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Community Blog Alibaba Cloud RDS PostgreSQL – Time Series Data Optimization

Alibaba Cloud RDS PostgreSQL – Time Series Data Optimization

This article talks about the time series function of Alibaba Cloud RDS PostgreSQL service and its best practices for optimizing data and methods to optimize data using PostgreSQL.

PostgreSQL is one of the most popular open-source databases. One advantage of PostgreSQL is that it can be optimized in many ways, such as data merging and data cleaning. Data merging and cleaning are necessary for several situations.

For example:

  1. When table details are updated (insert, update, delete), we need first to merge, and then obtain the newest value of each PK swiftly.
  2. When we have a large number of sensors which are continuously reporting data, we need to gather each sensor's latest reading promptly.
  3. We can use window query for this kind of operation, but we need to quickly retrieve batch data.

In general, there are four approaches to optimizing time sequence data.

  1. We can use recursion when there are few unique values and an unknown range.
  2. We can use subquery when there are few unique values, and their range is already determined.
  3. Window query is more appropriate than the above methods when there are many unique values.
  4. Stream computing is the best for all scenarios.

This document will only compare the first three methods. Stream computing does not need to be compared because it is the most powerful method in all scenarios.

Recursion vs. Subquery vs. Window Query

In our comparison, we will use a database with 5 million unique values as our data and compare these methods in the following situations.

Recursion

Scenario I. A large variety of unique values are available (1 million unique values)

Step 1: Create a table

\timing  
drop table test;  
create unlogged table test(id int , info text, crt_time timestamp);

Step 2: Construct data

insert into test select ceil(random()*1000000), md5(random()::text), clock_timestamp() from generate_series(1,5000000);

Step 3. Create an index

create index idx_test_1 on test (id, crt_time desc);

Step 4: Recursive query efficiency

explain (analyze,verbose,timing,costs,buffers) with recursive skip as (    
  (    
    select test as v from test where id in (select id from test where id is not null order by id,crt_time desc limit 1) limit 1  
  )    
  union all    
  (    
    select (  
      select t as v from test t where t.id>(s.v).id and t.id is not null order by id,crt_time desc limit 1  
    ) from skip s where (s.v).id is not null  
  )      -- The "where (s.v).id is not null" must be included. Else you will be stuck in an infinite loop.   
)     
select (t.v).id, (t.v).info, (t.v).crt_time from skip t where t.* is not null;   
                                                                                      QUERY PLAN                                                                                        
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 CTE Scan on skip t  (cost=54.35..56.37 rows=100 width=44) (actual time=0.042..6626.084 rows=993288 loops=1)  
   Output: (t.v).id, (t.v).info, (t.v).crt_time  
   Filter: (t.* IS NOT NULL)  
   Rows Removed by Filter: 1  
   Buffers: shared hit=3976934  
   CTE skip  
     ->  Recursive Union  (cost=0.91..54.35 rows=101 width=69) (actual time=0.034..6006.615 rows=993289 loops=1)  
           Buffers: shared hit=3976934  
           ->  Limit  (cost=0.91..0.93 rows=1 width=69) (actual time=0.033..0.033 rows=1 loops=1)  
                 Output: test.*  
                 Buffers: shared hit=8  
                 ->  Nested Loop  (cost=0.91..10.19 rows=500 width=69) (actual time=0.032..0.032 rows=1 loops=1)  
                       Output: test.*  
                       Buffers: shared hit=8  
                       ->  HashAggregate  (cost=0.48..0.49 rows=1 width=4) (actual time=0.021..0.021 rows=1 loops=1)  
                             Output: test_1.id  
                             Group Key: test_1.id  
                             Buffers: shared hit=4  
                             ->  Limit  (cost=0.43..0.47 rows=1 width=12) (actual time=0.016..0.016 rows=1 loops=1)  
                                   Output: test_1.id, test_1.crt_time  
                                   Buffers: shared hit=4  
                                   ->  Index Only Scan using idx_test_1 on public.test test_1  (cost=0.43..173279.36 rows=5000002 width=12) (actual time=0.015..0.015 rows=1 loops=1)  
                                         Output: test_1.id, test_1.crt_time  
                                         Index Cond: (test_1.id IS NOT NULL)  
                                         Heap Fetches: 1  
                                         Buffers: shared hit=4  
                       ->  Index Scan using idx_test_1 on public.test  (cost=0.43..9.64 rows=6 width=73) (actual time=0.009..0.009 rows=1 loops=1)  
                             Output: test.*, test.id  
                             Index Cond: (test.id = test_1.id)  
                             Buffers: shared hit=4  
           ->  WorkTable Scan on skip s  (cost=0.00..5.14 rows=10 width=32) (actual time=0.006..0.006 rows=1 loops=993289)  
                 Output: (SubPlan 1)  
                 Filter: ((s.v).id IS NOT NULL)  
                 Rows Removed by Filter: 0  
                 Buffers: shared hit=3976926  
                 SubPlan 1  
                   ->  Limit  (cost=0.43..0.49 rows=1 width=81) (actual time=0.005..0.005 rows=1 loops=993288)  
                         Output: t_1.*, t_1.id, t_1.crt_time  
                         Buffers: shared hit=3976926  
                         ->  Index Scan using idx_test_1 on public.test t_1  (cost=0.43..102425.17 rows=1666667 width=81) (actual time=0.005..0.005 rows=1 loops=993288)  
                               Output: t_1.*, t_1.id, t_1.crt_time  
                               Index Cond: ((t_1.id > (s.v).id) AND (t_1.id IS NOT NULL))  
                               Buffers: shared hit=3976926  
 Planning time: 0.354 ms  
 Execution time: 6706.105 ms  
(45 rows)

