The article will show you how to get your local Wordpress dev environment up on Mac.
http://hitchhackerguide.com/2011/02/18/wordpress-syntax-mode-for-panic-coda/
Performance measures whether an application can respond to a request within its defined service-level agreement (SLA). Scalability measures how well an application can maintain its performance under increasing load.
Session management is one of the key topics that all serious web developers and architects need to master with. This article will go through several key topics with you. They are:
Today, I will start walking through all these topics at a high level. A series of articles will be written to further develop on each topic if necessary. Lets start!
What do we need to pay attention when we store info in cookie?
At the first glance, session in server side sounds like a great solution. However, when it comes to scale, it always raises the concerns. Imagine you need to replicate client session state across multiple servers to achieve high availability. Both the replication time and memory resource limit will cause your system not able to scale linearly. To solve or minimize this, we selectively pick what kind of info we store in the session, use sticky session to avoid one session replication across all the machine or even try to store the state to the client if possible like using rich client UI (ex. Flex and Silverlight). A post will be written about this topic later on.
The idea is simple. Here is the regular event flow: Users interact with the UI, event is generated, broadcast via the event dispatcher (bubbling up the display hierarchy if enabled) and captured by any registered listeners, and a set of actions is taken in response. To understand it a bit more in detailed, you can check this article and play with its demo. In short, under the hood, it has 3 phases: capturing, targeting and bubbling. In the display list, from the top, it always starts from Stage as the root, then SystemManager, then your Application. The event is created by the flash player and travel down from Stage to the target component and bubble it up back to Stage (if enabled). In this round trip, it will trigger the event listeners' actions. Most components communicate with others using events which conveys useful information and data but only visual components (objects in the display list) can participate in the event flow described above.
in practice, you normally just need to worry about registering listener to the target component for a particular event propagated from it. You seldom need to understand the event flow above in detailed. However, if you want to exercise more control on the event like stopping the event from propagating, you need to understand how it works first.
Within Flex, by default, events only broadcast themselves to their parent component. If you want the event to broadcast to its parent's parent (and all the way up your component chain in the display object hierarchy), then you tell that event to bubble. If you don't want any component in chain cancels your event via stopPropagaton() or stopImmediatePropagation(), you can make it non-cancelable during event construction. The difference between stopPropagation and stopImmediatePropagation is that stopImmediatePropagation will not only prevent the event from moving to the next node, but it will also prevent any other listeners on that node from capturing their events.
Some events have an associated default behavior. For example, the doubleclick event has an associated default behavior that highlights the word under the mouse pointer at the time of the event. Your event listener can cancel this behavior by calling the preventDefault() method.
Now you understand the event flow. Next, you need to know how to create a custom event to carry additional info. This article helps you to achieve this goal.
To many of Flash/Flex programmers, it could be a bad news that Steve Job openly banned Flash on his devices like iPad. His message clearly stated that HTML5 can be used to replace the rich experience of Flash and it will be our future. He may be right about the cons of Flash. Nothing is perfect. However, I am surprised that he took a step further to ban Flash totally. Why couldn't he simply provide an option for users to turn it off if they want? I really doubt about his intent. Whenever I see something like that, it only reminds me what Microsoft did in the old days.
In fact, from his TOS, it stated clearly that the most important reason for Apple to ban Flash is because Apple doesn't want a third party layer of software come between the platform and the developer.
Allowing Flash — which is a development platform of its own — would just be too dangerous for Apple, a company that enjoys exerting total dominance over its hardware and the software that runs on it. Flash has evolved from being a mere animation player into a multimedia platform capable of running applications of its own. That means Flash would open a new door for application developers to get their software onto the iPhone: Just code them in Flash and put them on a web page. In so doing, Flash would divert business from the App Store, as well as enable publishers to distribute music, videos and movies that could compete with the iTunes Store – Brian on wired.com
As I mentioned in my last article, I was getting excited about the potential of Hive. Today, I decide to start my journey to learn this. I found a great introductory video that gives you a nice warm-up of using Hive (A basic knowledge of how hadoop and mapreduce work would be helpful for you to digest the material inside).
Hive is an SQL interface built on top of Hadoop. It supports Web access and JDBC. I am amazed how close the SQL syntax like the regular SQL for RDBMS. Below are some SQLs used in this tutorial.
