At this point I can see no possible explanation why this would occur.
Just a thought though, is it possible that data2011_01 was clustered on the
index at some point but data2013_01w has not been clustered?
If you cluster data2013_01w on the index, does the performance change?
On Mon, Feb 1, 2016 at 4:03 PM, Yu Nie <niey...@gmail.com> wrote:
> Melvin,
>
> Please see attached for the requests results. I ran two queries (each with
> a different taxiid that is next to each other) for each table. Note that
> for the large table one is much faster than the other because the shared
> cache was used for the second query. This does not work however for the
> small table.
>
> Many thanks for your willingness to help!
>
> Best, Marco
>
> On Mon, Feb 1, 2016 at 2:25 PM, melvin6925 <melvin6...@gmail.com> wrote:
>
>> Fine. Please rerun both explains and my queries and send ALL output to a
>> file (F8 in PGADMIN Sql). Then attach the file.
>>
>>
>>
>> Sent via the Samsung Galaxy S® 6, an AT&T 4G LTE smartphone
>> -------- Original message --------
>> From: Yu Nie <niey...@gmail.com>
>> Date: 2/1/2016 15:17 (GMT-05:00)
>> To: melvin6925 <melvin6...@gmail.com>
>> Subject: Re: [GENERAL] strange sql behavior
>>
>> Yes, absolutely.
>>
>> On Mon, Feb 1, 2016 at 2:12 PM, melvin6925 <melvin6...@gmail.com> wrote:
>>
>>> And were _your_ queries run on the same day at the same time within a
>>> few seconds of each other?
>>>
>>> Sent via the Samsung Galaxy S® 6, an AT&T 4G LTE smartphone
>>> -------- Original message --------
>>> From: Yu Nie <niey...@gmail.com>
>>> Date: 2/1/2016 15:01 (GMT-05:00)
>>> To: melvin6925 <melvin6...@gmail.com>
>>> Cc: Bill Moran <wmo...@potentialtech.com>, pgsql-general@postgresql.org
>>> Subject: Re: [GENERAL] strange sql behavior
>>>
>>> Thanks, Bill and Melvin!
>>>
>>> Just some quick note/answers before I absorb all the information
>>> provided by Bill.
>>>
>>> 1. I don't expect many users running queries against the tables,
>>> especially for the small table - since I just created it this morning, and
>>> nobody know about it except myself.
>>>
>>> 2. The setting in the config:
>>>
>>>
>>> shared_buffers = 512MB # min 128kB
>>> work_mem = 128MB # min 64kB
>>>
>>> 3. I am running a Windows 7 with 24 GB RAM. and my postgresal is 9.4.
>>>
>>> 4. here is the query I ran:
>>>
>>> SELECT n.nspname,
>>> s.relname,
>>> c.reltuples::bigint,
>>> n_tup_ins,
>>> n_tup_upd,
>>> n_tup_del,
>>> date_trunc('second', last_vacuum) as last_vacuum,
>>> date_trunc('second', last_autovacuum) as last_autovacuum,
>>> date_trunc('second', last_analyze) as last_analyze,
>>> date_trunc('second', last_autoanalyze) as last_autoanalyze
>>> ,
>>> round( current_setting('autovacuum_vacuum_threshold')::integer +
>>> current_setting('autovacuum_vacuum_scale_factor')::numeric * C.reltuples)
>>> AS av_threshold
>>> FROM pg_stat_all_tables s
>>> JOIN pg_class c ON c.oid = s.relid
>>> JOIN pg_namespace n ON (n.oid = c.relnamespace)
>>> WHERE s.relname NOT LIKE 'pg_%'
>>> AND s.relname NOT LIKE 'sql_%'
>>> AND s.relname IN ('data2013_01w', 'data2011_01')
>>> ORDER by 1, 2;
>>>
>>> I copied the result from PGAdmin directly, here it is again.:
>>>
>>> public;"data2011_01";784236864;784236885;0;0;"";"";"";"2016-01-19
>>> 17:31:08-06";156847423
>>> public;"data2013_01w";300786432;300786444;0;0;"";"";"2016-02-01
>>> 08:57:24-06";"2016-02-01 04:01:04-06";60157336
>>>
>>> On Mon, Feb 1, 2016 at 1:45 PM, melvin6925 <melvin6...@gmail.com> wrote:
>>>
>>>> Thanks Bill.
