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[GitHub] [incubator-doris] hf200012 commented on a change in pull request #6154: [DOC] Add docs of Runtime Filter
hf200012 commented on a change in pull request #6154:
URL: https://github.com/apache/incubator-doris/pull/6154#discussion_r663733418
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File path: docs/en/administrator-guide/runtime-filter.md
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@@ -0,0 +1,223 @@
+---
+{
+ "title": "Runtime Filter",
+ "language": "en"
+}
+---
+
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+
+# Runtime Filter
+Runtime Filter is a new feature officially added in Doris 0.15. It is designed to dynamically generate filter conditions for certain Join queries at runtime to reduce the amount of scanned data, avoid unnecessary I/O and network transmission, and speed up the query.
+
+It's design, implementation and effects, please refer to [ISSUE 6116](https://github.com/apache/incubator-doris/issues/6116).
+
+## Noun Interpretation
+* FE: Frontend, the front-end node of Doris. Responsible for metadata management and request access.
+* BE: Backend, the back-end node of Doris. Responsible for query execution and data storage.
+* Left table: the table on the left during Join query. Perform Probe operation. The order can be adjusted by Join Reorder.
+* Right table: the table on the right during Join query. Perform the Build operation. The order can be adjusted by Join Reorder.
+* Fragment: FE will convert the execution of specific SQL statements into corresponding fragments and send them to BE for execution. The corresponding Fragment is executed on the BE, and the results are aggregated and returned to the FE.
+* Join on clause: `Aa=Bb` in `A join B on Aa=Bb`, based on this to generate join conjuncts during query planning, including expr used by join Build and Probe, where Build expr is called in Runtime Filter src expr, Probe expr are called target expr in Runtime Filter.
+
+## Principle
+Runtime Filter is generated during query planning, constructed in HashJoinNode, and applied in ScanNode.
+
+For example, there is currently a Join query between the T1 table and the T2 table. Its Join mode is HashJoin. T1 is a fact table with 100,000 rows of data. T2 is a dimension table with 100 rows of data. Doris join The actual situation is:
+```
+| > HashJoinNode <
+| | |
+| | 100000 | 2000
+| | |
+| OlapScanNode OlapScanNode
+| ^ ^
+| | 100000 | 2000
+| T1 T2
+|
+```
+Obviously, scanning data for T2 is much faster than T1. If we take the initiative to wait for a while and then scan T1, after T2 sends the scanned data record to HashJoinNode, HashJoinNode calculates a filter condition based on the data of T2, such as the maximum value of T2 data And the minimum value, or build a Bloom Filter, and then send this filter condition to ScanNode waiting to scan T1, the latter applies this filter condition and delivers the filtered data to HashJoinNode, thereby reducing the number of probe hash tables and network overhead. This filter condition is Runtime Filter, and the effect is as follows:
+```
+| > HashJoinNode <
+| | |
+| | 6000 | 2000
+| | |
+| OlapScanNode OlapScanNode
+| ^ ^
+| | 100000 | 2000
+| T1 T2
+|
+```
+If the filter condition (Runtime Filter) can be pushed down to the storage engine, in some cases, the index can be used to directly reduce the amount of scanned data, thereby greatly reducing the scanning time. The effect is as follows:
+```
+| > HashJoinNode <
+| | |
+| | 6000 | 2000
+| | |
+| OlapScanNode OlapScanNode
+| ^ ^
+| | 6000 | 2000
+| T1 T2
+|
+```
+It can be seen that, unlike predicate push-down and partition cutting, Runtime Filter is a filter condition dynamically generated at runtime, that is, when the query is run, the join on clause is parsed to determine the filter expression, and the expression is broadcast to ScanNode that is reading the left table , Thereby reducing the amount of scanned data, thereby reducing the number of probe hash table, avoiding unnecessary I/O and network transmission.
+
+Runtime Filter is mainly used to optimize joins for large tables. If the amount of data in the left table is too small, or the amount of data in the right table is too large, the Runtime Filter may not achieve the expected effect.
+
+## Usage
+
+### Set session variable
+In addition to `runtime_filter_type`, other query options are used to adjust the Runtime Filter to achieve the best performance in a specific scenario, usually only after the performance test, to optimize the query that is resource-intensive, takes a long enough time to run, and has a high enough frequency .
+
+#### 1.runtime_filter_type
+The types of Runtime Filter used include Bloom Filter, MinMax Filter, and IN predicate. When multiple types are used, they are separated by commas. Note that you need to add quotation marks. For example:
+```
+set runtime_filter_type="BLOOM_FILTER,IN,MIN_MAX";
+```
+By default, only IN predicate is used conservatively. In some cases, the performance is higher when Bloom Filter, MinMax Filter, and IN predicate are used at the same time.
+
+- **Bloom Filter**: There is a certain misjudgment rate, resulting in filtered data less than expected, but it will not lead to inaccurate final results. In most cases, Bloom Filter can improve performance or have no significant impact on performance , But in some cases it will cause performance degradation.
