THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
This page documents Correlation Optimizer. It was originally introduced by HIVE-2206
and based on the idea of YSmart [1].
1. Overview
In Hadoop environments, an SQL query submitted to Hive will be evaluated in distributed systems. Thus, after generating a query operator tree representing the submitted SQL query, Hive needs to determine what operations can be executed in a task which will be evalauted in a single node. Also, since a MapReduce job can shuffle data data once, Hive also needs to cut the tree to multiple MapReduce jobs. It is important to cut an operator tree to multiple MapReduce in a good way, so the generated plan can evaluate the query efficiently.
When generating an operator tree for a given SQL query, Hive identifies when to shuffle the data through operations which may need to shuffle data. For example, a JOIN operation may need to shuffle the input data if input tables have not been distributed by join columns. However, in a complex query, it is possible that the input data of an operation which may need to shuffle the input data has already been partitioned in the desired way. For example, it is possible we can have a query like SELECT t1.key, sum(value) FROM t1 JOIN t2 ON (t1.key = t2.key) GROUP BY t1.key. In this example, both JOIN operation and GROUP BY operation may need to shuffle the input data. However, because the output of JOIN operation is the input of GROUP BY operation and it has been already partitioned by t1.key, we do not need to shuffle the data for GROUP BY operation. However, Hive is not aware this correlation between JOIN operation and GROUP BY operation and thus it will generate two separate MapReduce jobs to evaluate this query. Basically, we unnecessarily shuffle the data for GROUP BY operation. In a more complex query, correlation-unaware query planning can generate a very inefficient execution plan and result in poor performance.
Before we integrating Correlation Optimizer into Hive, Hive has ReduceSink Deduplication Optimizer which can figure out if we need to shuffle data for chained operators. However, to support more complex operator trees, we need a more general-purpose optimizer and a mechanism to correctly execute optimized plan. Thus, we have designed and implemented Correlation Optimizer and two operators for evaluating optimized plans. It is worth noting that it is better to use ReduceSink Deduplication Optimizer to handle simple cases first and then use Correlation Optimizer to handle more complex cases.
2. Examples
At first, let's take a look at three examples. For every query, we show the original operator tree generated by Hive and the optimized operator tree. To be concise, we only show the following operators, which are FileSinkOperator (FS), GroupByOperator (AGG), HashTableSinkOperator (HT), JoinOperator (JOIN), MapJoinOperator (MJ), and ReduceSinkOperator (RS). Also, in every query, we add comments (e.g. /*JOIN1*/) to indicate the node in the operator tree that an operation belongs to.
2.1 Example 1
SELECT tmp1.key, count(*)
FROM (SELECT key, avg(value) AS avg
FROM t1
GROUP BY /*AGG1*/ key) tmp1
JOIN /*JOIN1*/ t1 ON (tmp1.key = t2.key)
WHERE t1.value > tmp1.avg
GROUP BY /*AGG2*/ tmp1.key;
The original operator tree generated by Hive is shown below.
Figure 1: The original operator tree of Example 1 generated by Hive
This plan uses three MapReduce jobs to evaluate this query. However, AGG1, JOIN1, and AGG2 all require the column key to be the partitioning column for shuffling the data. Thus, we do not need to shuffle the data in the same way three times. We only need to shuffle the data once, and thus a single MapReduce job is needed. The optimized operator tree is shown below.
Figure 2: The optimized operator tree of Example 1
Since the input table of AGG1 and the left table of JOIN1 are both t1, when we use a single MapReduce job to evaluate this query, Hive only needs to scan t1 once. While, in the original plan, t1 is used in two MapReduce jobs, and thus it is scanned twice.
2.2 Example 2
SELECT tmp1.key, count(*)
FROM t1
JOIN /*JOIN1*/ (SELECT key, avg(value) AS avg
FROM t1
GROUP BY /*AGG1*/ key) tmp1 ON (t1.key = tmp1.key)
JOIN /*JOIN1*/ t2 ON (tmp1.key = t2.key)
WHERE t2.value > tmp1.avg
GROUP BY /*AGG2*/ t1.key;
The original operator tree generated by Hive is shown below.
Figure 3: The original operator tree of Example 2 generated by Hive
This example is similar to Example 1. The optimized operator tree only needs a single MapReduce job, which is shown below.
