He nui nā cores o nā CPU o kēia wā. No nā makahiki, ua hoʻouna nā noi i nā nīnau i nā ʻikepili i ka like. Inā he nīnau hōʻike ma nā lālani he nui i ka papaʻaina, e holo wikiwiki ana i ka hoʻohana ʻana i nā CPU he nui, a ua hiki iā PostgreSQL ke hana i kēia mai ka mana 9.6.
He 3 mau makahiki e hoʻokō ai i ka hiʻohiʻona hulina like - pono mākou e kākau hou i ke code ma nā pae like ʻole o ka hoʻokō ʻana i ka nīnau. Ua hoʻokomo ʻo PostgreSQL 9.6 i nā ʻenehana e hoʻomaikaʻi hou i ke code. Ma nā mana e hiki mai ana, ua hoʻokō like ʻia nā ʻano nīnau ʻē aʻe.
Nā palena
- Mai ʻae i ka hoʻokō like inā paʻa nā cores a pau, i ʻole e lohi nā noi ʻē aʻe.
- ʻO ka mea nui loa, ʻo ka hoʻoili like ʻana me nā waiwai WORK_MEM kiʻekiʻe e hoʻohana i ka hoʻomanaʻo nui - ʻo kēlā me kēia hash hui a i ʻole ʻano e lawe i ka hoʻomanaʻo work_mem.
- ʻAʻole hiki ke hoʻokō ʻia nā nīnau OLTP latency haʻahaʻa e ka hoʻokō like. A inā e hoʻihoʻi ka nīnau i hoʻokahi lālani, e hoʻolohi wale ka hana like.
- Makemake nā mea hoʻomohala e hoʻohana i ka benchmark TPC-H. Loaʻa paha kāu mau nīnau like no ka hoʻokō like ʻana.
- ʻO nā nīnau SELECT wale nō me ka laka ʻole predicate e hoʻokō like ʻia.
- I kekahi manawa, ʻoi aku ka maikaʻi o ka hoʻopaʻa inoa ʻana ma mua o ka nānā ʻana i ka papa ma ke ʻano like.
- ʻAʻole kākoʻo ʻia ka hoʻomaha ʻana i nā nīnau a me nā cursors.
- ʻAʻole like nā hana o ka puka makani a me nā hana i hoʻonohonoho ʻia.
- ʻAʻole loaʻa iā ʻoe kekahi mea i ka hana I/O.
- ʻAʻohe ʻano algorithm e hoʻokaʻawale like. Akā hiki ke hoʻokō like ʻia nā nīnau me nā ʻano ma kekahi mau ʻano.
- E hoʻololi iā CTE (ME ...) me kahi SELECT pūnana e hiki ai ke hana like.
- ʻAʻole i kākoʻo nā mea ʻikepili ʻaoʻao ʻekolu i ke kaʻina hana like (akā hiki iā lākou!)
- ʻAʻole kākoʻo ʻia ʻo FULL OUTER JOIN.
- Hoʻopau ʻo max_rows i ka hana like.
- Inā loaʻa i kahi nīnau kahi hana i hōʻailona ʻole ʻia PARALLEL SAFE, ʻo ia ke kaula hoʻokahi.
- Hoʻopau ka pae hoʻokaʻawale kālepa SERIALIZABLE i ka hana like.
Kaiapuni hoao
Ua ho'āʻo nā mea hoʻomohala PostgreSQL e hōʻemi i ka manawa pane o nā nīnau benchmark TPC-H. Hoʻoiho i ka pae hoʻohālikelike a . He hoʻohana mana ʻole kēia o ka TPC-H benchmark - ʻaʻole no ka ʻikepili a i ʻole ka hoʻohālikelike ʻana i nā lako.
- Hoʻoiho iā TPC-H_Tools_v2.17.3.zip (a i ʻole ka mana hou aku) .
- Hoʻololi i ka inoa makefile.suite i Makefile a hoʻololi e like me ka wehewehe ʻana ma aneʻi: . Hoʻopili i ke code me ke kauoha make.
