I am trying to migrate Oracle database to Postgres using foreign_database_wrapper by creating foreign tables.
But since the foreign tables acts like a view of Oracle so at the time of executing any query it fetches data on fly from the Original source and hence it increases the processing time.
As of now, in order to maintain physical data at Postgres end, I am creating table and inserting those data in it.
eg: create table employee_details as select * from emp_det;
where employee_details is a physical table and emp_det is a foreign table
But I felt this process is kind of redundant and time to time we need to manipulate this table(new insertion, updation or deletion)
So if anyone could share some related way where I can preserve these data with some other mode.
Regards,
See the identical Github issue.
oracle_fdw does not store the Oracle data on the PostgreSQL side.
Each access to a foreign table directly accesses the Oracle database.
If you want a copy of the data physically located in the PostgreSQL database, you can either do it like you described, or you could use a materialized view:
CREATE MATERIALIZED VIEW emp_det_mv AS SELECT * FROM emp_det;
That will do the same thing, but simpler. To refresh the data, you can run
REFRESH MATERIALIZED VIEW emp_det_mv;
Working on a project at the moment and we have to implement soft deletion for the majority of users (user roles). We decided to add an is_deleted='0' field on each table in the database and set it to '1' if particular user roles hit a delete button on a specific record.
For future maintenance now, each SELECT query will need to ensure they do not include records where is_deleted='1'.
Is there a better solution for implementing soft deletion?
Update: I should also note that we have an Audit database that tracks changes (field, old value, new value, time, user, ip) to all tables/fields within the Application database.
I would lean towards a deleted_at column that contains the datetime of when the deletion took place. Then you get a little bit of free metadata about the deletion. For your SELECT just get rows WHERE deleted_at IS NULL
You could perform all of your queries against a view that contains the WHERE IS_DELETED='0' clause.
Having is_deleted column is a reasonably good approach.
If it is in Oracle, to further increase performance I'd recommend partitioning the table by creating a list partition on is_deleted column.
Then deleted and non-deleted rows will physically be in different partitions, though for you it'll be transparent.
As a result, if you type a query like
SELECT * FROM table_name WHERE is_deleted = 1
then Oracle will perform the 'partition pruning' and only look into the appropriate partition. Internally a partition is a different table, but it is transparent for you as a user: you'll be able to select across the entire table no matter if it is partitioned or not. But Oracle will be able to query ONLY the partition it needs. For example, let's assume you have 1000 rows with is_deleted = 0 and 100000 rows with is_deleted = 1, and you partition the table on is_deleted. Now if you include condition
WHERE ... AND IS_DELETED=0
then Oracle will ONLY scan the partition with 1000 rows. If the table weren't partitioned, it would have to scan 101000 rows (both partitions).
The best response, sadly, depends on what you're trying to accomplish with your soft deletions and the database you are implementing this within.
In SQL Server, the best solution would be to use a deleted_on/deleted_at column with a type of SMALLDATETIME or DATETIME (depending on the necessary granularity) and to make that column nullable. In SQL Server, the row header data contains a NULL bitmask for each of the columns in the table so it's marginally faster to perform an IS NULL or IS NOT NULL than it is to check the value stored in a column.
If you have a large volume of data, you will want to look into partitioning your data, either through the database itself or through two separate tables (e.g. Products and ProductHistory) or through an indexed view.
I typically avoid flag fields like is_deleted, is_archive, etc because they only carry one piece of meaning. A nullable deleted_at, archived_at field provides an additional level of meaning to yourself and to whoever inherits your application. And I avoid bitmask fields like the plague since they require an understanding of how the bitmask was built in order to grasp any meaning.
if the table is large and performance is an issue, you can always move 'deleted' records to another table, which has additional info like time of deletion, who deleted the record, etc
that way you don't have to add another column to your primary table
That depends on what information you need and what workflows you want to support.
Do you want to be able to:
know what information was there (before it was deleted)?
know when it was deleted?
know who deleted it?
know in what capacity they were acting when they deleted it?
be able to un-delete the record?
be able to tell when it was un-deleted?
etc.