Scenario II. Few unique values are available (1,000 unique values)

Step 1: Create a table

\timing  
drop table test;  
create unlogged table test(id int , info text, crt_time timestamp);

Step 2: Construct data

insert into test select ceil(random()*1000), md5(random()::text), clock_timestamp() from generate_series(1,5000000);

Step 3: Create an index

create index idx_test_1 on test (id, crt_time desc);  
Step 4: Recursive query efficiency
Query statement stays unchanged  
                                                                                      QUERY PLAN                                                                                        
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 CTE Scan on skip t  (cost=55.09..57.11 rows=100 width=44) (actual time=0.046..8.859 rows=1000 loops=1)  
   Output: (t.v).id, (t.v).info, (t.v).crt_time  
   Filter: (t.* IS NOT NULL)  
   Rows Removed by Filter: 1  
   Buffers: shared hit=4007  
   CTE skip  
     ->  Recursive Union  (cost=0.91..55.09 rows=101 width=69) (actual time=0.039..8.203 rows=1001 loops=1)  
           Buffers: shared hit=4007  
           ->  Limit  (cost=0.91..1.67 rows=1 width=69) (actual time=0.038..0.038 rows=1 loops=1)  
                 Output: test.*  
                 Buffers: shared hit=8  
                 ->  Nested Loop  (cost=0.91..6335.47 rows=8333 width=69) (actual time=0.038..0.038 rows=1 loops=1)  
                       Output: test.*  
                       Buffers: shared hit=8  
                       ->  HashAggregate  (cost=0.48..0.49 rows=1 width=4) (actual time=0.021..0.021 rows=1 loops=1)  
                             Output: test_1.id  
                             Group Key: test_1.id  
                             Buffers: shared hit=4  
                             ->  Limit  (cost=0.43..0.47 rows=1 width=12) (actual time=0.016..0.017 rows=1 loops=1)  
                                   Output: test_1.id, test_1.crt_time  
                                   Buffers: shared hit=4  
                                   ->  Index Only Scan using idx_test_1 on public.test test_1  (cost=0.43..173279.55 rows=5000002 width=12) (actual time=0.015..0.015 rows=1 loops=1)  
                                         Output: test_1.id, test_1.crt_time  
                                         Index Cond: (test_1.id IS NOT NULL)  
                                         Heap Fetches: 1  
                                         Buffers: shared hit=4  
                       ->  Index Scan using idx_test_1 on public.test  (cost=0.43..6284.98 rows=5000 width=73) (actual time=0.015..0.015 rows=1 loops=1)  
                             Output: test.*, test.id  
                             Index Cond: (test.id = test_1.id)  
                             Buffers: shared hit=4  
           ->  WorkTable Scan on skip s  (cost=0.00..5.14 rows=10 width=32) (actual time=0.008..0.008 rows=1 loops=1001)  
                 Output: (SubPlan 1)  
                 Filter: ((s.v).id IS NOT NULL)  
                 Rows Removed by Filter: 0  
                 Buffers: shared hit=3999  
                 SubPlan 1  
                   ->  Limit  (cost=0.43..0.49 rows=1 width=81) (actual time=0.007..0.007 rows=1 loops=1000)  
                         Output: t_1.*, t_1.id, t_1.crt_time  
                         Buffers: shared hit=3999  
                         ->  Index Scan using idx_test_1 on public.test t_1  (cost=0.43..102425.80 rows=1666667 width=81) (actual time=0.007..0.007 rows=1 loops=1000)  
                               Output: t_1.*, t_1.id, t_1.crt_time  
                               Index Cond: ((t_1.id > (s.v).id) AND (t_1.id IS NOT NULL))  
                               Buffers: shared hit=3999  
 Planning time: 0.353 ms  
 Execution time: 8.980 ms  
(45 rows)

