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Community Blog Merging Time Series Data in Different Scenarios with PostgreSQL

Merging Time Series Data in Different Scenarios with PostgreSQL

This article discusses the implementation for merging time series data in Composite Indexes, Window Group Query Acceleration, and Extraordinary Recursive Acceleration.

By Digoal

Data merging is required in many scenarios. For example, for recorded table change details (insert, update, and delete), we need to merge details and quickly obtain the latest value of each PK based on these details.

Another example is that in some situations, we need to obtain the latest status of each of many sensors that are constantly uploading data.

We can use window queries to meet these scenario requirements. However, how can we implement acceleration and quickly obtain batch data?

To achieve this, we can make some optimizations.

Sensor Case Example

Take the previously mentioned sensor case as an example. Let's assume that these sensors are constantly uploading data and users need to query the latest value that each sensor is uploading.

Create the following test table.

create unlogged table sort_test(
  id serial8 primary key,  -- Primary key
  c2 int,  -- Sensor ID
  c3 int  -- Sensor value
);  
   
Write 10 million pieces of sensor test data
postgres=# insert into sort_test (c2,c3) select random()*100000, random()*100 from generate_series(1,10000000);
INSERT 0 10000000

The following is the query statement:

postgres=# explain (analyze,verbose,timing,costs,buffers) select id,c2,c3 from (select id,c2,c3,row_number() over(partition by c2 order by id desc) rn from sort_test) t where rn=1;
                                                                            QUERY PLAN                                                                            
------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Subquery Scan on t  (cost=10001512045.83..10001837045.83 rows=50000 width=16) (actual time=23865.363..44033.984 rows=100001 loops=1)
   Output: t.id, t.c2, t.c3
   Filter: (t.rn = 1)
   Rows Removed by Filter: 9899999
   Buffers: shared hit=54055, temp read=93801 written=93801
   ->  WindowAgg  (cost=10001512045.83..10001712045.83 rows=10000000 width=24) (actual time=23865.351..41708.460 rows=10000000 loops=1)
         Output: sort_test.id, sort_test.c2, sort_test.c3, row_number() OVER (?)
         Buffers: shared hit=54055, temp read=93801 written=93801
         ->  Sort  (cost=10001512045.83..10001537045.83 rows=10000000 width=16) (actual time=23865.335..31540.089 rows=10000000 loops=1)
               Output: sort_test.id, sort_test.c2, sort_test.c3
               Sort Key: sort_test.c2, sort_test.id DESC
               Sort Method: external merge  Disk: 254208kB
               Buffers: shared hit=54055, temp read=93801 written=93801
               ->  Seq Scan on public.sort_test  (cost=10000000000.00..10000154055.00 rows=10000000 width=16) (actual time=0.021..1829.135 rows=10000000 loops=1)
                     Output: sort_test.id, sort_test.c2, sort_test.c3
                     Buffers: shared hit=54055
 Planning time: 0.194 ms
 Execution time: 44110.560 ms
(18 rows)

To implement optimization, add a composite index and avoid SORT. Note that IDs require desc

postgres=# create index sort_test_1 on sort_test(c2,id desc); 
CREATE INDEX

SQL performance after optimization

postgres=# explain (analyze,verbose,timing,costs,buffers) select id,c2,c3 from (select id,c2,c3,row_number() over(partition by c2 order by id desc) rn from sort_test) t where rn=1;
                                                                            QUERY PLAN                                                                            
------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Subquery Scan on t  (cost=0.43..542565.80 rows=50000 width=16) (actual time=0.048..33844.843 rows=100001 loops=1)
   Output: t.id, t.c2, t.c3
   Filter: (t.rn = 1)
   Rows Removed by Filter: 9899999
   Buffers: shared hit=10029020 read=1
   ->  WindowAgg  (cost=0.43..417564.59 rows=10000097 width=24) (actual time=0.042..30490.662 rows=10000000 loops=1)
         Output: sort_test.id, sort_test.c2, sort_test.c3, row_number() OVER (?)
         Buffers: shared hit=10029020 read=1
         ->  Index Scan using sort_test_1 on public.sort_test  (cost=0.43..242562.89 rows=10000097 width=16) (actual time=0.030..18347.482 rows=10000000 loops=1)
               Output: sort_test.id, sort_test.c2, sort_test.c3
               Buffers: shared hit=10029020 read=1
 Planning time: 0.216 ms
 Execution time: 33865.321 ms
(13 rows)

If data to be extracted requires further processing, we can use cursors to obtain data in bulk. Since we are not required to display SORT, obtaining batch data is very fast, accelerating the overall data processing.

