Help Center> GaussDB> Distributed_3.x> SQL Optimization> Typical SQL Optimization Methods> Optimizing Statement Pushdown
Updated on 2024-05-21 GMT+08:00
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Optimizing Statement Pushdown

Statement Pushdown

Currently, the GaussDB optimizer can use three methods to develop statement execution policies in the distributed framework: generating a statement pushdown plan, a distributed execution plan, or a distributed execution plan for sending statements.

  • A statement pushdown plan pushes query statements from a CN down to DNs for execution and returns the execution results to the CN.
  • In a distributed execution plan, a CN compiles and optimizes query statements, generates a plan tree, and then sends the plan tree to DNs for execution. After the statements have been executed, execution results will be returned to the CN.
  • A distributed execution plan for sending statements pushes queries that can be pushed down (mostly base table scanning statements) to DNs for execution. Then, the plan obtains the intermediate results and sends them to the CN, on which the remaining queries are to be executed.

The third policy sends many intermediate results from DNs to the CN for further execution. In this case, the CN performance bottleneck (in bandwidth, storage, and computing) is caused by statements that cannot be pushed down to DNs. Therefore, you are advised not to use the query statements where only the third policy applies.

Statements cannot be pushed down if they have functions that do not support pushdown or syntax that does not support pushdown. Generally, you can rewrite the execution statements to solve the problem.

Typical Scenarios of Statement Pushdown

In the GaussDB optimizer, if you want to support statement pushdown, set the GUC parameter enable_fast_query_shipping to on. Generally, no execution plan operator is displayed after the EXPLAIN statement. If the keyword similar to "Data Node Scan on" exists, the statement has been pushed down to DNs for execution. The following describes statement pushdown and its supported scope from three scenarios.

1. Pushdown of single-table query statements

In a distributed database, to query a single table, whether the current statement can be pushed down depends on whether the CN needs to participate in calculation instead of simply collecting data. If the CN needs to further calculate the DN result, the statement cannot be pushed down. Generally, statements with keywords such as agg, windows function, limit/offset, sort, distinct cannot be pushed down.

  • Pushdown: Simple queries can be pushed down without further calculation on the CN.
    gaussdb=# explain select * from t where c1 > 1;
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Data Node Scan on "__REMOTE_FQS_QUERY__"  (cost=0.00..0.00 rows=0 width=0)
   Node/s: All datanodes
(2 rows)
  • Non-pushdown: A CN with the limit clause cannot simply send statements to DNs and collect data, which is inconsistent with the semantics of the limit clause.
    gaussdb=# explain select * from t limit 1;
                                     QUERY PLAN                                      
-------------------------------------------------------------------------------------
 Limit  (cost=0.00..0.00 rows=1 width=12)
   ->  Data Node Scan on "__REMOTE_LIMIT_QUERY__"  (cost=0.00..0.00 rows=1 width=12)
         Node/s: All datanodes
(3 rows)
  • Non-pushdown: A CN with the aggregate function cannot simply push down statements. Instead, it needs to further aggregate the results collected from DNs.
    gaussdb=# explain select sum(c1), count(*) from t;
                                     QUERY PLAN                                      
-------------------------------------------------------------------------------------
 Aggregate  (cost=0.10..0.11 rows=1 width=20)
   ->  Data Node Scan on "__REMOTE_GROUP_QUERY__"  (cost=0.00..0.00 rows=20 width=4)
         Node/s: All datanodes
(3 rows)

2. Pushdown of multi-table query statements

In the multi-table query scenario, whether a statement can be pushed down depends on the join condition and distribution columns. That is, if the join condition matches the distribution columns of the table, the statement can be pushed down. Otherwise, the statement cannot be pushed down. Generally, a replication table can be pushed down.
  • Create two hash distribution tables.
    gaussdb=# create table t(c1 int, c2 int, c3 int)distribute by hash(c1);
CREATE TABLE
gaussdb=# create table t1(c1 int, c2 int, c3 int)distribute by hash(c1);
CREATE TABLE
  • Pushdown: The join condition meets the hash distribution key attributes of two tables.
    gaussdb=# explain select * from t1 join t on t.c1 = t1.c1;
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Data Node Scan on "__REMOTE_FQS_QUERY__"  (cost=0.00..0.00 rows=0 width=0)
   Node/s: All datanodes
(2 rows)
  • Non-pushdown: The join condition does not meet the hash distribution key attribute. That is, t1.c2 is not the distribution key of t1.
    gaussdb=# explain select * from t1 join t on t.c1 = t1.c2;
                                         QUERY PLAN                                         
--------------------------------------------------------------------------------------------
 Hash Join  (cost=0.25..0.53 rows=20 width=24)
   Hash Cond: (t1.c2 = t.c1)
   ->  Data Node Scan on t1 "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=20 width=12)
         Node/s: All datanodes
   ->  Hash  (cost=0.00..0.00 rows=20 width=12)
         ->  Data Node Scan on t "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=20 width=12)
               Node/s: All datanodes
(7 rows)
  • Delete two hash distribution tables.
    gaussdb=# DROP TABLE t;
DROP TABLE
gaussdb=# DROP TABLE t1;
DROP TABLE

