动态过滤
本节介绍openLooKeng动态过滤特性。动态过滤适用于高选择性join场景，即大多数的probe侧的表在读取之后由于不匹配join条件而被过滤掉。 openLooKeng在查询运行时，依靠join条件以及build侧读出的数据，生成动态过滤条件，并作为额外的过滤条件应用到probe侧表的table scan阶段，从而减少参与join操作的probe表的数据量，有效地减少IO读取与网络传输。
适用场景
openLooKeng动态过滤主要应用于高选择性的join场景（包含针对分区表的分区裁剪以及非分区表的行过滤）。openLooKeng动态过滤当前适用于inner join，semi join 以及right join场景，适用于Hive connector，DC connector以及Memory connector。
使用
openLooKeng动态过滤特性依赖于分布式缓存组件，请参考State Store章节配置。
在/etc/config.properties需要配置如下参数
enable-dynamic-filtering=true
dynamic-filtering-data-type=BLOOM_FILTER
dynamic-filtering-max-per-driver-size=100MB
dynamic-filtering-max-per-driver-row-count=10000
dynamic-filtering-bloom-filter-fpp=0.1
上述属性说明如下：
enable-dynamic-filtering：是否开启动态过滤特性。dynamic-filtering-wait-time：等待动态过滤条件生成的最长等待时间，默认值是1s。dynamic-filtering-data-type：设置动态过滤类型，可选包含BLOOM_FILTER以及HASHSET，默认类型为BLOOM_FILTER。dynamic-filtering-max-size: 每个dynamic filter的大小上限，如果预估大小超过设定值，代价优化器不会生成对应的dynamic filter，默认值是1000000。dynamic-filtering-max-per-driver-size：每个driver可以收集的数据大小上限，默认值是1MB。dynamic-filtering-max-per-driver-row-count：每个driver可以收集的数据条目上限，默认值是10000。dynamic-filtering-bloom-filter-fpp：动态过滤使用的bloomfilter的FPP值，默认是0.1。
如果应用于Hive connector，需要对catalog/hive.properties如下修改：
hive.dynamic-filter-partition-filtering=true
hive.dynamic-filtering-row-filtering-threshold=5000
上述属性说明如下：
hive.dynamic-filter-partition-filtering：使用动态过滤条件根据分区值进行预先过滤，默认值是false。hive.dynamic-filtering-row-filtering-threshold：如果动态过滤条件大小低于阈值，则应用行过滤，默认值是2000。
执行计划
下面的例子展示了SQL语句如何应用动态过滤条件，在执行计划中标记为dynamicFilter。 可以使用explain命令查看动态过滤是否应用，也可以在webUI中的liveplan查看当前执行是否应用动态过滤。
create table table1 (id integer, year varchar);
create table table2 (id integer, total integer);
insert into table1 values (1, '2019'), (2, '2020'), (3, '2021');
insert into table2 values (1, 100), (2, 200);
Inner join:
explain select t1.id, t1.year from table1 t1, table2 t2 where t1.id = t2.id and t2.total = 200;
Query Plan
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Output[id, year]
│ Layout: [id:integer, year:varchar]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ RemoteExchange[GATHER]
│ Layout: [year:varchar, id:integer]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ InnerJoin[("id" = "id_0")][$hashvalue, $hashvalue_9]
│ Layout: [id:integer, year:varchar]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
│ Distribution: PARTITIONED
│ dynamicFilterAssignments = {id_0 -> 238}
├─ RemoteExchange[REPARTITION][$hashvalue]
│ │ Layout: [id:integer, year:varchar, $hashvalue:bigint]
│ │ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
│ └─ ScanFilterProject[table = memory:0, filterPredicate = true, dynamicFilter = {238 -> "id"}]
│ Layout: [id:integer, year:varchar, $hashvalue_8:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}
│ $hashvalue_8 := "combine_hash"(bigint '0', COALESCE("$operator$hash_code"("id"), 0))
│ year := 1
│ id := 0
└─ LocalExchange[HASH][$hashvalue_9] ("id_0")
│ Layout: [id_0:integer, $hashvalue_9:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
└─ RemoteExchange[REPARTITION][$hashvalue_10]
│ Layout: [id_0:integer, $hashvalue_10:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
└─ ScanFilterProject[table = memory:5, filterPredicate = ("total" = 200)]
Layout: [id_0:integer, $hashvalue_11:bigint]
Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}
$hashvalue_11 := "combine_hash"(bigint '0', COALESCE("$operator$hash_code"("id_0"), 0))
total := 1
id_0 := 0
Semi join:
explain select * from table1 where id in (select id from table2);
Query Plan
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Output[id, year]
│ Layout: [id:integer, year:varchar]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ RemoteExchange[GATHER]
│ Layout: [id:integer, year:varchar]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ FilterProject[filterPredicate = "expr_6"]
│ Layout: [id:integer, year:varchar]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}/{rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ Project[]
│ Layout: [id:integer, year:varchar, expr_6:boolean]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
└─ SemiJoin[id = id_1][$hashvalue, $hashvalue_16]
│ Layout: [id:integer, year:varchar, $hashvalue:bigint, expr_6:boolean]
│ Estimates: {rows: ? (?), cpu: ?, memory: ?, network: ?}
│ Distribution: PARTITIONED
│ dynamicFilterId: 279
├─ RemoteExchange[REPARTITION][$hashvalue]
│ │ Layout: [id:integer, year:varchar, $hashvalue:bigint]
│ │ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
│ └─ ScanFilterProject[table = memory:0, filterPredicate = true, dynamicFilter = {279 -> "id"}]
│ Layout: [id:integer, year:varchar, $hashvalue_15:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}
│ $hashvalue_15 := "combine_hash"(bigint '0', COALESCE("$operator$hash_code"("id"), 0))
│ year := 1
│ id := 0
└─ LocalExchange[SINGLE] ()
│ Layout: [id_1:integer, $hashvalue_16:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
└─ RemoteExchange[REPARTITION - REPLICATE NULLS AND ANY][$hashvalue_17]
│ Layout: [id_1:integer, $hashvalue_17:bigint]
│ Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: ?}
└─ ScanProject[table = memory:5]
Layout: [id_1:integer, $hashvalue_18:bigint]
Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: 0B}/{rows: ? (?), cpu: ?, memory: 0B, network: 0B}
$hashvalue_18 := "combine_hash"(bigint '0', COALESCE("$operator$hash_code"("id_1"), 0))
id_1 := 0
