Slow distinct query in SQL Server over large dataset - sql

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.

Related

Poor performance of SQL query with Table Variable or User Defined Type

I have a SELECT query on a view, that contains 500.000+ rows. Let's keep it simple:
SELECT * FROM dbo.Document WHERE MemberID = 578310
The query runs fast, ~0s
Let's rewrite it to work with the set of values, which reflects my needs more:
SELECT * FROM dbo.Document WHERE MemberID IN (578310)
This is same fast, ~0s
But now, the set is of IDs needs to be variable; let's define it as:
DECLARE #AuthorizedMembers TABLE
(
MemberID BIGINT NOT NULL PRIMARY KEY, --primary key
UNIQUE NONCLUSTERED (MemberID) -- and index, as if it could help...
);
INSERT INTO #AuthorizedMembers SELECT 578310
The set contains the same, one value but is a table variable now. The performance of such query drops to 2s, and in more complicated ones go as high as 25s and more, while with a fixed id it stays around ~0s.
SELECT *
FROM dbo.Document
WHERE MemberID IN (SELECT MemberID FROM #AuthorizedMembers)
is the same bad as:
SELECT *
FROM dbo.Document
WHERE EXISTS (SELECT MemberID
FROM #AuthorizedMembers
WHERE [#AuthorizedMembers].MemberID = Document.MemberID)
or as bad as this:
SELECT *
FROM dbo.Document
INNER JOIN #AuthorizedMembers AS AM ON AM.MemberID = Document.MemberID
The performance is same for all the above and always much worse than the one with a fixed value.
The dynamic SQL comes with help easily, so creating an nvarchar like (id1,id2,id3) and building a fixed query with it keeps my query times ~0s. But I would like to avoid using Dynamic SQL as much as possible and if I do, I would like to keep it always the same string, regardless the values (using parameters - which above method does not allow).
Any ideas how to get the performance of the table variable similar to a fixed array of values or avoid building a different dynamic SQL code for each run?
P.S. I have tried the above with a user defined type with same results
Edit:
The results with a temporary table, defined as:
CREATE TABLE #AuthorizedMembers
(
MemberID BIGINT NOT NULL PRIMARY KEY
);
INSERT INTO #AuthorizedMembers SELECT 578310
have improved the execution time up to 3 times. (13s -> 4s). Which is still significantly higher than dynamic SQL <1s.
Your options:
Use a temporary table instead of a TABLE variable
If you insist on using a TABLE variable, add OPTION(RECOMPILE) at the end of your query
Explanation:
When the compiler compiles your statement, the TABLE variable has no rows in it and therefore doesn't have the proper cardinalities. This results in an inefficient execution plan. OPTION(RECOMPILE) forces the statement to be recompiled when it is run. At that point the TABLE variable has rows in it and the compiler has better cardinalities to produce an execution plan.
The general rule of thumb is to use temporary tables when operating on large datasets and table variables for small datasets with frequent updates. Personally I only very rarely use TABLE variables because they generally perform poorly.
I can recommend this answer on the question "What's the difference between temporary tables and table variables in SQL Server?" if you want an in-depth analysis on the differences.

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.

