Re: [PERFORM] Performance problem with semi-large tables
SELECT * FROM shipment_status WHERE release_code_id = constant ORDER BY release_code_id DESC, date DESC LIMIT 100; I have done this in other queries where sorting by both release code and date were important. You are right, it is very fast and I do have this index in play. However, most of the time I retreive shipment's when their shipment_status all have the same release_code, which makes sorting kind of moot :/ I guess that answers your comment below. Ah, well in this case, ORDER BY release_code_id DESC seems of course useless because you only have one order_code_id, but it is in fact necessary to make the planner realize it can use the index on (release_code_id,date) for the ordering. If you just ORDER BY date, the planner will not use your index. Thanks again for your response. I'll try and clarify some metrics that I took a few days to figure out what would be the best join order. By running some count queries on the production database, I noticed there were only 8 rows in release_code. The filtered column is unique, Let's forget the shipments table for now. So you mean there is an unique, one-to-one relation between release_code_id and filtered_column ? The planner is not able to derermine this ahead of time ; and in your case, it's important that it be unique to be able to use the index to retrieve quickly the rows in (date DESC) order. So if you'll join only to ONE release_code_id, you can do this : (SELECT * FROM shipment_status WHERE release_code_id = (SELECT r.id FROM release_code WHERE r.filtered_column = '5' LIMIT 1) ORDER BY release_code_id DESC, date DESC LIMIT 100) Which is no longer a join and will get your shipment_status_id's very quickly. so that means the filter ratio is 0.125. However, the data distribution is not normal. When the filtered column is the constant '5', Postgres will join to 54% of the shipment_status rows. Since shipment_status has 32,000+ rows, this join is not a very good one to make. Sure ! The shipment table has 17k rows, but also due to the distribution of data, almost every shipment will join to a shipment_status with a release_code of '5'. For your information, this column indicates that a shipment has been released, as most shipments will move to this state eventually. The actual join ratio from shipment_status to shipment is about 98.5% of the rows in the shipment table, which is still basically 17k rows. I was simply curious how to make something like this faster. You see, it's the table size and the bad filters are really destroying this query example. I would never make a query to the database like this in practice, but I have similar queries that I do make that aren't much better (and can't be due to business requirements). For example, let's add another filter to get all the shipments with release code '5' that are 7 days old or newer. ss.date = current_date - 7 It's the order by + limit which makes the query behaves badly, and which forces use of kludges to use the index. If you add another condition like that, it should be a breeze. By analyzing the production data, this where clause has a filter ratio of 0.08, which is far better than the release_code filter both in ratio and in the number of rows that it can avoid joining. However, if I had this filter into the original query, Postgres will not act on it first - and I think it really should before it even touches release_code. Well I think too. What with the subqueries I wrote with the LIMIT inside the subquery ? Any better ? Normally the planner is able to deconstruct subqueries and change the order as it sees fit, but if there are LIMIT's I don't know. However, the planner (using EXPLAIN ANALYZE) will actually pick this filter last and will join 17k rows prematurely to release_code. In this example, I'd like force postgres to do the date filter first, join to release_code next, then finally to shipment. You could use the JOIN keywords to specify the join order youself. Another example is filtering by the driver_id, which is a foreign key column on the shipment table itself to a driver table. This has a filter ratio of 0.000625 when analyzing the production data. However, PostgreSQL will not act on this filter first either. The sad part is that since drivers are actually distributed more evenly in the database, it would filter out the shipment table from 17k to about 10 shipments on average. In most cases, it ends up being more than 10, but not more than 60 or 70, which is very good since some drivers don't have any shipments (I question why they are even in the database, but that's another story). As you can see, joining to shipment_status at this point (using the primary key index from shipment.current_status_id to shipment_status.id) should be extremely efficient. Yet, Postgres's planner/optimizer won't make the right call until might later in
Re: [PERFORM] Performance problem with semi-large tables
PFC [EMAIL PROTECTED] writes: For example, let's add another filter to get all the shipments with release code '5' that are 7 days old or newer. ss.date = current_date - 7 It's the order by + limit which makes the query behaves badly, and which forces use of kludges to use the index. If you add another condition like that, it should be a breeze. Actually, that date condition has its own problem, namely that the compared-to value isn't a constant. The 8.0 planner is able to realize that this is a pretty selective condition, but prior releases fall back on a very pessimistic default estimate. I'm sure that has something to do with Ken not being able to get it to use an index on date. regards, tom lane ---(end of broadcast)--- TIP 8: explain analyze is your friend
Re: [PERFORM] Performance problem with semi-large tables