- Hana i ka ʻikepili:
./dbgen -s 10hana i kahi waihona 23 GB. Ua lawa kēia no ka ʻike ʻana i ka ʻokoʻa o ka hana o nā nīnau like ʻole a me ka ʻole. - E hoʻohuli i nā faila
tblвcsv с forиsed. - Hoʻopili i ka waihona
pg_tpcha kope i nā failacsvвpg_tpch/dss/data. - E hana i nā nīnau me kahi kauoha
qgen. - Hoʻouka i ka ʻikepili i loko o ka waihona me ke kauoha
./tpch.sh.
Ka nānā ʻana i ke kaʻina hana parallel
ʻOi aku ka wikiwiki ʻaʻole ma muli o ka heluhelu like ʻana, akā no ka hoʻolaha ʻana o ka ʻikepili ma nā cores CPU he nui. I nā ʻōnaehana hana hou, ua hūnā maikaʻi ʻia nā faila data PostgreSQL. Me ka heluhelu ʻana ma mua, hiki ke kiʻi i kahi poloka nui aʻe mai ka waiho ʻana ma mua o nā noi daemon PG. No laila, ʻaʻole i kaupalena ʻia ka hana nīnau e ka disk I/O. Hoʻopau ia i nā pōʻai CPU i:
- heluhelu i nā lālani i kēlā me kēia manawa mai nā ʻaoʻao papa;
- hoʻohālikelike i nā waiwai string a me nā kūlana
WHERE.
E holo kāua i kahi nīnau maʻalahi select:
tpch=# explain analyze select l_quantity as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Seq Scan on lineitem (cost=0.00..1964772.00 rows=58856235 width=5) (actual time=0.014..16951.669 rows=58839715 loops=1)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 1146337
Planning Time: 0.203 ms
Execution Time: 19035.100 msHoʻopuka ka sequential scan i nā lālani he nui me ka ʻole o ka hōʻuluʻulu ʻana, no laila e hoʻokō ʻia ka nīnau e ka CPU hoʻokahi.
Inā hoʻohui SUM(), hiki iā ʻoe ke ʻike i ʻelua mau kahe hana e kōkua i ka wikiwiki o ka nīnau:
explain analyze select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=1589702.14..1589702.15 rows=1 width=32) (actual time=8553.365..8553.365 rows=1 loops=1)
-> Gather (cost=1589701.91..1589702.12 rows=2 width=32) (actual time=8553.241..8555.067 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=1588701.91..1588701.92 rows=1 width=32) (actual time=8547.546..8547.546 rows=1 loops=3)
-> Parallel Seq Scan on lineitem (cost=0.00..1527393.33 rows=24523431 width=5) (actual time=0.038..5998.417 rows=19613238 loops=3)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 382112
Planning Time: 0.241 ms
Execution Time: 8555.131 msHoʻohui like
Hoʻopuka ka node Parallel Seq Scan i nā lālani no ka hōʻuluʻulu ʻāpana. ʻOki ka node "Partial Aggregate" i kēia mau laina me ka hoʻohana ʻana SUM(). I ka hopena, e hōʻiliʻili ʻia ka helu SUM mai kēlā me kēia kaʻina hana e ka node "Gather".
Hoʻopili ʻia ka hopena hope e ka node "Finalize Aggregate". Inā loaʻa iā ʻoe kāu mau hana aggregation, mai poina e kaha iā lākou ma ke ʻano he "parallel safe".
Ka helu o nā kaʻina hana
Hiki ke hoʻonui ʻia ka helu o nā kaʻina hana me ka ʻole e hoʻomaka hou i ka kikowaena:
explain analyze select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=1589702.14..1589702.15 rows=1 width=32) (actual time=8553.365..8553.365 rows=1 loops=1)
-> Gather (cost=1589701.91..1589702.12 rows=2 width=32) (actual time=8553.241..8555.067 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=1588701.91..1588701.92 rows=1 width=32) (actual time=8547.546..8547.546 rows=1 loops=3)
-> Parallel Seq Scan on lineitem (cost=0.00..1527393.33 rows=24523431 width=5) (actual time=0.038..5998.417 rows=19613238 loops=3)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 382112
Planning Time: 0.241 ms
Execution Time: 8555.131 msHe aha ka hana ma ʻaneʻi? He 2 mau manawa hou aku ka hana, a ua lilo ka noi i 1,6599 manawa wikiwiki. He hoihoi ka helu ana. Loaʻa iā mākou 2 kaʻina hana a me 1 alakaʻi. Ma hope o ka hoʻololi ʻana ua lilo ia i 4+1.
