So I came in this morning and Windows 7 had rebooted my PC. I had 7.5GB of applications running across two operating systems (Ubuntu running in a Virtualbox - connected to lots of servers for work). This made me very mad. I had updated Windows Update to only download the updates, and let me choose when to install them. I had installed them - and suffered the annoying "Reboot now for Important updates to be installed" and I kept postponing it for 3 hours. The last thing I expected was to come in and find that windows got fed-up asking me and decided to reboot anyway.
Well, I found a way to stop it - and thought I'd share it here as Google seems to return mainly crap about the BETA version rebooting when it's about to expire. Convenient me thinks - this will mask any REAL issues with Windows 7 rebooting.
Anyway - here's the link: http://www.howtogeek.com/howto/windows-vista/prevent-windows-update-from-forcibly-rebooting-your-computer/
Friday 11 December 2009
Wednesday 2 December 2009
Google Profiles Turn Into OpenIDs
Google Profiles Turn Into OpenIDs: "
"
What this means is that you can sign into any site that accepts OpenID simply by using your Google Profile domain. Luckily, a few months ago Google started allowing these profiles to have vanity URLs, like /mgsiegler, instead of the previous /32090329039402903. Chris Messina, a huge proponent of the open web movement, has just sent out a picture of what signing in with OpenID via your Google Profile looks like (below).
Despite its good intentions, OpenID has yet to take off in mainstream usage. The problem, it seems, is largely about presentation. Most people have no idea which of their various accounts can be used as OpenIDs, or really even what OpenID is. Google backing it a bit more with these profiles obviously helps, but will it take OpenID mainstream? Probably not.
More interesting may be the second part of Fitzpatrick’s tweet. “Also, gmail webfinger declares that now too.” It’s not entirely clear what he means by that, but it would seem to suggest that we’re getting closer to being able to use our Gmail addresses as a web ID. WebFinger is a protocol being worked on by Fitzpatrick to allow you to attach information to your email address (in this case, you Gmail address), so it can be used as a solid means of identification.
Update: Kevin Marks (former Googler, now with BT) has pointed me to webfinger.org an example site built by Blaine Cook (formerly of Twitter). The site allows you to easily set up your Gmail account with Webfinger right now. As you can see in the example below from Cook’s account, many of the social networks Cook is a part of are pinned to his email address.
Crunch Network: CrunchGear drool over the sexiest new gadgets and hardware.
"
Google Profiles Turn Into OpenIDs
Google Profiles Turn Into OpenIDs: "
"
What this means is that you can sign into any site that accepts OpenID simply by using your Google Profile domain. Luckily, a few months ago Google started allowing these profiles to have vanity URLs, like /mgsiegler, instead of the previous /32090329039402903. Chris Messina, a huge proponent of the open web movement, has just sent out a picture of what signing in with OpenID via your Google Profile looks like (below).
Despite its good intentions, OpenID has yet to take off in mainstream usage. The problem, it seems, is largely about presentation. Most people have no idea which of their various accounts can be used as OpenIDs, or really even what OpenID is. Google backing it a bit more with these profiles obviously helps, but will it take OpenID mainstream? Probably not.
More interesting may be the second part of Fitzpatrick’s tweet. “Also, gmail webfinger declares that now too.” It’s not entirely clear what he means by that, but it would seem to suggest that we’re getting closer to being able to use our Gmail addresses as a web ID. WebFinger is a protocol being worked on by Fitzpatrick to allow you to attach information to your email address (in this case, you Gmail address), so it can be used as a solid means of identification.
Update: Kevin Marks (former Googler, now with BT) has pointed me to webfinger.org an example site built by Blaine Cook (formerly of Twitter). The site allows you to easily set up your Gmail account with Webfinger right now. As you can see in the example below from Cook’s account, many of the social networks Cook is a part of are pinned to his email address.
Crunch Network: CrunchGear drool over the sexiest new gadgets and hardware.
