I'm trying to run this query:
SELECT
COUNT(events.event_id) AS total_events,
COUNT(matches.fight_id) AS total_matches,
COUNT(players.fighter_id) AS total_players,
COUNT(DISTINCT events.organization) AS total_organizations,
COUNT(DISTINCT players.country) AS total_countries
FROM
events, matches, players
These are table details:
Events = 21k
Players = 90k
Matches = 155k
All of those are uniques, so the query's first 3 things will be those numbers. The other two values should be total_organizations, where the organization column is in the events (should return couple hundred), and total_countries should count distinct countries using country column in players table (also couple hundred).
All three of those ID columns are unique and indexed.
This query as it stands now takes forever. I never even have patience to see it complete. Is there a faster way of doing this?
Also, I need this to load these results on every page load, so should I just put this query in some hidden file, and set a cron job to run every midnight or something and populate a "totals" table or something so I can retrieve it from that table quickly?
Thanks!
First, remove the unnecessary join here; it's preventing most (if not all) of your indexes from being used. You want three different queries:
SELECT
COUNT(events.event_id) AS total_events,
COUNT(DISTINCT events.organization) AS total_organizations
FROM
events;
SELECT
COUNT(matches.fight_id) AS total_matches
FROM
matches;
SELECT
COUNT(players.fighter_id) AS total_players,
COUNT(DISTINCT players.country) AS total_countries
FROM
players;
This should go a long way to improving the performance of these queries.
Now, consider adding these indexes:
CREATE INDEX "events_organization" ON events (organization);
CREATE INDEX "players_country" ON events (country);
Compare the EXPLAIN SELECT ... results before and after adding these indexes. They might help and they might not.
Note that if you are using the InnoDB storage engine then all table rows will be visited anyway, to enforce transactional isolation. In this case, indexes will only be used to determine which table rows to visit. Since you are counting the entire table, the indexes will not be used at all.
If you are using MyISAM, which does not fully support MVCC, then COUNT() queries should be able to execute using only index cardinality, which will result in nearly instant results. This is possible because transactions are not supported on MyISAM, which means that isolation becomes a non-issue.
So if you are using InnoDB, then you may wind up having to use a cronjob to create a cache of this data anyway.
Related
I have 3 tables. All 3 tables have approximately 2 million rows. Everyday 10,000-100,000 new entries are entered. It takes approximately 10 seconds to finish the sql statement below. Is there a way to make this sql statement faster?
SELECT customers.name
FROM customers
INNER JOIN hotels ON hotels.cus_id = customers.cus_id
INNER JOIN bookings ON bookings.book_id = customers.book_id
WHERE customers.gender = 0 AND
customers.cus_id = 3
LIMIT 25 OFFSET 1;
Of course this statement works fine, but its slow. Is there a better way to write this code?
All database servers have a form of an optimization engine that is going to determine how best to grab the data you want. With a simple query such as the select you showed, there isn't going to be any way to greatly improve performance within the SQL. As others have said sub-queries won't helps as that will get optimized into the same plan as joins.
Reduce the number of columns, add indexes, beef up the server if that's an option.
Consider caching. I'm not a mysql expert but found this article interesting and worth a skim. https://www.percona.com/blog/2011/04/04/mysql-caching-methods-and-tips/
Look at the section on summary tables and consider if that would be appropriate. Does pulling every hotel, customer, and booking need to be up-to-the-minute or would inserting this into a summary table once an hour be fine?
A subquery don't help but a proper index can improve the performance so be sure you have proper index
create index idx1 on customers(gender , cus_id,book_id, name )
create index idex2 on hotels(cus_id)
create index idex3 on hotels(book_id)
I find it a bit hard to believe that this is related to a real problem. As written, I would expect this to return the same customer name over and over.
I would recommend the following indexes:
customers(cus_id, gender, book_id, name)
hotels(cus_id)
bookings(book_id)
It is really weird that bookings are not to a hotel.
First, these indexes cover the query, so the data pages don't need to be accessed. The logic is to start with the where clause and use those columns first. Then add additional columns from the on and select clauses.
Only one column is used for hotels and bookings, so those indexes are trivial.
The use of OFFSET without ORDER BY is quite suspicious. The result set is in indeterminate order anyway, so there is no reason to skip the nominally "first" value.
I had a table that is holding a domain and id
the query is
select distinct domain
from user
where id = '1'
the index is using the order idx_domain_id is faster than idx_id_domain
if the order of the execution is
(FROM clause,WHERE clause,GROUP BY clause,HAVING clause,SELECT
clause,ORDER BY clause)
then the query should be faster if it use the sorted where columns than the select one.
at 15:00 to 17:00 it show the same query i am working on
https://serversforhackers.com/laravel-perf/mysql-indexing-three
the table has a 4.6 million row.
time using idx_domain_id
time after change the order
This is your query:
select distinct first_name
from user
where id = '1';
You are observing that user(first_name, id) is faster than user(id, firstname).