ʻO kā mākou wikiwiki wikiwiki mai ka hana like ʻana: 5/3 = 1,66(6) mau manawa.
Pehea ia hana?
Nā kaʻina hana
Hoʻomaka mau ka hoʻokō noi me ke kaʻina alakaʻi. Hana ke alakaʻi i nā mea like ʻole a me kekahi hana like. ʻO nā kaʻina hana ʻē aʻe e hana i nā noi like i kapa ʻia nā kaʻina hana limahana. Hoʻohana ʻia ka hana parallel i nā ʻōnaehana (mai ka mana 9.4). Ma muli o ka hoʻohana ʻana o nā ʻāpana ʻē aʻe o PostgreSQL i nā kaʻina hana ma mua o nā kaula, hiki i kahi nīnau me nā kaʻina hana 3 hiki ke ʻoi aku ka wikiwiki o 4 ma mua o ka hana kuʻuna.
Pākuʻi
Kūkākūkā nā kaʻina hana me ke alakaʻi ma o ka queue memo (ma muli o ka hoʻomanaʻo like). Loaʻa i kēlā me kēia kaʻina 2 queues: no nā hewa a me nā tuples.
ʻEhia mau kaʻina hana e pono ai?
Hōʻike ʻia ka palena liʻiliʻi e ka ʻāpana . Lawe ka mea holo noi i nā kaʻina hana mai ka loko i kaupalena ʻia e ka ʻāpana . ʻO ka palena hope loa , ʻo ia hoʻi, ka huina o nā kaʻina hana hope.
Inā ʻaʻole hiki ke hoʻokaʻawale i kahi kaʻina hana, ʻo ka hana hoʻokahi ka hana.
Hiki i ka mea hoʻolālā nīnau ke hōʻemi i nā kahe hana ma muli o ka nui o ka papaʻaina a i ʻole ka papa kuhikuhi. Aia nā palena no kēia и .
set min_parallel_table_scan_size='8MB'
8MB table => 1 worker
24MB table => 2 workers
72MB table => 3 workers
x => log(x / min_parallel_table_scan_size) / log(3) + 1 workerʻO kēlā me kēia manawa he 3 manawa ka nui o ka papaʻaina min_parallel_(index|table)_scan_size, Hoʻohui ʻo Postgres i kahi kaʻina hana. ʻAʻole pili ka helu o nā kaʻina hana i nā koina. Paʻakikī nā hoʻokō paʻakikī i ka hilinaʻi pōʻai. Akā, hoʻohana ka mea hoʻolālā i nā lula maʻalahi.
Ma ka hoʻomaʻamaʻa, ʻaʻole kūpono kēia mau lula no ka hana ʻana, no laila hiki iā ʻoe ke hoʻololi i ka helu o nā kaʻina hana no kahi papa kikoʻī: ALTER TABLE ... SET (parallel_workers = N).
No ke aha ʻaʻole hoʻohana ʻia ka hana parallel?
Ma waho aʻe o ka papa inoa lōʻihi o nā kaohi ʻana, aia pū kekahi nā loiloi kumukūʻai:
- e pale i ka hana like ʻana o nā noi pōkole. Hoʻohālikelike kēia ʻāpana i ka manawa e hoʻomākaukau ai i ka hoʻomanaʻo, hoʻomaka i ke kaʻina hana, a me ka hoʻololi ʻikepili mua.
: Hiki ke hoʻopaneʻe ke kamaʻilio ma waena o ke alakaʻi a me nā limahana e like me ka nui o nā tuple mai nā kaʻina hana. Ke helu nei kēia ʻāpana i ke kumukūʻai o ka hoʻololi ʻikepili.
