I was reading about database partitioning from oracle documentation link(sales table). I got the concept that in range partitioning, you specify a column(or multiple columns) and give the corresponding range of values so that when you insert a value in table, it will go in related partitions making search efficient.
In the beginning of Range Partitioning section, it talks about the partitioning columns(sales_year, sales_month and sales_day) but those columns do not show in the create table statement. What exactly is partitioning columns? Are partitioning columns different from table columns?
No, partitioning columns are not different than table columns. I guess you are talking about the sale_year, sale_month, and sale_day columns in the documentation link.
I think that the example given in the link is incorrect. The partition is based on time_id and there are no sale_year, sale_month, and sale_day columns in the table. I think this piece of documentation is copy paste of older Oracle versions and not been updated appropriately in this case.
You can search for sale_year in the below links and find the correct example.
https://docs.oracle.com/cd/F49540_01/DOC/server.815/a67772/partiti.htm
https://books.google.co.in/books?id=z7UYI2-329MC&pg=PA462&lpg=PA462&dq=sale_year,+sale_month,+and+sale_day&source=bl&ots=j6_Pn7saNp&sig=YLwus8UqXzyhkG5_XxcY2wyV7kU&hl=en&sa=X&ved=0ahUKEwiOqPT3193MAhWBMI8KHUDPCCYQ6AEIGzAA#v=onepage&q=sale_year%2C%20sale_month%2C%20and%20sale_day&f=false
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I am working on a heavy record set database in MS SQL 2016. So I want to use row table partition feature to improve speed.
As we know partition feature is working on partition column of a table. Let's say [Date Column] of a table. In our scenario, have many tables that need to partition because of heaver record set in 5 to 7 tables. Each table not have that [Date column]. Also not possible to add that column in each table.
So is there any way I can select partition column of another table or something else.
The best option is to add a common column to all tables that you will then use to partition by.
You must already have a way of relating the different tables to each other so you can use this to tag each table with the correct Partition column.
This column could be as simple as an int with YYYYMM as values for monthly partitions.
You also need to make sure your queries are "Partition Aware".
This means that you should include this column in your WHERE Clause and also your JOIN Clauses for any queries.
Use Query Plans to make sure you are getting Partition Elimination on your queries.
If you can't change the model (but can add partitions???) then you could implement the partitioning with different columns in each table provided you have a single column in each table that you can partition on named ranges - but if you have 1-many relationships then it is unlikely that the child tables keys will be consecutive relative to the parent table. Note that this approach will make your "partition aware" queries more complex to craft.
I want to know the way how postgresql manage columns of table.
Say for e.g
I have created one table that contains 2 fields, so how postgresql manage these columns, table? In how many tables postgresql create entry for a single column ?
I would like to know the structure how the postgresql manage table and it's fields.
I only about pg_attribute table.
It would be good if anyone can share useful links.
Any help would be really appriciated.
Tables (and indexes) are organized in 8KB blocks in files in the data directory.
The column definitions are only in pg_attribute.
A table row with all its columns is stored together in one table block, and a table block can contain several such rows. In other words, PostgreSQL uses the traditional row oriented storage model.
Details can be read in the documentation.
Note: Don't use PostgreSQL 9.1 any more.
Index Organized Tables (IOTs) are tables stored in an index structure. Whereas a table stored
in a heap is unorganized, data in an IOT is stored and sorted by primary key (the data is the index). IOTs behave just like “regular” tables, and you use the same SQL to access them.
Every table in a proper relational database is supposed to have a primary key... If every table in my database has a primary key, should I always use an index organized table?
I'm guessing the answer is no, so when is an index organized table not the best choice?
Basically an index-organized table is an index without a table. There is a table object which we can find in USER_TABLES but it is just a reference to the underlying index. The index structure matches the table's projection. So if you have a table whose columns consist of the primary key and at most one other column then you have a possible candidate for INDEX ORGANIZED.
The main use case for index organized table is a table which is almost always accessed by its primary key and we always want to retrieve all its columns. In practice, index organized tables are most likely to be reference data, code look-up affairs. Application tables are almost always heap organized.
The syntax allows an IOT to have more than one non-key column. Sometimes this is correct. But it is also an indication that maybe we need to reconsider our design decisions. Certainly if we find ourselves contemplating the need for additional indexes on the non-primary key columns then we're probably better off with a regular heap table. So, as most tables probably need additional indexes most tables are not suitable for IOTs.
Coming back to this answer I see a couple of other responses in this thread propose intersection tables as suitable candidates for IOTs. This seems reasonable, because it is common for intersection tables to have a projection which matches the candidate key: STUDENTS_CLASSES could have a projection of just (STUDENT_ID, CLASS_ID).
I don't think this is cast-iron. Intersection tables often have a technical key (i.e. STUDENT_CLASS_ID). They may also have non-key columns (metadata columns like START_DATE, END_DATE are common). Also there is no prevailing access path - we want to find all the students who take a class as often as we want to find all the classes a student is taking - so we need an indexing strategy which supports both equally well. Not saying intersection tables are not a use case for IOTs. just that they are not automatically so.
I'd consider them for very narrow tables (such as the join tables used to resolve many-to-many tables). If (virtually) all the columns in the table are going to be in an index anyway, then why shouldn't you used an IOT.
