Re: Out of Memory and Configuration Problems (Big Computer)

Thanks Bill,

That sounds like good advice. I am rerunning my query with the python
function peppered with plpy.notice("msg") call.

Hopefully that'll shed some light on which inputs it's crashing on. Does
anyone know of a way to measure the memory being consumed by the
function/query so that I can spot any memory leak. I'm not very good at
debugging memory leaks..

Failing that, perhaps it is time to rewrite the function in SQL or
embrace the TSearch2 dictionaries. I was originally intending on
figuring out a way to extend the ISpell dictionary to match and replace
those keywords..

Thanks,
Tom

Bill Moran wrote:
> On 5/28/10 8:43:48 PM, Tom Wilcox wrote:
>> I ran this query:
>>
>> EXPLAIN ANALYZE UPDATE nlpg.match_data SET org = org;
>>
>> And I got this result:
>>
>> "Seq Scan on match_data (cost=0.00..9762191.68 rows=32205168 width=206)
>> (actual time=76873.592..357450.519 rows=27777961 loops=1)"
>> "Total runtime: 8028212.367 ms"
>
> That would seem to indicate that the problem is in your Python
> functions.
>
> Some ideas for next steps:
> * Perhaps it's just a few rows that have data in them that the
> function has difficulty with. Add some debugging/logging to
> the function and see if the row it bombs on has anything unusual
> in it (such as a very large text field)
> * While large, that function is fairly simplistic. You may want
> to consider rewriting it as an SQL function, which should be
> more efficient in any event.
>
>>
>>
>> On 28 May 2010 19:39, Tom Wilcox <hungrytom(at)googlemail(dot)com
>> <mailto:hungrytom(at)googlemail(dot)com>> wrote:
>>
>> Oops. Sorry about that.
>>
>> I am having this problem with multiple queries however I am
>> confident that a fair number may involve the custom plpython
>> "normalise" function which I have made myself. I didn't think it
>> would be complicated enough to produce a memory problem.. here it
>> is:
>>
>> -- Normalises common address words (i.e. 'Ground' maps to 'grd')
>> CREATE OR REPLACE FUNCTION normalise(s text) RETURNS text AS $$
>> ADDR_FIELD_DELIM = ' '
>>
>> # Returns distinct list without null or empty elements
>> def distinct_str(list):
>> seen = set()
>> return [x for x in list if x not in seen and not seen.add(x)
>> and x!=None and len(x)>0]
>>
>> # normalise common words in given address string
>> def normalise(match_data):
>> if match_data==None: return ''
>> import re
>> # Tokenise
>> toks = distinct_str(re.split(r'\s', match_data.lower()))
>> out = ''
>> for tok in toks:
>> ## full word replace
>> if tok == 'house' : out += 'hse'+ADDR_FIELD_DELIM
>> elif tok == 'ground' : out += 'grd'+ADDR_FIELD_DELIM
>> elif tok == 'gnd' : out += 'grd'+ADDR_FIELD_DELIM
>> elif tok == 'front' : out += 'fnt'+ADDR_FIELD_DELIM
>> elif tok == 'floor' : out += 'flr'+ADDR_FIELD_DELIM
>> elif tok == 'floors' : out += 'flr'+ADDR_FIELD_DELIM
>> elif tok == 'flrs' : out += 'flr'+ADDR_FIELD_DELIM
>> elif tok == 'fl' : out += 'flr'+ADDR_FIELD_DELIM
>> elif tok == 'basement' : out += 'bst'+ADDR_FIELD_DELIM
>> elif tok == 'subbasement' : out += 'sbst'+ADDR_FIELD_DELIM
>> elif tok == 'bsmt' : out += 'bst'+ADDR_FIELD_DELIM
>> elif tok == 'lbst' : out += 'lower bst'+ADDR_FIELD_DELIM
>> elif tok == 'street' : out += 'st'+ADDR_FIELD_DELIM
>> elif tok == 'road' : out += 'rd'+ADDR_FIELD_DELIM
>> elif tok == 'lane' : out += 'ln'+ADDR_FIELD_DELIM
