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Increase efficiency of initial message cache query 

In repeated trials, the initial data fetch used to take about 1100ms.
In practice, it was often taking >2000ms, probably due to caching
effects.  This commit cuts the time down to about 300ms in repeated
trials.

Note that the semantics are changed slightly in that we may no longer
get exactly 25000 messages.  However, holes in the message_id
sequence are currently very rare or non-existent so this shouldn't be
a problem and we don't care about the exact number of messages
anyway.

I believe the problem was that the query planner was unable to
effectively use the LIMIT clause to figure out that only a small
subset of zephyr_message was going to be needed.  Thus, it planned
for operating on the entire table and decided it could not use a more
efficient plan because work_mem, although large, would not be large
enough to execute the query over all of zephyr_message.

The original query was:

SELECT "zephyr_message"."id", "zephyr_message"."sender_id", "zephyr_message"."recipient_id", "zephyr_message"."subject", "zephyr_message"."content", "zephyr_message"."rendered_content", "zephyr_message"."rendered_content_version", "zephyr_message"."pub_date", "zephyr_message"."sending_client_id", "zephyr_userprofile"."id", "zephyr_userprofile"."password", "zephyr_userprofile"."last_login", "zephyr_userprofile"."email", "zephyr_userprofile"."is_staff", "zephyr_userprofile"."is_active", "zephyr_userprofile"."date_joined", "zephyr_userprofile"."full_name", "zephyr_userprofile"."short_name", "zephyr_userprofile"."pointer", "zephyr_userprofile"."last_pointer_updater", "zephyr_userprofile"."realm_id", "zephyr_userprofile"."api_key", "zephyr_userprofile"."enable_desktop_notifications", "zephyr_userprofile"."enter_sends", "zephyr_userprofile"."tutorial_status", "zephyr_realm"."id", "zephyr_realm"."domain", "zephyr_realm"."restricted_to_domain", "zephyr_recipient"."id", "zephyr_recipient"."type_id", "zephyr_recipient"."type", "zephyr_client"."id", "zephyr_client"."name" FROM "zephyr_message" INNER JOIN "zephyr_userprofile" ON ( "zephyr_message"."sender_id" = "zephyr_userprofile"."id" ) INNER JOIN "zephyr_realm" ON ( "zephyr_userprofile"."realm_id" = "zephyr_realm"."id" ) INNER JOIN "zephyr_recipient" ON ( "zephyr_message"."recipient_id" = "zephyr_recipient"."id" ) INNER JOIN "zephyr_client" ON ( "zephyr_message"."sending_client_id" = "zephyr_client"."id" ) ORDER BY "zephyr_message"."id" DESC LIMIT 25000;

with query plan:
 Limit  (cost=0.00..27120.95 rows=25000 width=362) (actual time=0.051..1121.282 rows=25000 loops=1)
   ->  Nested Loop  (cost=0.00..5330872.99 rows=4913981 width=362) (actual time=0.048..1081.014 rows=25000 loops=1)
         ->  Nested Loop  (cost=0.00..3932643.31 rows=4913981 width=344) (actual time=0.042..926.398 rows=25000 loops=1)
               ->  Nested Loop  (cost=0.00..2550275.29 rows=4913981 width=334) (actual time=0.035..752.524 rows=25000 loops=1)
                     Join Filter: (zephyr_message.sending_client_id = zephyr_client.id)
                     ->  Nested Loop  (cost=0.00..1739467.29 rows=4913981 width=320) (actual time=0.024..217.348 rows=25000 loops=1)
                           ->  Index Scan Backward using zephyr_message_pkey on zephyr_message  (cost=0.00..362510.09 rows=4913981 width=156) (actual time=0.014..42.097 rows=25000 loops=1)
                           ->  Index Scan using zephyr_userprofile_pkey on zephyr_userprofile  (cost=0.00..0.27 rows=1 width=164) (actual time=0.003..0.004 rows=1 loops=25000)
                                 Index Cond: (id = zephyr_message.sender_id)
                     ->  Materialize  (cost=0.00..1.17 rows=11 width=14) (actual time=0.001..0.010 rows=11 loops=25000)
                           ->  Seq Scan on zephyr_client  (cost=0.00..1.11 rows=11 width=14) (actual time=0.002..0.010 rows=11 loops=1)
               ->  Index Scan using zephyr_recipient_pkey on zephyr_recipient  (cost=0.00..0.27 rows=1 width=10) (actual time=0.002..0.003 rows=1 loops=25000)
                     Index Cond: (id = zephyr_message.recipient_id)
         ->  Index Scan using zephyr_realm_pkey on zephyr_realm  (cost=0.00..0.27 rows=1 width=18) (actual time=0.002..0.003 rows=1 loops=25000)
               Index Cond: (id = zephyr_userprofile.realm_id)
 Total runtime: 1141.408 ms

