I've got a fancy-schmancy "worksheet" style view in a Rails app that is taking way too long to load. (In dev mode, and yes I know there's no caching there, "Completed in 57893ms (View: 54975, DB: 855)") The worksheet is rendered using helper methods, because I couldn't stand maintaining umpteen teeny little partials for the different sorts of rows in the worksheet. Now I'm wondering whether partials might actually be faster?
I've profiled the page load and identified a few cases where object caching will shave a few seconds off, but the profile output suggests that a large chunk of time is spent simply looping through the Worksheet model's constituent objects and appending the string output from the helper. Here's an example of what I'm talking about:
def header_row(wksht)
content_tag(:thead, :class => "ioe") do
content_tag(:tr) do
html_row = []
for i in (0...wksht.class::NUM_COLS) do
html_row << content_tag(:th, h(wksht.column_headings[i].upcase),
:class => wksht.column_classes[i])
end
html_row.join("\n")
end
end
end
OTOH using partials means opening files, spinning off the Ruby interpreter, and in the long run, aggregating a bunch of strings, right? So I'm wondering whether there is another way to speed things up in the helpers. Should I be using something like a stringstream (does that exist in Ruby?), should I get rid of content_tag calls in favor of my own "" string interpolation... I'm willing to write my own performance tests, and share the results, if you have any suggested alternatives to the approach I've already taken.
As it's a fairly complex view (and has an editable version as well), I'd rather not rewrite-and-profile the whole thing more than once. :)
Some related reading:
http://www.viget.com/extend/helpers-vs-partials-a-performance-question/ (old)
http://www.breakingpointsystems.com/community/blog/ruby-string-processing-overhead/
http://blog.purepistos.net/index.php/2008/07/14/benchmarking-ruby-string-interpolation-concatenation-and-appending/
#tadman:
There are row totals and column totals (and more columnar arithmetic), and since they're not all just totals, but also depend on other "magic numbers" from the database, I implemented them in the Ruby code rather than Javascript. (DRY and unit testable.) Javascript is used only in the edit view, and just to add/delete rows (client side only) and to fetch a sheet with fresh totals when the cell contents change. It fetches the whole table because nearly half of the values get updated when an input cell changes.
The worksheet and its rows are actually virtual models; they don't live in the DB, but rather aggregate a boatload of real AR objects. They get created every time a view renders (but that takes 1.7 secs in dev mode, so I'm not worried about it).
I suppose I could transmit a matrix of numbers, rather than marked-up content, and have JS unpack it into the sheet. But that gets unmaintainable fast.
I ended up reading an excellent article at http://www.infoq.com/articles/Rails-Performance ("A Look At Common Performance Problems In Rails"). Then I followed the author's suggestion to cache computations during request processing:
def estimated_costs
#estimated_costs ||=
begin
# tedious vector math
end
end
Because my worksheet does stuff like the above over and over, and then builds on those results to calculate some more rows, this resulted in a 90% speedup right off the bat. Should have been plain as day, but it started with just a few totals, then I showed the prototype to the customer, and it snowballed from there :)
I also wondered whether my array-based math might be inefficient, so I replaced the Ruby Arrays of numbers with NArray (http://narray.rubyforge.org/). The speedup was negligible but the code's cleaner, so it's staying that way.
Finally, I put some object caching in place. The "magic numbers" in the database only change a few times a year at most, and some of them are encrypted, but they need to be used in most of the calculations. That's low-hanging fruit ripe for caching, and it shaved off another 1.25 seconds.
I'll look at eager loading of associations next, as there's probably some time to save there, and I'll do a quick comparison of sending "just the data" vs sending the HTML, as #tadman suggested. About the only partial I can cache is the navigation sidebar. All of the other content depends on the request parameters.
Thanks for your suggestions!
Internally all partials are translated into a block of executable Ruby code and run through exactly the same runtime as any helper methods. Periodically you can see glimpses of this when a malformed template causes the generated code to fail to compile.
