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Closed 10 years ago.
I made an application to collect data from users. Those data will be collected at different places and from these places will be sent to a central server. I need to design a validation plan for the central server in PostgreSQL. Data must be checked against various validations and if a validation fails a message must be thrown.
It is database to database transfer validation.
Yes you're on the right track, you'll either use triggers and/or check constraints to do this.
Also, PostgresQL has a very flexible type system. Make sure to select the most appropriate, restrictive types. You can even define custom types yourself.
UNIQUE constraints
CHECK Constraints
FOREIGN KEY constraints - tutorial
Triggers, which can call helper functions written in any supported procedural language. Triggers can RAISE EXCEPTION to abort a transaction.
Domain Types
EXCLUSION constraints in 9.2 and newer
Multi-column PRIMARY KEYs
Partial UNIQUE indexes
Note that instead of using varchar(length) you're usually better off using text and a check constraint.
Related
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Closed 10 years ago.
I am working in my first real world application that consists of keeping track of medical studies of a medium size medical office. The system needs to keep track of doctors, users, patients, study templates and study reports. The purpose of this program is to apply preformatted study template for any possible study, keep track of each patient's study and keep a easy to find file system. Each study report is saved in an specific folder as an html file that can be used or printed from Windows directly.
I estimate that at any given time would be about 20 active doctors, 30 different study templates, 12 users; the patients and study reports would be cumulative an will remain active indefinitely. I estimate that we are talking about 2000 new patient and 6000 new study reports a year.
I have almost completed the job, but initially I chose to store the data in a serialized file and I did not consider to use a database instead. Now, considering that the size of the data will rapidly grow, I believe that I should consider to work with a database instead. For many different reasons, especially I am concerned about the serialized file choice because I noticed that any change that I may make in the future in any class may conflict with the serialized file and stops me from reopening it. I appreciate any comments, how large a file is too large to work with? It is a serialize file acceptable in this case please pass me any ideas or comments. Thanks for the help
Your concern about breaking compatibility with these files is absolutely reasonable.
I solved the same problem in a small inventory project by taking these steps:
Setup of a DB server (MySQL)
Integration of hibernate into the project
Reimplementation of the serializable classes within a new package using JPA annotations (if the DB schema won't break, add the annotations to existing classes)
Generation of the DB schema using the JPA entitites
Implementation of an importer for existing objects (deserialization, conversion and persisting with referential integrity.
Import and validation of existing data objects
Any required refactoring from old classes to the new JPA entities within the whole project
Removal of old classes and their importer (should slumber in a repository)
Most people will say that you should use a database regardless. If this is a professional application you can't risk the data being corrupted and is a real possibility e.g. due to a bug in your code and someone using the program incorrectly.
It is the value of the data, not the size which matters here. Say it has been running for a year and the file becomes unusable. Are you going to tell them they should enter all the data again from scratch?
If its just an exercise, I still suggest you use a database as you will learn something. A popular choice is to use hibernate and it is CV++. ;)
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Closed 10 years ago.
Suppose you want to implement a graph which can have million number of nodes.But the nodes count will increase from 0 to million.Its uncertain whether it will reach the million mark or not.It may cross it to multi-million nodes also.
I know adjacency list is what is used for this.But a typical adjacency list has a data structure maintaining pointers to the linked lists.
What data structure then should be used to store the pointers to the adjacency list ?
For example take Facebook for that matter.It has millions of users. Suppose each user represents a node. Now all users are represented as nodes of a very big single graph and you want to do operations on it how will you store it ?
Well if you know the basics behind them, it shouldn't be too hard.
Generally you create an array called "buckets" that contain the key and value, with an optional pointer to create a linked list.
When you access the hash table with a key, you process the key with a custom hash function which will return an integer. You then take the modulus of the result and that is the location of your array index or "bucket". Then you check the unhashed key with the stored key, and if it matches, then you found the right place.
Otherwise, you've had a "collision" and must crawl through the linked list and compare keys until you match. (note some implementations use a binary tree instead of linked list for collisions).
Check out this fast hash table implementation:
http://attractivechaos.awardspace.com/khash.h.html
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Closed 10 years ago.
I have a word document that contains data dictionaries.
