2018 US Olympic Medals, Adjusted – 2

Last updated: 2018-02-23. The score is 15 to 6

Americans have become increasingly more racist and intolerant of their fellow citizens in the past few years. Rhetoric and policies by Donald Trump, Mike Pence and various leaders of the Republic Party have sought to treat people who are not Straight, White and Christian as second class citizens.

This series will be updated throughout the olympics to track separate US Olympic Medal counts for “Straight White Christian America” and citizens who are routinely persecuted for their ethnicity, religion or sexuality.

Medal Counts may be adjusted as more background information on athletes becomes available.

Team “Straight White Christian America”

Total: 15

Gold

event category athlete
Alpine Skiing: Giant Slalom Women’s Mikaela Shiffrin
Cross Country: Team Sprint Women’s Kikkan Randall, Jessie Diggins
Freestyle Skiing: Halfpipe Men’s David Wise
Hockey: Tourenment Women’s US Team
Snowboarding: Slopestyle Women’s Jamie Anderson
Snowboarding: Slopestyle Mens Redmond Gerard
Snowboarding: Halfpipe Men’s Shaun White

Silver

event category athlete
Alpine Skiing: Combined Women’s Mikaela Shiffrin
Freestyle Skiing: Slopestyle Men’s Nick Goepper
Freestyle Skiing: Halfpipe Men’s Alex Ferreirapotential $$
Luge: Singles Men’s Chris Mazdzer
Short Track: 1000m Men’s John-Henry Krueger
Snowboarding: Big Air Women’s Jamie Anderson

Bronze

event category athlete
Alpine Skiing: Downhill Women’s Lindsey Vonn
Freestyle Skiing: Halfpipe Women’s Brita Sigourney

Team “Routinely Persecuted America”

Total: 6

Gold

event category athlete
Snowboarding: Halfpipe Women’s Chloe Kim*,$

Silver

event category athlete
Bobsled Women’s Lauren Gibbs*, Elana Meyers Taylor

Bronze

event category athlete
Figure Skating Team Nathan Chen*,$, Alexa Scimeca Knierim, Chris Knierim, Mirai Nagasu*,$;, Adam Rippon, Alex Shibutani*, Maia Shibutani*, Bradie Tennell
Figure Skating: Ice Dance Team Alex Shibutani*, Maia Shibutani*
Snowboarding: Halfpipe Women’s Arielle Gold&
Speed Skating: Team Pursuit Women’s Heather Bergma, Brittany Bowe, Mia Manganello, Carlijn Schoutens$$

Global Standings

2018.02.23

A unified Team America is in 4th place overall with 21 medals. Without “Routinely Persecuted Americans”, “White Christian America” would drop to 15 medals – a tied 5th place (with France), behind the Netherlands.

Rank Country All Medals
1 Norway 37
2 Canada 27
3 Germany 26
4 USA 21
5 Netherlands 18
6 France 15
7 Russia 14

The disqualifiers

Key

mark disqualifier
* not white
$ immigrant
$$ child of immigrant(s)
lgbtq
& not christian

Notes – Researched

  • Adam Rippon – LGBTQ icon
  • Alex Shibutani – Asian, parents of Chinese descent
  • Arielle Gold – Jewish
  • Chloe Kim – Asian, parents immigrated from South Korea
  • Maia Shibutani – Asian, parents of Chinese descent
  • Mirai Nagasu – Asian, parents are Japanese citizens
  • Nathan Chen – Asian, parents immigrated from China
  • Carlijn Schoutens – Born in NJ to Dutch parents, raised in the Netherlands, moved back to USA to compete

Notes 2 – Needs more research

  • Alex Ferreira – Alex’s father played soccer for the River Plate club in Buenos Aries Argentina, however his birthplace has not yet been determined.

2018 US Olympic Medals, Adjusted

Last updated: 2018-02-21. The score is 10 to 6

Americans have become increasingly more racist and intolerant of their fellow citizens in the past few years. Rhetoric and policies by Donald Trump, Mike Pence and various leaders of the Republic Party have sought to treat people who are not Straight, White and Christian as second class citizens.

This page will be updated throughout the olympics to track separate US Olympic Medal counts for “Straight White Christian America” and citizens who are routinely persecuted for their ethnicity, religion or sexuality.

Medal Counts may be adjusted as more background information on athletes becomes available.

