Caching in Java + Spring

Caching is quite important to boost the application performance. There are many cache libraries in Java world, both commercial and open source. You should choose the existing library instead creating your own library. I see some guys in my previous companies develop their own cache, and it is not good for their project. Instead of spending time on project business features, guy spent much time to create the perfect library and maintain it through the project life cycle. Another case is using the Map and its relative classes for caching, it makes the application could not scalable and it does not provide the flexible caching mechanism the cache should have. Of course, we need to create the cache library if there is no good one in the market, but it is not true. There are many good cache libraries in Java world, one of them is Ehcache (http://ehcache.sourceforge.net/). Ehcache is the excellent library provides to community with high quality, good documentation, simple use and wide adoption by other popular frameworks. It takes development team 5-10 minutes to set up the caching (of course, if you are fresher you must spend more time to understand the caching mechanism), so why you do not use such library instead of creating your own library?

In our application, we use Spring as IoC container, though set up the Ehcache manager is the easy task but we would like to use it with our Spring beans and exposed it as Osgi service. Spring modules is the good library help us easy in configuration. It provides the consistent abstraction caching to our application, hence we can use our cache facade with other caching libraries such as JBoss cache, JCS, OScache without chaging our application code. You could download the Spring Modules at https://springmodules.dev.java.net/.

Here are the following steps to configure Ehcache in Spring application

Configuring caching via ehcache.xml file

[?xml version="1.0" encoding="UTF-8"?]

[ehcache xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="ehcache.xsd"]

[!--
Configure it if you have overflowToDisk or diskPersistent enabled for any cache.
java.io.tmpdir - Default temp file path
--]
[diskStore path="java.io.tmpdir" /]

[!--

Mandatory Default Cache configuration. These settings will be applied to caches
created programmtically using CacheManager.add(String cacheName).

The defaultCache has an implicit name "default" which is a reserved cache name.
--]
[defaultCache maxElementsInMemory="10000" eternal="false"
timeToIdleSeconds="120" timeToLiveSeconds="120" overflowToDisk="true"
diskSpoolBufferSizeMB="30" maxElementsOnDisk="10000000"
diskPersistent="false" diskExpiryThreadIntervalSeconds="120"
memoryStoreEvictionPolicy="LRU" /]

[!--

Sample cache named sampleCache1
This cache contains a maximum in memory of 10000 elements, and will expire
an element if it is idle for more than 5 minutes and lives for more than
10 minutes.

If there are more than 10000 elements it will overflow to the
disk cache, which in this configuration will go to wherever java.io.tmp is
defined on your system. On a standard Linux system this will be /tmp"

--]
[cache name="engroupCache" maxElementsInMemory="10000"
maxElementsOnDisk="10000000" eternal="false" overflowToDisk="true"
diskSpoolBufferSizeMB="20" timeToIdleSeconds="300"
timeToLiveSeconds="600" memoryStoreEvictionPolicy="LFU" /]
[/ehcache]

Declarating Ehcache in Spring context

[bean id="cacheManager"
class="org.springframework.cache.ehcache.EhCacheManagerFactoryBean"]
[property name="configLocation"]
[value]classpath:ehcache.xml[/value]
[/property]
[/bean]
[/pre]
[pre lang="xml" xml:space="preserve" xml:lang="xml"]
[bean id="cacheProviderFacade"
class="org.springmodules.cache.provider.ehcache.EhCacheFacade"]
[property name="cacheManager" ref="cacheManager" /]
[/bean]

Programmatic use

Using ehcache is so simple, a unit test to make the test whether configuration is set up correctly and take the simple put/get could explain all.

@RunWith(EngroupClassRunner.class)
@ContextConfiguration(locations = { "/META-INF/spring/cache-context.xml" })
public class CacheTest {
@Autowired
protected CacheProviderFacade cacheProviderFacade;

@Test
public void testCacheConfiguration() {
Assert.assertNotNull(cacheProviderFacade);

EhCacheCachingModel model = new EhCacheCachingModel();
model.setCacheName("engroupCache");

cacheProviderFacade.putInCache("a", model, new Integer(1));
Integer fromCache = (Integer) cacheProviderFacade.getFromCache("a",
model);
Assert.assertEquals(new Integer(1), fromCache);
}
}

Besides of using ehcache to create the session like variable in our application, we would like to use ehcache to cache SQL statement in iBatis and integrate with Terracotta to complete our distributing caching solution in our future project. I will keep the post on further caching usage in future posts. Keep reading!

use maven to generate auto generate schema

add the following to your pom.xml

change the content inside your srcFile tag and of course convert the [] to the diamond brakets.

