I know there are at least a few reasons one might not want to do this, but I'm going to want to do it anyway. I want to take a lot of PDFs, store them as blobs (so nodes can read them from the main SQL server).
What would be the best practice for an R user to read the PDF content as a string, serialize it, and insert into MYSQL as a blob?
I assume all I really need is a text representation of the pdf which can then be BLOB'ed
Related
I will be creating a structure more or less of the form:
type FileState struct {
LastModified int64
Hash string
Path string
}
I want to write these values to a file and read them in on subsequent calls. My initial plan is to read them into a map and lookup values (Hash and LastModified) using the key (Path). Is there a slick way of doing this in Go?
If not, what file format can you recommend? I have read about and experimented with with some key/value file stores in previous projects, but not using Go. Right now, my requirements are probably fairly simple so a big database server system would be overkill. I just want something I can write to and read from quickly, easily, and portably (Windows, Mac, Linux). Because I have to deploy on multiple platforms I am trying to keep my non-go dependencies to a minimum.
I've considered XML, CSV, JSON. I've briefly looked at the gob package in Go and noticed a BSON package on the Go package dashboard, but I'm not sure if those apply.
My primary goal here is to get up and running quickly, which means the least amount of code I need to write along with ease of deployment.
As long as your entiere data fits in memory, you should't have a problem. Using an in-memory map and writing snapshots to disk regularly (e.g. by using the gob package) is a good idea. The Practical Go Programming talk by Andrew Gerrand uses this technique.
If you need to access those files with different programs, using a popular encoding like json or csv is probably a good idea. If you just have to access those file from within Go, I would use the excellent gob package, which has a lot of nice features.
As soon as your data becomes bigger, it's not a good idea to always write the whole database to disk on every change. Also, your data might not fit into the RAM anymore. In that case, you might want to take a look at the leveldb key-value database package by Nigel Tao, another Go developer. It's currently under active development (but not yet usable), but it will also offer some advanced features like transactions and automatic compression. Also, the read/write throughput should be quite good because of the leveldb design.
There's an ordered, key-value persistence library for the go that I wrote called gkvlite -
https://github.com/steveyen/gkvlite
JSON is very simple but makes bigger files because of the repeated variable names. XML has no advantage. You should go with CSV, which is really simple too. Your program will make less than one page.
But it depends, in fact, upon your modifications. If you make a lot of modifications and must have them stored synchronously on disk, you may need something a little more complex that a single file. If your map is mainly read-only or if you can afford to dump it on file rarely (not every second) a single csv file along an in-memory map will keep things simple and efficient.
BTW, use the csv package of go to do this.
I am caching data in an application I am currently writing and was wondering which would be better to use a regular text file or a sqlite database to hold the cached data? Thanks.
EDIT:
I am using Zend_Cache so relationships are handled without the need of database. What I am caching is xml strings if saved as regular files can be as big as 60kBs.
Depends on the kind of data you're planning on storing.
If the data is related, and you'd want to retrieve the data based on those relationships...then use SQLite.
If the data is completely unrelated and you're just looking for a way to store a retreive plain text...then use a plain text file. It won't have the added overhead of calling in to SQLite.
If you don't need to persist the data in a file for any reason, and the data lends itself to key/value pair storage...you should look in to something like memcached so you don't have to deal with file IO.
If you really CACHE data (not store or transfer) may be better use cache solutions like memcached ?
60k is so small you probably wouldn't even bother caching the data, just keep it in memory.
The only reason to write it out to disk if you want to persist the data between sessions. In this case, using text/xml or sqlite wont make much difference.
I'm trying to start using plain text files to store data on a server, rather than storing them all in a big MySQL database. The problem is that I would likely be generating thousands of folders and hundreds of thousands of files (if I ever have to scale).
What are the problems with doing this? Does it get really slow? Is it about the same performance as using a Database?
What I mean:
Instead of having a database that stores a blog table, then has a row that contains "author", "message" and "date" I would instead have:
A folder for the specific post, then *.txt files inside that folder than has "author", "message" and "date" stored in them.
This would be immensely slower reading than a database (file writes all happen at about the same speed--you can't store a write in memory).
Databases are optimized and meant to handle such large amounts of structured data. File systems are not. It would be a mistake to try to replicate a database with a file system. After all, you can index your database columns, but it's tough to index the file system without another tool.
