I have two tables, studentrecordupdated and studentrecordlegacy, with student record legacy being in the legacy schema.
Both the forms have the same data, only difference being that the studentrecordlegacy table has record forms which are not in the studentrecordupdated table. I am joining these two tables on studentid.
How do I don't a rank over partition by using both tables (some of the records in the legacy table are more recent than in the updated table)?
Related
Since Postgres also supports partitioned tables, what is the use of child table.
Suppose there is a table of users which has a column created_date. We can store data in 2 ways:
We create many child tables of this user table and distribute the data of users on the basis of created_date (say, one table for every date, like user_jan01_21).
We can create a partitioned table with the partitioning key created_date
Then what is the difference between these solution?
Basically, I want to know what problem table inheritance can solve that partitioning cannot.
Another doubt I have: if I follow solution 1, and I query the user table without the ONLY keyword, will it scan all the child tables?
For example:
SELECT * FROM WHERE where created_date = current_date - 10;
If the objective is partitioning, as in your example, then there is no advantage in using table inheritance. Declarative partitioning is far superior in ease of use, performance and available features.
Table inheritance has uses that are unrelated to partitioning. Features that partitioning doesn't offer are:
the child table can have additional columns
a table can inherit from more than one table
With table inheritance, if you select from the parent table, you will also get all results from the child tables, just as if you had used UNION ALL to combine the results.
I am building a database in Postgres 11, and I would like to segment the information by partitioning tables. The appointment table is already partitioned by date ranges, and I would also like to partition the patient table; a partition of patients by each doctor.
The question is: How can I partition the patient table with list partitioning? That is to say, for this table I would have to make a direct partition relationship with the doctor table or I would have to use the intermediate table, since between the two mentioned tables there is a relationship of many to many.
Attached is an illustrative image.
For a many-to-many relationship you will need a mapping table, partitioning or not.
I wouldn't use an artificial primary key for the mapping table, but the combination of id_doctor and id_patient (they are artificial anyway). The same holds for the appointment table.
Since id_doctor is not part of the patient table (and shouldn't be), you cannot partition the patient table per doctor. Why would you want to do that? Partitioning is mostly useful for mass deletions (and to some extent for speeding up sequential scans) — is that your objective?
There is a wide-spread assumption that bigger tables should be partitioned just because they are big, but that is not the case. Index access to a partitioned table is — if anything — slightly slower than index access to a non-partitioned table. Do you have billions of patients?
I have following scenario while using postgresql -
No of tables - 100 ,
No of rows per table - ~ 10 Million .
All the tables have same schema E.g. each table contains daily call records of a company. So 100 tables contain call records of 100 days.
I want to make following type of queries on these tables -
For each column of each table get count of records having null value in that column.
So considering above scenario, what can be the major optimizations in table structures ? How should i prepare my query and does there exist any efficient way of querying for such cases
If you're using Postgres table inheritance, a simple select count(*) from calls where foo is null will work fine. It will use an index on foo provided null foo rows aren't too common.
Internally, that will do what you'd do manually without table inheritance, i.e. union all the result for each individual child table.
If you need to run this repeatedly, maintain the count in memcached or in another table.
We have 2 db2 instances, each with a DB having tables with differing sets of columns. For e.g. Table T1 has 5 columns in one DB while having 3 columns in the other DB.
We would like to replicate data from T1 from one DB to another. Whil replicating, we would additionally want to apply certain transformation so that the 5 columns in the source table can be mapped to 3 columns in the target.
SQL Server lets you modify the stored procs that insert the record in the target DB. Its called MCALL or XCALL mechanism.
Does DB2 have such a feature by which a source table having one schema can be replicated to a target table with a different schema?
Thanks,
Yash
There are various replication mechanisms that you can use with DB2, all of them allow you to manipulate replicated data. You didn't mention what type of replication you are planning to use; here's an example for SQL Replication: http://pic.dhe.ibm.com/infocenter/db2luw/v10r5/topic/com.ibm.swg.im.iis.db.repl.sqlrepl.doc/topics/iiyrssubmanipoverview.html
I store data in table named by month,such as data201201,data201202 and so on,table is created in the first day of month,all tables have same struct.I design this because each month ten million rows increase and I have only sqlserver standard version so Partition table can not be used.
Before use entity framework,i used sql string to query data,so I can change tablename in sql string by query condition.
Is these any way that I can query related table in Entity Framework?
I have two idea:
1.another table design that can support such big data and minor cost?
2.Intercept ef's query and change table name by query condition,and continue this query,but how and where i can do so?