在线时间:8:00-16:00
迪恩网络APP
随时随地掌握行业动态
扫描二维码
关注迪恩网络微信公众号
os: ubuntu 16.04 postgresql: 9.6.8 ip 规划 192.168.56.102 node2 postgresql help create indexpostgres=# \h create index Command: CREATE INDEX Description: define a new index Syntax: CREATE [ UNIQUE ] INDEX [ CONCURRENTLY ] [ [ IF NOT EXISTS ] name ] ON table_name [ USING method ] ( { column_name | ( expression ) } [ COLLATE collation ] [ opclass ] [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...] ) [ WITH ( storage_parameter = value [, ... ] ) ] [ TABLESPACE tablespace_name ] [ WHERE predicate ] [ USING method ] method 要使用的索引方法的名称。可以选择 btree、hash、 gist、spgist、 gin以及brin。 默认方法是btree。 hashhash 只能处理简单的等值比较, postgres=# drop table tmp_t0; DROP TABLE postgres=# create table tmp_t0(c0 varchar(100),c1 varchar(100)); CREATE TABLE postgres=# insert into tmp_t0(c0,c1) select md5(id::varchar),md5((id+id)::varchar) from generate_series(1,100000) as id; INSERT 0 100000 postgres=# create index idx_tmp_t0_1 on tmp_t0 using hash(c0); CREATE INDEX postgres=# \d+ tmp_t0 Table "public.tmp_t0" Column | Type | Collation | Nullable | Default | Storage | Stats target | Description --------+------------------------+-----------+----------+---------+----------+--------------+------------- c0 | character varying(100) | | | | extended | | c1 | character varying(100) | | | | extended | | Indexes: "idx_tmp_t0_1" hash (c0) postgres=# explain select * from tmp_t0 where c0 = 'd3d9446802a44259755d38e6d163e820'; QUERY PLAN ---------------------------------------------------------------------------- Index Scan using idx_tmp_t0_1 on tmp_t0 (cost=0.00..8.02 rows=1 width=66) Index Cond: ((c0)::text = 'd3d9446802a44259755d38e6d163e820'::text) (2 rows) 注意事项,官网特别强调:Hash索引操作目前不被WAL记录,因此存在未写入修改,在数据库崩溃后需要用REINDEX命令重建Hash索引。 同样,在完成初始的基础备份后,对于Hash索引的改变也不会通过流式或基于文件的复制所复制,所以它们会对其后使用它们的查询给出错误的答案。 正因为这些原因,Hash索引已不再被建议使用。 补充:Postgresql hash索引介绍 hash索引的结构当数据插入索引时,我们会为这个索引键通过哈希函数计算一个值。 PostgreSQL中的哈希函数始终返回“整数”类型,范围为2^32≈40亿。bucket桶的数量最初为2个,然后动态增加以适应数据大小。可以使用位算法从哈希码计算出桶编号。这个bucket将存放TID。 由于可以将与不同索引键匹配的TID放入同一bucket桶中。而且除了TID之外,还可以将键的源值存储在bucket桶中,但这会增加索引大小。为了节省空间,bucket桶只存储索引键的哈希码,而不存储索引键。 当我们通过索引查询时,我们计算索引键的哈希函数并获取bucket桶的编号。现在,仍然需要遍历存储桶的内容,并仅返回所需的哈希码匹配的TID。由于存储的“hash code - TID”对是有序的,因此可以高效地完成此操作。 但是,两个不同的索引键可能会发生以下情况,两个索引键都进入一个bucket桶,而且具有相同的四字节的哈希码。因此,索引访问方法要求索引引擎重新检查表行中的情况来验证每个TID。 映射数据结构到pageMeta page - 0号page,包含索引内部相关信息 Bucket pages - 索引的主要page,存储 “hash code - TID” 对 Overflow pages - 与bucket page的结构相同,在不足一个page时,作为bucket桶使用 Bitmap pages - 跟踪当前干净的overflow page,并可将其重新用于其他bucket桶 注意,哈希索引不能减小大小。虽然我们删除了一些索引行,但是分配的页面将不会返回到操作系统,只会在VACUUMING之后重新用于新数据。减小索引大小的唯一选项是使用REINDEX或VACUUM FULL命令从头开始重建索引 接下来看下hash索引如何创建demo=# create index on flights using hash(flight_no); demo=# explain (costs off) select * from flights where flight_no = 'PG0001'; QUERY PLAN ---------------------------------------------------- Bitmap Heap Scan on flights Recheck Cond: (flight_no = 'PG0001'::bpchar) -> Bitmap Index Scan on flights_flight_no_idx Index Cond: (flight_no = 'PG0001'::bpchar) (4 rows) 注意:10版本之前hash索引不记录到wal中,所以hash索引不能做recovery,当然也就不能复制了,但是从10版本以后hash所用得到了增强,可以记录到wal中,创建的时候也不会再有警告。 查看hash访问方法相关的操作函数demo=# select opf.opfname as opfamily_name, amproc.amproc::regproc AS opfamily_procedure from pg_am am, pg_opfamily opf, pg_amproc amproc where opf.opfmethod = am.oid and amproc.amprocfamily = opf.oid and am.amname = 'hash' order by opfamily_name, opfamily_procedure; opfamily_name | opfamily_procedure --------------------+------------------------- abstime_ops | hashint4extended abstime_ops | hashint4 aclitem_ops | hash_aclitem aclitem_ops | hash_aclitem_extended array_ops | hash_array array_ops | hash_array_extended bool_ops | hashcharextended bool_ops | hashchar bpchar_ops | hashbpcharextended bpchar_ops | hashbpchar bpchar_pattern_ops | hashbpcharextended bpchar_pattern_ops | hashbpchar bytea_ops | hashvarlena bytea_ops | hashvarlenaextended char_ops | hashcharextended char_ops | hashchar cid_ops | hashint4extended cid_ops | hashint4 date_ops | hashint4extended date_ops | hashint4 enum_ops | hashenumextended enum_ops | hashenum float_ops | hashfloat4extended float_ops | hashfloat8extended float_ops | hashfloat4 float_ops | hashfloat8 ... 