this dataset (with an immense cost), and then return only the first row.
I could of course filter by specific dates, but let's assume I don't know the period to filter by - how can I optimize this type of query in Amazon Redshift (something in the where clause maybe)?

Redshift's general strategy is to do a lot of scanning, but parallelize it. Any case that involves getting a single row is not going to be ideal. That said, you can do four things:
1. Reduce scanning, to a point
If country will always be the field filtered on, set the sortkey for the table to a compound sortkey on country first.
2. Eliminate the need for sorting
A more efficient way to do ORDER BY x LIMIT 1 is often MAX.
Then try
SELECT *
FROM installs
WHERE pk = (
SELECT MAX(pk) -- or install_date, if install date is unique
FROM installs
WHERE country = 'China'
)
3. Tailor the selected columns between row oriented and columnar
Asking a columnar database like Redshift to select * incurs costs for each column. Try selecting only the columns you need.
4. Add more nodes, so each node does less scanning
(Make sure the data is not set to distribution style all)

If you remove the ORDER BY, then it can work efficiently.
The requirement to sort the results means it needs to examine all rows where the country is China, which is not efficient for returning one item.
The where country = 'China' clause is efficient if SORTKEY = country since it can skip over any storage blocks that do not contain the desired value. This will be highly efficient if there are very relatively few rows that match.
If you are frequently querying for one-row results, then it might be worth storing such information in a separate table for faster lookup. The value could be calculated every day, or every hour, as necessary.

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.

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.

MySQL - Selecting data from multiple tables all with same structure but different data

Ok, here is my dilemma I have a database set up with about 5 tables all with the exact same data structure. The data is separated in this manner for localization purposes and to split up a total of about 4.5 million records.
A majority of the time only one table is needed and all is well. However, sometimes data is needed from 2 or more of the tables and it needs to be sorted by a user defined column. This is where I am having problems.
data columns:
id, band_name, song_name, album_name, genre
MySQL statment:
SELECT * from us_music, de_music where `genre` = 'punk'
MySQL spits out this error:
#1052 - Column 'genre' in where clause is ambiguous
Obviously, I am doing this wrong. Anyone care to shed some light on this for me?

I think you're looking for the UNION clause, a la
(SELECT * from us_music where `genre` = 'punk')
UNION
(SELECT * from de_music where `genre` = 'punk')

It sounds like you'd be happer with a single table. The five having the same schema, and sometimes needing to be presented as if they came from one table point to putting it all in one table.
Add a new column which can be used to distinguish among the five languages (I'm assuming it's language that is different among the tables since you said it was for localization). Don't worry about having 4.5 million records. Any real database can handle that size no problem. Add the correct indexes, and you'll have no trouble dealing with them as a single table.

Any of the above answers are valid, or an alternative way is to expand the table name to include the database name as well - eg:
SELECT * from us_music, de_music where `us_music.genre` = 'punk' AND `de_music.genre` = 'punk'

The column is ambiguous because it appears in both tables you would need to specify the where (or sort) field fully such as us_music.genre or de_music.genre but you'd usually specify two tables if you were then going to join them together in some fashion. The structure your dealing with is occasionally referred to as a partitioned table although it's usually done to separate the dataset into distinct files as well rather than to just split the dataset arbitrarily. If you're in charge of the database structure and there's no good reason to partition the data then I'd build one big table with an extra "origin" field that contains a country code but you're probably doing it for legitimate performance reason.
Either use a union to join the tables you're interested in http://dev.mysql.com/doc/refman/5.0/en/union.html or by using the Merge database engine http://dev.mysql.com/doc/refman/5.1/en/merge-storage-engine.html.

Your original attempt to span both tables creates an implicit JOIN. This is frowned upon by most experienced SQL programmers because it separates the tables to be combined with the condition of how.
The UNION is a good solution for the tables as they are, but there should be no reason they can't be put into the one table with decent indexing. I've seen adding the correct index to a large table increase query speed by three orders of magnitude.

The union statement cause a deal time in huge data. It is good to perform the select in 2 steps:
select the id
then select the main table with it

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