>> elif tok == 'rooms' : out += 'rm'+ADDR_FIELD_DELIM
>> elif tok == 'room' : out += 'rm'+ADDR_FIELD_DELIM
>> elif tok == 'no' : pass
>> elif tok == 'number' : pass
>> elif tok == 'and' : out += '&'+ADDR_FIELD_DELIM
>> elif tok == 'rear' : out += 'rr'+ADDR_FIELD_DELIM
>> elif tok == 'part' : out += 'pt'+ADDR_FIELD_DELIM
>> elif tok == 'south' : out += 's'+ADDR_FIELD_DELIM
>> elif tok == 'sth' : out += 's'+ADDR_FIELD_DELIM
>> elif tok == 'north' : out += 'n'+ADDR_FIELD_DELIM
>> elif tok == 'nth' : out += 'n'+ADDR_FIELD_DELIM
>> elif tok == 'west' : out += 'w'+ADDR_FIELD_DELIM
>> elif tok == 'wst' : out += 'w'+ADDR_FIELD_DELIM
>> elif tok == 'east' : out += 'e'+ADDR_FIELD_DELIM
>> elif tok == 'est' : out += 'e'+ADDR_FIELD_DELIM
>> elif tok == 'first' : out += '1st'+ADDR_FIELD_DELIM
>> elif tok == 'second' : out += '2nd'+ADDR_FIELD_DELIM
>> elif tok == 'third' : out += '3rd'+ADDR_FIELD_DELIM
>> elif tok == 'fourth' : out += '4th'+ADDR_FIELD_DELIM
>> elif tok == 'fifth' : out += '5th'+ADDR_FIELD_DELIM
>> elif tok == 'sixth' : out += '6th'+ADDR_FIELD_DELIM
>> elif tok == 'seventh' : out += '7th'+ADDR_FIELD_DELIM
>> elif tok == 'eighth' : out += '8th'+ADDR_FIELD_DELIM
>> elif tok == 'ninth' : out += '9th'+ADDR_FIELD_DELIM
>> elif tok == 'tenth' : out += '10th'+ADDR_FIELD_DELIM
>> elif tok == 'eleventh' : out += '11th'+ADDR_FIELD_DELIM
>> elif tok == 'twelfth' : out += '12th'+ADDR_FIELD_DELIM
>> elif tok == 'thirteenth' : out += '13th'+ADDR_FIELD_DELIM
>> elif tok == 'fourteenth' : out += '14th'+ADDR_FIELD_DELIM
>> elif tok == 'fifteenth' : out += '15th'+ADDR_FIELD_DELIM
>> elif tok == 'sixteenth' : out += '16th'+ADDR_FIELD_DELIM
>> elif tok == 'seventeenth' : out += '17th'+ADDR_FIELD_DELIM
>> elif tok == 'eighteenth' : out += '18th'+ADDR_FIELD_DELIM
>> elif tok == 'nineteenth' : out += '19th'+ADDR_FIELD_DELIM
>> elif tok == 'twentieth' : out += '20th'+ADDR_FIELD_DELIM
>> # numbers 0 - 20
>> elif tok == 'one' : out += '1'+ADDR_FIELD_DELIM
>> elif tok == 'two' : out += '2'+ADDR_FIELD_DELIM
>> elif tok == 'three' : out += '3'+ADDR_FIELD_DELIM
>> elif tok == 'four' : out += '4'+ADDR_FIELD_DELIM
>> elif tok == 'five' : out += '5'+ADDR_FIELD_DELIM
>> elif tok == 'six' : out += '6'+ADDR_FIELD_DELIM
>> elif tok == 'seven' : out += '7'+ADDR_FIELD_DELIM
>> elif tok == 'eight' : out += '8'+ADDR_FIELD_DELIM
>> elif tok == 'nine' : out += '9'+ADDR_FIELD_DELIM
>> elif tok == 'ten' : out += '10'+ADDR_FIELD_DELIM
>> elif tok == 'eleven' : out += '11'+ADDR_FIELD_DELIM
>> elif tok == 'twelve' : out += '12'+ADDR_FIELD_DELIM
>> elif tok == 'thirteen' : out += '13'+ADDR_FIELD_DELIM
>> elif tok == 'fourteen' : out += '14'+ADDR_FIELD_DELIM
>> elif tok == 'fifteen' : out += '15'+ADDR_FIELD_DELIM
>> elif tok == 'sixteen' : out += '16'+ADDR_FIELD_DELIM
>> elif tok == 'seventeen' : out += '17'+ADDR_FIELD_DELIM
>> elif tok == 'eighteen' : out += '18'+ADDR_FIELD_DELIM
>> elif tok == 'nineteen' : out += '19'+ADDR_FIELD_DELIM
>> elif tok == 'twenty' : out += '20'+ADDR_FIELD_DELIM
>> # town dictionary items
>> elif tok == 'borough' : pass
>> elif tok == 'city' : pass
>> elif tok == 'of' : pass
>> elif tok == 'the' : pass
>> # a few extras (from looking at voa)
>> elif tok == 'at' : pass
>> elif tok == 'incl' : pass
>> elif tok == 'inc' : pass
>> else: out += tok+ADDR_FIELD_DELIM
>> return out
>>
>> return normalise(s)
>> $$ LANGUAGE plpythonu;
>>
>>