If the record was deleted and un-deleted four times, is it sufficient for you to know that it is currently in an un-deleted state, or do you want to be able to tell what happened in the interim (including any edits between successive deletions!)?
Careful of soft-deleted records causing uniqueness constraint violations.
If your DB has columns with unique constraints then be careful that the prior soft-deleted records don’t prevent you from recreating the record.
Think of the cycle:
create user (login=JOE)
soft-delete (set deleted column to non-null.)
(re) create user (login=JOE). ERROR. LOGIN=JOE is already taken
Second create results in a constraint violation because login=JOE is already in the soft-deleted row.
Some techniques:
1. Move the deleted record to a new table.
2. Make your uniqueness constraint across the login and deleted_at timestamp column
My own opinion is +1 for moving to new table. Its take lots of
discipline to maintain the *AND delete_at = NULL* across all your
queries (for all of your developers)
You will definitely have better performance if you move your deleted data to another table like Jim said, as well as having record of when it was deleted, why, and by whom.
Adding where deleted=0 to all your queries will slow them down significantly, and hinder the usage of any of indexes you may have on the table. Avoid having "flags" in your tables whenever possible.
you don't mention what product, but SQL Server 2008 and postgresql (and others i'm sure) allow you to create filtered indexes, so you could create a covering index where is_deleted=0, mitigating some of the negatives of this particular approach.
Something that I use on projects is a statusInd tinyint not null default 0 column
using statusInd as a bitmask allows me to perform data management (delete, archive, replicate, restore, etc.). Using this in views I can then do the data distribution, publishing, etc for the consuming applications. If performance is a concern regarding views, use small fact tables to support this information, dropping the fact, drops the relation and allows for scalled deletes.
Scales well and is data centric keeping the data footprint pretty small - key for 350gb+ dbs with realtime concerns. Using alternatives, tables, triggers has some overhead that depending on the need may or may not work for you.
SOX related Audits may require more than a field to help in your case, but this may help.
Enjoy
Use a view, function, or procedure that checks is_deleted = 0; i.e. don't select directly on the table in case the table needs to change later for other reasons.
And index the is_deleted column for larger tables.
Since you already have an audit trail, tracking the deletion date is redundant.
I prefer to keep a status column, so I can use it for several different configs, i.e. published, private, deleted, needsAproval...
Create an other schema and grant it all on your data schema.
Implment VPD on your new schema so that each and every query will have the predicate allowing selection of the non-deleted row only appended to it.
http://download.oracle.com/docs/cd/E11882_01/server.112/e16508/cmntopc.htm#CNCPT62345
#AdditionalCriteria("this.status <> 'deleted'")
put this on top of your #entity
http://wiki.eclipse.org/EclipseLink/Examples/JPA/SoftDelete
I want to create a SQL Server table materialized view where I want to add an extra column named ID which is auto increment.
Is that possible?
No, that's not possible. The restrictions on indexed views prevent this.
The ID would not be stable anyway. It would change in unexpected ways when the underlying data changes. The view is not a persistent table. It reflects what the view definition says at all times.
Use something else as the key of the indexed view. Usually, there is a suitable combination of columns from the underlying tables.
Our database is part of a (specialized) desktop application.
The primary goal is to keep data about certain events.
Events happen every few minutes.
The data collected about events changes frequently with new data groups being added in and old ones swapped out almost monthly (the data comes in definite groups).
I have to put together a database to track the events. A first stab at that might be to simply have a single big table where each row is an event and that is basically what our data looks like, but this seems undesirable because of our constantly changing groups of data (i.e. the number of columns would either keep growing perpetually or we would constantly having this months database incompatible with last months database - ugh!). Because of this I am leadning toward the following even though it creates circular references. (But maybe this is a stupid idea)
Create tables like
Table Events
Table Group of the Month 1
Table Group of the Month 2
...
Table Events has:
A primary key whose deletion cascade to delete rows with foreign keys referencing it
A nullable foreighn key for each data group table
Each data group table has:
A primary key, whose deletion cascades to null out foreign keys referencing it
Columns for the data in that group
A non-nullable foreign key back to the event
This still leaves you with a growing, changing Event Table (as you need to add new foreign key columns for each new data group), just much less drastically. However it seems more modular to me than one giant table. Is this a good solution to this situation? If not, what is?