Small tables can be good candidates for IOTs as discussed by Richard Foote here
I consider the following kinds of tables excellent candidates for IOTs:
"small" "lookup" type tables (e.g. queried frequently, updated infrequently, fits in a relatively small number of blocks)
any table that you already are going to have an index that covers all the columns anyway (i.e. may as well save the space used by the table if the index duplicates 100% of the data)
From the Oracle Concepts guide:
Index-organized tables are useful when
related pieces of data must be stored
together or data must be physically
stored in a specific order. This type
of table is often used for information
retrieval, spatial (see "Overview of
Oracle Spatial"), and OLAP
applications (see "OLAP").
This question from AskTom may also be of some interest especially where someone gives a scenario and then asks would an IOT perform better than an heap organised table, Tom's response is:
we can hypothesize all day long, but
until you measure it, you'll never
know for sure.
An index-organized table is generally a good choice if you only access data from that table by the key, the whole key, and nothing but the key.
Further, there are many limitations about what other database features can and cannot be used with index-organized tables -- I recall that in at least one version one could not use logical standby databases with index-organized tables. An index-organized table is not a good choice if it prevents you from using other functionality.
All an IOT really saves is the logical read(s) on the table segment, and as you might have spent two or three or more on the IOT/index this is not always a great saving except for small data sets.
Another feature to consider for speeding up lookups, particularly on larger tables, is a single table hash cluster. When correctly created they are more efficient for large data sets than an IOT because they require only one logical read to find the data, whereas an IOT is still an index that needs multiple logical i/o's to locate the leaf node.
I can't per se comment on IOTs, however if I'm reading this right then they're the same as a 'clustered index' in SQL Server. Typically you should think about not using such an index if your primary key (or the value(s) you're indexing if it's not a primary key) are likely to be distributed fairly randomly - as these inserts can result in many page splits (expensive).
Indexes such as identity columns (sequences in Oracle?) and dates 'around the current date' tend to make for good candidates for such indexes.
An Index-Organized Table--in contrast to an ordinary table--has its own way of structuring, storing, and indexing data.
Index organized tables (IOT) are indexes which actually hold the data which is being indexed, unlike the indexes which are stored somewhere else and have links to actual data.
Microsoft in its MSDN entry about altering SQL 2005 partitions, listed a few possible approaches:
Create a new partitioned table with the desired partition function, and then insert the data from the old table into the new table by using an INSERT INTO...SELECT FROM statement.
Create a partitioned clustered index on a heap
Drop and rebuild an existing partitioned index by using the Transact-SQL CREATE INDEX statement with the DROP EXISTING = ON clause.
Perform a sequence of ALTER PARTITION FUNCTION statements.
Any idea what will be the most efficient way for a large scale DB (millions of records) with partitions based on the dates of the records (something like monthly partitions), where data spreads over 1-2 years?
Also, if I mostly access (for reading) recent information, will it make sense to keep a partition for the last X days, and all the rest of the data will be another partition? Or is it better to partition the rest of the data too (for any random access based on date range)?
I'd recommend the first approach - creating a new partitioned table and inserting into it - because it gives you the luxury of comparing your old and new tables. You can test query plans against both styles of tables and see if your queries are indeed faster before cutting over to the new table design. You may find there's no improvement, or you may want to try several different partitioning functions/schemes before settling on your final result. You may want to partition on something other than date range - date isn't always effective.
I've done partitioning with 300-500m row tables with data spread over 6-7 years, and that table-insert approach was the one I found most useful.
You asked about how to partition - the best answer is to try to design your partitions so that your queries will hit a single partition. If you tend to concentrate queries on recent data, AND if you filter on that date field in your where clauses, then yes, have a separate partition for the most recent X days.
Be aware that you do have to specify the partitioned field in your where clause. If you aren't specifying that field, then the query is probably going to hit every partition to get the data, and at that point you won't have any performance gains.
Hope that helps! I've done a lot of partitioning, and if you want to post a few examples of table structures & queries, that'll help you get a better answer for your environment.
In terms of performance and optimizations:
When constructing a table in SQL Server, does it matter what order I put the columns in?
Does it matter if my primary key is the first column?
When constructing a multi-field index, does it matter if the columns are adjacent?
Using ALTER TABLE syntax, is it possible to specify in what position I want to add a column?
If not, how can I move a column to a difference position?
In SQL Server 2005, placement of nullable variable length columns has a space impact - placing nullable variable size columns at the end of the definition can result in less space consumption.
SQL Server 2008 adds the "SPARSE" column feature which negates this difference.
See here.
I would say the answer to all those questions is NO, altough my experience with MS-SQL goes as far as SQL2000. Might be a different story in SQL2005
For the fourth bullet: No you can't specify where you want to add the column. Here is the syntax for ALTER TABLE: https://msdn.microsoft.com/en-us/library/ms190273.aspx
In MySQL they offer an ALTER TABLE ADD ... AFTER ... but this doesn't appear in T-SQL.
If you want to reorder the columns you'll have to rebuild the table.
Edit: For your last last bullet point, you'll have to DROP the table and recreate it to reorder the columns. Some graphical tools that manipulate SQL databases will do this for you and make it look like you're reordering columns, so you might want to look into that.
No to the first 3 because the index will hold the data and no the last once also
Column order does not matter while creating a table. We can arrange the columns while retrieving the data from the database. Primary key can be set to any column or combination of columns.
For the first bullet:
Yes, column order does matter, at least if you are using the deprecated BLOBs image, text, or ntext, and using SQL Server <= 2005.
In those cases, you should have those columns at the 'end' of the table, and there is a performance hit every time you retrieve one.
If you're retrieving the data from such a table using a DAL, this is the perfect place for the Lazy Load pattern.