I'm trying to commit a single blob directly to a repository using jgit. I know how to insert the blob and get its sha1, however I'm having difficulties constructing a tree for this scenario. I can't seem to figure out how to correctly use jgit's tree abstractions (TreeWalk and the like) to recursively construct a tree, almost identical to the previous commits', with only different parent trees of the blob.
What is the idiomatic way to do this in JGit?
The reason I ask, is because I'm writing a program that's a kind of editor for documents living in git repositories. In my case, the whole point of using git is to be able to have multiple versions of the documents (aka branches) at the same time. Since it's an editor I have to be able to commit changes, however since I want to see multiple versions of the document at the same time, checking out, modifying a file and committing using JGit porcelain API is not possible, it has to work directly with git objects.
The low-level API you can use for this is TreeFormatter together with CommitBuilder.
An example of using this can be seen here. In this case, it constructs one new tree object with multiple subtrees.
In your case, you probably have to recursively walk the tree and create the new tree objects on the path to the changed file and insert them bottom up. For the rest of the tree, you can use the existing tree IDs and don't have to descend into them. I recommend looking into TreeWalk#setRecursive and TreeWalk#setPostOrderTraversal.
Another option would be to create an in-core DirCache, fill it with DirCacheEntries from the commit and your updated entry, and then call DirCache#writeTree.
For example, can I put 100.000.000 documents in one plone folder?
In recent Plone releases, folders use a BTree-backed storage, so you can store as many objects in there as you like.
The biggest folder that I can access in a production environment currently stores 25k items.
You will, of course, need to deal with large numbers of items in one location appropriately. The usual caveats about really large numbers of content in a site apply.
I'd like to learn about using catalogs correctly.
I have about 30 useful content types, about 50 indexes in catalog.xml, and about 45 metadatas. There are just three types which account for most of the site's data - and I may need millions of these. I've been reading, and there's lots to do, but I want to have the basic configuration right before I begin all that.
This page told me that any non-default indexes should not be added to the portal_catalog. I've even read people explaining how removing one, or two of the default indexes makes a performance difference.
My question is: what are the rules for dividing up the indexes into different catalogs, and for selecting which catalog(s) index which type(s)?
So far I have created one additional catalog, used to catalog all indexes for my 'site-setup' objects (which I have caused to no longer be indexed in portal_catalog). The site-setup indexes are very often used, but more rarely modified than others, so I thought it was correct to separate them from objects which are reindexed more often. I'm not sure if that's the main consideration though.
Another similar question (a good example of the kind of thing I want to solve): how would you handle something like secondary workflow review_state variables? I give each workflow's review_state variable an index (and search on them quite often), but some of my workflows are only used on just a few types. (my most prolific objects have secondary workflows...)
I'd be very grateful for advice!
Campbell
This won't cover everything but I'll bring up some points..
Anything not in the portal_catalog won't work with collections, folder_contents view, getFolderContents method, search, portlet collections, related items(I think) and anything else the assumes you're using the portal_catalog.
I like to use an additional catalog when I need to be able to query the data but it only affects a sub-set of the content objects.
Use collective.indexing to speed up indexing operations.
Mount the catalogs on their own mount points so you can cache them differently from the rest of the site(so you can cache the whole catalog). Then, you can even serve the the catalogs from dedicated zeoserver.
Also, if your content doesn't have to be cataloged by the portal_catalog(with all the constraints listed), you may even want to think about if you need it as a full-fledged (archetype|dexterity) type in the first place. You can use a more slim repoze.catalog to catalog arbitrary objects(which could be very simple data) for whatever your purpose is and get even more performance. Or better yet, look into Solr for indexing it for VERY good performance.
On more thing, depending on the type of data you're storing, you could even look into using a relational database for a data store. But I don't know what kind of queries, indexes, data, etc you have...
30 different types seems like a lot but I don't know what your use case is. Care to share? Perhaps there is a better way to do it.
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'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