Figure 4: The optimized operator tree of Example 2
2.3 Example 3
SELECT count(distinct ws1.ws_order_number) as order_count,
sum(ws1.ws_ext_ship_cost) as total_shipping_cost,
sum(ws1.ws_net_profit) as total_net_profit
FROM web_sales ws1
JOIN /*MJ1*/ customer_address ca ON (ws1.ws_ship_addr_sk = ca.ca_address_sk)
JOIN /*MJ2*/ web_site s ON (ws1.ws_web_site_sk = s.web_site_sk)
JOIN /*MJ3*/ date_dim d ON (ws1.ws_ship_date_sk = d.d_date_sk)
LEFT SEMI JOIN /*JOIN4*/ (SELECT ws2.ws_order_number as ws_order_number
FROM web_sales ws2 JOIN /*JOIN1*/ web_sales ws3
ON (ws2.ws_order_number = ws3.ws_order_number)
WHERE ws2.ws_warehouse_sk <> ws3.ws_warehouse_sk) ws_wh1
ON (ws1.ws_order_number = ws_wh1.ws_order_number)
LEFT SEMI JOIN /*JOIN4*/ (SELECT wr_order_number
FROM web_returns wr
JOIN /*JOIN3*/ (SELECT ws4.ws_order_number as ws_order_number
FROM web_sales ws4 JOIN /*JOIN2*/ web_sales ws5
ON (ws4.ws_order_number = ws5.ws_order_number)
WHERE ws4.ws_warehouse_sk <> ws5.ws_warehouse_sk) ws_wh2
ON (wr.wr_order_number = ws_wh2.ws_order_number)) tmp1
ON (ws1.ws_order_number = tmp1.wr_order_number)
WHERE d.d_date >= '2001-05-01' and
d.d_date <= '2001-06-30' and
ca.ca_state = 'NC' and
s.web_company_name = 'pri';
The original operator tree generated by Hive is shown below.
Figure 5: The original operator tree of Example 3 generated by Hive
In this complex query, we will first have several MapJoins (MJ1, MJ2, and MJ3) which can be evaluated in the same Map phase. Since JOIN1, JOIN2, JOIN3, and JOIN4 use the same column as the join key, we can use a single MapReduce job to evaluate all operators before AGG1. The second MapReduce job will generate the final results. The optimized operator tree is shown below.
Figure 6: The optimized operator tree of Example 3
3. Intra-query Correlations
In Hive, a submitted SQL query needs to be evaluated in a distributed system. When evaluating a query, data may need to shuffled sometimes. Based on the nature of different data operations, operators in Hive can be divided to two categories.
- Operators which do not require data shuffling. Examples are
TableScanOperator,SelectOperatorandFilterOperator. - Operators which require data shuffling. Examples are
GroupByOperatorandJoinOperator.
For an operator requiring data shuffling, Hive will add one or multiple ReduceSinkOperators as parents of this operator (the number of ReduceSinkOperators depends on the number of inputs of the operator requiring data shuffling). Those ReduceSinkOperators form the boundary between the Map phase and Reduce phase. Then, Hive will cut the operator tree to multiple pieces (MapReduce tasks) and each piece can be executed in a MapReduce job.
For a complex query, it is possible that a input table is used by multiple MapReduce tasks. In this case, this table will be loaded multiple times when the original operator tree is used. Also, when generating those ReduceSinkOperators, Hive does not consider if the corresponding operator requiring data shuffling really needs a re-partitioned input data. For example, in the original operator tree of Example 1 (Figure 1), AGG1, JOIN1, and AGG2 require the data been shuffled in the same way because all of them require the column key to be the partitioning column in their corresponding ReduceSinkOperators. But, Hive is not aware this correlation between AGG1, JOIN1, and AGG2, and still generates three MapReduce tasks.
Correlation Optimizer aims to exploit two intra-qeury correlations mentioned above.
- Input Correlation: A input table is used by multiple MapReduce tasks in the original operator tree.
- Job Flow Correlation: Two dependent MapReduce tasks shuffle the data in the same way.
4. Correlation Detection
5. Operator Tree Transformation
6. Executing Optimized Operator Tree in the Reduce Phase
Currently, blocking operators in the reduce phase operator tree share the same keys. Other cases will be supported in future work.
6.1 Executing Operator Tree with Same Keys
7. Related Jiras
The umbrella jira is HIVE-3667.
7.1 Resolved Jiras
7.2 Unresolved Jiras
8. References
- Rubao Lee, Tian Luo, Yin Huai, Fusheng Wang, Yongqiang He, Xiaodong Zhang. YSmart: Yet another SQL-to-MapReduce Translator, ICDCS, 2011