\timing
begin;
declare c1 cursor for select id,c2,c3 from (select id,c2,c3,row_number() over(partition by c2 order by id desc) rn from sort_test) t where rn=1;
postgres=# fetch 100 from c1;
   id    | c2 | c3  
---------+----+-----
 9962439 |  0 |  93
 9711199 |  1 |  52
 9987709 |  2 |  65
 9995611 |  3 |  34
 9998766 |  4 |  12
 9926693 |  5 |  81
 ....
 9905064 | 98 |  44
 9991592 | 99 |  99
(100 rows)
Time: 31.408 ms  -- results are returned very quickly

Before optimization, SORT is displayed, so using cursors cannot improve the performance, and the first record obtained has been sorted.

drop index sort_test_1;

begin;
declare c1 cursor for select id,c2,c3 from (select id,c2,c3,row_number() over(partition by c2 order by id desc) rn from sort_test) t where rn=1;

postgres=# fetch 100 from c1;
....
Time: 22524.783 ms  -- results are returned after SORT is completed. This is very slow.

Example of Incremental Synchronization of Merged Data

When a materialized view is applied in Oracle, updating the same record only requires the last update instead of all the intermediate processes of each update.

We can use a similar method to implement grouping and obtain the last record.

create extension hstore;

create unlogged table sort_test1(
id serial8 primary key,  -- Primary key
  c2 int,  -- Target table PK
  c3 text,  -- insert or update or delete
  c4 hstore -- row
); 

create index idx_sort_test1_1 on sort_test1(c2,id desc);

select c2,c3,c4 from (select c2,c3,c4,row_number() over(partition by c2 order by id desc) rn from sort_test1) t where rn=1;

postgres=# explain select c2,c3,c4 from (select c2,c3,c4,row_number() over(partition by c2 order by id desc) rn from sort_test1) t where rn=1;
                                            QUERY PLAN                                             
---------------------------------------------------------------------------------------------------
 Subquery Scan on t  (cost=0.15..46.25 rows=4 width=68)
   Filter: (t.rn = 1)
   ->  WindowAgg  (cost=0.15..36.50 rows=780 width=84)
         ->  Index Scan using idx_sort_test1_1 on sort_test1  (cost=0.15..22.85 rows=780 width=76)
(4 rows)

Excellent Optimization Method for Sparse Columns

As we can see, the preceding optimization method only eliminates SORT and does not remove the number of blocks to be scanned.

In the event of very few groups (namely, sparse columns), we can use a more powerful optimization method – recursive queries.

Example

create type r as (c2 int, c3 int);

postgres=# explain (analyze,verbose,timing,costs,buffers) with recursive skip as (  
  (  
    select (c2,c3)::r as r from sort_test where id in (select id from sort_test where c2 is not null order by c2,id desc limit 1) 
  )  
  union all  
  (  
    select (
      select (c2,c3)::r as r from sort_test where id in (select id from sort_test t where t.c2>(s.r).c2 and t.c2 is not null order by c2,id desc limit 1) 
    ) from skip s where (s.r).c2 is not null
  )    -- "where (s.r).c2 is not null" must be added, otherwise it will end up with an endless loop. 
)   
select (t.r).c2, (t.r).c3 from skip t where t.* is not null; 