Why might this be the case? First, this could simply be an artifact of how your are doing the timing. If your table is really small (i.e. the data fits on a single data page), then indexes are generally not very useful for improving performance.
Second, if you are only running the queries once, then the first time you run the query, you might have a "cold cache". The second time, the data is already stored in memory, so it runs faster.
Other issues can come up as well. You don't specify what the timings are. Small differences can be due to noise and might be meaningless.
You don't provide enough information to give a more definitive explanation. That would include:
Repeated timings run on cold caches.
Size information on the table and the number of matching rows.
Layout information, particularly the type of id.
Explain plans for the two queries.
select distinct domain
from user
where id = '1'
Since id is the PRIMARY KEY, there is at most one row involved. Hence, the keyword DISTINCT is useless.
And the most useful index is what you already have, PRIMARY KEY(id). It will drill down the BTree to find id='1' and deliver the value of domain that is sitting right there.
On the other hand, consider
select distinct domain
from user
where something_else = '1'
Now, the obvious index is INDEX(something_else, domain). This is optimal for the WHERE clause, and it is "covering" (meaning that all the columns needed by the query exist in the index). Swapping the columns in the index will be slower. Meanwhile, since there could be multiple rows, DISTINCT means something. However, it is not the logical thing to use.
Concerning your title question (order of columns): The = columns in the WHERE clause should come first. (More details in the link below.)
DISTINCT means to gather all the rows, then de-duplicate them. Why go to that much effort when this gives the same answer:
select domain
from user
where something_else = '1'
LIMIT 1
This hits only one row, not all the 1s.
Read my Indexing Cookbook.
(And, yes, Gordon has a lot of good points.)
SELECT t1.*
FROM
( SELECT key_a,key_b,MAX(date) as date
FROM large_table
WHERE date <= **20150126**
group by key_a,key_b
) AS t2
JOIN large_table AS t1 USING(key_a,key_b ,date)
large_table = 1,223,001,206 rows of data
Primary Key key_a,key_b,date
key on key_b
key on date
There are numerous empty dates between rows for a & b that I want the most recent behind or on the "Date" entered.
Is it the Mysql Join settings causing it to be slow ?
I can copy the entire set of a & b data with an INSERT to a temp table just by selecting all the rows and then run the same query on the temp table, but why do multi queries (insert selected, then select from) when only 1 is needed.
The query above only has 4,128,548 total results in the temp insert all dates table, and the date specific returns under 180,000 total.
Not table optimization, not keys, is it Max sort length, Join Buffer size , I have 128 gig ram, on a 32 core server running this, there is no reason for it to be slow, just never bulk insert this large of a single table to run Join queries on prior if anyone else has dealt with tables this size any info greatly appreciated.
Edited query, yes it's late long day had Distinct when it wasn't needed or in actual query
WHERE date <= **20150126**
group by key_a,key_b
needs an index starting with date. It's about doing what you can with the WHERE clause, not sparse or dense.
Then... Since the inner query references only 3 columns, building a 'covering' index may be useful. (Probably useful in your case.) So, tack on the other two fields, in either order. Such as
INDEX(`date`, key_a, key_b)
For MyISAM this step is critical. For InnoDB, this is redundant, since each secondary key (such as your INDEX(date)) implicitly includes the rest of the fields of the PK.
No, the PRIMARY KEY(key_a, key_b, date) cannot serve the purpose. It's in the wrong order. Also, it is (if you are using InnoDB) "clustered" with the index.
The query above only has 4,128,548 total results in the temp insert all dates table, and the date specific returns under 180,000 total.
Sorry, I had trouble parsing that. I assume you are saying 4M rows had 'date<...' and the subquery delivered only 180K rows. Hence, the outer query also returned 180K rows.
The first goal is to get through the 4M rows as efficiently as possible. With the index I propose, that might be about 20K blocks (#16KB each) of index scanning. That's 300MB.
Next the MAX and GROUP BY are performed. At 300MB, this will involve a disk tmp table. (See max_heap_size and max_tmp_table_size.)
Then comes the JOIN to fetch t1.*. You are using a good technique for fetching a bunch of rows from a huge table, where you need a GROUP BY (or LIMIT or ...) that is clumsy when done the obvious way. It goes like this: Write the subquery to find the PKs. Get the best index for it. Then JOIN on the PK.
Now for the JOIN. (Again, I assume InnoDB.) Since you are JOINing on the PK, each lookup into t1 will be efficient -- drill down the PK's BTree to find a row. Do that 180K times.