3. Special scenarios

In some special scenarios, for example, a statement containing the WITH RECURSIVE clause cannot be pushed down.

Checking Whether the Execution Plan Has Been Pushed Down

Perform the following procedure to quickly determine whether the execution plan can be pushed down:

  1. Set the GUC parameter enable_fast_query_shipping to off to use the distributed framework policy for the query optimizer.

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    SET enable_fast_query_shipping = off;

  2. View the execution plan.

    If the execution plan contains Data Node Scan nodes, the SQL statements cannot be pushed down to DNs. If the execution plan contains Streaming nodes, the SQL statements can be pushed down to DNs.

    For example:

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    gaussdb=# select
count(ss.ss_sold_date_sk order by ss.ss_sold_date_sk)c1 
from store_sales ss, store_returns sr 
where 
sr.sr_customer_sk = ss.ss_customer_sk;

    The execution plan is as follows, which indicates that the SQL statement cannot be pushed down.

                                  QUERY PLAN
--------------------------------------------------------------------------
Aggregate
->  Hash Join
Hash Cond: (ss.ss_customer_sk = sr.sr_customer_sk)
->  Data Node Scan on store_sales "_REMOTE_TABLE_QUERY_"
Node/s: All datanodes
->  Hash
->  Data Node Scan on store_returns "_REMOTE_TABLE_QUERY_"
Node/s: All datanodes
(8 rows)

Syntax That Does Not Support Pushdown

SQL syntax that does not support pushdown is described using the following table definition examples:

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gaussdb=# CREATE TABLE CUSTOMER1
(
    C_CUSTKEY     BIGINT NOT NULL
  , C_NAME        VARCHAR(25) NOT NULL
  , C_ADDRESS     VARCHAR(40) NOT NULL
  , C_NATIONKEY   INT NOT NULL
  , C_PHONE       CHAR(15) NOT NULL
  , C_ACCTBAL     DECIMAL(15,2)   NOT NULL
  , C_MKTSEGMENT  CHAR(10) NOT NULL
  , C_COMMENT     VARCHAR(117) NOT NULL
)
DISTRIBUTE BY hash(C_CUSTKEY);
gaussdb=# CREATE TABLE test_stream(a int, b float); -- float does not support redistribution.
gaussdb=# CREATE TABLE sal_emp ( c1 integer[] ) DISTRIBUTE BY replication;
  • The returning statement cannot be pushed down. 
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    gaussdb=# explain update customer1 set C_NAME = 'a' returning c_name;
                               QUERY PLAN                                           
------------------------------------------------------------------
 Update on customer1  (cost=0.00..0.00 rows=30 width=187)
   Node/s: All datanodes
   Node expr: c_custkey
   ->  Data Node Scan on customer1 "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=30 width=187)
         Node/s: All datanodes
(5 rows)
  • If an SQL statement contains the aggregate functions using ORDER BY, this statement cannot be pushed down. 
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    gaussdb=# explain verbose select count ( c_custkey order by c_custkey) from customer1;
                               