Hoʻohui Loop Nested
PostgreSQL 9.6+ может выполнять вложенные циклы параллельно — это простая операция.
explain (costs off) select c_custkey, count(o_orderkey)
from customer left outer join orders on
c_custkey = o_custkey and o_comment not like '%special%deposits%'
group by c_custkey;
QUERY PLAN
--------------------------------------------------------------------------------------
Finalize GroupAggregate
Group Key: customer.c_custkey
-> Gather Merge
Workers Planned: 4
-> Partial GroupAggregate
Group Key: customer.c_custkey
-> Nested Loop Left Join
-> Parallel Index Only Scan using customer_pkey on customer
-> Index Scan using idx_orders_custkey on orders
Index Cond: (customer.c_custkey = o_custkey)
Filter: ((o_comment)::text !~~ '%special%deposits%'::text)Loaʻa ka hōʻiliʻili ma ka pae hope, no laila, ʻo Nested Loop Left Join kahi hana like. Ua hoʻokomo ʻia ʻo Parallel Index Only Scan ma ka mana 10 wale nō. He hana like ia me ka scan serial parallel. Kūlana c_custkey = o_custkey heluhelu i hoʻokahi kauoha no ke kaula o ka mea kūʻai aku. No laila, ʻaʻole like.
Hui Hash
Hana kēlā me kēia kaʻina hana i kāna papaʻaina hash a hiki i ka PostgreSQL 11. A inā ʻoi aku ma mua o ʻehā o kēia mau kaʻina hana, ʻaʻole e hoʻomaikaʻi ka hana. Ma ka mana hou, ua kaʻana like ka papaʻaina hash. Hiki i kēlā me kēia kaʻina hana ke hoʻohana i ka WORK_MEM e hana i ka papaʻaina hash.
select
l_shipmode,
sum(case
when o_orderpriority = '1-URGENT'
or o_orderpriority = '2-HIGH'
then 1
else 0
end) as high_line_count,
sum(case
when o_orderpriority <> '1-URGENT'
and o_orderpriority <> '2-HIGH'
then 1
else 0
end) as low_line_count
from
orders,
lineitem
where
o_orderkey = l_orderkey
and l_shipmode in ('MAIL', 'AIR')
and l_commitdate < l_receiptdate
and l_shipdate < l_commitdate
and l_receiptdate >= date '1996-01-01'
and l_receiptdate < date '1996-01-01' + interval '1' year
group by
l_shipmode
order by
l_shipmode
LIMIT 1;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=1964755.66..1964961.44 rows=1 width=27) (actual time=7579.592..7922.997 rows=1 loops=1)
-> Finalize GroupAggregate (cost=1964755.66..1966196.11 rows=7 width=27) (actual time=7579.590..7579.591 rows=1 loops=1)
Group Key: lineitem.l_shipmode
-> Gather Merge (cost=1964755.66..1966195.83 rows=28 width=27) (actual time=7559.593..7922.319 rows=6 loops=1)
Workers Planned: 4
Workers Launched: 4
-> Partial GroupAggregate (cost=1963755.61..1965192.44 rows=7 width=27) (actual time=7548.103..7564.592 rows=2 loops=5)
Group Key: lineitem.l_shipmode
-> Sort (cost=1963755.61..1963935.20 rows=71838 width=27) (actual time=7530.280..7539.688 rows=62519 loops=5)
Sort Key: lineitem.l_shipmode
Sort Method: external merge Disk: 2304kB
Worker 0: Sort Method: external merge Disk: 2064kB
Worker 1: Sort Method: external merge Disk: 2384kB
Worker 2: Sort Method: external merge Disk: 2264kB
Worker 3: Sort Method: external merge Disk: 2336kB
-> Parallel Hash Join (cost=382571.01..1957960.99 rows=71838 width=27) (actual time=7036.917..7499.692 rows=62519 loops=5)
Hash Cond: (lineitem.l_orderkey = orders.o_orderkey)
-> Parallel Seq Scan on lineitem (cost=0.00..1552386.40 rows=71838 width=19) (actual time=0.583..4901.063 rows=62519 loops=5)
Filter: ((l_shipmode = ANY ('{MAIL,AIR}'::bpchar[])) AND (l_commitdate < l_receiptdate) AND (l_shipdate < l_commitdate) AND (l_receiptdate >= '1996-01-01'::date) AND (l_receiptdate < '1997-01-01 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 11934691
-> Parallel Hash (cost=313722.45..313722.45 rows=3750045 width=20) (actual time=2011.518..2011.518 rows=3000000 loops=5)
Buckets: 65536 Batches: 256 Memory Usage: 3840kB
-> Parallel Seq Scan on orders (cost=0.00..313722.45 rows=3750045 width=20) (actual time=0.029..995.948 rows=3000000 loops=5)
Planning Time: 0.977 ms
Execution Time: 7923.770 msHōʻike maopopo ka nīnau 12 mai TPC-H i kahi pilina hash like. Hāʻawi kēlā me kēia kaʻina hana i ka hana ʻana i kahi papa hash maʻamau.
