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百年修得夫妻緣，可別讓小小的細節問題誤了終身！不妨看看《大日子》列出的婚前6大雷區，以免誤闖。

1 爭吵

雙方產生矛盾，由小變大，由芝麻綠豆膨脹成鋪天蓋地，罪魁禍首往往就是「爭吵」。

這是婚前經常發生的問題，也是最嚴重的問題。

談戀愛時雙方偶有拌嘴，男方往往會比較大度的遷就。一旦真正進入結婚準備期，直接面對人生大事了，女方眼裏往往容不得半粒沙子，甚至鑽牛角尖。平時看起來可愛的玩笑或者偶爾的打情罵俏，在心情格外煩躁激動的日子裏，往往會被誤解為其他含義，一些無傷大雅的小問題，如約會遲到、電話未接等，也可能成為不能容忍的矛盾。其實結婚本就是兩個不同的個體生活在一起，必定會產生摩擦和矛盾。重在調整好心態─ 既然已經決定了對方為終身伴侶，可不要因為一些瑕疵而否定了整塊美玉。

矛盾根由：

過於糾纏過火的玩笑、遲到、忘記約好的事等一些細小的事情，被一時的情緒控制了整個心態。

應對方案：

放鬆心情，調整心態，平靜面對自我的真實感受，擺脫情緒的一時控制。

2 生活習慣

新婚在即，是否已經幻想起了婚後甜蜜幸福的生活？是月光下二人相偎相依，綿綿蜜語在耳邊淌過；或者是回到家後驚喜地燭光晚餐，雖然看似並不美味，但入口卻別有一番回味。這些只是婚姻生活美好的一部分，如果你看到的是滿地亂放的襪、衣服，浴室用完沒有歸位的用具， 廚房裏還未清洗的鍋碗瓢盆和賴在沙發上看電視的那個人，你是否有想結束這種生活的衝動呢？為了體驗、適應婚後生活，許多男女往往在舉辦婚禮前就同居試婚，也正是因為如此，生活習慣的不同讓對方心理產生巨大的落差，這段試婚生活也以此作為終點了。

矛盾根由：生活習慣不同、生活分工不明確

應對方案：為了保持生活和諧，雙方要從戀愛轉向婚姻生活，可以向父母討教雙方生活的訣竅，對家庭中的事物進行分工，對對方生活中的小習慣持包容和認真地心態加以指正。

3工作因素

結婚籌備期長，真正的儀式只不過短短一天，在工作壓力巨大、工作節奏極快、工作競爭激烈的社會，要新人們利用空餘時間好好準備婚禮確實是一件非常頭疼的事。雖然現在有專業的公司替你辦妥，但很多細節的事還需要親自動手。在準備婚禮和工作之間，往往會產生衝突，雙方中的一方因為工作而不能很好的參與準備，加上沒有良好的溝通，那另一方快樂的心情霎時即被沖淡，分道揚鑣的念頭隨即滋長。

矛盾根由：

一方因為工作而忽視了對方，使對方過多承擔了責任，且沒有良好的互動溝通

應對方案：

在結婚準備前，雙方應根據工作和空餘時間安排進行計畫和分工，如遇突發情況應儘快與對方溝通，互相幫助，避免準備不及時和對方的埋怨。

4 婚禮中的差錯

作為新娘，最大的心願就是希望有一次完美的婚禮，但人非聖賢，孰能無過，完美的婚禮需要天時、地利、人和，偶爾的小差錯何必放在心上，破壞婚禮的氣氛呢？堵車、壞天氣、忘記道具等事情，在新娘眼裏都是破壞夢想婚禮的兇手，由於這些小問題，心情變得極度糟糕，牽連新郎、家人，甚至影響結婚的進程。矛盾根由：過於看重婚禮當天的表現