In the new code, we do two queries:

SELECT "zephyr_message"."id" FROM "zephyr_message" ORDER BY "zephyr_message"."id" DESC LIMIT 1

followed by:

SELECT "zephyr_message"."id", "zephyr_message"."sender_id", "zephyr_message"."recipient_id", "zephyr_message"."subject", "zephyr_message"."content", "zephyr_message"."rendered_content", "zephyr_message"."rendered_content_version", "zephyr_message"."pub_date", "zephyr_message"."sending_client_id", "zephyr_userprofile"."id", "zephyr_userprofile"."password", "zephyr_userprofile"."last_login", "zephyr_userprofile"."email", "zephyr_userprofile"."is_staff", "zephyr_userprofile"."is_active", "zephyr_userprofile"."date_joined", "zephyr_userprofile"."full_name", "zephyr_userprofile"."short_name", "zephyr_userprofile"."pointer", "zephyr_userprofile"."last_pointer_updater", "zephyr_userprofile"."realm_id", "zephyr_userprofile"."api_key", "zephyr_userprofile"."enable_desktop_notifications", "zephyr_userprofile"."enter_sends", "zephyr_userprofile"."tutorial_status", "zephyr_realm"."id", "zephyr_realm"."domain", "zephyr_realm"."restricted_to_domain", "zephyr_recipient"."id", "zephyr_recipient"."type_id", "zephyr_recipient"."type", "zephyr_client"."id", "zephyr_client"."name" FROM "zephyr_message" INNER JOIN "zephyr_userprofile" ON ( "zephyr_message"."sender_id" = "zephyr_userprofile"."id" ) INNER JOIN "zephyr_realm" ON ( "zephyr_userprofile"."realm_id" = "zephyr_realm"."id" ) INNER JOIN "zephyr_recipient" ON ( "zephyr_message"."recipient_id" = "zephyr_recipient"."id" ) INNER JOIN "zephyr_client" ON ( "zephyr_message"."sending_client_id" = "zephyr_client"."id" ) WHERE "zephyr_message"."id" > 4941883

with the message id filled in as the result of the first query.  The
new query differs from the original only in that its ORDER BY and
LIMIT clauses are replaced by a WHERE clause.  The second query has
query plan:

 Hash Join  (cost=709.30..28048.18 rows=20544 width=365) (actual time=41.678..279.261 rows=25041 loops=1)
   Hash Cond: (zephyr_message.recipient_id = zephyr_recipient.id)
   ->  Hash Join  (cost=102.98..27056.66 rows=20544 width=355) (actual time=3.686..190.730 rows=25041 loops=1)
         Hash Cond: (zephyr_message.sending_client_id = zephyr_client.id)
         ->  Hash Join  (cost=101.73..26772.94 rows=20544 width=341) (actual time=3.649..143.695 rows=25041 loops=1)
               Hash Cond: (zephyr_userprofile.realm_id = zephyr_realm.id)
               ->  Hash Join  (cost=99.99..26488.71 rows=20544 width=323) (actual time=3.578..96.746 rows=25041 loops=1)
                     Hash Cond: (zephyr_message.sender_id = zephyr_userprofile.id)
                     ->  Index Scan using zephyr_message_pkey on zephyr_message  (cost=0.00..26106.24 rows=20544 width=159) (actual time=0.017..41.980 rows=25041 loops=1)
                           Index Cond: (id > 4941883)
                     ->  Hash  (cost=83.33..83.33 rows=1333 width=164) (actual time=3.548..3.548 rows=1333 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 275kB
                           ->  Seq Scan on zephyr_userprofile  (cost=0.00..83.33 rows=1333 width=164) (actual time=0.006..1.646 rows=1333 loops=1)
               ->  Hash  (cost=1.33..1.33 rows=33 width=18) (actual time=0.064..0.064 rows=33 loops=1)
                     Buckets: 1024  Batches: 1  Memory Usage: 2kB
                     ->  Seq Scan on zephyr_realm  (cost=0.00..1.33 rows=33 width=18) (actual time=0.003..0.033 rows=33 loops=1)
         ->  Hash  (cost=1.11..1.11 rows=11 width=14) (actual time=0.027..0.027 rows=11 loops=1)
               Buckets: 1024  Batches: 1  Memory Usage: 1kB
               ->  Seq Scan on zephyr_client  (cost=0.00..1.11 rows=11 width=14) (actual time=0.003..0.013 rows=11 loops=1)
   ->  Hash  (cost=335.03..335.03 rows=21703 width=10) (actual time=37.974..37.974 rows=21761 loops=1)
         Buckets: 4096  Batches: 1  Memory Usage: 893kB
         ->  Seq Scan on zephyr_recipient  (cost=0.00..335.03 rows=21703 width=10) (actual time=0.004..18.443 rows=21761 loops=1)
 Total runtime: 299.300 ms

(imported from commit b2a70cccc47be7970df407c6be00eccd2e8be82a)
This commit is contained in:
Zev Benjamin 2013-04-24 19:30:19 -04:00
parent ccc5363f3c
commit a1634b12d3
1 changed files with 5 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
zephyr/lib/cache_helpers.py
View File

@ -23,6 +23,10 @@ def cache_save_message(message):
def cache_get_message(message_id): def cache_get_message(message_id):
return Message.objects.select_related().get(id=message_id) return Message.objects.select_related().get(id=message_id)
def message_fetch_objects():
max_id = Message.objects.only('id').order_by("-id")[0].id
return Message.objects.select_related().filter(id__gt=max_id - MESSAGE_CACHE_SIZE)
def message_cache_items(items_for_memcached, message): def message_cache_items(items_for_memcached, message):
items_for_memcached[message_cache_key(message.id)] = (message,) items_for_memcached[message_cache_key(message.id)] = (message,)
@ -58,8 +62,7 @@ cache_fillers = {
'client': (lambda: Client.objects.select_related().all(), client_cache_items, 3600*24*7, 10000), 'client': (lambda: Client.objects.select_related().all(), client_cache_items, 3600*24*7, 10000),
'recipient': (lambda: Recipient.objects.select_related().all(), recipient_cache_items, 3600*24*7, 10000), 'recipient': (lambda: Recipient.objects.select_related().all(), recipient_cache_items, 3600*24*7, 10000),
'stream': (lambda: Stream.objects.select_related().all(), stream_cache_items, 3600*24*7, 10000), 'stream': (lambda: Stream.objects.select_related().all(), stream_cache_items, 3600*24*7, 10000),
'message': (lambda: Message.objects.select_related().all().order_by("-id")[0:MESSAGE_CACHE_SIZE], 'message': (message_fetch_objects, message_cache_items, 3600 * 24, 1000),
message_cache_items, 3600 * 24, 1000),
'huddle': (lambda: Huddle.objects.select_related().all(), huddle_cache_items, 3600*24*7, 10000), 'huddle': (lambda: Huddle.objects.select_related().all(), huddle_cache_items, 3600*24*7, 10000),
'session': (lambda: Session.objects.all(), session_cache_items, 3600*24*7, 10000), 'session': (lambda: Session.objects.all(), session_cache_items, 3600*24*7, 10000),
} }