"
Google Profiles Turn Into OpenIDs
Google Profiles Turn Into OpenIDs: "
"
What this means is that you can sign into any site that accepts OpenID simply by using your Google Profile domain. Luckily, a few months ago Google started allowing these profiles to have vanity URLs, like /mgsiegler, instead of the previous /32090329039402903. Chris Messina, a huge proponent of the open web movement, has just sent out a picture of what signing in with OpenID via your Google Profile looks like (below).
Despite its good intentions, OpenID has yet to take off in mainstream usage. The problem, it seems, is largely about presentation. Most people have no idea which of their various accounts can be used as OpenIDs, or really even what OpenID is. Google backing it a bit more with these profiles obviously helps, but will it take OpenID mainstream? Probably not.
More interesting may be the second part of Fitzpatrick’s tweet. “Also, gmail webfinger declares that now too.” It’s not entirely clear what he means by that, but it would seem to suggest that we’re getting closer to being able to use our Gmail addresses as a web ID. WebFinger is a protocol being worked on by Fitzpatrick to allow you to attach information to your email address (in this case, you Gmail address), so it can be used as a solid means of identification.
Update: Kevin Marks (former Googler, now with BT) has pointed me to webfinger.org an example site built by Blaine Cook (formerly of Twitter). The site allows you to easily set up your Gmail account with Webfinger right now. As you can see in the example below from Cook’s account, many of the social networks Cook is a part of are pinned to his email address.
Crunch Network: CrunchGear drool over the sexiest new gadgets and hardware.
"
Saturday 28 November 2009
Restore a Single Table From mysqldump
Restore a Single Table From mysqldump: "Ok, this is inspired by another post on the same subject. I would have just commented, but there was a required signup. Ew.
While the method there with juggling grants works fine, I'd like to highlight another way that I think is much simpler: use grep & sed to create a file which contains only the table you want to restore.
# mysqldump gtowey > dump.sql
Given 3 tables in a mysqldump file, lets restore just t2
Now just use the line numbers to extract the table with sed:
sed -n '40,61 p' dump.sql > t2.sql
That's it, you have everything you need with no cleanup of mysql grants to do afterward.
PlanetMySQL Voting:
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While the method there with juggling grants works fine, I'd like to highlight another way that I think is much simpler: use grep & sed to create a file which contains only the table you want to restore.
mysql> show tables;
+------------------+
| Tables_in_gtowey |
+------------------+
| t1 |
| t2 |
| t3 |
+------------------+
3 rows in set (0.00 sec)
# mysqldump gtowey > dump.sql
Given 3 tables in a mysqldump file, lets restore just t2
# grep -n 'Table structure' dump.sql
19:-- Table structure for table `t1`
40:-- Table structure for table `t2`
61:-- Table structure for table `t3`
Now just use the line numbers to extract the table with sed:
sed -n '40,61 p' dump.sql > t2.sql
That's it, you have everything you need with no cleanup of mysql grants to do afterward.
PlanetMySQL Voting:
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Vote DOWN"
Monday 23 November 2009
Fast restore one database from a dump of ALL databases
Fast restore one database from a dump of ALL databases: "There have been times that I needed to restore one database from a dump file that contains all of the databases from the server. There are a few ways to accomplish this goal. One way would be to grep through the entire file for the table schema and insert statements. There are some problems with this method in some environments. For example, what if a table name in the targeted databases had the same name in another database on the same instance? The logic for grep now gets increasingly more complex.
It’s a good thing that the developers at MySQL already thought of this and implemented it, --one-database. In the example below I have created 4 databases, test, test[1-3] and filled each of them with 10K rows. I run mysqldump for all databases and put them in a file called test.all.sql then truncate test2.tbl1. Using mysql with the --one-database option I restore the data to its original state.
