Re: strange sql behavior

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 <melvin6925(at)gmail(dot)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 <nieyu04(at)gmail(dot)com>
> Date: 2/1/2016 15:17 (GMT-05:00)
> To: melvin6925 <melvin6925(at)gmail(dot)com>
> Subject: Re: [GENERAL] strange sql behavior
>
> Yes, absolutely.
>
> On Mon, Feb 1, 2016 at 2:12 PM, melvin6925 <melvin6925(at)gmail(dot)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 <nieyu04(at)gmail(dot)com>
>> Date: 2/1/2016 15:01 (GMT-05:00)
>> To: melvin6925 <melvin6925(at)gmail(dot)com>
>> Cc: Bill Moran <wmoran(at)potentialtech(dot)com>, pgsql-general(at)postgresql(dot)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 <melvin6925(at)gmail(dot)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 <wmoran(at)potentialtech(dot)com>
>>> Date: 2/1/2016 14:41 (GMT-05:00)
>>> To: Yu Nie <nieyu04(at)gmail(dot)com>
>>> Cc: Melvin Davidson <melvin6925(at)gmail(dot)com>, pgsql-general(at)postgresql(dot)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 <nieyu04(at)gmail(dot)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 <melvin6925(at)gmail(dot)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 <nieyu04(at)gmail(dot)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
>>>
>>
>>
>