Team “Straight White Christian America”

Total: 10

Gold

event category athlete
Slopestyle Snowboarding Mens Redmond Gerard
Slopestyle Snowboarding Women’s Jamie Anderson
Halfpipe Snowboarding Men’s Shaun White
Alpine Skiing: Giant Slalom Women’s Mikaela Shiffrin
Cross Country Women’s Kikkan Randall, Jessie Diggins

Silver

event category athlete
Singles Luge Men’s Chris Mazdzer
Freestyle Skiing: Slopestyle Men’s Nick Goepper
Short Track: 1000m Men’s John-Henry Krueger

Bronze

event category athlete
Alpine Skiing: Downhill Women’s Lindsey Vonn
Freestyle Skiing: Halfpipe Women’s Brita Sigourney

Team “Routinely Persecuted America”

Total: 6

Gold

event category athlete
Halfpipe Snowboarding Women’s Chloe Kim*,$

Silver

event category athlete
Bobsled Women’s Lauren Gibbs*, Elana Meyers Taylor

Bronze

event category athlete
Halfpipe Snowboarding Women’s Arielle Gold&
Figure Skating Team Nathan Chen*,$, Alexa Scimeca Knierim, Chris Knierim, Mirai Nagasu*,$;, Adam Rippon, Alex Shibutani*, Maia Shibutani*, Bradie Tennell
Figure Skating: Ice Dance Team Alex Shibutani*, Maia Shibutani*
Speed Skating: Team Pursuit Women’s Heather Bergma, Brittany Bowe, Mia Manganello, Carlijn Schoutens$$

Global Standings

2018.02.21

A unified Team America is in 5th place overall with 16 medals:

Rank Country All Medals
1 Norway 33
2 Germany 24
3 Canada 21
4 Netherlands 16
4 USA 16
6 France 14
7 Russia 13
8 Japan 11
9 Austria 10
10 South Korea 9
10 Italy 9
12 Sweden 8
12 Switzerland 8
14 China 7
15 Czech Republic 6

If the teams were split, Team “Straight White Christian America” drops down to the shared 9th place bracket and Team “Routinely Persecuted America” drops to the 15th bracket.

The disqualifiers

Key

mark disqualifier
* not white
$ immigrant
$$ child of immigrant(s)
lgbtq
& not christian

Notes

  • Adam Rippon – LGBTQ icon
  • Alex Shibutani – Asian, parents of Chinese descent
  • Arielle Gold – Jewish
  • Chloe Kim – Asian, parents immigrated from South Korea
  • Maia Shibutani – Asian, parents of Chinese descent
  • Mirai Nagasu – Asian, parents are Japanese citizens
  • Nathan Chen – Asian, parents immigrated from China
  • Carlijn Schoutens – Born in NJ to Dutch parents, raised in the Netherlands, moved back to USA to compete

a security vulnerability affecting most websites and open source projects, even facebook

While auditing some new changes to an Open Source project I often use, I wrote a test-suite to surface edge cases and discovered something I never noticed before: the ASCII Control Characters made it safely across the entire stack as-is. It didn’t raise any alarms or trigger escaping from Form controls, User Generated Content Sanitizers, or HTML Safe Markup rendering.

Form control systems generally only trigger an error with ASCII controls if the input validator is built with a Regular Expression that allows specific characters/ranges. Most just check to see valid ASCII characters. ASCII control characters do a lot of useful things like providing “newlines” and “tabs”. Most are irrelevant, but one is particularly problematic — the backspace character. “Backspaces” are problematic because Web Browsers, Text Editors and Developer IDEs typically consider most ASCII control characters to be non-printing or “invisible” and don’t render them onscreen, but they are still part of the “document”. If you copy a document or document part to your clipboard, the “invisible” characters copy too. If you then paste them into an interpreter window, they will be interpreted/execute in real-time — allowing a malicious user to carefully construct an evil payload.

An Example

Consider this trivial example with Python. It looks like we’re loading the yosemite library, right?:

import yosemite

Wrong. Pasted into a Python interpreter, this will appear:

import os

How? There are ASCII backspace control characters after certain letters. When pasted into an interpreter, they immediately “activate”. Using the escape sequences, the actual string above looks like:

import y\bose\bm\bi\bt\be

If you want to try this at home, you can generate a payload with this line:

open('payload.txt', 'w').write("import y\bose\bm\bi\bt\be ")

Given a few lines of text and some creativity, it is not hard to construct “hidden” code that deletes entire drives or does other nefarious activities.