[plugin]
[groupId]org.codehaus.mojo[/groupId]
[artifactId]sql-maven-plugin[/artifactId]
[version]1.0[/version]
[configuration]
[driver]${jdbc.driverClassName}[/driver]
[username]${jdbc.username}[/username]
[password]${jdbc.password}[/password]
[url]${jdbc.url}[/url]
[autocommit]true[/autocommit]
[skip]${maven.test.skip}[/skip]
[/configuration]
[executions]
[execution]
[id]create-schema[/id]
[phase]process-test-resources[/phase]
[goals]
[goal]execute[/goal]
[/goals]
[configuration]
[autocommit]true[/autocommit]
[srcFiles]
[srcFile]src/test/resources/${jdbc.groupId}-schema.sql[/srcFile]
[/srcFiles]
[/configuration]
[/execution]
[/executions]
[dependencies]
[dependency]
[groupId]${jdbc.groupId}[/groupId]
[artifactId]${jdbc.artifactId}[/artifactId]
[version]${jdbc.version}[/version]
[/dependency]
[/dependencies]
[/plugin]

Hibernate oneToMany lazy load correctly

I found lots of programmers are scratching their heads and try to find out why their lazy loading fetch strategy is not working correctly. There aren't many documents or tutorials in the web to tell you how to solve it.

To properly configure the hibernate lazy initialize collection, just follow these steps.

1). POJO Bean
1). override equal() and hashCode(). This is very important. I have seen a lots of programmer think these functions are not useful. without override these function, the Hibernate lazy loading will not work correctly.

2). in your @OneToMany and @ManyToOne relation attribute. indicate these are lazy loading. To do that. add fetch=fetchType.lazy inside the bracket eg. @ManyToOne(fetch=fetchType.lazy)

3). in your web.xml file add the following entities
<filter-mapping >
<filter-name>lazyLoadingFilter</filter-name>
<url-pattern>/*</url-pattern>
</filter-mapping>
and
<filter>
<filter-name>lazyLoadingFilter</filter-name>
<filter-class>org.springframework.orm.hibernate3.support.OpenSessionInViewFilter</filter-class>
</filter>

Hibernate enable lazy loading in your web app

In your web.xml file,

added the following

[filter-mapping]
[filter-name]lazyLoadingFilter[/filter-name>
[url-pattern]/*[/url-pattern]
[/filter-mapping]

and

[filter]
[filter-name]lazyLoadingFilter[/filter-name]
[filter-class]org.springframework.orm.hibernate3.support.OpenSessionInViewFilter[/filter-class]
[/filter]

replace [ and ] to > and <
damn blogspot editor

How to use OR to rewrite SQL Query

how to optimize the following SQL

select * from sys_user 

where user_code = 'bli' 

or user_code in 

(select grp_code 

from sys_grp 

where sys_grp.user_code = 'bli')

Execution Plan

----------------------------------------------------------

0  SELECT STATEMENT Optimizer=RULE

1  0   FILTER

2  1     TABLE ACCESS (FULL) OF 'SYS_USER'

3  1     INDEX (UNIQUE SCAN) OF 'PK_SYS_GRP' (UNIQUE)

Statistics

----------------------------------------------------------

14  recursive calls

4  db block gets

30590 consistent gets

0  physical reads

0  redo size

1723  bytes sent via SQL*Net to client

425  bytes received via SQL*Net from client

2  SQL*Net roundtrips to/from client

0  sorts (memory)

0  sorts (disk)

3  rows processed

the result of this query normally contains one or two rows, but sys_user table contains a lots of entries. How can we drive sys_grp?

the table information

Sql > select count(*)  from sys_grp;

count(*)

---------------------

25130

sql> select count(*) from sys_user;

Count(*)

----------------------

15190

optimize:

1) reduce the logical read is the base principle.

we will try to reduce the logical read to increase the sql query.

we can use or to rewrite this sql

select * from sys_user where user_code = 'bli' 

union all

select * from sys_user where user_code <> 'bli' 

and user_code in (select grp_code from sys_grp 

where sys_grp.user_code = 'bli')