>>>> Also, it's very important to include the headers with the queries!
>>>>
>>>> Marco,
>>>> There is no top secret information that is requested, so please do not
>>>> edit the output.
>>>>
>>>>
>>>>
>>>> Sent via the Samsung Galaxy S® 6, an AT&T 4G LTE smartphone
>>>> -------- Original message --------
>>>> From: Bill Moran <wmo...@potentialtech.com>
>>>> Date: 2/1/2016 14:41 (GMT-05:00)
>>>> To: Yu Nie <niey...@gmail.com>
>>>> Cc: Melvin Davidson <melvin6...@gmail.com>,
>>>> pgsql-general@postgresql.org
>>>> Subject: Re: [GENERAL] strange sql behavior
>>>>
>>>>
>>>> Came a little late to the thread, see many comments inline below:
>>>>
>>>> On Mon, 1 Feb 2016 13:16:13 -0600
>>>> Yu Nie <niey...@gmail.com> wrote:
>>>>
>>>> > Thanks a lot for your reply. I ran the query you suggested and here
>>>> are
>>>> > the results
>>>> >
>>>> > Large table: "public";"data2011_01";784236885;0;0;"";"";"";"2016-01-19
>>>> > 17:31:08-06";156847423
>>>> > Small table: "public";"data2013_01w";300786444;0;0;"";"";"2016-02-01
>>>> > 08:57:24-06";"2016-02-01 04:01:04-06";60157336
>>>>
>>>> You didn't do Melvin's query correctly. He specified 11 columns to
>>>> select, but you only show 10. Since you don't show the query you
>>>> actually ran, we don't know which of the numeric columns is missing.
>>>>
>>>> More information inline below:
>>>>
>>>> >
>>>> > On Mon, Feb 1, 2016 at 1:00 PM, Melvin Davidson <melvin6...@gmail.com
>>>> >
>>>> > wrote:
>>>> >
>>>> > > One thing to look at is the last time both tables were
>>>> vacuumed/analyzed.
>>>> > >
>>>> > > SELECT n.nspname,
>>>> > > s.relname,
>>>> > > c.reltuples::bigint,
>>>> > > n_tup_ins,
>>>> > > n_tup_upd,
>>>> > > n_tup_del,
>>>> > > date_trunc('second', last_vacuum) as last_vacuum,
>>>> > > date_trunc('second', last_autovacuum) as last_autovacuum,
>>>> > > date_trunc('second', last_analyze) as last_analyze,
>>>> > > date_trunc('second', last_autoanalyze) as last_autoanalyze
>>>> > > ,
>>>> > > round(
>>>> current_setting('autovacuum_vacuum_threshold')::integer +
>>>> > > current_setting('autovacuum_vacuum_scale_factor')::numeric *
>>>> C.reltuples)
>>>> > > AS av_threshold
>>>> > > FROM pg_stat_all_tables s
>>>> > > JOIN pg_class c ON c.oid = s.relid
>>>> > > JOIN pg_namespace n ON (n.oid = c.relnamespace)
>>>> > > WHERE s.relname NOT LIKE 'pg_%'
>>>> > > AND s.relname NOT LIKE 'sql_%'
>>>> > > AND s.relname IN ' "your_small_table", "your_large_table"'
>>>> > > ORDER by 1, 2;
>>>> > >
>>>> > >
>>>> > > Also, please confirm the indexes for both tables are using the same
>>>> method
>>>> > > (btree?).