Yes, I'm very well aware of VACUUM and VACUUM ANALYZE. I've even clusted the date index and so on to ensure faster performance. - Original Message - From: David Parker To: Ken Egervari ; pgsql-performance@postgresql.org Sent: Saturday, January 29, 2005 5:04 PM Subject: Re: [PERFORM] Performance problem with semi-large tables You don't mention if you have run VACUUM or VACUUM ANALYZE lately. That's generally one of the first things that folks will suggest. If you have a lot of updates then VACUUM will clean up dead tuples; if you have a lot of inserts then VACUUM ANALYZE will update statistics so that the planner can make better decisions (as I understand it). Another data point people will ask for in helping you will be EXPLAIN ANALYZE output from running the queries you think are slowing down. - DAP From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ken EgervariSent: Wednesday, January 26, 2005 9:17 PMTo: pgsql-performance@postgresql.orgSubject: [PERFORM] Performance problem with semi-large tables Hi everyone. I'm new to this forum and was wondering if anyone would be kind enough to help me out with a pretty severe performance issue. I believe the problem to be rather generic, so I'll put it in generic terms. Since I'm at home and not a work (but this is really bugging me), I can't post any specifics. However, I think my explaination will suffice. I have a 2 tables that are are getting large and will only get larger with time (expoentially as more users sign on to the system). Right the now, a table called 'shipment' contains about 16,000 rows and 'shipment_status' contains about 32,500 rows. These aren't massive rows (I keep reading about tables with millions), but they will definately get into 6 digits by next year and query performance is quite poor. Now, from what I can understand about tuning, you want to specify good filters, provide good indexes on the driving filter as well as any referencial keys that are used while joining. This has helped me solve performance problems many times in the past (for example, changing a query speed from 2 seconds to 21 milliseconds). However, I am now tuning queries that operate on these two tables and the filters aren't very good (the best is a filter ratio of 0.125) and the number of rows returned is very large (not taking into consideration limits). For example, consider something likethis query that takes ~1 second to finish: select s.*, ss.* from shipment s, shipment_status ss, release_code r where s.current_status_id = ss.id and ss.release_code_id = r.id and r.filtered_column = '5' order by ss.date desc limit 100; Release code is just a very small table of 8 rows by looking at the production data, hence the 0.125 filter ratio. However, the data distribution is not normal since the filtered column actually pulls out about 54% of the rows in shipment_status when it joins. Postgres seems to be doing a sequencial scan to pull out all of these rows. Next, it joins approx 17550 rows to shipment. Since this query has a limit, it only returns the first 100, which seems like a waste. Now, for this query, I know I can filter out the date instead to speed it up. For example, I can probably search for all the shipments in the last 3 days instead of limiting it to 100. But since this isn't a real production query, I only wanted to show it as an example since many times I cannot do a filter by the date (and the sort may be date or something else irrelavant). I'm just stressed out how I can make queries like this more efficient since all I see is a bunch of hash joins and sequencial scans taking all kinds of time. I guess here are my 2 questions: 1. Should I just change beg to change the requirements so that I can make more specific queries and more screens to access those? 2. Can you recommend ways so that postgres acts on big tables more efficiently? I'm not really interested in this specific case (I just made it up). I'm more interested in general solutions to this general problem of big table sizes with bad filters and where join orders don't seem to help much. Thank you very muchfor your help. Best Regards, Ken Egervari
Re: [PERFORM] Performance problem with semi-large tables
Well, postgres does what you asked. It will be slow, because you have a full table join. LIMIT does not change this because the rows have to be sorted first. I am aware that limit doesn't really affect the execution time all that much. It does speed up ORM though and keeps the rows to a manageable list so users don't have to look at thousands, which is good enough for me. My intention here is that the date was supposed to be a good filter. The date is in shipment_status so you should first get the shipment_status.id that you need and later join to shipment. This will avoid the big join : SELECT s.*, ss.* FROM (SELECT * FROM shipment_status WHERE release_code_id IN (SELECT r.id FROM release_code WHERE r.filtered_column = '5') ORDER BY date DESC LIMIT 100 ) as ss, shipment s WHERE s.current_status_id = ss.id ORDER BY date DESC LIMIT 100 Is this better ? This looks like it might be what I want. It's not that I was not aware of the correct join order. I used Dan Tow's diagram method and learned that filtering on date first is the best approach, then releae code, then finally shipment for this particular query. I just didn't know how to tell PostgreSQL how to do this. So are you suggesting as a general rule then that sub-queries are the way to force a specific join order in postgres? If that is the case, I will do this from now on. ---(end of broadcast)--- TIP 2: you can get off all lists at once with the unregister command (send unregister YourEmailAddressHere to [EMAIL PROTECTED])
Re: [PERFORM] Performance problem with semi-large tables