+ - Bloom Filter construction and application overhead is high, so when the filtering rate is low, or the amount of data in the left table is small, Bloom Filter may cause performance degradation.
+ - At present, only the Key column of the left table can be pushed down to the storage engine if the Bloom Filter is applied, and the test results show that the performance of the Bloom Filter is not pushed down to the storage engine.
+ - Currently Bloom Filter only has short-circuit logic when using expression filtering on ScanNode, that is, when the false positive rate is too high, the Bloom Filter will not continue to be used, but there is no short-circuit logic when the Bloom Filter is pushed down to the storage engine , So when the filtration rate is low, it may cause performance degradation.
+- **MinMax Filter**: Contains the maximum value and the minimum value, thereby filtering data smaller than the minimum value and greater than the maximum value. The filtering effect of the MinMax Filter is related to the type of the Key column in the join on clause and the data distribution of the left and right tables.
+ - When the type of the Key column in the join on clause is int/bigint/double, etc., in extreme cases, if the maximum and minimum values of the left and right tables are the same, there is no effect, otherwise the maximum value of the right table is less than the minimum value of the left table, or the minimum of the right table The value is greater than the maximum value in the left table, the effect is best.
+ - When the type of the Key column in the join on clause is varchar, etc., applying the MinMax Filter will often cause performance degradation.
+- **IN predicate**: Construct the IN predicate based on all the values of the Key listed in the join on clause on the right table, and use the constructed IN predicate to filter on the left table. Compared with the loom filter, the cost of construction and application is lower. The table on the right tends to have higher performance when the amount of data is small.
+ - By default, only the number of data rows in the right table is less than 1024 will be pushed down (can be adjusted by the `runtime_filter_max_in_num` in the session variable).
+ - Currently IN predicate does not implement a merge method, that is, it cannot be pushed down across Fragments, so currently when it is necessary to push down to the ScanNode of the left table of shuffle join, if Bloom Filter is not generated, then we will convert IN predicate to Bloom Filter for Process pushdown across Fragments, so even if the type only selects IN predicate, Bloom Filter may actually be applied;
+
+#### 2.runtime_filter_mode
+It is used to adjust the push-down strategy of Runtime Filter, including two strategies, LOCAL and GLOBAL, and the default setting is GLOBAL strategy.
+
+LOCAL: Relatively conservative, the constructed Runtime Filter can only be used in the same Fragment on the same instance (the smallest unit of query execution), that is, the Runtime Filter producer (the HashJoinNode that constructs the Filter) and the consumer (the ScanNode that uses the RuntimeFilter) The same Fragment, such as the general scene of broadcast join;
+
+GLOBAL: Relatively radical. In addition to satisfying the scenario of the LOCAL strategy, the Runtime Filter can also be combined and transmitted to different Fragments on different instances via the network. For example, Runtime Filter producers and consumers are in different Fragments, such as shuffle join. The query can be optimized in a wider range of scenarios.
+
+When building and applying Runtime Filters on different Fragments, the reasons and strategies for merging Runtime Filters can be found in [ISSUE 6116](https://github.com/apache/incubator-doris/issues/6116)
+
+#### 3.runtime_filter_wait_time_ms
+After the Runtime Filter is turned on, the ScanNode that joins the left table will wait for a period of time for each Runtime Filter assigned to itself and then scan the data. The default wait is 1s (1000ms, in milliseconds), that is, if the ScanNode is assigned 3 Runtime Filters , Then it will wait at most 3s.
+
+Because it takes time to build and merge the Runtime Filter, ScanNode will try to push down the Runtime Filter that arrives within the waiting time to the storage engine. If the waiting time is exceeded, ScanNode will directly start scanning data using the Runtime Filter that has arrived.
+
+If the Runtime Filter arrives after ScanNode starts scanning, ScanNode will not push the Runtime Filter down to the storage engine. Instead, it will use expression filtering on ScanNode based on the Runtime Filter for the data that has been scanned from the storage engine. The scanned data will not apply the Runtime Filter, so the intermediate data size obtained will be larger than the optimal solution, but serious cracking can be avoided.
+
+If the cluster is busy and there are many resource-intensive or long-time-consuming queries on the cluster, consider increasing the waiting time to avoid missing optimization opportunities for complex queries. If the cluster load is light, and there are many small queries on the cluster that only take a few seconds, you can consider reducing the waiting time to avoid an increase of 1s for each query.
+
+#### 4.runtime_filters_max_num
+The maximum number of Bloom Filters in the Runtime Filter that can be applied to each query, the default is 10. Because Bloom Filter construction and application costs are high, so if the generated Bloom Filter exceeds the maximum allowed number, the Bloom Filter with large selectivity is retained. Currently, only the number of Bloom Filters is limited, because compared to MinMax Filter and IN predicate It is more expensive to build and apply.
+```
+Selectivity = (HashJoinNode Cardinality / HashJoinNode left child Cardinality)
+```
+Because the cardinality of FE is currently inaccurate, the selectivity of Bloom Filter calculation here is inaccurate, so in the end it may only randomly reserve part of Bloom Filter.