It’s an Edge Case

This really only affects situations meeting both of the following two requirements:

  1. People retrieve code published via User Generated Content (or potentially main site articles)
  2. The code is copy/pasted directly into an interpreter or terminal (I’ve test this against Python, Ruby, and Node.JS)

The target audiences for this type of vulnerability is developers and “technology organizations”. The potential vectors for bad payloads are:

  • posts / articles / status
  • pastebins
  • bug-tracking / ticketing software
  • “how to reproduce” instructions on security vulnerability report forms (ironic, i know)

What is and isn’t affected

I tested dozens of “internet scale” websites, only two filtered out the backspace control character: StackOverflow and Twitter. WordPress.com filters out the backspace, but WordPress open source posts do not.

I focused my search on the types of sites that people would copy/paste code from: coding blogs, “engineering team” notes, discussion software, bugtrackers, etc. Everything but the two sites mentioned above failed.

Bad payloads were also accepted in:

  • Facebook
  • Multiple Pastebin and ‘fiddle’ services

Most Python/Ruby/PHP libraries allowed bad payloads through Form validation or UGC sanitization.

Did I file reports with anyone?

I previously filed security reports with a handful of projects and websites. I also offered patches to escape the backspace issue with some projects as well.

I decided to post this publicly because:

  • Open Source projects overwhelmingly believed the issue should be addressed by another component.
  • Commercial Websites believed this is a social engineering hack and dismissed it.

Small gotcha under Python’s PYTHONOPTIMIZE feature

A long, long, long time ago I started using interpreter optimizations to help organize code. If a block of code is within a constant (or expression of constants) that evaluate to False, then the block (or line) is optimized away.

if (false){ # this is optimized away }

Perl was very generous, and would allow for constants, true/false, and operations between the two to work.

Thankfully, Python has some optimizations available via the PYTHONOPTIMIZE environment variable (which can also be adjusted via the -o and -oo flags). They control the __debug__ constant, which is True (by default) or False (when optimizations are enabled), and will omit documentation strings if the level is 2 (or oo) or higher. Using these flags in a production environment allows for very verbose documentation and extra debugging logic in a development environment.

Unfortunately, I implemented this incorrectly in some places. To fine-tune some development debugging routines I had some lines like this:

if __debug__ and DEBUG_CACHE:
    pass

Python’s interpreter doesn’t optimize that away if DEBUG_CACHE is equal to False, or even if it IS False (i tested under 2.7 and 3.5). I should have realized this (or at least tested for it). I didn’t notice this until I profiled my app and saw a bunch of additional statistics logging that should not have been compiled.

The correct way to write the above is:

if __debug__:
    if DEBUG_CACHE:
        pass

Here is a quick test

trying running it with optimizations turned on:

export PYTHONOPTIMIZE=2
python test.py

and with them off

export PYTHONOPTIMIZE
python test.py

As far as the interpreter is concerned during the optimization phase, __debug__(False) and False is True.

import dis

def foo():
    """docstring"""
    if __debug__:
        print("debug message")
    return True

def bar():
    """docstring"""
    if __debug__ and False:
        print("debug message")
    return True

# ============

# this will be a lean function
dis.dis(foo)

print "- - - - - - - -"
# this will show extended logic
dis.dis(bar)

The default version of foo should look like this:

21           0 LOAD_CONST               1 ('debug message')
             3 PRINT_ITEM          
             4 PRINT_NEWLINE       

22           5 LOAD_GLOBAL              0 (True)
             8 RETURN_VALUE        

But you should see that the optimized version of foo creates some very lean code:

22           0 LOAD_GLOBAL              0 (True)
             3 RETURN_VALUE        

Now let’s look at the bar function when unoptimized

26           0 LOAD_GLOBAL              0 (__debug__)
             3 POP_JUMP_IF_FALSE       20
             6 LOAD_GLOBAL              1 (False)
             9 POP_JUMP_IF_FALSE       20

27          12 LOAD_CONST               1 ('debug message')
            15 PRINT_ITEM          
            16 PRINT_NEWLINE       
            17 JUMP_FORWARD             0 (to 20)

28     >>   20 LOAD_GLOBAL              2 (True)
            23 RETURN_VALUE        

Which generates the same code as the optimized version:

26           0 LOAD_GLOBAL              0 (__debug__)
             3 POP_JUMP_IF_FALSE       20
             6 LOAD_GLOBAL              1 (False)
             9 POP_JUMP_IF_FALSE       20

27          12 LOAD_CONST               1 ('debug message')
            15 PRINT_ITEM          
            16 PRINT_NEWLINE       
            17 JUMP_FORWARD             0 (to 20)

