What I am looking for is simple. I want 2 things:
1) In the typical Mac style, I want a great looking interface. I want to be able to see who did what, when, and why (assuming good commit messages from the developers).
2) Easy navigation through all the features. I am not planning on using any of the commands visually, I am still an archaic command line junkie.

One of my favorite features of git coming from Subversion is the ease of branching. I branch for everything now that I am using git. So in order to best track my changes, I was hoping for something to help me visualize my branches. I didn’t count this specifically in my desires because it wasn’t a requirement to be acceptable, but it definitely would have helped to tip the scales.

I found a three clients that I thought were worth looking at (4 if you count Gity, but I am still using Leopard and Gity is just for Snow Leopard).

First I wanted to look at Gitnub, but I couldn’t get it to run. So pass on that and move on.

Second I looked at Smartgit. Smartgit is technically not released yet (still in the alpha/beta stages), but is still a great product. It has that typical Java look and feel to it. Nothing too flashy, but has a lot of potential. I give them the benefit of the doubt since they are still in the early phases of production. Smartgit also has a much cleaner file browser/interface than any of the other UIs tested. Another clean item on the layout is tool tips. They are helpful because there are quite a few buttons which are otherwise unlabelled. There is a great button in the bottom right hand corner that maximizes the currently selected file into an automatic diff of HEAD vs. working tree. It is worth noting that Smartgit, when released will not be free software.

UPDATE: These are a few screen shots from SmartGit Beta 3. This is the current release of SmartGit.

Smartgit File Browser

SmartGit File Diff

Lastly I tried out GitX. GitX is the closest thing to what I am looking for. I would like the navigation to be a little more descriptive and the ability to see blame in a file, but it’s otherwise pretty good. GitX provides a great branch/merge view. Shows the line of the HEAD and where it came from. It’s extremely handy when you’re working with a lot of branches. The colored diffs were also very nice to look at and I have always been a fan of things are easier for me to read. These items (visualized branches and colored diffs) are a scale tipper for me.

UPDATE: These are a few screen shots from the stable version of GitX.

GitX Main Screen

GitX Main File Browser

GitX Current Changes

I guess I still have a little time to wait for a solid Git client to meet my criteria. If I could combine the cleanliness and branching look of GitX with the usability of SmartGit (with its up and coming featureset), then I would be set. If you know of a good Git clients for the Mac that I don’t know about, leave it in the comments section.

First thing you’ll need to do is download FancyBox and copy the stylesheets, images, and Javascript files to their proper/desired location in your Rails app. Style the window according to your likings.

Whether it is right or wrong, I did this entirely in the view, without even pulling the Javascript out into the application.js (or even another Javascript file for that matter). My reason was that I only want the modal window showing up on this page. If you want your modal window to show up somewhere else (or on every page), then put the code in your layout. But remember that this call will be executed every time the page loads. I put mine in a profile page which doesn’t get accessed that often so the conditional is not checked quite as frequently.

My application uses Facebook Connect and grabs the users Facebook Proxy email address (FB app developers will know what this is). So I check if that’s the email I have for the user. If yes, then I pop up a modal window on page load only once to get their regular email address and possibly a password so they can login to their account without Facebook Connect if they want. When the modal window is shown, a variable is set in the cookie (note that this cookie is shared with authlogic for sessions) to ensure that the modal window isn’t shown again.

The explanation of the code is below with comments in the code.

The code that goes in the view is as follows:

Now you have a modal window that is only shown once.

If it takes that engineer, admin, developer or whatever 10 minutes to get focused after each interruption and the initial getting into the office and getting into the swing of things; that means that out of an 8 hour day, 1 hour is wasted just refocusing. Refocusing just puts you back on the issue, it doesn’t put you back in the zone. Some engineers only get in the zone once per day. At that rate, you can massively waste someone’s productivity with a 10 second interruption.

What’s my point? Good question. That commercial/question/interruption that someone is pushing onto that engineer could be the straw that broke the camel’s back on a deadline. So be aware of the situation that your people are in, who is talking to them, who has access to them, and who takes advantage of that access. Those precious periods of concentration can afford you a huge win or bring about a big loss.

The problem is that this doesn’t interpolate the newline character. (For more information on interpolation, see Wikipedia here.) In order to have the newline interpolated, you need to add the command line switch ‘-e‘.

This will force Bash to interpolate any non-literal characters in the quotes. Note: Unlike Perl, single or double quotes don’t matter here when Bash is deciding whether or not to interpolate the new line characters.