Statistics

----------------------------------------------------------

          0  recursive calls

          0  db block gets

         130 consistent gets

          0  physical reads

          0  redo size

       1723  bytes sent via SQL*Net to client

        425  bytes received via SQL*Net from client

          2  SQL*Net roundtrips to/from client

          1  sorts (memory)

          0  sorts (disk)

          3  rows processed

Execution Plan

----------------------------------------------------------

0      SELECT STATEMENT Optimizer=RULE

1    0   UNION-ALL

2    1     TABLE ACCESS (BY INDEX ROWID) OF 'SYS_USER'

3    2       INDEX (UNIQUE SCAN) OF 'PK_SYS_USER' (UNIQUE)

4    1     NESTED LOOPS

5    4       VIEW OF 'VW_NSO_1'

6    5         SORT (UNIQUE)

7    6           TABLE ACCESS (BY INDEX ROWID) OF 'SYS_GRP'

8    7             INDEX (RANGE SCAN) OF 'FK_SYS_USER_CODE' (NON-UNIQUE)

9    4       TABLE ACCESS (BY INDEX ROWID) OF 'SYS_USER'

10    9         INDEX (UNIQUE SCAN) OF 'PK_SYS_USER' (UNIQUE)

with this query, the logically read reduces to 130.  From 30590 to 130 is a huge performance jump

DBMS_STAT tables and backup the stat informaiton

when we use DBMS_STAT to analyze the DB, we need to save previous analyze data in case of the stat data decrease the system performance.

to create a stat backup.
login to sqlplus

SQL> begin
2 dbms_stats.create_stat_table(ownname => 'TEST',stattab => 'STAT_TABLE');
3 end;
4 /

analyze information
SQL> BEGIN
2 --DBMS_STATS.delete_table_stats(ownname => 'TEST',tabname => 'A');
3 DBMS_STATS.gather_table_stats(ownname => 'TEST',tabname => 'A');
4 END;
5 /

export analyzed data to stat_table
SQL> BEGIN
2 dbms_stats.export_table_stats(ownname => 'TEST',tabname => 'A',stattab => 'STAT_TABLE');
3 END;
4 /

check how many rows in a table
SQL> select count(*) from stat_table;

delete analyzed data
SQL> BEGIN
2 DBMS_STATS.delete_table_stats(ownname => 'TEST',tabname => 'A');
3 END;
4 /

import analyzed data
SQL> BEGIN
2 DBMS_STATS.import_table_stats(ownname => 'TEST',tabname => 'A',stattab => 'STAT_TABLE');
3 END;
4 /


Done

Oracle show parameters tips and tricks

To check Oracle parameters, you can use "show Parameters". Show parameters can be run as fuzzy query.

for instance, if I want to check "db_" parameters, I can use

SQL> show parameter db_;

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

db_16k_cache_size big integer 0

db_2k_cache_size big integer 0

db_32k_cache_size big integer 0

db_4k_cache_size big integer 0

db_8k_cache_size big integer 0

db_block_buffers integer 0

db_block_checking string FALSE

db_block_checksum string TRUE

db_block_size integer 8192

db_cache_advice string ON

db_cache_size big integer 0

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

db_create_file_dest string

db_create_online_log_dest_1 string

db_create_online_log_dest_2 string

db_create_online_log_dest_3 string

db_create_online_log_dest_4 string

db_create_online_log_dest_5 string

db_domain string

db_file_multiblock_read_count integer 8

db_file_name_convert string

db_files integer 200

db_flashback_retention_target integer 1440

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

db_keep_cache_size big integer 0

db_name string Ora813

db_recovery_file_dest string /home/oracle/product/10.2.0/flash

_recovery_area

db_recovery_file_dest_size big integer 2G

db_recycle_cache_size big integer 0

db_unique_name string Ora813

db_writer_processes integer 1

dbwr_io_slaves integer 0

rdbms_server_dn string

standby_archive_dest string %ORACLE_HOME%/RDBMS

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

standby_file_management string MANUAL

SQL> spool off;

I have difficulty to remember all these parameters. Oracle have already thought about these and you can use fuzzy to query the parameters.

