I'm referring to file organization in DBMS. But I can't understand what is Blocking Records. If you can please explain me the term Blocking Records.
Blocking record is, File records divide into Blocks. A database is a collection of large amount of related data. In case of RDBMS (Relational Database Management System), the data is stored in the form of relations or tables.
when stored the data stored in tables but actually this huge amount of data is stored in the form of files in physical memory.(A File is a collection of related records stored on the secondary storage)
There are various strategies for mapping file records into blocks of disk
1. Spanned Mapping -: suppose when File record is too large. hence stored inside the block even if it can only be stored partially hence we stored a record of file in two blocks.
2. Unspanned Mapping -: the record of a file is stored inside the block only if it can be stored completely inside it.
Related
I use schemas in PostgreSQL for organizing my huge accounting database. At the end of every year I make a reconcile process by creating a new schema for the next year.
Are the files of the new schema physically separated from the old schema? Or all schemas stored on the hard disk together?
This is a vital thing for me because at the end of every year I've huge tables with millions of records which means I'll call heavy queries soon (I didn't plan for it when I decided to choose PostgreSQL).
Schemas are namespaces so they are a "logical" thing, not a physical thing.
As documented in the manual each table is represented as one (or more files) inside the directory corresponding to the database the table is created in. The namespaces (schemas) are not reflected in the physical database layout.
In general you shouldn't care about the storage of the database to begin with and your SQL queries will not know where the actual data is stored.
"millions" of rows is not considered "huge" these days. If you do run in performance problems, you will tune your query using e.g. indexes or by rewriting it to a more efficient solution. In rare cases partitioning a table can help with really huge tables - but we are talking hundreds of millions or even billions of rows. With medium to small sized tables, partitioning usually doesn't help with performance.
If I am correct, SQLite stores a database per file, and a file can't store more than one databases.
How does PostgreSQL store a database in terms of file(s)? Does it also store a database per file, and a file can't store more than one databases?
(SQLite uses more than one file for the rollback journal or when in WAL mode.)
The PostgreSQL database file layout is documented in its documentation:
Each table and index is stored in a separate file. For ordinary relations, these files are named after the table or index's filenode number, which can be found in pg_class.relfilenode. […] in addition to the main file (a/k/a main fork), each table and index has a free space map …, which stores information about free space available in the relation. The free space map is stored in a file named with the filenode number plus the suffix _fsm. Tables also have a visibility map, stored in a fork with the suffix _vm, to track which pages are known to have no dead tuples. […]
I need to know - Is it any possibility to restore data in collection or database if it was dropped?
The OS, by default (or in the case of Windows: any case) will not allow you to restore deleted data. You will need a third party program which can read the sectors. It is also good to note that while database drops will delete the files collection drops will not, instead they get nulled.
Dropping a collection should make it near on impossible to retrieve the data since the hard drive sectors that were used have now been overwritten with new data (basically one pass 0).
So the files may be recoverable on a database drop but that is still questionable.
I have little doubt, I have a field of type oid in my database I'm saving the text files, I wonder if I can somehow retrieve the name with which you saved the file. Do not know if in some way to do so successfully *lo_export* or another method.
Thanks in advance for the help.
PostgreSQL does not create a separate file for each large object stored in the database; a fairly normal heap table with btree index is used. So large objects are broken into smaller pieces for easier space management by the database, and intermingled within the 1GB segment files used by that table.
Taking a quick peek at the pg_class table, I see an entry for pg_largeobject, which I think is where all objects stored with the "lo" large object feature are stored. On my system I see a relfilenode of 11869, which means that the initial file for storing data would be 11869 and subsequent files would be 11869.1, 11869.2, etc. I don't know whether there is any way for the relfilenode to be reassigned for large objects, but you should probably check your pg_class entry to be sure.
Generally, large objects stored in the database should not be accessed except through the "lo" functions provided. If you want separate files and the ability to access them directly, you should probably save them directly to disk and store the filename or a URI. You could save them to disk from inside a PostgreSQL function, or externally.
I have loaded a huge CSV dataset -- Eclipse's Filtered Usage Data using PostgreSQL's COPY, and it's taking a huge amount of space because it's not normalized: three of the TEXT columns is much more efficiently refactored into separate tables, to be referenced from the main table with foreign key columns.
My question is: is it faster to refactor the database after loading all the data, or to create the intended tables with all the constraints, and then load the data? The former involves repeatedly scanning a huge table (close to 10^9 rows), while the latter would involve doing multiple queries per CSV row (e.g. has this action type been seen before? If not, add it to the actions table, get its ID, create a row in the main table with the correct action ID, etc.).
Right now each refactoring step is taking roughly a day or so, and the initial loading also takes about the same time.
From my experience you want to get all the data you care about into a staging table in the database and go from there, after that do as much set based logic as you can most likely via stored procedures. When you load into the staging table don't have any indexes on the table. Create the indexes after the data is loaded into the table.
Check this link out for some tips http://www.postgresql.org/docs/9.0/interactive/populate.html