To quote Apple, “It’s the world’s first multi-touch mouse.” It’s a wireless mouse that attaches to any computer that has a keyboard, mouse and Bluetooth via Bluetooth. It’s sleek just like everything else Apple makes. But the best part is that (as of now), its only $69. Good work Apple.

I could go on and on about why I think its cool and what it can do, but why waste time on my website reading a summary, just check it out on Apple’s web site: http://www.apple.com/magicmouse/

Cassandra is the production database that’s in use on Facebook for handling their email system and Digg.

One thing that I would like to note is that when I tested TC, I used the Perl API for both TC and TT. I tried both the Perl API and the Ruby API. I couldn’t get the Ruby API (written by Evan Weaver of Twitter) it to work at all with the Cassandra server (although I am sure the server included with the gem works well). I initially struggled quite a bit with the UUID aspects of the Perl API until I finally gave up and changed the ColumnFamily CompareWith type from

to

Then everything was working well and I began to write my tests. The layout that I ended up using is going to be one that works in a schemaless fashion. I created 2 consistent columns per user: email and person_id. Here is where it gets interesting and different for those of us used to RDBMS’s and having less columns. For this particular project, every time a user is sent an email, there is a “row” (I call it a row for those unfamiliar with Cassandra terminology, it is actually a column) added in the format of: send_dates_<date> (note the structure below). The value of this column is the mailing campaign id sent to the user on this date. This means that if the user receives 365 emails per year at one a day, then there will be 365 rows (or Cassandra columns) that start with send_dates_ and end with YYYY-MM-DD. Note the data structure below in a JSON ish format.

To understand all the data structures in Cassandra better, I strongly recommend reading WTF Is A SuperColumn Cassandra Data Model and Up And Running With Cassandra. They are written by folks at Digg and Twitter respectively and are well worth the reads.

So for my first iteration, I simply loaded up the data in the format mentioned above. Every insert does an insert of an email and person_id just in case they aren’t there to begin with. The initial data set has approximately 3.6 million records. This caused all sorts of problems with the default configurations (ie kept crashing on me). The changes I made to the default configuration are as follows:

• Change the maximum file descriptors from 1024 (system default) to 65535 (or unlimited)
• Change the default minimum and maximum Java -Xms256M -Xmx2G (could not get the data to load past 2.5 million records without upping max memory values)

It was interesting to note that the write latency of about 3.6 million records was 0.004 ms. Also the data compaction brought the size of the records on disk down from 13G to 1.4G. Those figures are being achieved with the reads and writes happening on the same machines.

The load of the second data set took a mere 30m when compared to loading that same data set into Tokyo Cabinet which took closer to 180m.

Now that there is a dataset worth working with, it’s time to start the read tests.

For the first test, I am doing a simple get of the email column. This is just to iterate over the column and find out the approximate speed of the read operations.

For the second test, I made use of a Cassandra feature called get_slice. Since I have columns that are in the format send_dates_YYYY-MM-DD, I used get_slice to grab all column names on a per-row (each email address) basis that were between send_dates_2009-09-29 and send_dates_2009-10-29. The maximum amount that could be matched were 2 (since I only loaded 2 days worth of data into the data base). I used data from a 3rd day so I can get 0, 1, or 2 as results.

This first run is using the Perl version of the script.

Subsequent runs I began to run into API issues and rewrote the same script in Python to see if the more well tested Thrift Python API was faster than the Thrift Perl API (and wouldn’t give me timeout issues). The Perl timeout issues ended up being fixable, but I continued with the tests in Python.

Ultimately with Cassandra, there was quite a bit of a learning curve. But in my opinion is well worth it. Cassandra is an extremely powerful database system that I plan on continuing to explore in greater detail with a few more in depth tests. If you have the chance, take a look at Cassandra.

Interestingly enough, these methods are only available during the initial table creation. If you want to add a reference to a model that is created later, you have to do it the old fashioned way, by just adding a column:

Doing it this way is clean, easy, and definitely meets the KISS principle. But I do find it interesting that one can’t add an association later in the game. Sometimes the Rails way is just KISS and adding the column by hand.

The code for the gem is available from github here. Other inifile documentation is available here. The rest of the inifile documentation is a good reference but doesn’t contain any examples.

For some reason (which I don’t understand so please feel free to explain it in the comments if you know), you have to do more than just the standard require statement for this gem. At the top of your Ruby code, add the lines below. Make sure that you replace the directory location with your directory location.