So are you suggesting as a general rule then that sub-queries are the way to force a specific join order in postgres? If that is the case, I will do this from now on. I'll try to explain a bit better... Here's your original query : select s.*, ss.* from shipment s, shipment_status ss, release_code r where s.current_status_id = ss.id and ss.release_code_id = r.id and r.filtered_column = '5' order by ss.date desc limit 100; If you write something like : SELECT * FROM shipment_status WHERE release_code_id = constant ORDER BY release_code_id DESC, date DESC LIMIT 100; In this case, if you have an index on (release_code_id, date), the planner will use a limited index scan which will yield the rows in index order, which will be very fast. However, if you just have an index on date, this won't help you. In your case, moreover, you don't use release_code_id = constant, but it comes from a join. So there may be several different values for release_code_id ; thus the planner can't use the optimization, it has to find the rows with the release_code_id first. And it can't use the index on (release_code_id, date) to get the rows in sorted order precisely because there could be several different values for the release_code_id. And then it has to sort by date. I hope this makes it clearer. If you are absolutely sure there is only one row in release_code with r.filtered_column = '5', then this means release_code_id is a constant and your query could get a huge speedup by writing it differently. ---(end of broadcast)--- TIP 9: the planner will ignore your desire to choose an index scan if your joining column's datatypes do not match
Re: [PERFORM] Performance problem with semi-large tables
Thanks again for your response. I'll try and clarify some metrics that I took a few days to figure out what would be the best join order. By running some count queries on the production database, I noticed there were only 8 rows in release_code. The filtered column is unique, so that means the filter ratio is 0.125. However, the data distribution is not normal. When the filtered column is the constant '5', Postgres will join to 54% of the shipment_status rows. Since shipment_status has 32,000+ rows, this join is not a very good one to make. The shipment table has 17k rows, but also due to the distribution of data, almost every shipment will join to a shipment_status with a release_code of '5'. For your information, this column indicates that a shipment has been released, as most shipments will move to this state eventually. The actual join ratio from shipment_status to shipment is about 98.5% of the rows in the shipment table, which is still basically 17k rows. I was simply curious how to make something like this faster. You see, it's the table size and the bad filters are really destroying this query example. I would never make a query to the database like this in practice, but I have similar queries that I do make that aren't much better (and can't be due to business requirements). For example, let's add another filter to get all the shipments with release code '5' that are 7 days old or newer. ss.date = current_date - 7 By analyzing the production data, this where clause has a filter ratio of 0.08, which is far better than the release_code filter both in ratio and in the number of rows that it can avoid joining. However, if I had this filter into the original query, Postgres will not act on it first - and I think it really should before it even touches release_code. However, the planner (using EXPLAIN ANALYZE) will actually pick this filter last and will join 17k rows prematurely to release_code. In this example, I'd like force postgres to do the date filter first, join to release_code next, then finally to shipment. Another example is filtering by the driver_id, which is a foreign key column on the shipment table itself to a driver table. This has a filter ratio of 0.000625 when analyzing the production data. However, PostgreSQL will not act on this filter first either. The sad part is that since drivers are actually distributed more evenly in the database, it would filter out the shipment table from 17k to about 10 shipments on average. In most cases, it ends up being more than 10, but not more than 60 or 70, which is very good since some drivers don't have any shipments (I question why they are even in the database, but that's another story). As you can see, joining to shipment_status at this point (using the primary key index from shipment.current_status_id to shipment_status.id) should be extremely efficient. Yet, Postgres's planner/optimizer won't make the right call until might later in the plan. SELECT * FROM shipment_status WHERE release_code_id = constant ORDER BY release_code_id DESC, date DESC LIMIT 100; In this case, if you have an index on (release_code_id, date), the planner will use a limited index scan which will yield the rows in index order, which will be very fast. I have done this in other queries where sorting by both release code and date were important. You are right, it is very fast and I do have this index in play. However, most of the time I retreive shipment's when their shipment_status all have the same release_code, which makes sorting kind of moot :/ I guess that answers your comment below. However, if you just have an index on date, this won't help you. In your case, moreover, you don't use release_code_id = constant, but it comes from a join. So there may be several different values for release_code_id ; thus the planner can't use the optimization, it has to find the rows with the release_code_id first. And it can't use the index on (release_code_id, date) to get the rows in sorted order precisely because there could be several different values for the release_code_id. And then it has to sort by date. Well, the filtered column is actually unique (but it's not the primary key). Should I just make it the primary key? Can't postgres be equally efficient when using other candidate keys as well? If not, then I will definately change the design of my database. I mostly use synthetic keys to make Hibernate configuration fairly straight-forward and to make it easy so all of my entities extend from the same base class. I hope this makes it clearer. If you are absolutely sure there is only one row in release_code with r.filtered_column = '5', then this means release_code_id is a constant and your query could get a huge speedup by writing it differently. You mean by avoiding the filter on number and avoiding the join? You see, I never thought joining to release_code should be so bad since