28     >>   20 LOAD_GLOBAL              2 (True)
            23 RETURN_VALUE

So let’s add a new function, biz

def biz():
    """docstring"""
    if __debug__:
        if False:
            print("debug message")
    return True

The unoptimized code:

33           0 LOAD_GLOBAL              0 (False)
             3 POP_JUMP_IF_FALSE       14

34           6 LOAD_CONST               1 ('debug message')
             9 PRINT_ITEM          
            10 PRINT_NEWLINE       
            11 JUMP_FORWARD             0 (to 14)

35     >>   14 LOAD_GLOBAL              1 (True)
            17 RETURN_VALUE    

And a lot of that gets optimized away:

35           0 LOAD_GLOBAL              0 (True)
             3 RETURN_VALUE        

Not many people use/abuse how the interpreter compiles code to their advantage, but if you do — pay attention to constructs like this.

Using __debug__ is a great way to hide logging code on a production environment. The evaluation of __debug__ only happens once, when Python first compiles the code, so there is very little overhead.

deleted dogpile forks

I previously maintained some forks of Mike Bayer’s excellent dogpile.cache package that enabled some aggressive caching techniques, additional logging functions, and fixes to unit tests on Redis.

dogpile has since had some upstream changes, so I’ve decided to delete the forks. most of the ‘problems’ I’ve had have been solved, and compatibility will be better maintained via plugins.

Optimized Archiving of Historical Time-Series Data for Analytics

The (forthcoming) Aptise platform features a web-indexer that tabulates a lot of statistical data each day for domains across the global internet.

While we don’t currently need this data, we may need it in the future. Keeping it in the “live” database is not really a good idea – it’s never used and just becomes a growing stack of general crap that automatically gets backed-up and archived every day as part of standard operations. It’s best to keep production lean and move this unnecessary data out-of-sight and out-of-mind.

An example of this type of data is a daily calculation on the relevance of each given topic to each domain that is tracked. We’re primarily concerned with conveying the current relevance, but in the future we will address historical trends.

Let’s look at the basic storage requirements of this as a PostgreSQL table:

| Column    | Format  | Size    |
| --------- | ------- | ------- |
| date      | Date    | 8 bytes |
| domain_id | Integer | 4 bytes |
| topic_id  | Integer | 4 bytes |
| count     | Integer | 4 bytes |

Each row is taking 20 bytes, 8 of which are due to date alone.

Storing this in PostgreSQL across thousands of domains and tags every day takes a significant chunk of storage space – and this is only one of many similar tables that primarily have archival data.

An easy way to save some space for archiving purposes is to segment the data by date, and move the storage of date information from a row and into the organizational structure.

If we were to keep everything in Postgres, we would create an explicit table for the date. For example:

CREATE TABLE report_table_a__20160101 (domain_id INT NOT NULL, topic_id INT NOT NULL, count INT NOT NULL DEFAULT 0);

| Column    | Format  | Size    |
| --------- | ------- | ------- |
| domain_id | Integer | 4 bytes |
| topic_id  | Integer | 4 bytes |
| count     | Integer | 4 bytes |

This structure conceptually stores the same data, but instead of repeating the date in every row, we record it only once within the table’s name. This simple shift will lead to a nearly 40% reduction in size.

In our use-case, we don’t want to keep this in PostgreSQL because the extra data complicates automated backups and storage. Even if we wanted this data live, having it within hundreds of tables is a bit much overkill. So for now, we’re going to export the data from a single date into a new file.

SELECT domain_id, topic_id, count FROM table_a WHERE date = '2016-01-01';

And we’re going to save that into a comma delimited file:

table_a-20160101.csv

I skipped a lot of steps above because I do this in Python — for reasons I’m about to explain.

As a raw csv file, my date-specific table is still pretty large at 7556804 bytes — so let’s consider ways to compress it:

Using standard zip compression, we can drop that down to 2985257 bytes. That’s ok, but not very good. If we use xz compression, it drops to a slightly better 2362719.

We’ve already compressed the data to 40% the original size by eliminating the date column, so these numbers are a pretty decent overall improvement — but considering the type of data we’re storing, the compression is just not very good. We’ve got to do better — and we can.

We can do much better and actually it’s pretty easy (!). All we need to do is understand compression algorithms a bit.

Generally speaking, compression algorithms look for repeating patterns. When we pulled the data out of PostgreSQL, we just had random numbers.