Example:
mysql> select min(anum), max(anum) from test.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 0 | 10000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test1.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 10001 | 20000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test2.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 20001 | 30000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test3.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 30001 | 40000 |
+-----------+-----------+
1 row in set (0.01 sec)
shell> mysqldump -uroot -S mysql.sock --opt --all-databases > test.all.sql
mysql> use test2;
mysql> select count(*) from tbl1;
+----------+
| count(*) |
+----------+
| 10000 |
+----------+
1 row in set (0.01 sec)
mysql> truncate table tbl1;
Query OK, 0 rows affected (0.05 sec)
mysql> select count(*) from tbl1;
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (0.00 sec)
shell> mysql -uroot -S mysql.sock --one-database test2 < test.all.sql
mysql> select min(anum), max(anum) from test.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 0 | 10000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test1.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 10001 | 20000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test2.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 20001 | 30000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test3.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 30001 | 40000 |
+-----------+-----------+
1 row in set (0.01 sec)
As you can see test2.tbl1 has the original data back in and we did not have to write our own script to accomplish this.
PlanetMySQL Voting:
Vote UP /
Vote DOWN"
It’s a good thing that the developers at MySQL already thought of this and implemented it, --one-database. In the example below I have created 4 databases, test, test[1-3] and filled each of them with 10K rows. I run mysqldump for all databases and put them in a file called test.all.sql then truncate test2.tbl1. Using mysql with the --one-database option I restore the data to its original state.
Example:
mysql> select min(anum), max(anum) from test.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 0 | 10000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test1.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 10001 | 20000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test2.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 20001 | 30000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test3.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 30001 | 40000 |
+-----------+-----------+
1 row in set (0.01 sec)
shell> mysqldump -uroot -S mysql.sock --opt --all-databases > test.all.sql
mysql> use test2;
mysql> select count(*) from tbl1;
+----------+
| count(*) |
+----------+
| 10000 |
+----------+
1 row in set (0.01 sec)
mysql> truncate table tbl1;
Query OK, 0 rows affected (0.05 sec)
mysql> select count(*) from tbl1;
+----------+
| count(*) |
+----------+
| 0 |
+----------+
1 row in set (0.00 sec)
shell> mysql -uroot -S mysql.sock --one-database test2 < test.all.sql
mysql> select min(anum), max(anum) from test.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 0 | 10000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test1.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 10001 | 20000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test2.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 20001 | 30000 |
+-----------+-----------+
1 row in set (0.01 sec)
mysql> select min(anum), max(anum) from test3.tbl1;
+-----------+-----------+
| min(anum) | max(anum) |
+-----------+-----------+
| 30001 | 40000 |
+-----------+-----------+
1 row in set (0.01 sec)
As you can see test2.tbl1 has the original data back in and we did not have to write our own script to accomplish this.
PlanetMySQL Voting:
Vote UP /
Vote DOWN"
Tuesday 17 November 2009
mk-query-digest now understands HTTP
mk-query-digest now understands HTTP: "
PlanetMySQL Voting:
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Vote DOWN"
You used to use mk-query-digest to aggregate and report on MySQL’s slow query log. Then it got the ability to grab query events from polling SHOW PROCESSLIST. Next we thought, really, how hard can it be to implement the libmysql wire protocol, so we can sniff TCP packets? … it’s hard, but not that hard as it turns out. But why stop there, why not implement memcached protocol too? I think you can see where this is headed.