Although it stands to reason that helper methods are faster than partials, and a straightforward string interpolation is faster still, it's hard to say if the performance gain from this would make it worth pursuing. Rendering a very large number of partials can be a bottleneck in terms of logging in the development environment, but in a production environment their impact seems less severe.
The only way to figure this one out is to benchmark your pages using two different rendering methods.
As you point out, caching is where you get the big gains. Using Memcached to save large chunks of pre-rendered HTML content can give you exponentially faster load times. Rendering 10,000 rows into HTML will always be slower than retrieving the same snippet from the Rails.cache subsystem.
It's also the case that the content you don't render is always rendered the quickest, so anything you can do to reduce the amount of content you generate for each helper call will provide big gains. If you're building a large spread-sheet style app that's entirely dependent on JavaScript, you may find that bundling up the data as a JSON array and expanding it client-side is significantly faster than unrolling the HTML on the server and shipping it over that way.
Related
I maintain a website of events taking place in my city.
My website homepage is a 5-days calendar with all the events taking place from today to 4 days in the future.
I have done quite a good job with my "ActiveRecord" code as I have around ~120ms spent in MySQL (double checked with RackMiniProfiler) to load ~200-300 events.
However my response time is very slow (1.5s - 2s).
Most time is spent in instantiation of AR's objects from my queries
Using ObjectSpace, I see that ~6k AR::Base objects are instantiated for +300 events that are displayed.
The reason why there are so many objects is that my Event model has many associated models (e.g. venue, occurrences, categories, etc...), all of which contain bits of information I need to show.
As expected, profiling proved ActiveRecord's object instantiation as the most consuming task during the request.
I am not experienced enough in neither ActiveRecord nor its performance.
Is such speed expected or should my objects be instantiated much faster?
Should I move away from AR and use simple ruby hashes?
Is this the Rails standard when my data model is too complex?
========= UPDATE 1 =========
This pastebin contains the service class I use to load the events for a single day in the calendar.
I hope it's understandable, I did not have time to properly document it since it's still a work in progress to improve performance.
========= UPDATE 2 =========
Loading all these objects has another drawback: it causes GC runs while the page is being rendered, adding ~100ms after every n events are rendered, which becomes a total overhead of ~500ms.
To give you an idea of how much data I'm loading (sadly 99% of it is needed), if I dump to JSON I get a 47K file.
========= UPDATE 3 =========
As mentioned by #TheSuper, even though it does not improve AR's performance, fragment caching is indeed my friend as I'm rendering quite the amount of data while under GC runs. Applying fragment caching yielded a 1-1.2s improvement, which is HUGE.
However I still cannot overcome the 600ms wall of AR.
A possible improvement are "selective includes" discussed in this answer, for the few cases where I need a small portion of the attributes of an included model, but this is ugly and inflexible.
I am trying to index about 3 million text documents in solr. About 1/3 of these files are emails that have about 1-5 paragraphs of text in them. The remaining 2/3 files only have a few words to sentences each.
It takes Lucid/Solr nearly 1 hour to fully index the entire dataset I'm working with. I'm trying to find ways to optimize this. I have setup Lucid/Solr to only commit every 100,000 files, and it indexes the files in batches of 50,000 files at once. Memory isn't an issue anymore, as it consistently stays around 1GB of memory because of the batching.
The entire dataset has to be indexed initially. It's like a legacy system that has to be loaded to a new system, so the data has to be indexed and it needs to be as fast as possible, but I'm not sure what areas to look into to optimize this time.
I'm thinking that maybe there's a lot of little words like "the, a, because, should, if, ..." that are causing a lot of overhead and are just "noise" words. I am curious if I cut them out if it would drastically speed up the indexing time. I have been looking at the Lucid docs for a while, but I can't seem to find a way to specify what words not to index. I came across the term "stop list" but didn't see much more than a reference to it in passing.
Are there other ways to make this indexing go faster or am I just stuck with a 1 hour indexing time?