Query with a UNION sub-query takes a very long time

I've been having an odd problem with some queries that depend on a sub query. They run lightning fast, until I use a UNION statement in the sub query. Then they run endlessly, I've given after 10 minutes. The scenario I'm describing now isn't the original one I started with, but I think it cuts out a lot of possible problems yet yields the same problem. So even though it's a pointless query, bear with me!
I have a table:
tblUser - 100,000 rows
tblFavourites - 200,000 rows
If I execute:
SELECT COUNT(*)
FROM tblFavourites
WHERE userID NOT IN (SELECT uid FROM tblUser);
… then it runs in under a second. However, if I modify it so that the sub query has a UNION, it will run for at least 10 minutes (before I give up!)
SELECT COUNT(*)
FROM tblFavourites
WHERE userID NOT IN (SELECT uid FROM tblUser UNION SELECT uid FROM tblUser);
A pointless change, but it should yield the same result and I don't see why it should take any longer?
Putting the sub-query into a view and calling that instead has the same effect.
Any ideas why this would be? I'm using SQL Azure.
Problem solved. See my answer below.
UNION generate unique values, so the DBMS engine makes sorts.
You can use safely UNION ALL in this case.
UNION is really doing a DISTINCT on all fields in the combined data set. It filters out dupes in the final results.
Is Uid indexed? If not it may take a long time as the query engine:
Generates the first result set
Generates the second result set
Filters out all the dupes (which is half the records) in a hash table
If duplicates aren't a concern (and using IN means they won't be) then use UNION ALL which removes the expensive Sort/Filter step.
UNION's are usually implemented via temporary in-memory tables. You're essentially copying your tblUser two times into memory, WITH NO INDEX. Then every row in tblFavourites incur a complete table scan over 200,000 rows - that's 200Kx200K=40 billion double-row scans (because the query engine must get the uid from both table rows)
If your tblUser has an index on uid (which is definitely true because all tables in SQL Azure must have a clustered index), then each row in tblFavourites incurs a very fast index lookup, resulting in only 200Kxlog(100K) =200Kx17 = 200K row scans, each with 17 b-tree index comparisons (which is much faster than reading the uid from a row on a data page), so it should equate to roughly 200Kx(3-4) or 1 million double-row scans. I believe newer versions of SQL server may also build a temp hash table containing just the uid's, so essentially it gets down to 200K row scans (assuming hash table lookup to be trivial).
You should also generate your query plan to check.
Essentially, the non-UNION query runs around 500,000 times faster if tblUser has an index (should be on SQL Azure).
It turns out the problem was due to one of the indexes ... tblFavourites contained two foreign keys to the primary key (uid) in tblUser:
userId
otherUserId
both columns had the same definition and same indexes, but I discovered that swapping userId for otherUserId in the original query solved the problem.
I ran:
ALTER INDEX ALL ON tblFavourites REBUILD
... and the problem went away. The query now executes almost instantly.
I don't know too much about what goes on behind the scenes in Sql Server/Azure ... but I can only imagine that it was a damaged index or something? I update statistics frequently, but that had no effect.
Thanks!
---- UPDATE
The above was not fully correct. It did fix the problem for around 20 minutes, then it returned. I have been in touch with Microsoft support for several days and it seems the problem is to do with the tempDB. They are working on a solution at their end.
I just ran into this problem. I have about 1million rows to go through and then I realized that some of my IDs were in another table, so I unioned to get the same information in one "NOT EXISTS." I went from the query taking about 7 sec to processing only 5000 rows after a minute or so. This seemed to help. I absolutely hate the solution, but I've tried a multitude of things that all end up w/the same extremely slow execution plan. This one got me what I needed in about 18 sec.
DECLARE #PIDS TABLE ([PID] [INT] PRIMARY KEY)
INSERT INTO #PIDS SELECT DISTINCT [ID] FROM [STAGE_TABLE] WITH(NOLOCK)
INSERT INTO #PIDS SELECT DISTINCT [OTHERID] FROM [PRODUCTION_TABLE] WITH(NOLOCK)
WHERE NOT EXISTS(SELECT [PID] FROM #PIDS WHERE [PID] = [OTHERID]
SELECT (columns needed)
FROM [ORDER_HEADER] [OH] WITH(NOLOCK)
INNER JOIN #PIDS ON [OH].[SOME_ID] = [PID]
(And yes I tried "WHERE EXISTS IN..." for the final select... inner join was faster)
Please let me say again, I personaly feel this is really ugly, but I actually use this join twice in my proc, so it's going to save me time in the long run. Hope this helps.
Doesn't it make more sense to rephrase the questions from
"UserIds that aren't on the combined list of all the Ids that apper in this table and/or that table"
to
"UserIds that aren't on this table AND aren't on that table either
SELECT COUNT(*)
FROM tblFavourites
WHERE userID NOT IN (SELECT uid FROM tblUser)
AND userID NOT IN (SELECT uid FROM tblUser);

SQL WHERE ID IN (id1, id2, ..., idn)