SQL> show parameter db_c;

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

db_cache_advice string ON

db_cache_size big integer 0

db_create_file_dest string

db_create_online_log_dest_1 string

db_create_online_log_dest_2 string

db_create_online_log_dest_3 string

db_create_online_log_dest_4 string

db_create_online_log_dest_5 string

SQL> spool off;

Oracle lag and Lead

LAG(EXPRESSION,[OFFSET],[DEFAULT])

SQL> with a as (select 1 id,'a' name from dual
2 union
3 select 2 id,'b' name from dual
4 union
5 select 3 id,'c' name from dual
6 union
7 select 4 id,'d' name from dual
8 union
9 select 5 id,'e' name from dual
10 )
11 select id,name,lag(id,1,'')over(order by name) from a
12 /

ID NAME LAG(ID,1,'')OVER(ORDERBYNAME)
---------- ---- -----------------------------
1 a
2 b 1
3 c 2
4 d 3
5 e 4



LEAD(EXPRESION,[OFFSET],[DEFAULT])

SQL> with a as (select 1 id,'a' name from dual
2 union
3 select 2 id,'b' name from dual
4 union
5 select 3 id,'c' name from dual
6 union
7 select 4 id,'d' name from dual
8 union
9 select 5 id,'e' name from dual
10 )
11 select id,name,lead(id,1,'')over(order by name) from a
12 /

ID NAME LEAD(ID,1,'')OVER(ORDERBYNAME)
---------- ---- ------------------------------
1 a 2
2 b 3
3 c 4
4 d 5
5 e

Use Maven to manage your Java Project

Maven have lots of cool features. In this post, I am writing for how to craete single executable jar file that includes all the dependended libraries. This plug-in is called assembly.

for how to use this plugin. please visit http://maven.apache.org/plugins/maven-assembly-plugin

Oracle Analytic Fucntions

Oracle Analytic Fucntions

The general syntax of analytic function is:

Function(arg1,..., argn) OVER ( [PARTITION BY <...>] [ORDER BY <....>] [] )

is like "ROW " or "RANK " 

the usages are:

Types of Analytical Functions

Ranking Functions

This family consists of rank, dense_rank, row_number, ntile,percent_rank and cume_dist.

See also dense rank vs rank vs row_number.

Window Functions

Window Functions consist of all agregate functions (sum,max, avg...)

Reporting Functions

These are queries that ask something like: find the sales that contribute 10 % or more to total sales. The most prominent function for this is ratio_to_report.

Lag/Lead Functions

Lag/Lead functions make it possible to access values in other rows than the current one. The corresponding functions are lag and lead.

The partition clause

An important part in analytic functions is the Partition Claus

measuring Oracle cache hit rate

rem-------------------------------------------

rem measuring cache hitrate

rem ------------------------------------------

-- Collecting initial cache hit rate...

SELECT ROUND((1-(phy.value / (cur.value + con.value)))*100,2) "Cache Hit Ratio"

FROM v$sysstat cur, v$sysstat con, v$sysstat phy

WHERE cur.name = 'db block gets'

AND con.name = 'consistent gets'

AND phy.name = 'physical reads'

/

-- intervent to increase cache hit rate...

DECLARE

v_dummy dual.dummy%TYPE;

BEGIN

FOR I IN 1..1000 LOOP

SELECT dummy INTO v_dummy FROM dual;

END LOOP;

END;

/

-- collect again...

SELECT ROUND((1-(phy.value / (cur.value + con.value)))*100,2) "Cache Hit Ratio"

FROM v$sysstat cur, v$sysstat con, v$sysstat phy

WHERE cur.name = 'db block gets'

AND con.name = 'consistent gets'

AND phy.name = 'physical reads'

/

Quick and Easy ways to copy Oracle Schema method 1

exp/imp can be used to export/import data to or from schema

the syntax is 

to export:

$exp username/password inctype=[complete||incremental] file=file.dmp

to import:

imp username/password inctype= RECTORE FULL=Y FILE=file.dmp

please note. to import, you need to create enough room for rollback.

next time, I will take about to use new tool called datapump to export/import schema. data pump only available for oracle 10+.

5 easy steps to create a PDF Document in Java

Recently, my company has asked me to create some histograms in the pdf format. 

The first thing comes to my mind is to use Jasper Report. After talking to one of my colleagues, I decided to use iText to generate the pdf document.