Hui Hui
He ʻano like ʻole ka hui ʻana. Mai hopohopo inā ʻo kēia ka pae hope loa o ka nīnau - hiki ke holo like.
-- Query 2 from TPC-H
explain (costs off) select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment
from part, supplier, partsupp, nation, region
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and p_size = 36
and p_type like '%BRASS'
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'AMERICA'
and ps_supplycost = (
select
min(ps_supplycost)
from partsupp, supplier, nation, region
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'AMERICA'
)
order by s_acctbal desc, n_name, s_name, p_partkey
LIMIT 100;
QUERY PLAN
----------------------------------------------------------------------------------------------------------
Limit
-> Sort
Sort Key: supplier.s_acctbal DESC, nation.n_name, supplier.s_name, part.p_partkey
-> Merge Join
Merge Cond: (part.p_partkey = partsupp.ps_partkey)
Join Filter: (partsupp.ps_supplycost = (SubPlan 1))
-> Gather Merge
Workers Planned: 4
-> Parallel Index Scan using <strong>part_pkey</strong> on part
Filter: (((p_type)::text ~~ '%BRASS'::text) AND (p_size = 36))
-> Materialize
-> Sort
Sort Key: partsupp.ps_partkey
-> Nested Loop
-> Nested Loop
Join Filter: (nation.n_regionkey = region.r_regionkey)
-> Seq Scan on region
Filter: (r_name = 'AMERICA'::bpchar)
-> Hash Join
Hash Cond: (supplier.s_nationkey = nation.n_nationkey)
-> Seq Scan on supplier
-> Hash
-> Seq Scan on nation
-> Index Scan using idx_partsupp_suppkey on partsupp
Index Cond: (ps_suppkey = supplier.s_suppkey)
SubPlan 1
-> Aggregate
-> Nested Loop
Join Filter: (nation_1.n_regionkey = region_1.r_regionkey)
-> Seq Scan on region region_1
Filter: (r_name = 'AMERICA'::bpchar)
-> Nested Loop
-> Nested Loop
-> Index Scan using idx_partsupp_partkey on partsupp partsupp_1
Index Cond: (part.p_partkey = ps_partkey)
-> Index Scan using supplier_pkey on supplier supplier_1
Index Cond: (s_suppkey = partsupp_1.ps_suppkey)
-> Index Scan using nation_pkey on nation nation_1
Index Cond: (n_nationkey = supplier_1.s_nationkey)Aia ka "Merge Join" node ma luna o ka "Gather Merge". No laila ʻaʻole hoʻohana ka hoʻohui ʻana i ka hana parallel. Akā ke kōkua mau nei ka "Parallel Index Scan" me ka ʻāpana part_pkey.
Hoʻohui ma nā ʻāpana
Ma ka PostgreSQL 11 disabled by default: loaʻa ke kumukūʻai hoʻonohonoho. Hiki ke hoʻohui ʻia nā pākaukau me nā ʻāpana like ʻole. Ma kēia ala e hoʻohana ai ʻo Postgres i nā papa hash liʻiliʻi. Hiki i kēlā me kēia pilina o nā ʻāpana ke like.