//———- Set up your tables in HIVE —————–
SHOW TABLES;CREATE TABLE shakespeare (freq INT, word STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t’ STORED AS TEXTFILE;
DESCRIBE shakespeare;
//———- Load data into Hive table from Hadoop HDFS ——————-
LOAD DATA INPATH “shakespeare_freq” INTO TABLE shakespeare;//———- Query against the data using hive sql interface ————–
select * from shakespeare limit 10;
select * from sakespeare where freq > 100 sort by freq asc limit 10;
select freq, count(1) as f2 from shakespeare group by freq sort by f2 desc limit 10;//show me the plan
explain select freq, count(1) as f2 from shakespeare group by freq sort by f2 desc limit 10;//———- Create a merge table and populate it using dataset joining by 2 different tables
insert overwrite table merged select s.word, s.freq, k.freq from shakespeare s join kjv k on (s.word = k.word);//———- Query the merge table ———————
select word, shake_f, kjv_f, (shake_f+kjv_f) as ss from merged sort by ss limit 20;
To prepare the data for Hive to load in, the demo uses another mapreduce job to achieve. Remember to delete the log before doing Hive table load.
hadoop jar $HADOOP_HOME/hadoop-*-examples.jar grep input shakespeare_freq ‘\w+’
//remove the mapreduce job log
hadoop fs -rmr shakespeare_freq/_logs
Often time, large scale data processing system always IO bound. So for mapreduce job, your mapper is always waiting for data to load from disk. Hadoop mitigates the problem via during parallel load from lots of hard drives. However, a single hard drive is still max out at 75MB/s read as physical limit and nothing we can do about this. In order to achieve good speed, the key is to eliminate # of hadoop pass
Since Hive is on top of Hadoop’s HDFS, it will have the same restrictions as it. So, you cannot do UPDATE, DELETE and INSERT records as regular RDMS. However, you can do bulk load to add more new files (data) to the table and you can do delete a file from Hive.
Hive needs to store metadata of the tables out from the HDFS. You can use regular rdms to achieve the job. But when you start Hive locally, it will seek for the local metastore. So, in distributed environment, you may need to centralize the metastore in a remote location. There is wiki on the Hive site that documents how to set it up.
Cloudera Hadoop Training: Hive Tutorial Screencast from Cloudera on Vimeo.
I ever worked for a display ad network company that collects over 400 million of impression/ click logs per day. With this amount of data, my ex-company bought a supercomputer and cross their fingers that it can handle the grow in both volume and analytic demand of the data. It is obviously not a scalable solution. However, what is the best solution?
Although I haven’t worked for this company anymore, it is still an interesting problem to solve. I have a great friend who proposed a shared nothing solution for this company. The solution is to partition the data across a set of Postgresql databases and put Greenplum on top of them to parallelize the query —there is no disk-level sharing or contention to be concerned with (i.e. it is a ‘shared-nothing’ architecture). I like this approach. The only thing is that Greenplum is not free and it may be difficult for a startup to face this upfront cost. Apart from that, this setting requires all the databases are running on the same network that hindered us to move this in the elastic cloud like Amazon EC2.
Later on, I joined a great company in the same industry that seeks for a solution in the cloud to host its data warehouse. So, I got a chance to revisit this problem. During the research, I came across an interesting technology – column-based database (eg. infobright and lucid db). The idea of column-based data store is that traditional database stores and fetches data in row from data files into the memory. It is inefficient if your query only requires few columns for computation. So, column-based data stores your data in column with effective compression algorithm due to all values in it has the same data type. This solution is great but it doesn’t do MPP (ie. massive parallel processing) and it is also not ready for cloud yet.
Here comes another solution. That is Hive on top of Hadoop on top of Amazon cloud. It is an interesting idea. Check out this video to learn about this.
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If you are not sure what Hadoop is and want to get some warm up in massive computing. I suggest you go through the following 5 excellent Google lectures.
I want to summarize some new and interesting Java 5 features in this article and how they change the way I code.
I use int constants to make my life easier b/c it can avoid typo. However, it has several drawbacks:
Use new enum type in Java 5:
public enum Apple {FUJI, PIPPIN, GRANNY_SMITH}
Enum is full-fledged final class that export one instance for each enumeration constant via a public static final field.
If elements of an enumerated types are used primarily in sets, it is traditional to use the int enum pattern, assigning a different power of 2 to each constant like READ = 1 << 2, WRITE = 1 <<1, EXECUTE = 1 << 0 to represent permissions per each entity in Unix. This representation lets you use the bitwise OR operation to combine several constants into a set, known as a bit field. The bit field representation also lets you perform set operations such as union and intersection efficiently using bitwise arithmetic. But bit fields have all the disadvantages of int enum mentioned above.