>> Here's the create script for the table from pgAdmin (I hope that
>> will be good enough instead of \d as I can't do that right now)..
>>
>> -- Table: nlpg.match_data
>>
>> -- DROP TABLE nlpg.match_data;
>>
>> CREATE TABLE nlpg.match_data
>> (
>> premise_id integer,
>> usrn bigint,
>> org text,
>> sao text,
>> "level" text,
>> pao text,
>> "name" text,
>> street text,
>> town text,
>> pc postcode,
>> postcode text,
>> match_data_id integer NOT NULL DEFAULT
>> nextval('nlpg.match_data_match_data_id_seq1'::regclass),
>> addr_str text,
>> tssearch_name tsvector,
>>
>> CONSTRAINT match_data_pkey1 PRIMARY KEY (match_data_id)
>> )
>> WITH (
>> OIDS=FALSE
>> );
>> ALTER TABLE nlpg.match_data OWNER TO postgres;
>> ALTER TABLE nlpg.match_data ALTER COLUMN "name" SET STATISTICS
>> 10000;
>>
>>
>> -- Index: nlpg.index_match_data_mid
>>
>> -- DROP INDEX nlpg.index_match_data_mid;
>>
>> CREATE INDEX index_match_data_mid
>> ON nlpg.match_data
>> USING btree
>> (match_data_id);
>>
>> -- Index: nlpg.index_match_data_pc
>>
>> -- DROP INDEX nlpg.index_match_data_pc;
>>
>> CREATE INDEX index_match_data_pc
>> ON nlpg.match_data
>> USING btree
>> (pc);
>>
>> -- Index: nlpg.index_match_data_pid
>>
>> -- DROP INDEX nlpg.index_match_data_pid;
>>
>> CREATE INDEX index_match_data_pid
>> ON nlpg.match_data
>> USING btree
>> (premise_id);
>>
>> -- Index: nlpg.index_match_data_tssearch_name
>>
>> -- DROP INDEX nlpg.index_match_data_tssearch_name;
>>
>> CREATE INDEX index_match_data_tssearch_name
>> ON nlpg.match_data
>> USING gin
>> (tssearch_name);
>>
>> -- Index: nlpg.index_match_data_usrn
>>
>> -- DROP INDEX nlpg.index_match_data_usrn;
>>
>> CREATE INDEX index_match_data_usrn
>> ON nlpg.match_data
>> USING btree
>> (usrn);
>>
>> As you can see, no FKs or triggers..
>>
>> I am running: EXPLAIN ANALYZE UPDATE nlpg.match_data SET org = org;
>>
>> However, as it should take around 90mins (if it is linear) then I
>> thought I would send this now and follow up with the results once it
>> finishes. (Has taken 2hours so far..)
>>
>> Thanks very much for your help.
>>
>> Tom
>>
>>
>> On 28 May 2010 17:54, "Bill Moran" <wmoran(at)potentialtech(dot)com
>> <mailto:wmoran(at)potentialtech(dot)com>> wrote:
>>>
>>> In response to Tom Wilcox <hungrytom(at)googlemail(dot)com
>>> <mailto:hungrytom(at)googlemail(dot)com>>:
>>>
>>> > In addition, I have discovered that the update query that runs
>>> on each row
>>> > of a 27million row ta...
>>>
>>> You're not liable to get shit for answers if you omit the mailing
>>> list from
>>> the conversation, especially since I know almost nothing about
>>> tuning
>>> PostgreSQL installed on Windows.
>>>
>>> Are there multiple queries having this problem? The original
>>> query didn't
>>> have normalise() in it, and I would be highly suspicious that a
>>> custom
>>> function may have a memory leak or other memory-intensive
>>> side-effects.
>>> What is the code for that function?
>>>
>>> For example, does:
>>> UPDATE nlpg.match_data SET org = org WHERE match_data_id;
>>> finish in a reasonable amount of time or exhibit the same out of
>>> memory
>>> problem?
>>>
>>> It'd be nice to see a \d on that table ... does it have any
>>> triggers or
>>> cascading foreign keys?
>>>
>>> And stop
>>>
>>> --
>>>
>>> Bill Moran
>>> http://www.potentialtech.com
>>> http://people.collaborativefusion.com/~wmoran/
>>> <http://people.collaborativefusion.com/%7Ewmoran/>
>>>
>>
>
>