We met similar problem recently. We can't use solrj as the request and response have to go through some applications, so we take the following steps:
Creating Custom Solr Type to Stream Large Text Field!
Use GZipOutput/InputStream and Bse64Output/InputStream to compress the large text. This can reduce size of text about 85%, this can reduce the time to transfer the request/response.
To reduce memory usage at client side:
2.1 We use stream api(GSon stream or XML Stax) to read doc one by one.
2.2 Define a custom Solr Field Type: FileTextField which accepts FileHolder as value. FileTextField will eventually pass a reader to Lucene. Lucene will use the reader to read content and add to index.
2.3 When the text field is too big, first uncompress it to a temp file, create a FileHolder instance, then set the FileHolder instance as field value.
It seems from your query that Indexing time is really important for your application. Solr is a great search engine however if you need super fast indexing time and if that is a very important criteria for you, than you should go with Sphinx Search Engine. It wont take much of time for you to quickly setup and benchmark your results using Sphinx.
There can be ways (like the one you have mentioned, stopwords etc.) to optimize however whatever you do with respect to indexing time Solr won't be able to beat Sphinx. I have done benchmarking myself.
I too love Solr a lot because of its ease of use, its out of box great features like N-Gram Indexing, Faceting, Multi-core, Spelling Correctors and its integration with other apache products etc.. but when it comes to Optimized Algorithms (be it Index size, Index time etc.) Sphinx rocks!!
Sphinx too is open source. Try that out.
I have a text file sized 300MB, I want to count the occurrences of each 10,000 substrings in the file. I want to know how to do it fast.
Now, I use the following code:
content = IO.read("path/to/mytextfile")
Word.each do |w|
w.occurrence = content.scan(w.name).size
w.save
end
Word is an ActiveRecord class.
It took me almost 1 day to finish the counting. Is there anyway to do it faster? Thanks.
Edit1:
Thank you again. I am running rails 2.3.9. The name filed of words table contains what I am searching for, and it contains only unique values. Instead of using Word.each, I use batch(1000 rows a time) load. It should help.
I rewrited the whole code with the idea from bpaulon. Now it only took a few hours to finish the counting.
I profiled the new version code, now the largest time costing methods are utf8 encode supported string truncating code
def truncate(n)
self.slice(/\A.{0,#{n}}/m)
end
and characters counting code
def utf8_length
self.unpack('U*').size
end
Any other faster methods to replace them?
Your use of scan creates an array, counts the size of it, then throws it away. If you have a lot of occurrences of the substring inside a big file, you will create a big array temporarily, potentially burning up CPU time with memory management, but that should still run pretty quickly, even with 300MB.
Because Word is an ActiveRecord class, it is dependent on the schema and any indexes in your database, plus any issues your database server might be having. If the database is not optimized or is responding slowly or the query used to retrieve the data is not efficient, then the iteration will be slow. You might find it a lot faster to grab groups of Word so they are in RAM, then iterate over them.
And, if the database and your code are running on the same machine, you could be suffering from resource constraints like having only one drive, not enough RAM, etc.
Without knowing more about your environment and hardware it's hard to say.
EDIT:
I can grab the substrings into an array/hash first, then add the count results to the array or hash, and write the results back to database after all the counting is done. You think it be faster, right?
No, I doubt that will help a lot, and, without knowing where the problem lies all you might do is make the problem worse because you'll have to load 10,000 records as objects from the database, then build a 10,000 element hash or array which will also be in memory along with the DB records, then write them out.
Ruby will only use a single core, currently, but you can gain speed by using Ruby 1.9+. I'd recommend installing RVM and letting it manage your Ruby. Be sure to read the instructions on that page, then run rvm notes and follow those directions.
What is your Word model and the underlying schema and indexes look like? Is the database on the same machine?
EDIT: From looking at your table schema, you have no indexes except for id which really won't help much for normal look-ups. I'd recommend presenting your schema on Stack Overflow's sibling site https://dba.stackexchange.com/ and explain what you want to do. At a minimum I'd add a key to the text fields to help avoid full table scans for any searches you do.
