What is the most efficient way to count rows in a table in SQLite? - sql

I've always just used "SELECT COUNT(1) FROM X" but perhaps this is not the most efficient. Any thoughts? Other options include SELECT COUNT(*) or perhaps getting the last inserted id if it is auto-incremented (and never deleted).
How about if I just want to know if there is anything in the table at all? (e.g., count > 0?)


The best way is to make sure that you run SELECT COUNT on a single column (SELECT COUNT(*) is slower) - but SELECT COUNT will always be the fastest way to get a count of things (the database optimizes the query internally).
If you check out the comments below, you can see arguments for why SELECT COUNT(1) is probably your best option.


To follow up on girasquid's answer, as a data point, I have a sqlite table with 2.3 million rows. Using select count(*) from table, it took over 3 seconds to count the rows. I also tried using SELECT rowid FROM table, (thinking that rowid is a default primary indexed key) but that was no faster. Then I made an index on one of the fields in the database (just an arbitrary field, but I chose an integer field because I knew from past experience that indexes on short fields can be very fast, I think because the index is stored a copy of the value in the index itself). SELECT my_short_field FROM table brought the time down to less than a second.


If you are sure (really sure) that you've never deleted any row from that table and your table has not been defined with the WITHOUT ROWID optimization you can have the number of rows by calling:
select max(RowId) from table;
Or if your table is a circular queue you could use something like
select MaxRowId - MinRowId + 1 from
(select max(RowId) as MaxRowId from table) JOIN
(select min(RowId) as MinRowId from table);
This is really really fast (milliseconds), but you must pay attention because sqlite says that row id is unique among all rows in the same table. SQLite does not declare that the row ids are and will be always consecutive numbers.


The fastest way to get row counts is directly from the table metadata, if any. Unfortunately, I can't find a reference for this kind of data being available in SQLite.
Failing that, any query of the type
SELECT COUNT(non-NULL constant value) FROM table
should optimize to avoid the need for a table, or even an index, scan. Ideally the engine will simply return the current number of rows known to be in the table from internal metadata. Failing that, it simply needs to know the number of entries in the index of any non-NULL column (the primary key index being the first place to look).
As soon as you introduce a column into the SELECT COUNT you are asking the engine to perform at least an index scan and possibly a table scan, and that will be slower.


I do not believe you will find a special method for this. However, you could do your select count on the primary key to be a little bit faster.


sp_spaceused 'table_name' (exclude single quote)
this will return the number of rows in the above table, this is the most efficient way i have come across yet.
it's more efficient than select Count(1) from 'table_name' (exclude single quote)
sp_spaceused can be used for any table, it's very helpful when the table is exceptionally big (hundreds of millions of rows), returns number of rows right a way, whereas 'select Count(1)' might take more than 10 seconds. Moreover, it does not need any column names/key field to consider.

Related

how to speed up a clustered index scan while selecting all fields on range of rows or all the rows

I have a table
Books(BookId, Name, ...... , PublishedYear)
I do have about 30 fields in my Books table, where BookId is the primary key (Identity column). I have about 2 million records for this table.
I know select * is evil performance killer..
I have a situation to select range of rows or all the rows having all the columns in it.
Select * from Books;
this query takes more than 2 seconds to scan through the data page and get all the records. On checking the execution it still uses the Clustered index scan.
Obviously 2 seconds my not be that bad, however when this table has to be joined with other tables which is executed in batch is taking time over 15 minutes (There are no duplicate records though on the final result at completion as the count is matching). The join criteria is pretty simple and yields no duplication.
Excluding this table alone has the batch execution completed in sub seconds.
Is there a way to optimize this having said that I will have to select all the columns :(
Thanks in advance.

I've just run a batch against my developer instance, one SELECT specifying all Columns and one using *. There is no evidence (nor should there) that there is any difference aside from the raw parsing of my input. If I remember correctly, that old saying really means: Do not SELECT columns you are not using, they use up resources without benefit.
When you try to improve performance in your code, always check your assumptions, they might only apply to some older version (of sql server etc) or other method.

Index on VARCHAR column

I have a table of 32,589 rows, and one of the columns is called 'Location' and is a Varchar(40) column type. The column holds a location, which is actually a suburb, all uppercase text.
A function that uses this table does a:
IF EXISTS(SELECT * FROM MyTable WHERE Location = 'A Suburb')
...
Would it be beneficial to add an index to this column, for efficiency? This is more a read-only table, so not much edits or inserts except for maintanance.

Without an index SQL Server will have to perform a table scan to find the first instance of the location you're looking for. You might get lucky and have the value be in one of the first few rows, but it could be at row 32,000, which would be a waste of time. Adding an index only takes a few second and you'll probably see a big performance gain.