A short example of the ini file that we will be working with:

To get the id parameter of the ini file assuming you know its in the [foo]section, you can use the code below. Notice the parameter section of the new object instantiator. The reason for this is that ini files are pretty abstract can have a few variations on format. Therefore you can specify the comment style and parameter definition style during object instantiation. My ini files use the ‘=’ to assign parameters

Using the above code the id variable now contains the contents of the id parameter from the ini file.

Ultimately I am working on a comparison of Cassandra and Tokyo Cabinet, but I will get to more on Cassandra later.

Ideally the tests I am going to be doing are fairly simple. I am going to be loading a few million rows into a TCT database (which is a table database in TC terms) and then loading key, value pairs into the database. The layout in a hash format is basically going to be as follows:

I ran these tests in the following formats for INSERTing the data into the a table database and as serialized data in a hash database. It is necessary to point out that the load on this machine is the normal load. Therefore it cannot be a true benchmark. Since the conditions are not optimal (but really, when are they ever), take the results with a grain of salt. Also, there is some data munging going on during every iteration to grab the email addresses and other data. All this is being done through the Perl API and Tokyo Tyrant. The machine that this is running on is a Dual Dual Core 2.5GHz Intel Xeon processor with 16G of memory.

For the first round, a few things should be noted:

• The totals referenced below are email address counts add/modified in the db
• I am only using 1 connection to the Tokyo Tyrant DB and it is currently setup to handle 8 threads
• I didn’t do any memory adjustment on startup, so the default (which is marginal) is in use
• I am only using the standard put operations, not putcat, putkeep, or putnr (which I will be using later)

The results of the table database are as follows. It is also worth noting the size of the table is around 410M on disk.

The structure for the hash database (seeing as its only key value) is as follows:

The results of loading the same data into a hash database are as follows. It is also worth noting the size of the table is around 360M on disk. This is significantly smaller than the 410M of the table database containing the same style data.

For the second round, I loaded a second days worth of data in to the database. I used the same layouts as above with the following noteworthy items:

• I did a get first prior to the put to decide whether to use put or putcat
• The new data structure is now either “2009-09-30,2009-10-01″ or “2009-09-30|1995,2009-10-01|1996″

Results of the hash database test round 2:

Results of the table database test round 2:

When it comes down to the final implementation, I will likely be parallelizing the put in some form. I would like to think that a database designed for this sort of thing works best in a concurrent environment (especially considering the default startup value is 8 threads).

It is obvious that when it comes to load times, that the hash database is much faster. Now its time to run some queries and see how this stuff goes down.

So I ran some queries first against the table database. I grabbed a new list of 3.6 million email addresses and iterated over the list, grabbed the record from the table database and counted how many dates (via array value counts) were entered for that email address. I ran the script 4 times and results were as follows. I typically throw out the first run since caching kicks in for the other runs.

Doing the same thing for the hash database, I got the following result set:

I think the results speak for themselves. A hash database is obviously faster (which is something most of us assumed from the beginning). The rest of time comes form programmatic comparisons like date comparisons in specific slices of the array. Load times can be sped up using concurrency, but given the requirements of the project, the gets have to be done in this sequential fashion.

Now its on to testing Cassandra in a similar fashion for comparison.

Like any corporation, Oxford is in the business of making money and in this case, they are doing so by deciding not to pay for my PT. In the long run, the lack of rehabilitation will likely leave me in a weakened state and generally more prone to injury once I go back to my skydiving, motorcycle riding, MMA, and BASE jumping ways. If Oxford had said, let’s make sure he can walk and then we’ll cut him off, at least he’ll have a foundation and be less prone to injury; then they might be saving a bit of money on me in the long run.

So what does this sob story have to do with IT? A decision made now in order to save money can end up costing you more of time and money in the long run. And since time is money, sometimes a little bit of planning can go a long way. Should you add the feature now because your biggest client wants it by Friday. Well if you do that, then you might lose a few smaller clients along the way and the word of mouth may be more damaging than temporarily upsetting that large client.

Perhaps you set up Nagios and immediately turned on alerting without learning the thresholds that your machines typically sit at. Then you get a whole set of alerts and you spend more time trying to sort through the real problem ones versus the ones that just have a slightly abnormal operating level then you would if you just looked at your machines thresholds to begin with.

There are a million examples that could be listed here. The point is, before jumping into a decision, try to make sure that you’re not going to be paying for it in the long run. A little planning can go a long way.