應對方案：盡人事，知天命，婚前儘量安排好細節工作，預料到可能出現的差錯，雙方

共同解決，如無解決辦法，那就忽略，把這當成是上天給你的安排，自有他的道理。

5家人磨合

結婚不僅僅是兩個人的事，更是兩個家庭的事。

許多愛情，往往在最終階段由於彼此的家庭矛盾而凋謝，終難成就正果。作為結合的一對，站在自己家人和愛人的立場從中調解周旋才是保證婚姻生活圓滿的基礎。現實生活中往往不是如此，都是養育自己幾十年的父母、相處二十幾年的兄弟姐妹，都希望結婚能夠順應家人的意願。但是兩家人往往有不同的想法，女方喜歡氣派；男方喜歡溫馨，對婚宴不同的要求自然讓兩家的長輩互生芥蒂······男方和女方夾在中間，一不小心可能導致一拍兩散的結局。

矛盾根由：

缺乏心理互換立場；缺乏做好「中間人」的技巧。

應對方案：

1 . 男女雙方應多安排雙方父母見面，交流感情，對父母合理的要求和建議予以考慮，不要一味遷就自己的父母或對方；

2 . 對於比較難於融合的矛盾，要講究溝通技巧，儘量規避雙方的直接正面接觸，而由自己充當中間人加以調和；

3 . 自己家人情緒激動時說出來的「晦氣話」，往往成為傷害對方感情的利刃，所以要儘量避免傳遞給對方。

6心態變化

結婚後往往都會覺得你的另一半似乎變了，以前的體貼細緻、善解人意似乎在一點點消失，從前的溫柔瀟灑、專注用心也似乎一去不復返，究其原因是雙方心態的變化。從單身到結婚，也許意味著人的成熟，也許也意味著浪漫和用心的消逝。一旦要步入婚禮的殿堂，對兩人關係的要求、行為的出發點就會產生變化：她/他不是你的女朋友或者男朋友，而是你的妻子/丈夫，所以你似乎有權力可以指揮她/他做一些事情， 似乎可以不用再細心的打扮、精心的準備， 似乎你覺得他/ 她應該負更多的責任，而你有更多的要求等待她/他完成······是人變了嗎？不是，是心態、身份不同了。

矛盾根由：

戀愛與婚姻沒有很好地過渡

應對方案：

婚後側重點雖然是生活，但是婚姻同樣需要滋潤，兩個人共同的生活需要更多的心思來設計。不要把「實實在在地生活」理解為「死死板板地過日子」，也不能把婚姻當做某種權利濫施濫用。

A developer colleague (let's call him Pete because that's his name) asked me today about fill factors and the performance considerations behind choosing a fill factor for an index...so I thought, since it was fresh in my mind, I'd blog it. His basic situation was he's developing a middle-tier feature around a new table that has 200,000 rows in it and it took him about 2 minutes to delete those 200,000 rows, which seemed excessive. He suspected it was something to do with the fill factor he was choosing (I don't actually know what fill factor he chose) and so he asked me to explain a little about fill factors.

Well...

The fill factor applied to an index defines how full each leaf level page is when the index is created (or rebuilt with DBCC DBREINDEX(), which, for all intents & purposes, is basically the same as doing a DROP INDEX followed by a CREATE INDEX). If you specify a fill factor on 90 then that means each leaf page in the index will be 90% full of data initially. Fill factors are more relevant with clustered indexes (the actual table data) simply because clustered indexes tend to be much wider than your non-clustered indexes but the same principles apply to both clustered and non-clustered indexes.

So, why leave empty space in an index page? Well, what happens when a page becomes 100% full and SQL Server needs to insert another row in that page? What SQL Server does is take the full page, split it in 2 roughly equally filled pages (i.e 50% full) and then insert the new row into the appropriate of those 2 new pages, both of which should now have enough free space for the new row. This is known as a page split. As you can imagine, page splits increase the I/O activity on an index (allocating new pages, moving data, shuffling pages in the b-tree to put them in the right place, in addition to the actual insert you originally wanted to do) and so hurt performance. So you want to try to minimise page splits.

So does that mean I should just create my indexes with a fill factor of, say, 5%? (This was how the actual conversation with Pete went.) Well, if each page was only 5% full (as opposed to 100% full) then you'd need 20 times as many pages (each of which is 8k) to store the data. So we're talking about a pretty big index which is mostly full of empty space. That's great in terms of inserts & modifications (assuming you've got enough disk space to handle indexes that are suddenly 20 times larger than before) - you wouldn't expect page splits very often. However, if you're retrieving a range of data from that index with a SELECT statement then that data is most likely going to be spread out across 20 times more pages than before. So your range scans are going to have to hit 20 times more pages to return the same amount of data as before, hence 20 times more I/O.