Subquery

Scenario I. A large variety of unique values are available (1 million unique values)

Step 1: Subquery query efficiency

Subquery is inefficient if the value range of an ID is too broad.

A unique ID table needs to be maintained. Here we use generate_series as a replacement for testing.

explain (analyze,verbose,timing,costs,buffers) select (select test from test where id=t.id order by crt_time desc limit 1) from generate_series(1,1000000) t(id);  
                                                                 QUERY PLAN                                                                    
---------------------------------------------------------------------------------------------------------------------------------------------  
 Function Scan on pg_catalog.generate_series t  (cost=0.00..1976.65 rows=1000 width=32) (actual time=70.682..2835.109 rows=1000000 loops=1)  
   Output: (SubPlan 1)  
   Function Call: generate_series(1, 1000000)  
   Buffers: shared hit=3997082  
   SubPlan 1  
     ->  Limit  (cost=0.43..1.97 rows=1 width=77) (actual time=0.002..0.002 rows=1 loops=1000000)  
           Output: test.*, test.crt_time  
           Buffers: shared hit=3997082  
           ->  Index Scan using idx_test_1 on public.test  (cost=0.43..9.64 rows=6 width=77) (actual time=0.002..0.002 rows=1 loops=1000000)  
                 Output: test.*, test.crt_time  
                 Index Cond: (test.id = t.id)  
                 Buffers: shared hit=3997082  
 Planning time: 0.119 ms  
 Execution time: 2892.712 ms  
(14 rows)

Scenario II. Few unique values are available (1,000 unique values)

Step 1: Subquery query efficiency

Query statement changes

explain (analyze,verbose,timing,costs,buffers) select (select test from test where id=t.id order by crt_time desc limit 1) from generate_series(1,1000) t(id);  
                                                                   QUERY PLAN                                                                     
------------------------------------------------------------------------------------------------------------------------------------------------  
 Function Scan on pg_catalog.generate_series t  (cost=0.00..1699.41 rows=1000 width=32) (actual time=0.107..7.041 rows=1000 loops=1)  
   Output: (SubPlan 1)  
   Function Call: generate_series(1, 1000)  
   Buffers: shared hit=4000  
   SubPlan 1  
     ->  Limit  (cost=0.43..1.69 rows=1 width=77) (actual time=0.006..0.007 rows=1 loops=1000)  
           Output: test.*, test.crt_time  
           Buffers: shared hit=4000  
           ->  Index Scan using idx_test_1 on public.test  (cost=0.43..6284.98 rows=5000 width=77) (actual time=0.006..0.006 rows=1 loops=1000)  
                 Output: test.*, test.crt_time  
                 Index Cond: (test.id = t.id)  
                 Buffers: shared hit=4000  
 Planning time: 0.131 ms  
 Execution time: 7.126 ms  
(14 rows)