So now mk-query-digest is a tool that can understand and “do stuff with” a variety of query/response types of information. The latest is HTTP. HTTP traffic is just a query-response flow of events, perfectly suitable for response-time analysis. Example:
baron@kanga:~$ mk-query-digest sample-http.txt --type http
# 1.6s user time, 100ms system time, 14.20M rss, 17.07M vsz
# Overall: 56 total, 30 unique, 1.27 QPS, 0.06x concurrency ______________
# total min max avg 95% stddev median
# Exec time 3s 746us 563ms 49ms 293ms 110ms 3ms
# Time range 2009-11-14 07:04:50.997506 to 2009-11-14 07:05:34.969818
# bytes 0 0 0 0 0 0 0
# Transmit 10s 0 1s 176ms 777ms 301ms 28us
# Query 1: 0.28 QPS, 0.02x concurrency, ID 0x198704D47EE1BF0C at byte 103987
# pct total min max avg 95% stddev median
# Count 21 12
# Exec time 24 670ms 3ms 563ms 56ms 13ms 150ms 12ms
# Hosts 12 12.38.8.23... (1), 121.44.246... (1)... 10 more
# Time range 2009-11-14 07:04:51.763526 to 2009-11-14 07:05:34.969818
# bytes 0 0 0 0 0 0 0 0
# Status co 1 200
# Transmit 62 6s 0 1s 514ms 900ms 376ms 580ms
# Virtual h 1 www.mysqlperformanceblog.com
# Query_time distribution
# 1us
# 10us
# 100us
# 1ms ####################################
# 10ms ################################################################
# 100ms #########
# 1s
# 10s+
get www.mysqlperformanceblog.com/feed/\G
... snip a bunch of output ...
# Rank Query ID Response time Calls R/Call Item
# ==== ================== ================ ===== ======== ================
# 1 0x198704D47EE1BF0C 0.6700 25.5% 12 0.0558 GET www.mysqlperformanceblog.com/feed/
# 2 0x2CBDA396697F8145 0.3408 13.0% 1 0.3408 GET www.mysqlperformanceblog.com/
# 3 0x2E18EB8C0CD9AED9 0.3100 11.8% 1 0.3100 GET www.mysqlperformanceblog.com/page/2/
# 4 0x4686B61E6556B753 0.3042 11.6% 1 0.3042 GET www.mysqlperformanceblog.com/
# 5 0x247CABBCB1B76C01 0.2705 10.3% 1 0.2705 GET www.mysqlperformanceblog.com/page/28/
# 6 0x8C239A43A9C80FD2 0.2373 9.0% 1 0.2373 GET www.mysqlperformanceblog.com/
# 7 0x4D4095C546E65CD4 0.1959 7.5% 1 0.1959 GET www.mysqlperformanceblog.com/2008/11/26/
# 8 0x49CC22FAC68CD475 0.1906 7.3% 1 0.1906 GET /favicon.ico
This is suitable for lots of things. We’re trying to look at the most useful protocols, because the variety of inputs is really unlimited; we could implement almost anything that fits into the notion of query and response. For example, the memcached protocol is becoming something of a lingua franca for a lot of different systems, so there’s a big value-add. HTTP has been used a long time as a transport layer for REST, SOAP, and so on (CouchDB anyone?). Valid, and interesting, suggestions are Sphinx, PostgreSQL, and Gearman. (Please offer to sponsor any that you want to see.)
Back to HTTP: implementing it gives an easy way to measure website response time, including useful things like 95th percentile goodness. And from there, you can drill down into the performance of the work done for these requests. If you want to get really fancy, you can even capture some samples of netstat at the same time as you tcpdump traffic for HTTP, memcached, and MySQL — so you can blame database queries and memcached requests on specific HTTP requests!
Related posts:
- An alternative to the MySQL Query Analyzer MySQL just
- Seeking input on a badness score for query execution Suppose th
- Analyze and optimize memcached usage with Maatkit Ryan poste
Related posts brought to you by Yet Another Related Posts Plugin.
PlanetMySQL Voting:
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Vote DOWN"
Monday 16 November 2009
QuickTip: Make life easier with ssh-copy-id
QuickTip: Make life easier with ssh-copy-id: "
"
How many times have you ran through this series of events?
$ cat ~/.ssh/id_dsa.pub
...copy output to clipboard...
$ ssh myhost
...enter password...
myhost$ vi ~./ssh/authorized_keys
...paste public key and save...
myhost$ exit
Thanks to bash's tab completion, I happened upon ssh-copy-id. Instead of all that above, just do this:
$ ssh-copy-id myhost
...enter password...
You're done!
