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Database dataset from updating

In other words this process will insert new rows on day start and will update the inner array every 3 minutes.

The advantages of this solution: We turned OFF the autovacuum for performance issues, doing it with a batch every night. Given the fact that you're just logging data, I would suggest a daily table, where you store the data for one day.

[A] TABLE main_segments_history( id_segment integer NOT NULL, day date NOT NULL, day_slices bigint[], CONSTRAINT main_segments_history_pk PRIMARY KEY (id_segment,day) ) [B] TABLE current_segment_release_state( id_segment integer NOT NULL, release_date timestamptz, ... CONSTRAINT currsegm_release_state_pk PRIMARY KEY (id_segment,release_date) ) There is a back-end process that elaborate the network.

It inserts or updates the states of each segment, every 3 minutes.

Consider that in this way we have almost between 90M and 190M UPDATES each day, this is also the number of rows completely rewritten by postgre SQL (as you certainly know an UPDATE will flag the row deleted and then a NEW row is inserted) every day. Instead of having 190 M updates/day, you may have 190 M (smaller) inserts/day; and as many updates as there are segments.

More, the UPDATE is a great time-consuming operation creating often a delay on the writes. CREATE TABLE main_segments_history ( id_segment integer NOT NULL, day date NOT NULL, day_slices bigint[], CONSTRAINT main_segments_history_pk PRIMARY KEY (id_segment, day) ) ; CREATE TABLE dayly_segments ( id_segment integer NOT NULL, day date NOT NULL, id_slice integer NOT NULL, slice bigint, PRIMARY KEY (id_segment, day, id_slice) ) ; INSERT INTO dayly_segments (id_segment, day, id_slice, slice) SELECT id_segment, '2017-01-01', id_slice, (random()*1e7)::bigint FROM generate_series (1, 200) AS s1(id_segment) CROSS JOIN generate_series (1, 20*24) AS s2(id_slice) ; -- Move segments from dayly_segments to main_segment_history INSERT INTO main_segments_history (id_segment, day, day_slices) SELECT id_segment, day, (SELECT array_agg(slice) FROM (SELECT slice FROM dayly_segments s1 WHERE s1.id_segment = s0.id_segment AND s1= s0ORDER BY id_slice) AS s2) FROM (SELECT DISTINCT id_segment, day FROM dayly_segments s0 WHERE day = '2017-01-01' ) AS s0 ; -- Delete them from original DELETE FROM dayly_segments WHERE day = '2017-01-01' ; -- At this point, you should also...

Dim str Select As String = _ "SELECT * FROM Categories" Dim da As Sql Data Adapter = New Sql Data Adapter(str Select, cn) ' Set the data adapter object's UPDATE, INSERT, and DELETE ' commands. Select("Category Name = 'Dairy Products'")(0) row("Description") = "Milk and stuff" ' Add a record. New Row( ) row("Category Name") = "Software" row("Description") = "Fine code and binaries" dt. For each row that is to be changed, added, or deleted, the parameters are replaced with values from the row, and the resulting SQL statement is issued to the database.

When you cache updates, changes to a dataset (such as posting changes or deleting records) are stored locally instead of being written directly to the dataset's underlying table.They also can't see any changes you make until you apply the cached updates.Because of this, cached updates may not be appropriate for applications that work with volatile data, as you may create or encounter too many conflicts when trying to merge your changes into the database.However, cached data is local to your application and is not under transaction control.This means that while you are working on your local, in-memory, copy of the data, other applications can be changing the data in the underlying database table.

Dim autogen As New Sql Command Builder(da) ' Load a data set. Fill(ds, "Categories") ' Get a reference to the "Categories" Data Table. This property contains an Sql Parameter Collection object that in turn contains one Sql Parameter object for each formal parameter.

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Database dataset from updating introduction

Database dataset from updating

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