Window Query

Scenario I. A large variety of unique values are available (1 million unique values)

Step 1: Window query efficiency

explain (analyze,verbose,timing,costs,buffers) select id,info,crt_time from (select row_number() over (partition by id order by crt_time desc) as rn, * from test) t where rn=1;  
postgres=# explain (analyze,verbose,timing,costs,buffers) select id,info,crt_time from (select row_number() over (partition by id order by crt_time desc) as rn, * from test) t where rn=1;  
                                                                       QUERY PLAN                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------  
 Subquery Scan on t  (cost=0.43..310779.41 rows=25000 width=45) (actual time=0.027..6398.308 rows=993288 loops=1)  
   Output: t.id, t.info, t.crt_time  
   Filter: (t.rn = 1)  
   Rows Removed by Filter: 4006712  
   Buffers: shared hit=5018864  
   ->  WindowAgg  (cost=0.43..248279.39 rows=5000002 width=53) (actual time=0.026..5973.497 rows=5000000 loops=1)  
         Output: row_number() OVER (?), test.id, test.info, test.crt_time  
         Buffers: shared hit=5018864  
         ->  Index Scan using idx_test_1 on public.test  (cost=0.43..160779.35 rows=5000002 width=45) (actual time=0.019..4058.476 rows=5000000 loops=1)  
               Output: test.id, test.info, test.crt_time  
               Buffers: shared hit=5018864  
 Planning time: 0.121 ms  
 Execution time: 6446.901 ms  
(13 rows)

Scenario II. Few unique values are available (1,000 unique values)

Step 1: Window query efficiency

Query statement stays unchanged

                                                                       QUERY PLAN                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------  
 Subquery Scan on t  (cost=0.43..310779.61 rows=25000 width=45) (actual time=0.027..6176.801 rows=1000 loops=1)  
   Output: t.id, t.info, t.crt_time  
   Filter: (t.rn = 1)  
   Rows Removed by Filter: 4999000  
   Buffers: shared hit=4744850 read=18157  
   ->  WindowAgg  (cost=0.43..248279.58 rows=5000002 width=53) (actual time=0.026..5822.576 rows=5000000 loops=1)  
         Output: row_number() OVER (?), test.id, test.info, test.crt_time  
         Buffers: shared hit=4744850 read=18157  
         ->  Index Scan using idx_test_1 on public.test  (cost=0.43..160779.55 rows=5000002 width=45) (actual time=0.020..4175.082 rows=5000000 loops=1)  
               Output: test.id, test.info, test.crt_time  
               Buffers: shared hit=4744850 read=18157  
 Planning time: 0.108 ms  
 Execution time: 6176.924 ms  
(13    rows)

Comparison Diagram of Horizontal Efficiency

Data Volume Number of Unique Values Window Query (ms) Subquery (ms) Recursive Query (ms)
5 million 1 million 6.446 2.892 6.706
5 million 1 million 6.176 7 9

Conclusion

With the rise of the IoT, the world is generating increasing amounts of time-sequenced data, and in situations where we have to provide services based on that data, calculating the newest values in the data and those in the sliding window is crucial.

Alibaba Cloud RDS PostgreSQL is the best choice in open-source databases as it provides several solutions to the same problems. With reference to data optimization methods, we can conclude that:

  1. Recursion is suitable when there are few unique values but with an unknown range.
  2. Subquery is suitable when there are few unique values, and the range is determined.
  3. Window query is more appropriate than subquery when there are many unique values.
  4. Stream computing is the best option for all scenarios.

Visit the PostgreSQL official documentation to learn more.

PostgreSQL Database Database Management ApsaraDB for RDS Database Optimization
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Comments

Jantje Cloud May 31, 2018 at 3:32 am

Thanks for the share!

Alibaba Clouder June 7, 2018 at 8:54 am

You're welcome!

Alibaba Clouder June 7, 2018 at 8:53 am

You're welcome!

Alibaba Clouder

2,599 posts | 762 followers

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