Generally speaking, read performance of an index is directly proportional to the fill factor and write performance is inversely proportional to the fill factor. Damned if you do, damned if you don't. This is one of the reasons you've got to know the data you're playing with. Do you optimise the index for reads or writes? Generally speaking, once again, an index will be used much more for reads than writes (Microsoft estimates that reads typically outnumber writes by at least a factor of 5 to 10). So, will this index (or the table in general) mostly get used for reads or writes? Also, when new rows get inserted into the index are they going to be allocated at the end of the index (in sequential order, like an IDENTITY column for example) or are they going to be all over the place (like new uniqueidentifier values assigned with NEWID())? The answers to these two questions will be very important in determining what initial fill factor to set when creating your index (or rebuilding it later), assuming you don't want to stick with the server default (set with sp_configure).

Firstly, since indexes are generally read more than written to, you should probably err on the side of larger fill factors (for example, at least greater than 50%). If you're expecting a large percentage of writes on the index then you'll want a lower fill factor to provide some room for future adds. On the other hand if you're not expecting many writes in the index then you should fill the pages more (if you don't change the server default fill factor and you don't specify what fill factor you want when creating an index then SQL Server will fill each page as much as it can, i.e. 100%). That's pretty straight forward.

However, you may also be able to get away with a very high fill factor if new rows will almost always go at the end of the index (not in the middle) and existing rows will rarely, if ever, get changed. This will create a hotspot at the end of the index and page splits will be rare because almost all new rows will go at the end of the index resulting in simple new page allocations rather than page splits. The classic example is a clustered index on an identity column; this is one argument for the surrogate key side of the famous (or should I say infamous?) surrogate key versus natural key debate. But I won't get into that can of worms here.

The basic theory behind fill factors that I've described here should stand you in good steed to come up with general rules of thumb when designing your indexes. But, since it's essentially a trade-off between read performance and write performance, that will really only give you a starting point. From there it's a case of trial and error, bearing in mind how changes in fill factor affect performance. If you're getting excessive page splits then rebuild your index with a smaller fill factor. If I/Os are high during SELECT statements and it's affecting performance then you may be able to rebuild your index with a bigger fill factor. (Fill factors are only a very minor point to consider during query optimisation; good query design and table normalisation will usually play a much bigger role.)

One more thing to think about is that with page splits comes index fragmentation. Fragmentation is really pretty unavoidable but excessive page splits will lead to greater index fragmentation and reduced I/O performance, both read performance and write performance (and I/O is typically the bottleneck in RDBMSs). But index fragmentation is a topic for a whole other blog. And, by the way, I think Pete's slow deletes problem had more to do with waits/locks on the resources he was trying to delete than fill factors...but anyway...

Clustered Index

1. Leaf node contains the actual data pages.
2. The data row of the table are sorted and stored in the table based on their clustered index key (i.e. based on the index column(s)).
3. You can have only one clustered index per table.
4. The RowLocator in Clustered Index is the clustered Index key.

Non Clustered Index

1. Leaf node contains index pages instead of data pages.
2. You can have up to 249 Non Clustered Index per table.
3. The data row of the table are not sorted and stored in the table based on their clustered index key (i.e. based on the index column(s)).
4. The row locator in Non Clustered Index is a pointer to the row. Row locator is built based on the following.
   ROW ID (RowLocator)= file identifier + page number + row number on the page
5. You can have the functionality of Non key Columns (Included Columns) in case of Non Clustered Index.
6. Non key columns are stored only at the leaf level whereas Key columns (Non Clustered Index Key columns) are stored at all the levels of non clustered index.

What are Heaps?

1. Heaps are tables without clustered index.
2. Data Rows are not stored in a particular order.
3. Sequences of the Data pages are not ordered as well as they are not linked in a linked list.