What might help more is to read: Retrieving Multiple Objects in Batches from "Active Record Query Interface".
Also, look at the SQL being emitted when your Word.each is running. Is it something like "select * from word"? If so, Rails is pulling in 10,000 records to iterate over them one by one. If it is something like "select * from word where id=1" then for every record you have a database read followed by a write when you update the count. That is the scenario that the "Retrieving Multiple Objects in Batches" link will help fix.
Also, I am guessing that content is the text you are searching for, but I can't tell for sure. Is it possible you have duplicated text values causing you to do scans more than once for the same text? If so, select your records using a unique condition on that field and then update your counts for all matching records at one time.
Have you profiled your code to see if Ruby itself can help you pinpoint the problem? Modify your code a little to process 100 or 1000 records. Start the app with the -r profile flag. When the app exits profiler will output a table showing where time was spent.
What version of Rails are you running?
I think you could approach this problem differently
You do not need to scan the file this many times, you could create a db, like in mongo or mysql, and for each word you find, you fetch the db for it and then adds on some "counter" field.
You could ask me "but then I will have to scan my database a lot and it could take a lot more". Well, sure you wouldn't ask this, but it won't take more time because databases are focused in IO, besides you could always index it.
EDIT: There is no way to delimit at all?? Let's say that where you have the a Word.name string you really holds a (not simple) regex. Could the regex contain the \n? Well, if the regex can contain any value, you should estimate the maximum size of string the regex can fetch, double it, and scan the file by that ammount of chars but moving the cursor by that number.
Lets say your estimate of the maximum your regex could fetch it is like 20 chars nad your file has from 0 to 30000 chars. You pass each regex you have from 0 to 40 chars, then again from 20 to 60, from 40 to 80, etc...
You should also hold the position you found of your smaller regex so it wouldn't repeat it.
Finally, this solution seems to be not worth the effort, your problem may have a greater solution based on what that regexes are, but it will be faster than invoke scan Words.count times your your 300Mb string.
You could load your entire "Word" table into a Trie, then do back-tracking since you said there are no delimiters in the text.
So for each character in the text, go down the Trie of words. If you hit a word, increment its count. "Going down the trie" involves three cases:
There's no node at this character. (If you're mid-search, pop the back-tracking stack)
There's a node at this character. (But it's not a Word)
There's a node at this character. (It's a Word - increment and "dirty")
Back-tracking is just keeping track of places you want to go after you've exhausted this "search" of the Trie, which is when you run out of nodes to visit. This will probably be each character you visit that is a root of the Trie.
After you've done this, you can then visit all the nodes you changed and just update the records they represent.
This will take some time to implement, but will surely be faster than each & scan.
I'm trying to perform a daily operation on a larger than normal dataset (2m+ records). However, Rails seems to take a very long time performing operations on such a dataset. Operations like
Dataset.all.each do |data|
...
end
take a very long time to complete (I assume this is because it can't fit all the items into memory at once, right?).
Does anyone have any strategies on how I could handle this situation? I know SQL would probably speed up the process, but I'm looking to use the Rails environment as I can do many more complicated things to the data than I can with just SQL statements.
You want to use ActiveRecord's find_each for this.
Dataset.find_each do |data|
...
end
When processing a large set of rows, a database is very fast and efficient, it what they were designed for. I would recommend attempting to do all this processing in SQL if you want max performance. If you prefer to use Rails, or it is impossible to do everything you want in SQL, you might attempt to do some pre-processing in SQL and the remainder in Rails. Short of that, 2m+ rows is a lot to loop over, even if each only takes a fraction of a second it add up to a long time.
I am a newbie working in a simple Rails app that translates a document (long string) from a language to another. The dictionary is a table of terms (a string regexp to find and substitute, and a block that ouputs a substituting string). The table is 1 million records long.
Each request is a document that wants to be translated. In a first brutish force approach I need to run the whole dictionary against each request/document.