Thursday 12 November 2009
How to tweak your Linux machine’s history command
How to tweak your Linux machine’s history command: "
"
If you are somewhat familiar with using the Linux or UNIX command line interface it is quite likely that you have used the history command. For those not familiar with it the history command allows you to search through the commands you have already run on your computer’s command line.
An example of its usage is:
# history | grep “kill”
This will search for a command containing the term kill through the history of commands you have run on your computer. This is a basic example of the history command. There are certain limitations in this command. Let’s take a look at how you can tweak it a bit to work more effectively for you. One of the limitations I find bugging me quite often is that if you are running two terminal session at the same time the commands from the first session are erased from the history of commands. Not the smartest way the command should work, I think. Well let’s fix it then.
Log in to your account and launch a shell terminal. In your user’s home directory you should find a file called
.bashrc. If this file does not exist, create it. To open the file use a command like the following, replacing user with your username:# vim /home/user/.bashrc
Add the following lines to the .bashrc file without any changes whatsoever:
shopt -s histappend
PROMPT_COMMAND=’history -a’
And voila, problem solved. Henceforth all your commands from single sessions as well as simultaneous sessions will be written to the command history file, and no overwriting will happen.
Another useful change I like to make to the history command’s configuration is removing duplicates from the history of commands. This way I get access to a lot more data and get it quicker. Open the .bashrc file once again and add the following lines in a new line:
export HISTCONTROL=”ignoredups”
export HISTIGNORE=”&:ls:[bf]g:exit”
Save the file and exit the text editor. Now the history command will check to see if a command already exists in the log file or not before adding any new information. A last modification I make to the history command’s configuration is adding spell check. Add the line shown below to the file .bashrc to help the history command check for spelling errors in command and suggest a valid command:
shopt -s cdspell
Save and exit. Now your Linux machine will be able to figure out when you type gerp instead of grep.
---
Related Articles at Simple Help:
- How to see a detailed history of the commands run in Linux
How to post to Twitter from the Linux command line
How to set the date on your Linux machine
How to install and manage packages in Linux with RPM
How to setup and use YUM on Fedora Linux
"
Wednesday 11 November 2009
Initial InnoDB Compression Test Results
Initial InnoDB Compression Test Results: "I've completed an initial round of testing InnoDB with data compression and the results are extremely promising. The testing was performed by setting up two shadows of a production database. The shadows execute the exact same queries as the production database to collect performance statistics but the query results are ignored.
The production server uses Fusion-io PCI-Express flash storage and had 290GB of data. Both shadows were running on traditional spinning disks, so I wasn't sure they would be able to keep up with the load. The first shadow was configured with MySQL 5.0.84 with the uncompressed 'compact' InnoDB storage format. The second shadow was configured with MySQL 5.1.38 and the InnoDB 1.0.4 plugin with 2x compression. With compression enabled on all tables except 3 containing pre-compressed data, the InnoDB data size shrunk to 170GB, a 41% reduction.
When the shadows were first started up with empty caches, the compressed shadow ran noticeably slower as decompression slowed down every read from disk. Once the cache was warm, however, the compressed shadow ran significantly faster. InnoDB can store both compressed and uncompressed pages in its cache. When the cache is full, it can discard uncompressed pages but retain the compressed pages, allowing a much larger number of pages to stay into memory. For a spinning disk-based server, decompressing a page is much faster than re-reading it from disk.
The numbers speak for themselves; With compression enabled the cache miss rate dropped from 0.51% to 0.36% and the time spent waiting for IO dropped from 19% to 7.6%. The CPU load increased from 5% to 9% but our workload is rarely limited by CPU speed. The average time spent performing queries for the profile page, one of our most important metrics, was 4x faster with compression enabled.
Unexpectedly the amount of write IO went up with compression enabled. Because we have more pages in memory, insertions into secondary indexes are more often performed immediately instead of being deferred and combined in the insert buffer. Overall though the reduced read IO dramatically outweighed the increase in write IO. The net results look like a very promising way to make better use of our server hardware.