Now, java.util package provides the EnumSet to efficient represent sets of value drawn from single enum type. This class implements Set interface and internally use bit vector to represent set of values. For example, if you enum types has 64 values, the entire EnumSet can be represented as a single long, so its performance is comparable to the bit field.
The EnumSet class provides three benefits a normal set does not:
An annotation is a new language feature introduced in J2SE 5.0. Simply put, annotations allow developers to mark classes, methods, and members with secondary information that is not part of the operating code.You can see annotation is a way to extend Java language.
Before annotation from Java 5, you may use naming patterns to indicate that some program elements like method demanded special treatment by a tool or a framework. Like JUnit required its users to name the test methods with the pattern like testXXX(). It works but with some big disadvantages:
Annotation can solve this problem. To use it, you can:
Below are some related articles I feel useful:
Nowadays there are many tools available on the Net ranging from IM to cloud computing that certainly lowers the barrier for entrepreneurs like us. Today, I am going to list out the tools that helps me to run my company:
In our daily life, we deal with lots of data. The data normally is stored in text format for the ease of human to read. With the large amount of data we have, we need ways to deal with it. There are several things we frequently do on the data: Search, Filter, Sort and Analysis. In Linux, there are some powerful commands that I can use: cat, grep, find, sort, unique and etc. I found those commands quite powerful. So, I decide to put these down as my reference. This tutorial I will go over the basic text processing commands and how we use them together to achieve the tasks we often encounter in our workplace.
The power of “cat” is not just output a file to screen but to concatenates a list of file content and stream through the pipe to another program as input.
cat * | sort
The power of find is to list out the matched filenames based on metadata of the files like type, size, create date…
“grep” helps you to list out the file(s) with the content that match the pattern(s) in regular expression. You can use it as content search across the files in your file system.
grep -H -R --color -n -P abc *
option:
“cut” extracts sections from each line of input. (example of usage). Below the command will extract the 5th field and the rest from each line of file A using delimiter colon.
cut -d ":" -f 5- fileA
option:
The sort command sorts a file according to fields–the individual pieces of data on each line. By default, sort assumes that the fields are just words separated by blanks, but you can specify an alternative field delimiter if you want (such as commas or colons). Output from sort is printed to the screen, unless you redirect it to a file.
donor.data
Bay Ching 500000 China
Jack Arta 250000 Indonesia
Cruella Lumper 725000 MalaysiaLet’s take this sample donors file and sort it according to the donation amount. The following shows the command to sort the file on the second field (last name) and the output from the command:
sort +1 -2 donors.data
Jack Arta 250000 Indonesia
Bay Ching 500000 China
Cruella Lumper 725000 Malaysia
If the file is delimited by comma, you can use -t , to tell the sort the delimiter. You can use -u to output the uniqueness as well.
sort -t: +1 -2 company.data To sort the file on the third field (department name) and suppress the duplicates, use this command: sort -t: -u +2 company.data Note that the line for Ed Jucacion did not print, because he’s in Sales, and we asked the command (with the -u flag) to suppress lines that were the same in the sort field.
Nasium, Jim:031762:Marketing
Jucacion, Ed:396082:Sales
Itorre, Jan:406378:Sales
Ancholie, Mel:636496:Research
Nasium, Jim:031762:Marketing
Ancholie, Mel:636496:Research
Itorre, Jan:406378:Sales
option:
uniq – line level uniqueness. It prints the unique lines in a sorted file, retaining only one of a run of matching lines. Optionally, it can show only lines that appear exactly once, or lines that appear more than once. uniq requires sorted input since it compares only consecutive lines.
option:
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wc – word count. Apart from word count, it also does the following
“tr” translate or delete characters. It is used for data cleaning job. Can we do pattern replacement?
tr ‘[:lower:]‘ ‘[:upper:]‘
The above command will convert all the lowest case to upper case.
tr ‘.’ ‘/’
The above will convert all the . character to /. And for translation, you cannot have -d option on. You may be asking when would we do that. Here is the common use case – convert window files to unix formatted file:
tr -d ‘\r’ < input_dos_file.txt > output_unix_file.txt
option:
“tr” can do character replacement. But if you want to do pattern replacement, you need to use sed. usage: sed -e s/pattern/replacement/flags
sed -e s/one/another
sed -e s/[aeiou]/_/g
Note the use of the “g” flag so that you apply the pattern/replacement to every match instead of just the first one.
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