                         QUERY PLAN                                        
------------------------------------------------------------------ Aggregate  (cost=2.50..2.51 rows=1 width=8)
   Output: count(customer1.c_custkey ORDER BY customer1.c_custkey)
   ->  Data Node Scan on customer1 "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=30 width=8)
         Output: customer1.c_custkey
         Node/s: All datanodes
         Remote query: SELECT c_custkey FROM ONLY public.customer1 WHERE true
(6 rows)
  • If an SQL statement contains COUNT(DISTINCT expr) and columns in COUNT(DISTINCT expr) do not support redistribution, this statement cannot be pushed down. 
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    gaussdb=# explain verbose select count(distinct b) from test_stream;
                                          QUERY PLAN                                           
------------------------------------------------------------------ Aggregate  (cost=2.50..2.51 rows=1 width=8)
   Output: count(DISTINCT test_stream.b)
   ->  Data Node Scan on test_stream "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=30 width=8)
         Output: test_stream.b
         Node/s: All datanodes
         Remote query: SELECT b FROM ONLY public.test_stream WHERE true
(6 rows)
  • A statement containing DISTINCT ON cannot be pushed down. 
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    gaussdb=# explain verbose select distinct on (c_custkey) c_custkey from customer1 order by c_custkey;
                                            QUERY PLAN                                             
------------------------------------------------------------------ Unique  (cost=49.83..54.83 rows=30 width=8)
   Output: customer1.c_custkey
   ->  Sort  (cost=49.83..52.33 rows=30 width=8)
         Output: customer1.c_custkey
         Sort Key: customer1.c_custkey
         ->  Data Node Scan on customer1 "_REMOTE_TABLE_QUERY_"  (cost=0.00..0.00 rows=30 width=8)
               Output: customer1.c_custkey
               Node/s: All datanodes
               Remote query: SELECT c_custkey FROM ONLY public.customer1 WHERE true
(9 rows)
  • A statement containing array expressions cannot be pushed down. 
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    gaussdb=# explain verbose select array[c_custkey,1] from customer1 order by c_custkey;

                          QUERY PLAN                                                    
------------------------------------------------------------------ Sort  (cost=49.83..52.33 rows=30 width=8)
   Output: (ARRAY[customer1.c_custkey, 1::bigint]), customer1.c_custkey
   Sort Key: customer1.c_custkey
   ->  Data Node Scan on "__REMOTE_SORT_QUERY__"  (cost=0.00..0.00 rows=30 width=8)
         Output: (ARRAY[customer1.c_custkey, 1::bigint]), customer1.c_custkey
         Node/s: All datanodes
         Remote query: SELECT ARRAY[c_custkey, 1::bigint], c_custkey FROM ONLY public.customer1 WHERE true ORDER BY 2
(7 rows)
  • The following table describes the scenarios where a statement containing WITH RECURSIVE cannot be pushed down in the current version, as well as the causes.

    No.

    Scenario

    Cause of Not Supporting Pushdown

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    The query contains foreign tables.

    LOG: SQL can't be shipped, reason: RecursiveUnion contains ForeignScan is not shippable (In this table, LOG describes the cause of not supporting pushdown.)

    In the current version, queries containing foreign tables do not support pushdown.

    2

    Multiple node groups exist.

    LOG: SQL can't be shipped, reason: With-Recursive under multi-nodegroup scenario is not shippable

    In the current version, pushdown is supported only when all base tables are stored and computed in the same node group.

    3

    UNION does not contain ALL, and deduplication is required.

    LOG: SQL can't be shipped, reason: With-Recursive does not contain "ALL" to bind recursive & none-recursive branches

    For example:

    WITH recursive t_result AS (
SELECT dm,sj_dm,name,1 as level
FROM test_rec_part
WHERE sj_dm > 10
UNION
SELECT t2.dm,t2.sj_dm,t2.name||' > '||t1.name,t1.level+1
FROM t_result t1
JOIN test_rec_part t2 ON t2.sj_dm = t1.dm
)
SELECT * FROM t_result t;

    4

    A base table contains a system catalog.

    LOG: SQL can't be shipped, reason: With-Recursive contains system table is not shippable

    Example:

    WITH RECURSIVE x(id) AS
(
select count(1) from pg_class where oid=1247
UNION ALL
SELECT id+1 FROM x WHERE id < 5
), y(id) AS
(
select count(1) from pg_class where oid=1247
UNION ALL
SELECT id+1 FROM x WHERE id < 10
)
SELECT y.*, x.* FROM y LEFT JOIN x USING (id) ORDER BY 1;

    5

    Only VALUES is used for scanning base tables. In this case, the statement can be executed on the CN, and DNs are unnecessary.

    LOG: SQL can't be shipped, reason: With-Recursive contains only values rte is not shippable

    Example:

    WITH RECURSIVE t(n) AS (
VALUES (1)
UNION ALL
SELECT n+1 FROM t WHERE n < 100
)
SELECT sum(n) FROM t;

    6

    Only the recursion part has correlation conditions of correlated subqueries, and the non-recursion part has no correlation condition.