Any suggestions?
P.S. We are using SQL Express or SQL Compact (we are currently experimenting with which one suits us best)
Why not use basically the single table approach and store the changing event data as XML in an XML column? You can even use XSD schemas to account for the changing data types, and you can add indexes on XML data if fast query performance on some XML data is required.
A permanently changing DB schema wouldn't really be an option if I were to implement such a database.
You should not have foreign keys for each data group table in the event table.
The event table already has an event_id which is in each data group table. So you can get from event to the child tables. Furthermore there will be old rows in the event table that are not in the latest data group table. So you really can't have a foreign key.
That said, I would wonder whether there is additional structure in the data group tables that can be used to clean up your design. Without knowing anything about what they look like I can't say. But if there is, consider taking advantage of it! (A schema that changes every month is a pretty bad code smell.)
Store your data at as granular a level as possible. It might be as simple as:
EventSource int FK
EventType int FK
Duration int
OccuredOn datetime
Get the data right and as simple as possible in the first place, and then
Aggregate via views or queries. Your instincts are correct about the ever changing nature of the columns - better to control that in T-SQL than in DDL.
I faced this problem a number of years ago with logfiles for massive armies of media players, and where I ultimately ended up was taking this data and creating a OLAP cube out of it. OLAP is another approach to database design where the important thing is optimizing it for reporting and "sliceability". It sounds like you're on that track, where it would be very useful to be able to look at a quick month's view of data, then a quarter's, and then back down to a week's. This is what OLAP is for.
Microsoft's technology for this is Analysis Services, which comes as part of Sql Server. If you didn't want to take the entire plunge (OLAP has a pretty steep learning curve), you could also look at doing a selectively denormalized database that you populated each night with ETL from your source database.
HTH.
How it is possible to create clustered indexes on a view in SQL Server 2008.
View is not a real table so there is no sense in physical arrangement of the data that clustered index creates.
Where do I miss the point?
An index always exists on disk. When you create the index, you are materialising the rows of the view on disk even if the view itself is not "real" rows.
MSDN White paper with an explanation
This is a somewhat simplified explanation. There's lots of technical hoo-hah going on under the hood, but it sounded like you wanted a general "wassup" explanation.
A view is, essentially, a pre-written and stored query; whenever you access the view, you're retrieving and plugging that pre-written query into your current query. (Leastways this is how I think of it.)
So these "basic" views read data that's stored in tables already present in the database/on the hard drive. When you build a clustered index on a view, what you are really doing is making a second physical copy of the data that is referenced by the view. For example, if you have table A, create view vA as "select * from A", and then build a clustered index on that view, what you end up with is two copies of the data on the hard drive.
This can be useful if table A is very large, and you want quick access to a small subset of the table (such as only 2-3 columns, or only where Status = 1, or you want quick access to the data that requires an ugly join to produce.)
The fun comes in when you update table A (really, any of the tables referenced by the view), as any changes to the "base" table must also be made to the "view" table. Not a good idea in heavily used OLTP systems.
FYI, I believe SQL's "Indexed Views" are called "Materialized Views" in Oracle. For my money, Materialized View is a much better name/description.
Though a view is not a real object, the clustered index is.
And the rows the view returns can be sorted and stored.
However, to be indexable, the view should satisfy a number of conditions.
Mostly they make sure that the results are persisted and the updates to the underlying table can be easily tracked in a view (so that the index will not have to be rebuilt each time the underlying table is updated).
For instance, SUM(*) and COUNT_BIG(*) are distributive functions:
SUM(set1) + SUM(set2) = SUM(set1 + set2)
COUNT_BIG(set1) + COUNT_BIG(set2) = COUNT_BIG(set1 + set2)
, so it's easy to recalculate values of SUM and COUNT_BIG when the table is changed, using only the view rows and the values of the columns affected.
However, it's not the case with other aggregates, so they are not allowed in an indexed view.