I concur with #Brian Shamblen answer.
Also, try using TOP 1 in the inner select
IF EXISTS(SELECT TOP 1 * FROM MyTable WHERE Location = 'A Suburb')
You don't have to select all the records matching your criteria for EXISTS, one is enough.

An opportunistic approach to performance tuning is usually a bad idea.
To answer the specific question - if your function is using location in a where clause, and the table has more than a few hundred rows, and the values in the location column are not all identical, creating an index will speed up your function.
Whether you notice any difference is hard to say - there may be much bigger performance problems lurking in the database, and you might be fixing the wrong problem.

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.

Slow distinct query in SQL Server over large dataset

We're using SQL Server 2005 to track a fair amount of constantly incoming data (5-15 updates per second). We noticed after it has been in production for a couple months that one of the tables has started to take an obscene amount of time to query.
The table has 3 columns:
id -- autonumber (clustered)
typeUUID -- GUID generated before the insert happens; used to group the types together
typeName -- The type name (duh...)
One of the queries we run is a distinct on the typeName field:
SELECT DISTINCT [typeName] FROM [types] WITH (nolock);
The typeName field has a non-clusted, non-unique ascending index on it. The table contains approximately 200M records at the moment. When we run this query, the query took 5m 58s to return! Perhaps we're not understanding how the indexes work... But I didn't think we mis-understood them that much.
To test this a little further, we ran the following query:
SELECT DISTINCT [typeName] FROM (SELECT TOP 1000000 [typeName] FROM [types] WITH (nolock)) AS [subtbl]
This query returns in about 10 seconds, as I would expect, it's scanning the table.
Is there something we're missing here? Why does the first query take so long?
Edit: Ah, my apologies, the first query returns 76 records, thank you ninesided.
Follow up: Thank you all for your answers, it makes more sense to me now (I don't know why it didn't before...). Without an index, it's doing a table scan across 200M rows, with an index, it's doing an index scan across 200M rows...
SQL Server does prefer the index, and it does give a little bit of a performance boost, but nothing to be excited about. Rebuilding the index did take the query time down to just over 3m instead of 6m, an improvement, but not enough. I'm just going to recommend to my boss that we normalize the table structure.
Once again, thank you all for your help!!

You do misunderstand the index. Even if it did use the index it would still do an index scan across 200M entries. This is going to take a long time, plus the time it takes to do the DISTINCT (causes a sort) and it's a bad thing to run. Seeing a DISTINCT in a query always raises a red flag and causes me to double check the query. In this case, perhaps you have a normalization issue?

There is an issue with the SQL Server optimizer when using the DISTINCT keyword. The solution was to force it to keep the same query plan by breaking out the distinct query separately.
So we took queries such as:
SELECT DISTINCT [typeName] FROM [types] WITH (nolock);
and break it up into the following:
SELECT typeName INTO #tempTable1 FROM types WITH (NOLOCK)
SELECT DISTINCT typeName FROM #tempTable1
Another way to get around it is to use a GROUP BY, which gets a different optimization plan.

I doubt SQL Server will even try to use the index, it'd have to do practically the same amount of work (given the narrow table), reading all 200M rows regardless of whether it looks at the table or the index. If the index on typeName was clustered it may reduce the time taken as it shouldn't need to sort before grouping.
If the cardinality of your types is low, how about maintaining a summary table which holds the list of distinct type values? A trigger on insert/update of the main table would do a check on the summary table and insert a new record when a new type is found.

As others have already pointed out - when you do a SELECT DISTINCT (typename) over your table, you'll end up with a full table scan no matter what.
So it's really a matter of limiting the number of rows that need to be scanned.
The question is: what do you need your DISTINCT typenames for? And how many of your 200M rows are distinct? Do you have only a handful (a few hundred at most) distinct typenames??
If so - you could have a separate table DISTINCT_TYPENAMES or something and fill those initially by doing a full table scan, and then on inserting new rows to the main table, just always check whether their typename is already in DISTINCT_TYPENAMES, and if not, add it.
That way, you'd have a separate, small table with just the distinct TypeName entries, which would be lightning fast to query and/or to display.
Marc

A looping approach should use multiple seeks (but loses some parallelism). It might be worth a try for cases with relatively few distinct values compared to the total number of rows (low cardinality).
Idea was from this question:
select typeName into #Result from Types where 1=0;
declare #t varchar(100) = (select min(typeName) from Types);
while #t is not null
begin
set #t = (select top 1 typeName from Types where typeName > #t order by typeName);
if (#t is not null)
insert into #Result values (#t);
end
select * from #Result;
And looks like there are also some other methods (notably the recursive CTE #Paul White):
different-ways-to-find-distinct-values-faster-methods
sqlservercentral Topic873124-338-5

My first thought is statistics. To find last updated:
SELECT
name AS index_name,
STATS_DATE(object_id, index_id) AS statistics_update_date
FROM
sys.indexes
WHERE
object_id = OBJECT_ID('MyTable');
Edit: Stats are updated when indexes are rebuilt, which I see are not maintained
My second thought is that is the index still there? The TOP query should still use an index.
I've just tested on one of my tables with 57 million rows and both use the index.