Generate a pdf document is not a rocket science. Here are the steps how I do it in Netbeans( I believe it should be same as in Eclipse). I assume you have installed Maven plugin for your Netbeans. If you haven't done so, please install it. Maven will make your programming life much easier.

1. Setup the environment first.

1). Create a maven project in NetBeans.

2). open the project description file pom.xml. this file is located inside your Project Files.

3). inside the dependencies tag, add an iText dependeny

      <dependency>

         <groupid>com.lowagie</groupid>

         <artifactid>itext</artifactid>

         <version>2.1.5</version>

         </dependency>

   

4). right click on the Libraries folder and select download all libraries.

     2.  inside your main function.

Document doc = new Document(); 

       PdfWriter.getInstance(doc, new FileOutputStream("/tmp/helloWorld.pdf")); 

       doc.open();

       doc.add(new Paragraph("Hello from iText PDF"));

        doc.close();

    3. click on Run.

    4. a file named helloWorld.pdf should located in /tmp directory.

 

    5. there is no step 5.

Here is just the sample to illustruate how easy it is to create PDF file by using Netbean and Maven. If you have some questions, please let me know.

       

Difference between "in (...)" and "= any(...)" queries when using arrays

I was playing with "in (...)" and "= any (...)" queries and found a
difference between them and I wonder:

why this works:

select * from table_of_integers where integer_column = any (array[5,6]);

and this doesn't:

select * from table_of_integers where integer_column in (array[5,6]);

Although documentation says:

9.20.4. ANY/SOME

[...]
SOME is a synonym for ANY. IN is equivalent to = ANY.
[...]

I thought that if IN is equivalent to = any, both queries above should work.

after "google is your friend", I am able to find my answers.

the answers are

select * from table_of_integers where integer_column = any (array[5,6]);

This checks if integer_column matches any value in the specified array.

select * from table_of_integers where integer_column in (array[5,6]);

This checks if integer_column IS an array[5,6] (which it isn't).

Converting an ext3 filesystem to ext4

since ext4 is backwards compatible, you can mount an ext3 partition as ext4:

mount -t ext4 /dev/device /mnt/mountpoint

where device is the device you want to mount, and mountpoint is where you want to mount it.

The filesystem will not, however, use many of the new features of ext4. You can enable those with the following:

Warning: This will make the filesystem unable to be mounted as ext3

tune2fs -O extents,uninit_bg,dir_index /dev/device

It is important that you then run fsck, to make sure everything is ok:

fsck -pf /dev/device

Note: Using this method, new files will be created using the "extents" method, but old files will not be automatically converted

Appfuse experience

appfuse is an opensource framework. it glues some of the best Java Open source frameworks out there.  

Appfuse is build on top of Spring framework. it can be use JPA, Hibernate and iBatic for the data layer persistence. 

you can download appfuse from www.appfuse.org

Poor man VPN using SSH Tunnelling

Tips: Alter postgresql.conf to increase PostgreSQL performance

The default configuration PostgreSQL ships with is a very solid configuration aimed at everyone's best guess as to how an "average" database on "average" hardware should be setup. This article aims to help PostgreSQL users of all levels better understand PostgreSQL performance tuning.

There are several postmaster options that can be set that drastically affect performance, below is a list of the most commonly used and how they effect performance:

• max_connections = — This option sets the maximum number of database backend to have at any one time. Use this feature to ensure that you do not launch so many backends that you begin swapping to disk and kill the performance of all the children. Depending on your application it may be better to deny the connection entirely rather than degrade the performance of all of the other children.
• shared_buffers = — Editing this option is the simplest way to improve the performance of your database server. The default is pretty low for most modern hardware. General wisdom says that this should be set to roughly 25% of available RAM on the system. Like most of the options I will outline here you will simply need to try them at different levels (both up and down ) and see how well it works on your particular system. Most people find that setting it larger than a third starts to degrade performance.
• effective_cache_size = — This value tells PostgreSQL's optimizer how much memory PostgreSQL has available for caching data and helps in determing whether or not it use an index or not. The larger the value increases the likely hood of using an index. This should be set to the amount of memory allocated to shared_buffers plus the amount of OS cache available. Often this is more than 50% of the total system memory.
• work_mem = — This option is used to control the amount of memory using in sort operations and hash tables. While you may need to increase the amount of memory if you do a ton of sorting in your application, care needs to be taken. This isn't a system wide parameter, but a per operation one. So if a complex query has several sort operations in it it will use multiple work_mem units of memory. Not to mention that multiple backends could be doing this at once. This query can often lead your database server to swap if the value is too large. This option was previously called sort_mem in older versions of PostgreSQL.
• max_fsm_pages = — This option helps to control the free space map. When something is deleted from a table it isn't removed from the disk immediately, it is simply marked as "free" in the free space map. The space can then be reused for any new INSERTs that you do on the table. If your setup has a high rate of DELETEs and INSERTs it may be necessary increase this value to avoid table bloat.
• fsync = — This option determines if all your WAL pages are fsync()'ed to disk before a transactions is committed. Having this on is safer, but can reduce write performance. If fsync is not enabled there is the chance of unrecoverable data corruption. Turn this off at your own risk.
• commit_delay = and commit_siblings = — These options are used in concert to help improve performance by writing out multiple transactions that are committing at once. If there are commit_siblings number of backends active at the instant your transaction is committing then the server waiting commit_delay microseconds to try and commit multiple transactions at once.
• random_page_cost = — random_page_cost controls the way PostgreSQL views non-sequential disk reads. A higher value makes it more likely that a sequential scan will be used over an index scan indicating that your server has very fast disks.

If this is still confusing to you, Revolution Systems does offer a PostgreSQL Tuning Service

Note that many of these options consume shared memory and it will probably be necessary to increase the amount of shared memory allowed on your system to get the most out of these options.

Hardware Issues

Obviously the type and quality of the hardware you use for your database server drastically impacts the performance of your database. Here are a few tips to use when purchasing hardware for your database server (in order of importance):

• RAM — The more RAM you have the more disk cache you will have. This greatly impacts performance considering memory I/O is thousands of times faster than disk I/O.
• Disk types — Obviously fast Ultra-320 SCSI disks are your best option, however high end SATA drives are also very good. With SATA each disk is substantially cheaper and with that you can afford more spindles than with SCSI on the same budget.
• Disk configuration — The optimum configuration is RAID 1+0 with as many disks as possible and with your transaction log (pg_xlog) on a separate disk ( or stripe ) all by itself. RAID 5 is not a very good option for databases unless you have more than 6 disks in your volume. With newer versions of PostgreSQL you can also use the tablespaces option to put different tables, databases, and indexes on different disks to help optimize performance. Such as putting your often used tables on a fast SCSI disk and the less used ones slower IDE or SATA drives.
• CPUs — The more CPUs the better, however if your database does not use many complex functions your money is best spent on more RAM or a better disk subsystem.

In general the more RAM and disk spindles you have in your system the better it will perform. This is because with the extra RAM you will access your disks less. And the extra spindles help spread the reads and writes over multiple disks to increase throughput and to reduce drive head congestion.

Another good idea is to separate your application code and your database server onto different hardware. Not only does this provide more hardware dedicated to the database server, but the operating system's disk cache will contain more PostgreSQL data and not other various application or system data this way.

For example, if you have one web server and one database server you can use a cross-over cable on a separate ethernet interface to handle just the web server to database network traffic to ensure you reduce any possible bottlenecks there. You can also obviously create an entirely different physical network for database traffic if you have multiple servers that access the same database server.

Writing your own Velocity macro

Velocity is a Java Template Engine. Velocity can be generate Highlighted source code view, SQL, reports in variable forms. By using velocity, developers can seperate the code logic and the views.

these are the logics

#if #else #end -- conditional
#foreach #end -- Iteration

#set() assign new variable value

#stop stop execution the following
#evaluate() evaluate a function
#define

#macro

to define your own macro

#macro(invoke $__p_page)
   #if($__p_page.startsWith("/"))
       #parse($__p_page)
   #else
       #set($__uri = $resource.this_vm())
       #set($__path = $__uri.substring(0, $__uri.lastIndexOf("/")))
       #parse("$__path/$__p_page")
   #end
#end

to use it.

#invoke( hello.vm)

NC linux command

Today, I discover a nice network utility tool call nc

you can use it for troubleshooting your network apps.

Ford Escape

Yes, finally. I got my Ford Escape today.

I like this small suv very much. Compare to my 98 Acura Integra. This car is big and comfy.

will post some pictures when I have time