Examples

Clustered Index Example

Clustered can be created in the following ways

1. Create Table with Primary key – this will by default create clustered index based on the primary key defined.
2. Create clustered Index using the CREATE CLUSTERED INDEX command.

–create table with primary key
CREATE TABLE Employee (empno NUMERIC (10) PRIMARY KEY, EmpName Varchar(10));

–check the existence of Index for the table

SELECT * FROM SYS.INDEXES WHERE OBJECT_ID = (SELECT OBJECT_ID FROM SYS.OBJECTS WHERE NAME=‘EMPLOYEE’); 
 




–create table without primary key

CREATE TABLE Employee2 (empno NUMERIC (10), EmpName Varchar(10));

–Create Clustered Index using Clustered Index  Command

CREATE CLUSTERED INDEX IDX_CLUST_EMP ON Employee2 (empno);

–check the existence of Index for the table

SELECT * FROM SYS.INDEXES WHERE OBJECT_ID = (SELECT OBJECT_ID FROM SYS.OBJECTS WHERE NAME=‘Employee2′);

    

Non Clustered Index Example

– Adding a column the existing employee table

ALTER TABLE EMPLOYEE
ADD TAX_ID NUMERIC(10);

–create non clustered index on TAX_ID column
CREATE INDEX IDX_NON_CLUST ON EMPLOYEE (TAX_ID);

–check the existence of Index for the table
SELECT * FROM SYS.INDEXES WHERE OBJECT_ID = (SELECT OBJECT_ID FROM SYS.OBJECTS WHERE NAME=‘EMPLOYEE’);

This is a very basic and a quick information on the differences between Clustered and Non Clustered Indexes in SQL Server.

傳統釐定鑽石價值高低的標準是「4C」制度，即卡、色澤、淨度、和切割。淨度

淨度（Clarity）以鑽石內的瑕疵多少缺定。瑕疵可能是天然的雜質或裂痕。瑕疵的數量、位置、大小等都會影響評級。鑽石壙開採出來的鑽石中，只有20%可以成為寶石，其餘的因為瑕疵較多通常只能作工業用途。而20%的寶石級鑽石中，大部分都包含肉眼可見的瑕疵。在此級別以上的鑽石較為大眾喜愛。至於屬完美級別的鑽石更為罕有，被稱為「博物館級」鑽石。通常使用10倍放大鏡觀察鑽石內部及表面瑕疵的大小、數量、分佈及對鑽石光彩影響的程度，分出等級。一般通行的淨度分級如下：

GIA - The Gemological Institute of America
IGI - International Gemmological Institute
SIG - SIGMA International Gemology
AGS - American Gem Society Laboratories

大致上你於問題內已排出4間機構既認受性 & quality order~
簡單而言,
認受性: 於行內普及程度, 規模及聲譽等
quality: 對寶石評級既準確性及分量等

本人認為AGS係最好的

AGS 50分出一張DQD REPORT 要 US$85 再加INSURANCE同FREIGHT CHARGE, HK代理MIN收 US$50 TOTAL US$135，X7.8 = HK$1053 一張

AGS係最貴的一張CERT 亦都係最好~原因係~

AGS的CERT係最早有CUT GRADE~還要早過GIA出

AGS REPORT的鑽石比例，會標示於圖上~令銷費者更易明白比例的位置

證書上以彩色小圖例明淨度的符號的解釋~令銷費者更易明白

AGS 證書上例明 LIGHT PERFORMANCE的 GRADE 令銷費者明白鑽石光源折射的級數

AGS 證書於CUT GRADE之下更會細分 小項~以等知CUT GRAED包含的詳細資料

AGS 證書的PACKING 精美~以黑面硬身膠套保護證書

現時所有證書只可於美國研究所做鑽石評級~加強品質的控制

好多GIA grade excellent cut 既 AGS 會變左 Fair .. 連 Good 都冇... 呢個亦都係我煩惱左好耐既原因..
講 Cut Grade.. AGS is simply the best..

[Refer: http://www1.uwants.com/viewthread.php?tid=5570421]

[Reference]

http://blog.blueshop.com.tw/hent/archive/2008/03/15/54518.aspx