我有一个包含300万行和1.3GB大小的表。在我的笔记本电脑上使用4GB RAM运行Postgres 9.3。
explain analyze
select act_owner_id from cnt_contacts where act_owner_id = 2
我在cnt_contacts.act_owner_id上有btree键定义为：
CREATE INDEX cnt_contacts_idx_act_owner_id
ON public.cnt_contacts USING btree (act_owner_id, status_id);
查询在大约5秒内运行
Bitmap Heap Scan on cnt_contacts (cost=2598.79..86290.73 rows=6208 width=4) (actual time=5865.617..5875.302 rows=5444 loops=1)
Recheck Cond: (act_owner_id = 2)
-> Bitmap Index Scan on cnt_contacts_idx_act_owner_id (cost=0.00..2597.24 rows=6208 width=0) (actual time=5865.407..5865.407 rows=5444 loops=1)
Index Cond: (act_owner_id = 2)
Total runtime: 5875.684 ms"
为什么要这么久？
work_mem = 1024MB;
shared_buffers = 128MB;
effective_cache_size = 1024MB
seq_page_cost = 1.0 # measured on an arbitrary scale
random_page_cost = 15.0 # same scale as above
cpu_tuple_cost = 3.0
答案 0 :(得分：2)
您正在笔记本电脑上选择分散在1.3 GB表格中的5444条记录。您期待需要多长时间？
看起来你的索引没有被缓存，因为它无法在缓存中维持，或者因为这是你第一次使用它的那部分。如果重复运行完全相同的查询会发生什么？相同的查询但具有不同的常量？
在“explain（analyze，buffers）”下运行查询将有助于获取其他信息，特别是如果您首先启用track_io_timing。
答案 1 :(得分：2)
好的，PG有大表，索引和长时间执行。让我们思考如何改善计划和减少时间的方法。您编写和删除行。 PG写和删除元组，表和索引可以膨胀。为了良好的搜索，PG将索引加载到共享缓冲区并且您需要尽可能保持索引清洁。选择PG读取共享缓冲区而不是搜索。尝试设置缓冲区内存并减少索引和表膨胀，保持数据库清理。
你做什么和思考：
1）只需检查索引重复项，并且您的索引具有良好的选择：
WITH table_scans as (
SELECT relid,
tables.idx_scan + tables.seq_scan as all_scans,
( tables.n_tup_ins + tables.n_tup_upd + tables.n_tup_del ) as writes,
pg_relation_size(relid) as table_size
FROM pg_stat_user_tables as tables
),
all_writes as (
SELECT sum(writes) as total_writes
FROM table_scans
),
indexes as (
SELECT idx_stat.relid, idx_stat.indexrelid,
idx_stat.schemaname, idx_stat.relname as tablename,
idx_stat.indexrelname as indexname,
idx_stat.idx_scan,
pg_relation_size(idx_stat.indexrelid) as index_bytes,
indexdef ~* 'USING btree' AS idx_is_btree
FROM pg_stat_user_indexes as idx_stat
JOIN pg_index
USING (indexrelid)
JOIN pg_indexes as indexes
ON idx_stat.schemaname = indexes.schemaname
AND idx_stat.relname = indexes.tablename
AND idx_stat.indexrelname = indexes.indexname
WHERE pg_index.indisunique = FALSE
),
index_ratios AS (
SELECT schemaname, tablename, indexname,
idx_scan, all_scans,
round(( CASE WHEN all_scans = 0 THEN 0.0::NUMERIC
ELSE idx_scan::NUMERIC/all_scans * 100 END),2) as index_scan_pct,
writes,
round((CASE WHEN writes = 0 THEN idx_scan::NUMERIC ELSE idx_scan::NUMERIC/writes END),2)
as scans_per_write,
pg_size_pretty(index_bytes) as index_size,
pg_size_pretty(table_size) as table_size,
idx_is_btree, index_bytes
FROM indexes
JOIN table_scans
USING (relid)
),
index_groups AS (
SELECT 'Never Used Indexes' as reason, *, 1 as grp
FROM index_ratios
WHERE
idx_scan = 0
and idx_is_btree
UNION ALL
SELECT 'Low Scans, High Writes' as reason, *, 2 as grp
FROM index_ratios
WHERE
scans_per_write <= 1
and index_scan_pct < 10
and idx_scan > 0
and writes > 100
and idx_is_btree
UNION ALL