>>>> > >
>>>> > > On Mon, Feb 1, 2016 at 1:35 PM, Yu Nie <niey...@gmail.com> wrote:
>>>> > >
>>>> > >> Hi there,
>>>> > >>
>>>> > >> Recently I am working with a large amount of taxis GIS data and had
>>>> > >> encountered some weird performance issues. I am hoping someone in
>>>> this
>>>> > >> community can help me figure it out.
>>>> > >>
>>>> > >> The taxi data were loaded in 5 minute block into a table. I have
>>>> two
>>>> > >> separate such tables, one stores a month of data with about 700
>>>> million
>>>> > >> rows, another stores about 10 days of data with about 300 million
>>>> rows.
>>>> > >> The two tables have the exactly same schema and indexes. There are
>>>> two
>>>> > >> indexes: one on taxiid (text), and the other on the time stamp
>>>> (date
>>>> > >> time). In order to process the data, I need to get all points for
>>>> a single
>>>> > >> taxis; to do that, I use something like:
>>>> > >> select * from table1 where taxiid = 'SZB00S41' order by time;
>>>> > >> What puzzled me greatly is that this query runs consistently much
>>>> faster
>>>> > >> for the large table than for the small table, which seems to
>>>> contradict
>>>> > >> with intuition. At the end of message you may find explain
>>>> (analyze
>>>> > >> buffer) results of two particular queries for the same taxiid (one
>>>> for each
>>>> > >> table). You can see that it took much longer (more than 20 times)
>>>> to get
>>>> > >> 20k rows from the small table than to get 44 k rows from the large
>>>> table.
>>>> > >> Interestingly it seems that the planner does expect about 1/3 work
>>>> for the
>>>> > >> small table query - yet for some reason, it took much longer to
>>>> fetch the
>>>> > >> rows from the small table. Why there is such a huge performance
>>>> between
>>>> > >> the two seemingly identical queries executed on two different
>>>> tables?
>>>> > >>
>>>> > >> Is is because the data on the second table is on some mysteriously
>>>> > >> "broken part" of the disk? what else could explain such a bizarre
>>>> > >> behavior? Your help is greatly appreciated.
>>>> > >>
>>>> > >> The above behavior is consistent through all queries. Another
>>>> issue I
>>>> > >> identified is that for the large table, the query can use the
>>>> shared buffer
>>>> > >> more effectively. For example, after you query one taxiid and
>>>> immediately
>>>> > >> following that query run the same query for another taxi whose id
>>>> ranks
>>>> > >> right behind the first id, then shared hit buffers would be quite
>>>> high (and
>>>> > >> the query would run much faster); this however never works for the
>>>> small
>>>> > >> table.
>>>> > >>
>>>> > >> Thanks a lot!
>>>> > >>
>>>> > >> Best, Marco
>>>> > >>
>>>> > >>
>>>> > >> Results for the small table: it took 141 seconds to finish. The
>>>> planning
>>>> > >> time is 85256.31
>>>> > >>
>>>> > >> "Sort (cost=85201.05..85256.31 rows=22101 width=55) (actual
>>>> > >> time=141419.499..141420.025 rows=20288 loops=1)"
>>>> > >> " Sort Key: "time""
>>>> > >> " Sort Method: quicksort Memory: 3622kB"
>>>> > >> " Buffers: shared hit=92 read=19816"
>>>> > >> " -> Bitmap Heap Scan on data2013_01w (cost=515.86..83606.27
>>>> > >> rows=22101 width=55) (actual time=50.762..141374.777 rows=20288
>>>> loops=1)"
>>>> > >> " Recheck Cond: ((taxiid)::text = 'SZB00S41'::text)"
>>>> > >> " Heap Blocks: exact=19826"
>>>> > >> " Buffers: shared hit=92 read=19816"
>>>>
>>>> ^^
>>>> Note that despite this table being smaller, Postgres had to read 19816
>>>> blocks from disk, which is 2.5x more than the larger table.