    LOG: SQL can't be shipped, reason: With-Recursive recursive term correlated only is not shippable

    Example:

    select  a.ID,a.Name,
(
with recursive cte as (
select ID, PID, NAME from b where b.ID = 1
union all
select parent.ID,parent.PID,parent.NAME
from cte as child join b as parent on child.pid=parent.id
where child.ID = a.ID
)
select NAME from cte limit 1
) cName
from
(
select id, name, count(*) as cnt
from a group by id,name
) a order by 1,2;

    7

    The replicate plan is used for limit in the non-recursion part but the hash plan is used in the recursion part, resulting in conflicts.

    LOG: SQL can't be shipped, reason: With-Recursive contains conflict distribution in none-recursive(Replicate) recursive(Hash)

    Example:

    WITH recursive t_result AS (
select * from(
SELECT dm,sj_dm,name,1 as level
FROM test_rec_part
WHERE sj_dm < 10 order by dm limit 6 offset 2)
UNION all
SELECT t2.dm,t2.sj_dm,t2.name||' > '||t1.name,t1.level+1
FROM t_result t1
JOIN test_rec_part t2 ON t2.sj_dm = t1.dm
)
SELECT * FROM t_result t;

    8

    recursive of multiple-layers are nested. That is, a recursive is nested in the recursion part of another recursive.

    LOG: SQL can't be shipped, reason: Recursive CTE references recursive CTE "cte"

    Example:

    with recursive cte as
(
select * from rec_tb4 where id<4
union all
select h.id,h.parentID,h.name from
(
with recursive cte as
(
select * from rec_tb4 where id<4
union all
select h.id,h.parentID,h.name from rec_tb4 h inner join cte c on h.id=c.parentID
)
SELECT id ,parentID,name from cte order by parentID
) h
inner join cte  c on h.id=c.parentID
)
SELECT id ,parentID,name from cte order by parentID,1,2,3;
    Delete the tables. 
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    gaussdb=# DROP TABLE CUSTOMER1;
DROP TABLE
gaussdb=# DROP TABLE test_stream;
DROP TABLE
gaussdb=# DROP TABLE sal_emp;
DROP TABLE

Functions That Do Not Support Pushdown

The following describes the volatility of functions. In GaussDB, every function has a volatility classification, with the possibilities being:

  • IMMUTABLE

    Indicates that the function always returns the same result if the parameter values are the same.

  • STABLE

    Indicates that the function cannot modify the database, and that within a single table scan it will consistently return the same result for the same parameter value, but its result varies by SQL statements.

  • VOLATILE

    Indicates that the function value can change in a single table scan and no optimization is performed.

The volatility of a function can be obtained by querying for its provolatile column in pg_proc. The value i indicates immutable, s indicates stable, and v indicates volatile. The valid values of the proshippable column in pg_proc are t, f, and NULL. This column and the provolatile column together describe whether a function is pushed down.

  • If the provolatile of a function is i, the function can be pushed down regardless of the value of proshippable.
  • If the provolatile of a function is s or v, the function can be pushed only if the value of proshippable is t.
  • CTEs containing random, exec_hadoop_sql, or exec_on_extension are not pushed down, because pushdown may lead to incorrect results.

When creating a customized function, you can specify the values of provolatile and proshippable. For details, see CREATE FUNCTION.

In scenarios where a function does not support pushdown, perform one of the following as required:

  • If it is a system function, replace it with a functionally equivalent one.
  • If it is a customized function, check whether its provolatile and proshippable are correctly defined.

Example: Customized Functions

Define a customized function that generates fixed output for a certain input as the immutable type.

Take the sales information of TPC Benchmark DS (TPC-DS) as an example. If you want to write a function to calculate the discount data of a product, you can define the function as follows:

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CREATE FUNCTION func_percent_2 (NUMERIC, NUMERIC) RETURNS NUMERIC
AS 'SELECT $1 / $2 WHERE $2 > 0.01'
LANGUAGE SQL
VOLATILE;

Run the following statements:

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SELECT func_percent_2(ss_sales_price, ss_list_price)
FROM store_sales;

The execution plan is as follows.

func_percent_2 is not pushed down, and ss_sales_price and ss_list_price are executed on a CN. In this case, a large amount of resources on the CN is consumed, and the performance deteriorates as a result.

In this example, the function generates the same output when the same input is provided. Therefore, we can modify the function to the following one:

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CREATE FUNCTION func_percent_1 (NUMERIC, NUMERIC) RETURNS NUMERIC
AS 'SELECT $1 / $2 WHERE $2 > 0.01'
LANGUAGE SQL
IMMUTABLE;

Run the following statement:

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SELECT func_percent_1(ss_sales_price, ss_list_price)
FROM store_sales;

The execution plan is as follows.

The func_percent_1 function is pushed down to DNs for execution.