SELECT 'Seldom Used Large Indexes' as reason, *, 3 as grp
FROM index_ratios
WHERE
index_scan_pct < 5
and scans_per_write > 1
and idx_scan > 0
and idx_is_btree
and index_bytes > 100000000
UNION ALL
SELECT 'High-Write Large Non-Btree' as reason, index_ratios.*, 4 as grp
FROM index_ratios, all_writes
WHERE
( writes::NUMERIC / ( total_writes + 1 ) ) > 0.02
AND NOT idx_is_btree
AND index_bytes > 100000000
ORDER BY grp, index_bytes DESC )
SELECT reason, schemaname, tablename, indexname,
index_scan_pct, scans_per_write, index_size, table_size
FROM index_groups;
2）检查表格和索引是否膨胀？
SELECT
current_database(), schemaname, tablename, /*reltuples::bigint, relpages::bigint, otta,*/
ROUND((CASE WHEN otta=0 THEN 0.0 ELSE sml.relpages::FLOAT/otta END)::NUMERIC,1) AS tbloat,
CASE WHEN relpages < otta THEN 0 ELSE bs*(sml.relpages-otta)::BIGINT END AS wastedbytes,
iname, /*ituples::bigint, ipages::bigint, iotta,*/
ROUND((CASE WHEN iotta=0 OR ipages=0 THEN 0.0 ELSE ipages::FLOAT/iotta END)::NUMERIC,1) AS ibloat,
CASE WHEN ipages < iotta THEN 0 ELSE bs*(ipages-iotta) END AS wastedibytes
FROM (
SELECT
schemaname, tablename, cc.reltuples, cc.relpages, bs,
CEIL((cc.reltuples*((datahdr+ma-
(CASE WHEN datahdr%ma=0 THEN ma ELSE datahdr%ma END))+nullhdr2+4))/(bs-20::FLOAT)) AS otta,
COALESCE(c2.relname,'?') AS iname, COALESCE(c2.reltuples,0) AS ituples, COALESCE(c2.relpages,0) AS ipages,
COALESCE(CEIL((c2.reltuples*(datahdr-12))/(bs-20::FLOAT)),0) AS iotta -- very rough approximation, assumes all cols
FROM (
SELECT
ma,bs,schemaname,tablename,
(datawidth+(hdr+ma-(CASE WHEN hdr%ma=0 THEN ma ELSE hdr%ma END)))::NUMERIC AS datahdr,
(maxfracsum*(nullhdr+ma-(CASE WHEN nullhdr%ma=0 THEN ma ELSE nullhdr%ma END))) AS nullhdr2
FROM (
SELECT
schemaname, tablename, hdr, ma, bs,
SUM((1-null_frac)*avg_width) AS datawidth,
MAX(null_frac) AS maxfracsum,
hdr+(
SELECT 1+COUNT(*)/8
FROM pg_stats s2
WHERE null_frac<>0 AND s2.schemaname = s.schemaname AND s2.tablename = s.tablename
) AS nullhdr
FROM pg_stats s, (
SELECT
(SELECT current_setting('block_size')::NUMERIC) AS bs,
CASE WHEN SUBSTRING(v,12,3) IN ('8.0','8.1','8.2') THEN 27 ELSE 23 END AS hdr,
CASE WHEN v ~ 'mingw32' THEN 8 ELSE 4 END AS ma
FROM (SELECT version() AS v) AS foo
) AS constants
GROUP BY 1,2,3,4,5
) AS foo
) AS rs
JOIN pg_class cc ON cc.relname = rs.tablename
JOIN pg_namespace nn ON cc.relnamespace = nn.oid AND nn.nspname = rs.schemaname AND nn.nspname <> 'information_schema'
LEFT JOIN pg_index i ON indrelid = cc.oid
LEFT JOIN pg_class c2 ON c2.oid = i.indexrelid
) AS sml
ORDER BY wastedbytes DESC
3）你是否从硬盘清理未使用的元组？真空时间到了吗？
SELECT
relname AS TableName
,n_live_tup AS LiveTuples
,n_dead_tup AS DeadTuples
FROM pg_stat_user_tables;
4）想一想。如果db中有10条记录，10条中有8条id = 2，那就意味着你的索引选择性很差，这样PG就会扫描所有8条记录。但是你尝试使用id！= 2 index会很好用。尝试设置好的选择索引。
5）使用适当的列类型获取数据。如果你可以为你的列使用更少的kb类型，只需转换它。
6）只需检查数据库和条件。检查一下这个开始page 只是尝试在表中看到数据库中未使用的数据，必须清理索引，检查索引的选择性。尝试使用其他brin索引获取数据，尝试重新创建索引。