For example, a variable called FUEL is described as follows:
FUEL -- What type of fuel does it take?
1 Gas
2 Diesel
3 Hybrid
4 Flex fuel
7 OTHER, SPECIFY
I want to convert the document into a PostgreSQL table. Do you have any suggestions?
In general, this sort of thing takes two stages: 1st, massage the data into a sane tabular format using text processing tools and scripting, or with something like Excel.
Once you have a tabular format, output the data as CSV (say, with Save As in Excel) and load it into PostgreSQL using the COPY command or psql's \copy after running appropriate CREATE TABLE commands to define a table structure that matches the structure of the CSV.
Edit: Given the updated post, I'd say you probably have to write a simple parser for this, unless the document contains internal structured markup. Save the document as plain text. Now write a script in a language like Perl or Python that looks for the heading that defines the variable, extracts the capitalied variable name and the description from that line, then reads numbered options until it runs out and is ready to read the next variable. If the document is uniformly structured this should only take a few lines of code with some basic regular expressions; you could probably even do it in awk. Have the script either write CSV ready for importing later, or use database interfaces like DBD::Pg (Perl) or psycopg2 (Python) to store the data directly.
If you don't know any scripting tools, you'll either need to learn or get very good at copy and paste.
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Closed 11 years ago.
I have been looking at data integration methods Global as view and Local as view, but I can not find any examples of how queries would be formed for these, could anyone give me examples of how these methods of data integration can be queried using GAV and LAV please
I am specifically asking about GAV and LAV here
I know that GAV (Global as view) is described over data sources and that LAV (local as view) is described over the mediated schema. However I am not totally sure what those terms mean, nor how they affect the query produced.
There is a wikipedia page for GAV, with no example of a query, and there isn't a wikipedia page for LAV sadly
I think these terms are not widely used in industry - the only references I can see for them appear to arise from academic work. They apply to Enterprise Information Integration - a genre of technology where a client-side reporting or integration layer is placed over existing databases without actually persisting the data into a separate reporting database.
Essentially, 'Global As View' describes where data is transformed into a unified representation before reporting queries are issued. In a data warehouse (where the data is transformed and persisted into a separate database) this view would be the data warehouse tables. An EII tool can do this by issuing queries to the underlying data sources and merging it into the centralised schema. EII is not a widely used technology, though.
'Local as view' techniques query all the sources individually and then merge the result sets together. Conceptually, this is an act of making up several queries to the different sources that produce result sets in the same format, but source the data from wherever it is found in the underlying systems. The data integration is then done in the reporting layer.
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Closed 12 years ago.
which is faster and usefull ? accumulator , register , or stack?
Registers are the fastest. An accumulator is also a register in which intermediate arithmetic and logic results are stored (info from Wikipedia).
A stack will be slower since it's a region of memory, and memory will always be slower than registers.
However, you will always have more memory available than registers since CPU storage is very costly.
Bottom line: they're all useful and their speed is inversely proportional to their available storage.
Those questions without any context about CPU architecture or other information what you want to accomplish cannot be answered in any useful way.
Usually the accumulator is just one of the registers - modern CPUs don't differentiate anymore, so for old one accu might be faster - or actually the only register allowing you certain operations. Registers are always faster then external memory, but there are just a limited amount of them (and they need to be explicitely named by the compiler/assember).
The stack is an area of RAM used to store data. So that's slower for sure :)
Quistion is not quite correct. "Fast" is related to the operations, not to the registers and etc. Another point - there is nothing about architecture of CPU in first message :-)
Depending on CPU architecture accumulator is a register but can have a special implementation. This way the operations that use accumulateor usualy faster than register operations.
About stack. Some processors have no support of Register-Register operations(i.e. input-output processor). That case some operations on the stack can be faster because it is not required to calculate effective address.
Register are always faster because it doesn't go get data into the memory, but be more clear about the situation.
Registers are usefull when you have many like x64 or Arm architecture.
Generally, registers are faster because they are actually part of the microprocessor. And the accumulator is just one of the register (the one that normally stores the result of various operations).
The stack is just memory like any other memory, allocated for the purpose of tracking return addresses and local variables.
But you can't use registers for everything because there are only a very limited number of them available.
If you explained why you were asking these questions, they might make a little more sense.