可以用这些函数计算相关类型的哈希码hank=# select hashtext('zhang'); hashtext ------------- -1172392837 (1 row) hank=# select hashint4(10); hashint4 ------------- -1547814713 (1 row) hash索引相关的属性hank=# select a.amname, p.name, pg_indexam_has_property(a.oid,p.name) hank-# from pg_am a, hank-# unnest(array['can_order','can_unique','can_multi_col','can_exclude']) p(name) hank-# where a.amname = 'hash' hank-# order by a.amname; amname | name | pg_indexam_has_property --------+---------------+------------------------- hash | can_order | f hash | can_unique | f hash | can_multi_col | f hash | can_exclude | t (4 rows) hank=# select p.name, pg_index_has_property('hank.idx_test_name'::regclass,p.name) hank-# from unnest(array[ hank(# 'clusterable','index_scan','bitmap_scan','backward_scan' hank(# ]) p(name); name | pg_index_has_property ---------------+----------------------- clusterable | f index_scan | t bitmap_scan | t backward_scan | t (4 rows) hank=# select p.name, hank-# pg_index_column_has_property('hank.idx_test_name'::regclass,1,p.name) hank-# from unnest(array[ hank(# 'asc','desc','nulls_first','nulls_last','orderable','distance_orderable', hank(# 'returnable','search_array','search_nulls' hank(# ]) p(name); name | pg_index_column_has_property --------------------+------------------------------ asc | f desc | f nulls_first | f nulls_last | f orderable | f distance_orderable | f returnable | f search_array | f search_nulls | f (9 rows) 由于hash函数没有特定的排序规则,所以一般的hash索引只支持等值查询,可以通过下面数据字典看到,所有操作都是“=”,而且hash索引也不会处理null值,所以不会标记null值,还有就是hash索引不存储索引键,只存储hash码,所以不会 index-only扫描,也不支持多列创建hash索引 hank=# select opf.opfname AS opfamily_name, hank-# amop.amopopr::regoperator AS opfamily_operator hank-# from pg_am am, hank-# pg_opfamily opf, hank-# pg_amop amop hank-# where opf.opfmethod = am.oid hank-# and amop.amopfamily = opf.oid hank-# and am.amname = 'hash' hank-# order by opfamily_name, hank-# opfamily_operator; opfamily_name | opfamily_operator --------------------+------------------------------------------------------------ abstime_ops | =(abstime,abstime) aclitem_ops | =(aclitem,aclitem) array_ops | =(anyarray,anyarray) bool_ops | =(boolean,boolean) bpchar_ops | =(character,character) bpchar_pattern_ops | =(character,character) bytea_ops | =(bytea,bytea) char_ops | =("char","char") cid_ops | =(cid,cid) date_ops | =(date,date) enum_ops | =(anyenum,anyenum) float_ops | =(real,real) float_ops | =(double precision,double precision) float_ops | =(real,double precision) float_ops | =(double precision,real) hash_hstore_ops | =(hstore,hstore) integer_ops | =(integer,bigint) integer_ops | =(smallint,smallint) integer_ops | =(integer,integer) integer_ops | =(bigint,bigint) integer_ops | =(bigint,integer) integer_ops | =(smallint,integer) integer_ops | =(integer,smallint) integer_ops | =(smallint,bigint) integer_ops | =(bigint,smallint) interval_ops | =(interval,interval) jsonb_ops | =(jsonb,jsonb) macaddr8_ops | =(macaddr8,macaddr8) macaddr_ops | =(macaddr,macaddr) name_ops | =(name,name) network_ops | =(inet,inet) numeric_ops | =(numeric,numeric) oid_ops | =(oid,oid) oidvector_ops | =(oidvector,oidvector) pg_lsn_ops | =(pg_lsn,pg_lsn) range_ops | =(anyrange,anyrange) reltime_ops | =(reltime,reltime) text_ops | =(text,text) text_pattern_ops | =(text,text) time_ops | =(time without time zone,time without time zone) timestamp_ops | =(timestamp without time zone,timestamp without time zone) timestamptz_ops | =(timestamp with time zone,timestamp with time zone) timetz_ops | =(time with time zone,time with time zone) uuid_ops | =(uuid,uuid) xid_ops | =(xid,xid) 从10版本开始,可以通过pageinspect插件查看hash索引的内部情况安装插件 create extension pageinspect; 查看0号page hank=# select hash_page_type(get_raw_page('hank.idx_test_name',0)); hash_page_type ---------------- metapage (1 row) 查看索引中的行数和已用的最大存储桶数 hank=# select ntuples, maxbucket hank-# from hash_metapage_info(get_raw_page('hank.idx_test_name',0)); ntuples | maxbucket ---------+----------- 1000 | 3 (1 row) 可以看到1号page是bucket,查看此bucket page的活动元组和死元组的数量, 也就是膨胀度,以便维护索引 hank=# select hash_page_type(get_raw_page('hank.idx_test_name',1)); hash_page_type ---------------- bucket (1 row) hank=# select live_items, dead_items hank-# from hash_page_stats(get_raw_page('hank.idx_test_name',1)); live_items | dead_items ------------+------------ 407 | 0 (1 row) 以上为个人经验,希望能给大家一个参考,也希望大家多多支持极客世界。如有错误或未考虑完全的地方,望不吝赐教。 |
请发表评论