>>>>
>>>> > >> " -> Bitmap Index Scan on data2013_01w_ixtaxiid
>>>> > >> (cost=0.00..510.33 rows=22101 width=0) (actual time=26.053..26.053
>>>> > >> rows=20288 loops=1)"
>>>> > >> " Index Cond: ((taxiid)::text = 'SZB00S41'::text)"
>>>> > >> " Buffers: shared hit=4 read=78"
>>>> > >> "Planning time: 0.144 ms"
>>>> > >> "Execution time: 141421.154 ms"
>>>> > >>
>>>> > >> Results for the large table: it took 5 seconds to finish. The
>>>> planning
>>>> > >> time is 252077.10
>>>> > >> "Sort (cost=251913.32..252077.10 rows=65512 width=55) (actual
>>>> > >> time=5038.571..5039.765 rows=44204 loops=1)"
>>>> > >> " Sort Key: "time""
>>>> > >> " Sort Method: quicksort Memory: 7753kB"
>>>> > >> " Buffers: shared hit=2 read=7543"
>>>> > >> " -> Bitmap Heap Scan on data2011_01 (cost=1520.29..246672.53
>>>> > >> rows=65512 width=55) (actual time=36.935..5017.463 rows=44204
>>>> loops=1)"
>>>> > >> " Recheck Cond: ((taxiid)::text = 'SZB00S41'::text)"
>>>> > >> " Heap Blocks: exact=7372"
>>>> > >> " Buffers: shared hit=2 read=7543"
>>>> > >> " -> Bitmap Index Scan on data2011_01_ixtaxiid
>>>> > >> (cost=0.00..1503.92 rows=65512 width=0) (actual time=35.792..35.792
>>>> > >> rows=44204 loops=1)"
>>>>
>>>> ^^
>>>> Note that the larger table took LONGER to do the index work than the
>>>> smaller table, which probably means there's nothing wrong with your
>>>> disks or anything ... that's the behavior I would expect.
>>>>
>>>> > >> " Index Cond: ((taxiid)::text = 'SZB00S41'::text)"
>>>> > >> " Buffers: shared hit=2 read=171"
>>>> > >> "Planning time: 0.127 ms"
>>>> > >> "Execution time: 5042.134 ms"
>>>>
>>>> So, what I'm seeing, is that Postgres is able to figure out _which_ rows
>>>> to fetch faster on the small table than the large table, which is what
>>>> you would expect, since a smaller index should be faster than a large
>>>> one.
>>>>
>>>> However, when it goes to actually fetch the row data, it takes
>>>> significantly longer on the small table, despite the fact that it's
>>>> only fetching 1/3 as many rows. It is, however, doing 2.5x as many
>>>> disk reads to get those rows: For the large table, it reads 61MB from
>>>> disk, but it reads 160MB to get all the data for the smaller table.
>>>>
>>>> How the data was inserted into each table could lead to similar data
>>>> being clustered on common pages on the large table, while it's spread
>>>> across many more pages on the small table.
>>>>
>>>> That still doesn't explain it all, though. 2.5x the disk
>>>> activity normally wouldn't equate to 28x the time required. Unless
>>>> you're disks are horrifically slow? Does this server have a lot of
>>>> other activity against the disks? I.e. are other people running
>>>> queries that you would have to contend with, or is the server a VM
>>>> sharing storage with other VMs, or even a combined use server that
>>>> has to share disk access with (for example) a web or mail server
>>>> as well? Is the performance difference consistently ~28x?
>>>>
>>>> Other things: what is shared_buffers set to? The queries would seem
>>>> to indicate that this server has less than 1M of those two tables
>>>> cached in memory at the time you ran those queries, which seems to
>>>> suggest that either you've got shared_buffers set very low, or that
>>>> there are a lot of other tables that other queries are accessing at
>>>> the time you're running these. Perhaps installing pg_buffercache to
>>>> have a look at what's using your shared_buffers would be helpful.
>>>>
>>>> --
>>>> Bill Moran
>>>>
>>>
>>>
>>
>
--
*Melvin Davidson*
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