An indexed view can make this faster.
create view alltypes
with schemabinding as
select typename, count_big(*) as kount
from dbo.types
group by typename
create unique clustered index idx
on alltypes (typename)
The work to keep the view up to date on each change to the base table should be moderate (depending on your application, of course -- my point is that it doesn't have to scan the whole table each time or do anything insanely expensive like that.)
Alternatively you could make a small table holding all values:
select distinct typename
into alltypes
from types
alter table alltypes
add primary key (typename)
alter table types add foreign key (typename) references alltypes
The foreign key will make sure that all values used appear in the parent alltypes table. The trouble is in ensuring that alltypes does not contain values not used in the child types table.

I should try something like this:
SELECT typeName FROM [types] WITH (nolock)
group by typeName;
And like other i would say you need to normalize that column.

An index helps you quickly find a row. But you're asking the database to list all unique types for the entire table. An index can't help with that.
You could run a nightly job which runs the query and stores it in a different table. If you require up-to-date data, you could store the last ID included in the nightly scan, and combine the results:
select type
from nightlyscan
union
select distinct type
from verybigtable
where rowid > lastscannedid
Another option is to normalize the big table into two tables:
talbe1: id, guid, typeid
type table: typeid, typename
This would be very beneficial if the number of types was relatively small.

I could be missing something but would it be more efficient if an overhead on load to create a view with distinct values and query that instead?
This would give almost instant responses to the select if the result set is significantly smaller with the overhead over populating it on each write though given the nature of the view that might be trivial in itself.
It does ask the question how many writes compared to how often you want the distinct and the importance of the speed when you do.

What's the most efficient way to check the presence of a row in a table?

Say I want to check if a record in a MySQL table exists. I'd run a query, check the number of rows returned. If 0 rows do this, otherwise do that.
SELECT * FROM table WHERE id=5
SELECT id FROM table WHERE id=5
Is there any difference at all between these two queries? Is effort spent in returning every column, or is effort spent in filtering out the columns we don't care about?
SELECT COUNT(*) FROM table WHERE id=5
Is a whole new question. Would the server grab all the values and then count the values (harder than usual), or would it not bother grabbing anything and just increment a variable each time it finds a match (easier than usual)?
I think I'm making a lot of false assumptions about how MySQL works, but that's the meat of the question! Where am I wrong? Educate me, Stack Overflow!

Optimizers are pretty smart (generally). They typically only grab what they need so I'd go with:
SELECT COUNT(1) FROM mytable WHERE id = 5

The most explicit way would be
SELECT WHEN EXISTS (SELECT 1 FROM table WHERE id = 5) THEN 1 ELSE 0 END
If there is an index on (or starting with) id, it will only search, with maximum efficiency, for the first entry in the index it can find with that value. It won't read the record.
If you SELECT COUNT(*) (or COUNT anything else) it will, under the same circumstances, count the index entries, but not read the records.
If you SELECT *, it will read all the records.

Limit your results to at most one row by appending LIMIT 1, if all you want to do is check the presence of a record.
SELECT id FROM table WHERE id=5 LIMIT 1
This will definitely ensure that no more than one row is returned or processed. In my experience, LIMIT 1 (or TOP 1 depending in the DB) to check for existence of a row makes a big difference in terms of performance for large tables.
EDIT: I think I misread your question, but I'll leave my answer here anyway if it's of any help.

I would think this
SELECT null FROM table WHERE id = 5 LIMIT 1;
would be faster than this
SELECT 1 FROM table WHERE id = 5 LIMIT 1;
but the timer says the winner is "SELECT 1".

For the first two queries, most people will generally say, always specify exactly what you need and leave the rest. Effort isn't all specific as bandwidth could be spent in returning data that you aren't even going to do anything with.
As for the previous answer will do for your result set, unless you're dealing with a language that supports affected rows. This can sometimes work when getting data to collect information on how many rows were returned in the last query. You'll need to look at your interface documentation as to how to get that information.

The difference between your 3 queries depends on how you've built your index. Only returning the primary key is likely to be faster as MySQL will have your index in memory, and not have to hit disk. Adding the LIMIT 1 is also a good trick that will speed up the optimizer significantly in early 5.0.x branches and earlier.
try EXPLAIN SELECT id FROM table WHERE id=5 and check the Extras column for the presence of USING INDEX. If its there, then you're query is coming straight from the index, and is going to be much faster.