I need to write a query to retrieve a big list of ids.
We do support many backends (MySQL, Firebird, SQLServer, Oracle, PostgreSQL ...) so I need to write a standard SQL.
The size of the id set could be big, the query would be generated programmatically. So, what is the best approach?
1) Writing a query using IN
SELECT * FROM TABLE WHERE ID IN (id1, id2, ..., idn)
My question here is. What happens if n is very big? Also, what about performance?
2) Writing a query using OR
SELECT * FROM TABLE WHERE ID = id1 OR ID = id2 OR ... OR ID = idn
I think that this approach does not have n limit, but what about performance if n is very big?
3) Writing a programmatic solution:
foreach (var id in myIdList)
{
var item = GetItemByQuery("SELECT * FROM TABLE WHERE ID = " + id);
myObjectList.Add(item);
}
We experienced some problems with this approach when the database server is queried over the network. Normally is better to do one query that retrieve all results versus making a lot of small queries. Maybe I'm wrong.
What would be a correct solution for this problem?
Option 1 is the only good solution.
Why?
Option 2 does the same but you repeat the column name lots of times; additionally the SQL engine doesn't immediately know that you want to check if the value is one of the values in a fixed list. However, a good SQL engine could optimize it to have equal performance like with IN. There's still the readability issue though...
Option 3 is simply horrible performance-wise. It sends a query every loop and hammers the database with small queries. It also prevents it from using any optimizations for "value is one of those in a given list"
An alternative approach might be to use another table to contain id values. This other table can then be inner joined on your TABLE to constrain returned rows. This will have the major advantage that you won't need dynamic SQL (problematic at the best of times), and you won't have an infinitely long IN clause.
You would truncate this other table, insert your large number of rows, then perhaps create an index to aid the join performance. It would also let you detach the accumulation of these rows from the retrieval of data, perhaps giving you more options to tune performance.
Update: Although you could use a temporary table, I did not mean to imply that you must or even should. A permanent table used for temporary data is a common solution with merits beyond that described here.
What Ed Guiness suggested is really a performance booster , I had a query like this
select * from table where id in (id1,id2.........long list)
what i did :
DECLARE #temp table(
ID int
)
insert into #temp
select * from dbo.fnSplitter('#idlist#')
Then inner joined the temp with main table :
select * from table inner join temp on temp.id = table.id
And performance improved drastically.
First option is definitely the best option.
SELECT * FROM TABLE WHERE ID IN (id1, id2, ..., idn)
However considering that the list of ids is very huge, say millions, you should consider chunk sizes like below:
Divide you list of Ids into chunks of fixed number, say 100
Chunk size should be decided based upon the memory size of your server
Suppose you have 10000 Ids, you will have 10000/100 = 100 chunks
Process one chunk at a time resulting in 100 database calls for select
Why should you divide into chunks?
You will never get memory overflow exception which is very common in scenarios like yours.
You will have optimized number of database calls resulting in better performance.
It has always worked like charm for me. Hope it would work for my fellow developers as well :)
Doing the SELECT * FROM MyTable where id in () command on an Azure SQL table with 500 million records resulted in a wait time of > 7min!
Doing this instead returned results immediately:
select b.id, a.* from MyTable a
join (values (250000), (2500001), (2600000)) as b(id)
ON a.id = b.id
Use a join.
In most database systems, IN (val1, val2, …) and a series of OR are optimized to the same plan.
The third way would be importing the list of values into a temporary table and join it which is more efficient in most systems, if there are lots of values.
You may want to read this articles:
Passing parameters in MySQL: IN list vs. temporary table
I think you mean SqlServer but on Oracle you have a hard limit how many IN elements you can specify: 1000.
Sample 3 would be the worst performer out of them all because you are hitting up the database countless times for no apparent reason.
Loading the data into a temp table and then joining on that would be by far the fastest. After that the IN should work slightly faster than the group of ORs.
For 1st option
Add IDs into temp table and add inner join with main table.
CREATE TABLE #temp (column int)
INSERT INTO #temp (column)
SELECT t.column1 FROM (VALUES (1),(2),(3),...(10000)) AS t(column1)
Try this
SELECT Position_ID , Position_Name
FROM
position
WHERE Position_ID IN (6 ,7 ,8)
ORDER BY Position_Name

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

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.