PlanetMySQL Voting:
Vote UP /
Vote DOWN"
The production server uses Fusion-io PCI-Express flash storage and had 290GB of data. Both shadows were running on traditional spinning disks, so I wasn't sure they would be able to keep up with the load. The first shadow was configured with MySQL 5.0.84 with the uncompressed 'compact' InnoDB storage format. The second shadow was configured with MySQL 5.1.38 and the InnoDB 1.0.4 plugin with 2x compression. With compression enabled on all tables except 3 containing pre-compressed data, the InnoDB data size shrunk to 170GB, a 41% reduction.
When the shadows were first started up with empty caches, the compressed shadow ran noticeably slower as decompression slowed down every read from disk. Once the cache was warm, however, the compressed shadow ran significantly faster. InnoDB can store both compressed and uncompressed pages in its cache. When the cache is full, it can discard uncompressed pages but retain the compressed pages, allowing a much larger number of pages to stay into memory. For a spinning disk-based server, decompressing a page is much faster than re-reading it from disk.
The numbers speak for themselves; With compression enabled the cache miss rate dropped from 0.51% to 0.36% and the time spent waiting for IO dropped from 19% to 7.6%. The CPU load increased from 5% to 9% but our workload is rarely limited by CPU speed. The average time spent performing queries for the profile page, one of our most important metrics, was 4x faster with compression enabled.
Unexpectedly the amount of write IO went up with compression enabled. Because we have more pages in memory, insertions into secondary indexes are more often performed immediately instead of being deferred and combined in the insert buffer. Overall though the reduced read IO dramatically outweighed the increase in write IO. The net results look like a very promising way to make better use of our server hardware.
PlanetMySQL Voting:
Vote UP /
Vote DOWN"
Video: Building a MySQL Slave and Keeping it in Sync
Video: Building a MySQL Slave and Keeping it in Sync: "
PlanetMySQL Voting:
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Last night at the Boston MySQL User Group I presented on how to get a consistent snapshot to build a slave, how to use mk-table-checksum to check for differences between masters and slaves on an ongoing basis, and how to use tools such as mk-table-sync and mysqldump to sync the data if there are any discrepancies.
The slides are online at http://technocation.org/files/doc/slave_sync.pdf.
The video can be watched on youtube at http://www.youtube.com/watch?v=Un0wqYKmbWY or directly in your browser with the embedded player below:
PlanetMySQL Voting:
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Saturday 7 November 2009
Google Open Sources Set Of Closure Tools It Uses To Build Its Own Apps
Google Open Sources Set Of Closure Tools It Uses To Build Its Own Apps: "
"
The first tool is called the Closure Compiler, which helps developers optimize their JavaScript code by removing extra portions of comments and code. The Compiler also has a sister program called Inspector — a plugin for Firebug that allows developers to view their optimized code in the browser, with all of their original variable names and other data restored (typically optimized code strips variable names and formatting that makes it very difficult to read). Google is also releasing the Compiler as a web app and a RESTful API.
Google’s second release is a Library that includes many common functions used for building JavaScript apps. Here’s how Google’s blog post describes it:
The Closure Library is a JavaScript library that provides many features that are useful for building web applications across browers. It helps to abstract browser differences and provides clean class and function interfaces for common tasks. Besides DOM interaction and UI tools, the library includes tools for arrays, objects, string manipulation and much more. The library also implements a namespacing and import standard, which helps keep scripts small and load fast when used with the Closure Compiler.
Finally, Google is releasing a new set of templates for Java and JavaScript:
Closure Templates simplify the task of dynamically creating HTML and have an easy-to-read syntax. They allow you to write templates for reusable HTML and UI elements that can be used within JavaScript or on the server side in Java. They also have excellent run-time performance because they are never parsed on the client side.
Crunch Network: CrunchGear drool over the sexiest new gadgets and hardware.
"
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