Databases are built for rapid data access and retrieval. File systems are built for data storage. Use the right tool for the job. In this case, it's absolutely a database.
That being said, if you want to create HTML files for the posts and then store those locales in a DB so that you can easily get to them, then that's definitely a good solution (a la Movable Type).
But if you store these things on a file system, how can you find out your latest post? Most prolific author? Most controversial author? All of those things are trivial with a database, and very hard with a file system. Stick with the database, you'll be glad you did.
It is really depends:
What is file size
What durability requirements do you have?
How many updates do you perform?
What is file system?
It is not obvious that MySQL would be faster:
I did once such comparison for small object in order to use it as sessions storage for CppCMS. With one index (Key Only) and Two indexes (primary key and secondary timeout).
File System: XFS ext3
-----------------------------
Writes/s: 322 20,000
Data Base \ Indexes: Key Only Key+Timeout
-----------------------------------------------
Berkeley DB 34,400 1,450
Sqlite No Sync 4,600 3,400
Sqlite Delayed Commit 20,800 11,700
As you can see, with simple Ext3 file system was faster or as fast as Sqlite3 for storing data because it does not give you (D) of ACID.
On the other hand... DB gives you many, many important features you probably need, so
I would not recommend using files as storage unless you really need it.
Remember, DB is not always the bottle neck of the system
Forget about long-winded answers, here's the simplest reasons why storing data in plaintext files is a bad idea:
It's near-impossible to query. How would you sort blog posts by date? You'd have to read all the files and compare their date, or maintain your own index file (basically, write your own database system.)
It's a nightmare to backup. tar cjf won't cut it, and if you try you may end up with an inconsistent snapshot.
There's probably a dozen other good reasons not to use files, it's hard to monitor performance, very hard to debug, near impossible to recover in case of error, there's no tools to handle them, etc...
I think the key here is that there will be NO indexing on your data. SO to retrieve anything in say a search would be rediculously slow compared to an indexed database. Also, IO operations are expensive, a database could be (partially) in memory, which makes the data available much faster.
You don't really say why you won't use a database yourself... But in the scenario you are describing I would definitely use a DB over folder any day, for a couple of reasons. First of all, the blog scenario seems very simple but it is very easy to imagine that you, someday, would like to expand it with more functionality such as search, more post details, categories etc.
I think that growing the model would be harder to do in a folder structure than in a DB.
Also, databases are usually MUCH faster that file access due to indexing and memory caching.
IIRC Fudforum used the file-storage for speed reasons, it can be a lot faster to grab a file than to search a DB index, retrieve the data from the DB and send it to the user. You're trading the filesystem interface with the DB and DB-library interfaces.
However, that doesn't mean it will be faster or slower. I think you'll find writing is quicker on the filesystem, but reading faster on the DB for general issues. If, like fudforum, you have relatively immutable data that you want to show several posts in one, then a file-basd approach may be a lot faster: eg they don't have to search for every related post, they stick it all in 1 text file and display it once. If you can employ that kind of optimisation, then your file-based approach will work.
Also, mail servers work in the file-based approach too, the Maildir format stores each email message as a file in a directory, not in a database.
one thing I would say though, you'll be better storing everything in 1 file, not 3. The filesystem is better at reading (and caching) a single file than it is with multiple ones. So if you want to store each message as 3 parts, save them all in a single file, read it to get any of the parts and just display the one you want to show.
...and then you want to search all posts by an author and you get to read a million files instead of a simple SQL query...
Databases are NOT faster. Think about it: In the end they store the data in the filesystem as well. So the question if a database is faster depends strongly on the access path.
If you have only one access path, which correlates with your file structure the file system might be way faster then a database. Just make sure you have some caching available for the filesystem.
Of course you do loose all the nice things of a database:
- transactions
- flexible ways to index data, and therefore access data in a flexible way reasonably fast.
- flexible (though ugly) query language
- high recoverability.
The scaling really depends on the filesystem used. AFAIK most file system have some kind of upper limit for number of files (totally or per directory), though on the new ones this is often very high. For hundreds and thousands of files with some directory structure to keep directories to a reasonable size it should be possible to find a well performing file system.