tpch=# set enable_partitionwise_join=t;
tpch=# explain (costs off) select * from prt1 t1, prt2 t2
where t1.a = t2.b and t1.b = 0 and t2.b between 0 and 10000;
QUERY PLAN
---------------------------------------------------
Append
-> Hash Join
Hash Cond: (t2.b = t1.a)
-> Seq Scan on prt2_p1 t2
Filter: ((b >= 0) AND (b <= 10000))
-> Hash
-> Seq Scan on prt1_p1 t1
Filter: (b = 0)
-> Hash Join
Hash Cond: (t2_1.b = t1_1.a)
-> Seq Scan on prt2_p2 t2_1
Filter: ((b >= 0) AND (b <= 10000))
-> Hash
-> Seq Scan on prt1_p2 t1_1
Filter: (b = 0)
tpch=# set parallel_setup_cost = 1;
tpch=# set parallel_tuple_cost = 0.01;
tpch=# explain (costs off) select * from prt1 t1, prt2 t2
where t1.a = t2.b and t1.b = 0 and t2.b between 0 and 10000;
QUERY PLAN
-----------------------------------------------------------
Gather
Workers Planned: 4
-> Parallel Append
-> Parallel Hash Join
Hash Cond: (t2_1.b = t1_1.a)
-> Parallel Seq Scan on prt2_p2 t2_1
Filter: ((b >= 0) AND (b <= 10000))
-> Parallel Hash
-> Parallel Seq Scan on prt1_p2 t1_1
Filter: (b = 0)
-> Parallel Hash Join
Hash Cond: (t2.b = t1.a)
-> Parallel Seq Scan on prt2_p1 t2
Filter: ((b >= 0) AND (b <= 10000))
-> Parallel Hash
-> Parallel Seq Scan on prt1_p1 t1
Filter: (b = 0)ʻO ka mea nui, ʻo ka hoʻohui ʻana i nā ʻāpana e like wale nō inā nui kēia mau ʻāpana.
Pākuʻi Kūlike
hiki ke hoʻohana ʻia ma kahi o nā poloka ʻokoʻa i nā kahe hana like ʻole. Hana ʻia kēia me nā nīnau UNION ALL. ʻO ka hemahema ka liʻiliʻi o ka parallelism, no ka mea, ʻo kēlā me kēia kaʻina hana hana wale nō 1 noi.
Aia he 2 mau kaʻina hana e holo nei ma ʻaneʻi, ʻoiai ua hoʻohana ʻia ka 4.
tpch=# explain (costs off) select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day union all select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '2000-12-01' - interval '105' day;
QUERY PLAN
------------------------------------------------------------------------------------------------
Gather
Workers Planned: 2
-> Parallel Append
-> Aggregate
-> Seq Scan on lineitem
Filter: (l_shipdate <= '2000-08-18 00:00:00'::timestamp without time zone)
-> Aggregate
-> Seq Scan on lineitem lineitem_1
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)ʻO nā loli nui loa
- WORK_MEM kaupalena ka hoʻomanaʻo i kēlā me kēia kaʻina hana, ʻaʻole wale nā nīnau: work_mem nā kaʻina hana pili = nui ka hoʻomanaʻo.
- - ehia ka nui o nā mea hana e hoʻohana ai ka papahana hoʻokō no ka hana like ʻana mai ka papahana.
- - hoʻoponopono i ka huina o nā kaʻina hana i ka helu o nā cores CPU ma ke kikowaena.
- - like, akā no nā kaʻina hana like.
Nā hopena
E like me ka mana 9.6, hiki i ka hana like ke hoʻomaikaʻi nui i ka hana o nā nīnau paʻakikī e nānā ana i nā lālani a i ʻole nā kuhikuhi. Ma PostgreSQL 10, hiki ke hoʻohana ʻia ka hana like ʻana ma ka paʻamau. E hoʻomanaʻo e hoʻopau iā ia ma nā kikowaena me kahi hana OLTP nui. ʻAi ʻia ka nui o nā kumu waiwai i ka nānā ʻana a i ʻole ka helu helu helu. Inā ʻaʻole ʻoe e holo ana i kahi hōʻike ma ka ʻikepili holoʻokoʻa, hiki iā ʻoe ke hoʻomaikaʻi i ka hana noiʻi ma ka hoʻohui ʻana i nā index i nalowale a i ʻole ka hoʻohana ʻana i ka ʻāpana kūpono.
kūmole
Source: www.habr.com