We can help the compression algorithm do its job by giving it better input. One way is through sorting:

SELECT domain_id, topic_id, count FROM table_a WHERE date = '2016-01-01' ORDER BY domain_id ASC, topic_id ASC, count ASC;

As a raw csv, this sorted date-specific table is still the same exact 7556804 bytes.

Look what happens when we try to compress it:

Using standard zip compression, we can drop that down to 1867502 bytes. That’s pretty good – we’re at 25.7% the size of the raw file AND it’s 60% the size of the non-sorted zip. That is a huge difference! If we use xz compression, we drop down to 1280996 bytes. That’s even better at 17%.

17% compression is honestly great, and remember — this is compressing the data that is already 40% smaller because we shifted the date column out. If we consider what the filesize with the date column would be, we’re actually at 10% compression. Wonderful.

I’m pretty happy with these numbers, but we can still do better — and without much more work.

As I said above, compression software looks for patterns. Although the sorting helped, we’re still a bit hindered because our data is in a “row” storage format. Consider this example:

1000,1000,1
1000,1000,2
1000,1000,3
1001,1000,1
1001,1000,2
1001,1000,3

There are lots of repeating patterns there, but not as many as if we represented the same information in a “column” storage format:

1000,1000,1000,1001,1001,1001
1000,1000,1000,1000,1000,1000
1,2,3,1,2,3

This is the same data, but as you can see it’s much more “machine” friendly – there are larger repeating patterns.

This transformation from row to column is an example of “transposing an array of data`; performing it (and reversing it) is incredibly easy with Python’s standard functions.

Let’s see what happens when I use transpose_to_csv below on the data from my csv file

def transpose_to_csv(listed_data):
    """given an array of `listed_data` will turn a row-store into a col-store (or vice versa)
    reverse with `transpose_from_csv`"""
    zipped = zip(*listed_data)
    list2 = [','.join([str(i) for i in zippedline]) for zippedline in zipped]
    list2 = '\n'.join(list2)
    return list2

def transpose_from_csv(string_data):
    """given a string of csvdata, will revert the output of `transpose_to_csv`"""
    destringed = string_data.split('\n')
    destringed = [line.split(',') for line in destringed]
    unzipped = zip(*destringed)
    return unzipped

As a raw csv, my transposed file is still the exact same size at 7556804 bytes.

However, if I zip the file – it drops down to 1425585 bytes.

And if I use xz compression… I’m now down to 804960 bytes.

This is a HUGE savings without much work.

The raw data in postgres was probably about 12594673 bytes (based on the savings, the file was deleted).
Stripping out the date information and storing it in the filename generated a 7556804 bytes csv file – a 60% savings.
Without thinking about compression, just lazily “zipping” the file created a file 2985257 bytes.
But when we thought about compression: we sorted the input, transposed that data into a column store; and applied xz compression; we resulted in a filesize of 804960 bytes – 10.7% of the csv size and 6.4% of the estimated size in PostgreSQL.

This considerably smaller amount of data can not be archived onto something like Amazon’s Glacier and worried about at a later date.

This may seem like a trivial amount of data to worry about, but keep in mind that this is a DAILY snapshot, and one of several tables. At 12MB a day in PostgreSQL, one year of data takes over 4GB of space on a system that is treated for high-priority data backups. This strategy turns that year of snapshots into under 300MB of information that can be warehoused on 3rd party systems. This saves us a lot of time and even more money! In our situation, this strategy is applied to multiple tables. Most importantly, the benefits cascade across our entire system as we free up space and resources.

These numbers could be improved upon further by finding an optimal sort order, or even using custom compression algorithms (such as storing the deltas between columns, then compressing). This was written to illustrate a quick way to easily optimize archived data storage.

The results:

Format Compression Sorted? Size % csv+date
csv+date 12594673 100
row csv 7556804 60
row csv zip 2985257 23.7
row csv xz 2362719 18.8
row csv Yes 7556804 60
row csv zip Yes 1867502 14.8
row csv xz Yes 1280996 10.2
col csv Yes 7556804 60
col csv zip Yes 1425585 11.3
col csv xz Yes 804960 6.4

Note: I purposefully wrote out the ASC on the sql sorting above, because the sort order (and column order) does actually factor into the compression ratio. On my dataset, this particular column and sort order worked the best — but that could change based on the underlying data.

The Dangers of URL Shorteners

In preparation for the next release, I just did an audit of all the errors that Aptise/Cliquedin encountered while indexing content.

Out of a few million URLs, there were 35k “well formed” urls that couldn’t be parsed due to “critical errors”.