                                                                                           QUERY PLAN                                                                                           
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 CTE Scan on skip t  (cost=302.97..304.99 rows=100 width=8) (actual time=0.077..4184.770 rows=100001 loops=1)
   Output: (t.r).c2, (t.r).c3
   Filter: (t.* IS NOT NULL)
   Rows Removed by Filter: 1
   Buffers: shared hit=800947, temp written=476
   CTE skip
     ->  Recursive Union  (cost=0.91..302.97 rows=101 width=32) (actual time=0.066..3970.580 rows=100002 loops=1)
           Buffers: shared hit=800947
           ->  Nested Loop  (cost=0.91..2.95 rows=1 width=32) (actual time=0.064..0.066 rows=1 loops=1)
                 Output: ROW(sort_test_1.c2, sort_test_1.c3)::r
                 Buffers: shared hit=8
                 ->  HashAggregate  (cost=0.47..0.48 rows=1 width=8) (actual time=0.044..0.044 rows=1 loops=1)
                       Output: sort_test_2.id
                       Group Key: sort_test_2.id
                       Buffers: shared hit=4
                       ->  Limit  (cost=0.43..0.46 rows=1 width=12) (actual time=0.036..0.036 rows=1 loops=1)
                             Output: sort_test_2.id, sort_test_2.c2
                             Buffers: shared hit=4
                             ->  Index Only Scan using sort_test_1 on public.sort_test sort_test_2  (cost=0.43..267561.43 rows=10000000 width=12) (actual time=0.034..0.034 rows=1 loops=1)
                                   Output: sort_test_2.id, sort_test_2.c2
                                   Index Cond: (sort_test_2.c2 IS NOT NULL)
                                   Heap Fetches: 1
                                   Buffers: shared hit=4
                 ->  Index Scan using sort_test_pkey on public.sort_test sort_test_1  (cost=0.43..2.45 rows=1 width=16) (actual time=0.011..0.012 rows=1 loops=1)
                       Output: sort_test_1.id, sort_test_1.c2, sort_test_1.c3
                       Index Cond: (sort_test_1.id = sort_test_2.id)
                       Buffers: shared hit=4
           ->  WorkTable Scan on skip s  (cost=0.00..29.80 rows=10 width=32) (actual time=0.037..0.038 rows=1 loops=100002)
                 Output: (SubPlan 1)
                 Filter: ((s.r).c2 IS NOT NULL)
                 Rows Removed by Filter: 0
                 Buffers: shared hit=800939
                 SubPlan 1
                   ->  Nested Loop  (cost=0.92..2.96 rows=1 width=32) (actual time=0.034..0.035 rows=1 loops=100001)
                         Output: ROW(sort_test.c2, sort_test.c3)::r
                         Buffers: shared hit=800939
                         ->  HashAggregate  (cost=0.49..0.50 rows=1 width=8) (actual time=0.023..0.023 rows=1 loops=100001)
                               Output: t_1.id
                               Group Key: t_1.id
                               Buffers: shared hit=400401
                               ->  Limit  (cost=0.43..0.48 rows=1 width=12) (actual time=0.021..0.021 rows=1 loops=100001)
                                     Output: t_1.id, t_1.c2
                                     Buffers: shared hit=400401
                                     ->  Index Only Scan using sort_test_1 on public.sort_test t_1  (cost=0.43..133557.76 rows=3333333 width=12) (actual time=0.019..0.019 rows=1 loops=100001)
                                           Output: t_1.id, t_1.c2
                                           Index Cond: ((t_1.c2 > (s.r).c2) AND (t_1.c2 IS NOT NULL))
                                           Heap Fetches: 100000
                                           Buffers: shared hit=400401
                         ->  Index Scan using sort_test_pkey on public.sort_test  (cost=0.43..2.45 rows=1 width=16) (actual time=0.006..0.007 rows=1 loops=100000)
                               Output: sort_test.id, sort_test.c2, sort_test.c3
                               Index Cond: (sort_test.id = t_1.id)
                               Buffers: shared hit=400538
 Planning time: 0.970 ms
 Execution time: 4209.026 ms
(54 rows)

Fast FETCH is still supported

postgres=# begin;
BEGIN
Time: 0.079 ms
postgres=# declare cur cursor for with recursive skip as (  
  (  
    select (c2,c3)::r as r from sort_test where id in (select id from sort_test where c2 is not null order by c2,id desc limit 1) 
  )  
  union all  
  (  
    select (
      select (c2,c3)::r as r from sort_test where id in (select id from sort_test t where t.c2>(s.r).c2 and t.c2 is not null order by c2,id desc limit 1) 
    ) from skip s where (s.r).c2 is not null
  )    -- "where (s.r).c2 is not null" must be added, otherwise it will end up with an endless loop. 
)   
select (t.r).c2, (t.r).c3 from skip t where t.* is not null; 
DECLARE CURSOR
Time: 1.240 ms
postgres=# fetch 100 from cur;
    r     
----------
 (0,93)
 (1,52)
 (2,65)
.....
  (97,78)
 (98,44)
 (99,99)
(100 rows)

Time: 4.314 ms

The extraordinary recursive optimization enables 10 times better performance and allows 10 million records to be filtered in just 4 seconds.

PostgreSQL Database ApsaraDB for RDS Time Series Data Data Merging Composite Index Window Query
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