If those 180K lookups are scattered around the table, then this could be 180K disk hits.
Total effort: 20K + 180K = 200K disk hits, possibly less. On commodity spinning disks, this would take about 30 minutes (plus time for the tmp table). (No, only one core will be used. Anyway, I/O is probably the bottleneck.)
OPTIMIZE TABLE -- almost always useless.
I assume innodb_buffer_pool_size is about 90G? If things are going to be cached, that is where it would happen (for InnoDB). Since 200K blocks is 3GB, it could be easily cached. That is, if you run the query twice, the first might be 30 minutes, but the second might be less than 3 minutes.
To get more numbers, you could do:
FLUSH STATUS;
SELECT ...;
SHOW SESSION STATUS;
and look for 'Handler%', '%sort%', 'Innodb%' and maybe a few others.
What version are you running? Recent versions have a leapfrog technique that works better for max+groupby than what I described. I think it is called MRR. If so, your PK is actually optimal. (Hmmm... I should play around with that.)
PARTITIONing -- I don't see any benefit (for this query).
I have about 1 million rows on users table and have columns A AA B BB C CC D DD E EE F FF by example to count int values 0 & 1
SELECT
CityCode,SUM(A),SUM(B),SUM(C),SUM(D),SUM(E),SUM(F),SUM(AA),SUM(BB),SUM(CC),SUM(DD),SUM(EE),SUM(FF)
FROM users
GROUP BY CityCode
Result 8 rows in set (24.49 sec).
How to make my statement more faster?
Use explain to to know the excution plan of your query.
Create atleast one or more Index. If possible make CityCode primary key.
Try this one
SELECT CityCode,SUM(A),SUM(B),SUM(C),SUM(D), SUM(E),SUM(F),SUM(AA),SUM(BB),SUM(CC),SUM(DD),SUM(EE),SUM(FF)
FROM users
GROUP BY CityCode,A,B,C,D,E,F,AA,BB,CC,DD,EE,FF
Create an index on the CityCode column.
I believe it is not because of SUM(), try to say select CityCode from users group by CityCode; it should take neary the same time...
Use better hardware
increase caching size - if you use InnoDB engine, then increase the innodb_buffer_pool_size value
refactor your query to limit the number of users (if business logic permits that, of course)
You have no WHERE clause, which means the query has to scan the whole table. This will make it slow on a large table.
You should consider how often you need to do this and what the impact of it being slow is. Some suggestions are:
Don't change anything - if it doesn't really matter
Have a table which contains the same data as "users", but without any other columns that you aren't interested in querying. It will still be slow, but not as slow, especially if there are bigger ones
(InnoDB) use CityCode as the first part of the primary key for table "users", that way it can do a PK scan and avoid any sorting (may still be too slow)
Create and maintain some kind of summary table, but you'll need to update it each time a user changes (or tolerate stale data)
But be sure that this optimisation is absolutely necessary.
I'd like to know which of the followings would execute faster in MySQL database. The table would have 200 - 1000 entries.
SELECT id
from TABLE
order by id desc
limit 1
or
SELECT count(id)
from TABLE
The story is the Table is cached. So this query is to be executed every time before cache retrieval to determine whether the cache data is invalid by comparing the previous value.
So if there exists a even less expensive query, please kindly let me know. Thanks.
If you
start from 1
never have any gaps
use the InnoDB engine
id is not nullable
Then the 2nd could run [ever so marginally] faster due to not having to visit table data at all (count is stored in metadata).
Otherwise,
if the table has NO index on ID (causing a SCAN), the 2nd one is faster
Barring both the above
the first one is faster
And if you actually meant to ask SELECT .. LIMIT 1 vs SELECT MAX(id).. then the answer is actually that they are the same for MySQL and most sane DBMS, whether or not there is an index.
I think, the first query will run faster, as the query is limited to be executed for one row only, 200-1000 may not matter that much in this case.
As already pointed out in the comments, your table is so small it really doesn't what your solution will be. For this reason the select count(id) should be used as it expresses the intent and doesn't need any further processing.
Now select count(id) comes with an alternative select count(*). These two are not synonyms. select count(*) will count the number of rows and use a cached value if possible when select count(id) counts the number of non null values of the column id exists. If the id columns is set as not null then the cached row count may be used.
The selection between count(*) and count(id) depends once again on your intent. In the general case, count(*) describes the intent better.
The there is the possibility of count(1) which is actually a synonym of count(*) when using mysql but the interpretation may vary if end up using a different RDBMS.
The performance of each type of count also varies depending on whether you are using MyISAM or InnoDB. The row counts are cached on the former but not on the latter, if I've understood correctly.
In the end, you should rely on query plans and running tests and measuring their performance rather than these general ramblings.