Since the dictionary will run whole every time (from the first record to the last), instead of loading the table of records of the dictionary with each document, I think the best would be to have the whole dictionary as an array in memory.
I know it is not the most efficient, but the dictionary has to run whole at this point.
1.- If no efficiency can be gained by restructuring the document and dictionary (meaning it is not possible to create smaller subsets of the dictionary). What is the best design approach?
2.- Do you know of similar projects that I can learn from?
3.- Where should I look to learn how to load such a big table into memory (cache?) at rails startup?
Any answer to any of the posed questions will be greatly appreciated. Thank you very much!
I don't think your web hoster will be happy with a solution like this. This script
dict = {}
(0..1000_000).each do | num |
dict[/#{num}/] = "#{num}_subst"
end
consumes a gigabyte of RAM on my MBP for storing the hash table. Another approach will be to store your substitutions marshaled in memcached so that you could (at least) store them across machines.
require 'rubygems'
require 'memcached'
#table = Memcached.new("localhost:11211")
retained_keys = (0..1000_000).each do | num |
stored_blob = Marshal.dump([/#{num}/, "#{num}_subst"])
#table.set("p#{num}", stored_blob)
end
You will have to worry about keeping the keys "hot" since memcached will expire them if they are not needed.
The best approach however, for your case, would be very simple - write your substitutions to a file (one line per substitution) and make a stream-filter that reads the file line by line, and replaces from this file. You can also parallelize that by mapping work on this, say, per letter of substitution and replacing markers.
But this should get you started:
require "base64"
File.open("./dict.marshal", "wb") do | file |
(0..1000_000).each do | num |
stored_blob = Base64.encode64(Marshal.dump([/#{num}/, "#{num}_subst"]))
file.puts(stored_blob)
end
end
puts "Table populated (should be a 35 meg file), now let's run substitutions"
File.open("./dict.marshal", "r") do | f |
until f.eof?
pattern, replacement = Marshal.load(Base64.decode64(f.gets))
end
end
puts "All replacements out"
To populate the file AND load each substitution, this takes me:
real 0m21.262s
user 0m19.100s
sys 0m0.502s
To just load the regexp and the string from file (all the million, piece by piece)
real 0m7.855s
user 0m7.645s
sys 0m0.105s
So this is 7 seconds IO overhead, but you don't lose any memory (and there is huge room for improvement) - the RSIZE is about 3 megs. You should easily be able to make it go faster if you do IO in bulk, or make one file for 10-50 substitutions and load them as a whole. Put the files on an SSD or a RAID and you got a winner, but you get to keep your RAM.
In production mode, Rails will not reload classes between requests. You can keep something in memory easily by putting it into a class variable.
You could do something like:
class Dictionary < ActiveRecord::Base
##cached = nil
mattr_accessor :cached
def self.cache_dict!
##cached = Dictionary.all
end
end
And then in production.rb:
Dictionary.cache_dict!
For your specific questions:
Possibly write the part that's inefficient in C or Java or a faster language
Nope, sorry. Maybe you could do a MapReduce algorithm to distribute the load across servers.
See above.
This isn't so much a specific answer to one of your questions as a process recommendation. If you're having (or anticipating) performance issues, you should be using a profiler from the get-go.
Check out this tutorial: How to Profile Your Rails Application.
My experience on a number of platforms (ANSI C, C#, Ruby) is that performance problems are very hard to deal with in advance; rather, you're better off implementing something that looks like it might be performant then load-testing it through a profiler.
Then, once you know where your time is being spent, you can expend some effort on optimisation.
If I had to take a guess, I'd say the regex work you'll be performing will be as much of a performance bottleneck as any ActiveRecord work. But without verifying that with a profiler, that guess is of little value.
If you use something like cache_fu, you can then leverage something like memcache without doing any work yourself. If you are trying to bring 1 MM rows into memory, being able to leverage the distributed nature of memcache will probably be useful.