Most of these 35k errors are due to URL shorteners. A small percentage of them are from shorteners that have dead/broken links. A much larger percentage of them are from shorteners that just do not seem to perform well at all.

I hate saying bad things about any company, but speaking as a former publisher — I feel the need to talk bluntly about this type of stuff. After pulling out some initial findings, I ran more tests on the bad urls from multiple unrelated IP addresses to make sure I wasn’t being singled out for “suspicious” activity. Unfortunately, the behavior was consistent.

The worst offenders are:

* wp.me from WordPress
* ctx.ly from Adobe Social Marketing
* trib.al from SocialFlow when used with custom domains fom Bitly

The SocialFlow+Bitly system was overrepresented because of the sheer number of their clients and urls they handle — and I understand that may skews things — but they have some interesting architecture decisions that seem to be reliably bad for older content. While I would strongly recommend that people NOT use any of these url shortening services, I more strongly recommend that people do not use SocialFlow’s system with a Custom Domain through bitly. I really hate saying this, but the performance is beyond terrible — it’s totally unacceptable.

The basic issue across the board is this: these systems perform very well in redirecting people to newer content (they most likely have the mapping of newer unmaksed urls in a cache), but they all start to stall when dealing with older content that probably needs a database query. I’m not talking about waiting a second or two for a redirect to happen — I’m consistently experiencing wait times from these systems that are reliably over 20 seconds long — and often longer. When using a desktop/mobile web browser, the requests reliably timeout and the browser just gives up.

While wp.me and ctx.ly have a lag as they lookup a url to redirect users to a masked url, the SocialFlow + Bitly combo has a design element that works differently:

1. Request a custom shortened url `http://m.example.com/12345`
2. The shortened url is actually served by Bitly, and you will wait x seconds for lookup + redirect
3. Redirected to a different short url on trib.al (SocialFlow) http://trib.al/abcde
4. Wait x seconds for second lookup + redirect
5. Redirected to end url `http://example.com/VERY+LONG+URL”

All the shorteners could do a better job with speeding up access to older and “less popular” content. But if I were SocialFlow, I would remove Bitly from the equation and just offer custom domains myself. Their service isn’t cheap, and from the viewpoint of a publisher — wait times like this are totally unacceptable.

WARNING: Dreamhost's One-Click Install for Trac still has a security flaw

I first noticed this two years ago (http://www.destructuring.net/2012/03/23/dreamhost-ux-security-flaw/) and contacted Dreamhost. It has yet to be fixed.

If you create a “Private” subversion repository on Dreamhost ( username + password are required to view ) and then add a “One Click Install” of Trac to that private repository ( which is marked as “Private” in their installer ), the Trac instance does not have any security permissions. The entirety of your source code is readable through the Trac browser.

Here’s a illustration:

• Private SVN Repository – http://svn.2xlp.com/ExampleTracSvn/svn
• Default Trac Install – http://svn.2xlp.com/ExampleTracSvn/trac/browser/README.txt

While many people may want to have a Publicly readable repo for ticketing, I think it’s safe to say that most people who use a “One Click Install” are not familiar enough with the intricacies of Trac to know about it’s permissions system.

If you’re affected the easiest fix you can implement, is to add a .htaccess file to your trac directory.

A better fix, is to get off Dreamhost’s OneClickInstall entirely. The Trac One-Click-Install is a halfassed and terrible approach.

Dreamhost did something smart with their Subversion install. Your home directory has a `svn` subdirectory which contains some specific files for each subversion repo:

* RepoName/ ( the actual repo )
* RepoName.access (a .htaccess file for your repo )
* RepoName.passwd ( a htpassed file for the repo’s access file )

It’s a very smart an elegant solution. The Trac install, however, is anything but.

1. Dreamhost installs one version of the Trac library in your home directory for each trac instance. If you have 5 tracs, you have 5 directories like `~/webroot_svn_2xlp_com_ExampleTracSvn_trac_trac`

2. Dreamhost installs the entire Trac environment and database in a web directory. The configuration files, database, everything, are available in an Apache served directory — controlled only by .htaccess files.

A better way to manage Trac, is to create a `~/trac` subdirectory of your home folder, and centralize all your trac projects. You can then use .htaccess files and symlinks to expose the relevant directories to the internet at large.

This will guard you against situations where an erroneous .htaccess file renders your contents as raw-data ( it happens ).

If you have more than one Trac installation, you would probably benefit from installing Trac as a Python library and having multiple projects reference it.