+
+#### 5. Bloom Filter related parameters
+- `runtime_bloom_filter_min_size`: the minimum length of Bloom Filter in Runtime Filter (in bytes), the default is 1048576 (1M);
+
+- `runtime_bloom_filter_max_size`: the maximum length of Bloom Filter in Runtime Filter (in bytes), the default is 16777216 (16M);
+
+- `runtime_bloom_filter_size`: The default length of Bloom Filter in Runtime Filter (in bytes), the default is 2097152 (2M);
+
+Because it is necessary to ensure that the length of the Bloom Filter constructed by each HashJoinNode is the same to be merged, the length of the Bloom Filter is currently calculated in the FE query planning.
+
+If you can get the number of data rows (Cardinality) in the statistical information of the join table on the right, you will try to use Cardinality as NDV, the default false detection rate fpp is 0.05, and the Bloom that contains NDV unique elements and the false detection rate is lower than fpp is calculated by the formula The minimum number of bytes required by the filter, rounded to the nearest power of 2 (log value with 2 as the base), and limits the upper and lower limits of the length of the final Bloom Filter.
+
+If the Cardinality of the join right table is not available, the default Bloom Filter length will be used.
+
+#### 6.runtime_filter_max_in_num
+If the number of rows in the right table of the join is greater than this value, we will not generate an IN predicate, and the default is 1024;
+
+### View Runtime Filter generated by query
+The query plan that can be displayed by the `explain` command includes the join on clause information used by each Fragment, as well as comments on the generation and use of the Runtime Filter by the Fragment, so as to confirm whether the Runtime Filter is applied to the desired join on clause.
+- The comment contained in the Fragment that generates the Runtime Filter, such as `runtime filters: filter_id[type] <- table.column`.
+- Use the comment contained in the fragment of Runtime Filter such as `runtime filters: filter_id[type] -> table.column`.
+
+The query in the following example uses a Runtime Filter with ID RF000.
+```
+CREATE TABLE test (t1 INT) DISTRIBUTED BY HASH (t1) BUCKETS 2 PROPERTIES("replication_num" = "1");
+INSERT INTO test VALUES (1), (2), (3), (4);
+
+CREATE TABLE test2 (t2 INT) DISTRIBUTED BY HASH (t2) BUCKETS 2 PROPERTIES("replication_num" = "1");
+INSERT INTO test2 VALUES (3), (4), (5);
+
+EXPLAIN SELECT t1 FROM test JOIN test2 where test.t1 = test2.t2;
++-------------------------------------------------------------------+
+| Explain String |
++-------------------------------------------------------------------+
+| PLAN FRAGMENT 0 |
+| OUTPUT EXPRS:`t1` |
+| |
+| 4:EXCHANGE |
+| |
+| PLAN FRAGMENT 1 |
+| OUTPUT EXPRS: |
+| PARTITION: HASH_PARTITIONED: `default_cluster:ssb`.`test`.`t1` |
+| |
+| 2:HASH JOIN |
+| | join op: INNER JOIN (BUCKET_SHUFFLE) |
+| | equal join conjunct: `test`.`t1` = `test2`.`t2` |
+| | runtime filters: RF000[in] <- `test2`.`t2` |
+| | |
+| |----3:EXCHANGE |
+| | |
+| 0:OlapScanNode |
+| TABLE: test |
+| runtime filters: RF000[in] -> `test`.`t1` |
+| |
+| PLAN FRAGMENT 2 |
+| OUTPUT EXPRS: |
+| PARTITION: HASH_PARTITIONED: `default_cluster:ssb`.`test2`.`t2` |
+| |
+| 1:OlapScanNode |
+| TABLE: test2 |
++-------------------------------------------------------------------+
+-- 上面`runtime filters`的行显示了`PLAN FRAGMENT 1`的`2:HASH JOIN`生成了ID为RF000的IN predicate，
+-- 其中`test2`.`t2`的key values仅在运行时可知，
+-- 在`0:OlapScanNode`使用了该IN predicate用于在读取`test`.`t1`时过滤不必要的数据。
+
+SELECT t1 FROM test JOIN test2 where test.t1 = test2.t2;
+-- 返回2行结果[3, 4];
+
+-- 通过query的profile（set is_report_success=true;）可以查看查询内部工作的详细信息，
+-- 包括每个Runtime Filter是否下推、等待耗时、以及OLAP_SCAN_NODE从prepare到接收到Runtime Filter的总时长。
+RuntimeFilter:in:
+ - HasPushDownToEngine: true
+ - AWaitTimeCost: 0ns
+ - EffectTimeCost: 2.76ms
+
+-- 此外，在profile的OLAP_SCAN_NODE中还可以查看Runtime Filter下推后的过滤效果和耗时。
+ - RowsVectorPredFiltered: 9.320008M (9320008)
+ - VectorPredEvalTime: 364.39ms
+```
+
Review comment:
It should be English here
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