#Eric's comment:
It depends on what you need. If you only need the content of exact on file per query, and you can determine the location and name of the file in a deterministic way the direct access is faster than what a database does, which is roughly:
access a bunch of index entries, in order to
access a bunch of table rows (rdbms typically read blocks that contain multiple rows), in order to
pick a single row from the block.
If you look at it: you have indexes and additional rows in memory, which make your caching inefficient, where is the the speedup of a db supposed to come from?
Databases are great for the general case. But if you have a special case, there is almost always a special solution that is better in some sense.
if you are preferred to go away with RDBMS, why dont u try the other open source key value or document DBs (Non- relational Dbs)..
From ur posting i understand that u r not goin to follow any ACID properties of relational db.. it would be better to adapt other key value dbs (mongodb,coutchdb or hyphertable) instead of your own file system implementation.. it will give better performance than the existing approaches..
Note: I am not also expert in this.. just started working on MongoDB and find useful in similar scenarios. just wanted to share in case u r not aware of these approaches
My application allows user to upload pdf files and store them on the webserver for later viewing. I store the name of the file, location, size, upload date, user name etc in an SQL server database.
I'd like to be able to programatically, just after a file is uploaded, generate a list of keywords (maybe everything except common words) and store them in the sql database as well so that subsequent users can do keyword searches...
Suggestions on how to approach this task? Does these type of routine already exist?
EDIT: Just to clarify my requirements, I wouldn't be concerned with doing OCR, I don't know the insides' of PDF's, but I understand that if it was generated by an app, such as Word->PDF Print, the text of the document is searchable...so really my first task, and the intent of my question is, how do I access the text of a PDF file from an asp.net app? OCR on scanned PDF's is probably beyond my requirements at this point.
As a first step you should extract all text from the PDF.
ghostscript and pdftotext can do this, the PDFBox is another option.
There are certainly other tools as well.
Then you can remove all stopwords and duplicates and write it to the database.
I has been mentioned that this does not work for scanned PDF documents but this is only half the truth. On the one hand there are lots of scanned PDFs which have text additionally embeded, because that is what some scanners drivers do (Canon CanoScan drivers performs OCR and generate searchable PDFs). On the other hand documents generated with LaTeX that contain non-ASCCII characters return garbage in my experience (even when I copy and paste in acrobat).
The only problem I foresee of grabbing every non-common word is that you'll dilute your search results and have to query the DB for more pdfs. One website to look at is Scribd which does something similar to what you are talking about doing with users uploading files and people being able to view them online via a flash app.
That is very interesting topic. The question is how many keywords do you need to define one PDF. If you say:
3 to 10 - I would check methods of text categorization such as bayesian classifier or K-NN (that method will group PDF files into clusters which are similar). I know that similar algorithms are used to filter spam. But it is a system that need input for example if you add keywords to 100 PDF this system will learn the schemas. I am not an expert but this is one way to do it.
more than 10 - then I would suggest brute force -> filter common words -> get most frequent words for a specific document.
I would explore first option. You must surely check such methods as "text categorization", "auto tagging", "text mining", "automatic keyword extraction".
Some links :
http://en.wikipedia.org/wiki/Naive_Bayes_classifier
Keyword Extraction Using Naive Bayes
If you are planning on indexing PDF documents, you should consider using a dedicated text search engine like Lucene. Lucene provides features that will be difficult to implement using only SQL and a relational database. You will still need to extract the text from the PDF documents, but won't have to worry about filtering out common words. By filtering out common words, you will completely lose the ability to do phrase searches.
I'm trying to read through the documentation on Berkeley DB XML, and I think I could really use a developer's blog post or synopsis of when they had a problem that found the XML layer atop Berkeley DB was the exact prescription for.
Maybe I'm not getting it, but it seems like they're both in-process DBs, and ultimately you will parse your XML into objects or data, so why not start by storing your data parsed, rather than as XML?
Ultimately I want my data stored in some reasonable format.
If that data started as XML and I want to retrieve it/them using XQuery, without the XML layer, I have to write a lot of code to do the XQuery by myself, and perhaps even worse to know my XML well enough to be able to have a reasonable storage system for it.
Conversely, so long as the performance of the system allows, I can forget about that part of the back end, and just worry about my XML document and up (i.e. to the user) level and leave the rest as a black box. It gives me the B-DB storage goodness, but I get to use it from a document-centric perspective.