EnterpriseDB has been running a research project to improve the performance of heavily updated tables. We have a number of approaches prototyped and we'd like to discuss the best of these now on -hackers for community input and patch submission to PostgreSQL core.
The most important step with any proposal is to agree that we have an issue that needs improvement, discuss how widespread that issue is and find some clear test cases that show up the problems. Tests are: 1. pgbench reveals performance that will degrade over a long period. 2. DBT-2 reveals performance that will degrade over a long period. Many tests over a 2 hour period don't fully show this, especially when the test is cafeully tuned. 3. Some common scenarios in applications are where some rows of a table are "hot" from being constantly updated, while others are not. An example of such a test case is the truckin' test, included here. It's based directly on a specific customer application, but its been generalised to make sure the underlying design pattern is clear. These tests reveal the following issues, all of which are known: - update performs inserts into indexes, as well as into heap blocks - VACUUM can remove heap blocks easily, but performs much worse on indexes, making VACUUM a less good solution. We have now been able to speed up index VACUUM, but this require us to scan the whole index for correct locking. VACUUM scans the whole table, whereas dead rows may well be localised. Heap-needs-vacuum-bitmap has been proposed here, but no solution currently exists for vacuuming only parts of indexes and so proposals for concurrent vacuums are now being considered. - indexes that have been stretched apart by updates do not ever coalesce again and require regular REINDEX, which is not yet possible concurrently; the contention caused by this would be catastrophic for performance, even if anybody knew of a way to do this concurrently. - There are specific issues with the optimizer's ability to understand dead row numbers, which can in some cases lead to SeqScan plans that are inappropriate when tables grow because of updates. This is a red-herring that can lead to people thinking the situation is worse than it is; that needs fixing, but the core issues mentioned above remain. To alleviate these problems we've added features such as WITH fillfactor for heaps and table-level autovacuum tuning. Tuning all of these features to good effect is an art form that is beyond the reasonable for most users. Many internal optimizations have been made in this area and as can be seen, many are still required to achieve better performance. The proposal about to be made takes a more radical approach and re-examines the architecture of the heap, to allow us to consider much faster designs for heavy UPDATEs. Although initially radical, the proposal appears to be fully MVCC correct, crash safe as well as being much faster under heavy updates, while approximately neutral in other cases with no major downsides. Why should we care? The UPDATE case has obvious use-cases in a business design pattern I'll call CustomerAccountDebit which is pervasive in pay-per-use websites, banks, telephone companies, road traffic monitoring etc etc. It's also pervasive in Data Warehousing where summary tables/materialized views are regularly updated to maintain a current picture of spending, movements or any other accumulation of event detail. It's everywhere, basically. Your various viewpoints on the above are welcome, but assuming for the moment that you agree so far, we can move towards the proposal... These discussions will likely be lengthy if taken seriously and need to cover a range of different topics to ensure we cover what we know and ensure we listen to all the feedback everybody gives. To that end, I'd like to introduce two colleagues of mine to the community, Pavan Deolasee and Nikhil Sontakke who have been working hard on developing the prototypes and measuring/tuning them respectively. I would stress that we are not bringing our first prototype to the table, but actually design #5. We think you'll be interested, but we won't take that for granted. Our next steps will be to - discuss various other approaches to the problem, and why we are now proposing one specific approach and receive "why dont we..." feedback and additional ideas (Simon) - discuss the proposal in technical depth, explain the challenges that remain and ask for feedback and input on those, with specific regard to low-level coding (Pavan) - present details of performance testing done so far (Nikhil) - explain the measures we have taken to prove the correctness of our approach for MVCC, crash safety and PITR (Simon) Each of these areas will be started as a separate thread of discussion on -hackers, to allow us to stay focused on those topics. But before we do that, any comments on the above? --- The truckin test case included here consists of a complex update function that is executed by a custom pgbench script, on the postgres database. psql -f truckin.sql postgres for each test pgbench -n -f truckin.pgb postgres -- Simon Riggs EnterpriseDB http://www.enterprisedb.com
\setrandom vid 1 10000 \setrandom npoints 100 1000 select simulate_vehicle_route(:vid,:npoints);
drop table if exists vehicle_position_current; create table vehicle_position_current ( vehicle_id integer not null ,position integer not null ); create unique index vpc_idx on vehicle_position_current (vehicle_id); drop table if exists vehicle_position_history; create table vehicle_position_history ( vehicle_id integer not null ,log_datetime timestamp not null default current_timestamp ,position integer not null ); create index vph_idx on vehicle_position_history (vehicle_id); create or replace function set_vehicle_position(p_veh_id integer, p_position integer) returns integer as $$ declare p integer; begin select into p position from vehicle_position_current where vehicle_id = p_veh_id; if not found then insert into vehicle_position_current (vehicle_id, position) values (p_veh_id, 1); insert into vehicle_position_history (vehicle_id, position) values (p_veh_id, 1); else insert into vehicle_position_history (vehicle_id, position) values (p_veh_id, p); update vehicle_position_current set position = position + 1 where vehicle_id = p_veh_id; end if; return 0; end; $$ language plpgsql; create or replace function simulate_vehicle_route(p_veh_id integer, npoints integer) returns integer as $$ declare i integer; begin for i in 1..npoints loop perform set_vehicle_position(p_veh_id, i); end loop; return 0; end; $$ language plpgsql; insert into vehicle_position_history select generate_series(1,10000000),current_timestamp,1; insert into vehicle_position_current select generate_series(1,10000000),1;
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