Just thought i should have a quick guide for configuring an optimized for innodb my.cnf
use innodb_data_home_dir & innodb_data_file_path to specify the location and size of the tablespace.
innodb_data_home_dir = /path/to/data
innodb_data_file_path = ibdata1:50M;ibdata2:50M:autoextend:max:10G
innodb_autoextend_increment = 10
This asks innodb to create two files ibdata1 & ibdata2 (as tablespace) and keep them in the /path/to/data directory. Both would be of 50 MB initially, but ibdata2 would expand automatically by innodb_autoextend_increment MBs (yes the value is in MB) till the max size of 10 GB. InnoDB forms the directory path for each data file by textually concatenating the value of innodb_data_home_dir to the data file name, adding a path name separator (slash or backslash) between values if necessary.
By default innodb creates all its tables in the tablespace specified by the innodb_data_file_path directory - in a single file - as specified. But there is another option using which you can ask innodb to create tablespace for each table.
innodb_file_per_table
Innodb would now store each newly created table in its own table_name.ibd file - which would contain both the data and the index.
Lets look at some optimization and performance settings for innodb
innodb_additional_mem_pool_size=4M
If you have a large number of innodb tables, you will need to increase this value. It is used to store data dictionary information and other internal data structures. If the value is low, you might see warning messages in the mysql error log.
innodb_autoinc_lock_mode=2
Innodb uses some table level locking for generating auto increment values - which is configurable. If the value is set to 0 (traditional mode), for all insert statements, a table level auto_inc lock is obtained and maintained till the end of the insert statement. If the value is set to 1(consecutive lock mode), all bulk insert-like statements obtain and maintain the auto_inc lock on the whole table. But simple inserts use a new locking method where a lightweight mutex is used during the allocation of auto-increment values, and no table-level AUTO-INC lock is used. If the value is set to 2 (interleaved lock mode), no insert-like statements use the table-level AUTO-INC lock, and multiple statements can execute at the same time. This is the fastest and most scalable lock mode, but it is not safe when using statement-based replication or recovery scenarios when SQL statements are replayed from the binary log. Here auto-increment values are guaranteed to be unique and monotonically increasing across all concurrently executing insert-like statements. However, because multiple statements can be generating numbers at the same time (that is, allocation of numbers is interleaved across statements), the values generated for the rows inserted by any given statement may not be consecutive.
innodb_buffer_pool_size=1024M
The larger the buffer pool the less disk I/O would be needed to access data in tables. Generally buffer pool should be around 50% of available memory size. Very large values can result in paging and slow down the system.
innodb_commit_concurrency=4
The number of threads that can commit at the same time. Should be equal to the number of available cpus or cores.
innodb_flush_log_at_trx_commit=2
If the value is 0, the log buffer is written out to the log file once per second and the flush to disk operation is performed on the log file, but nothing is done at a transaction commit. When the value is 1 (the default), the log buffer is written out to the log file at each transaction commit and the flush to disk operation is performed on the log file. When the value is 2, the log buffer is written out to the file at each commit, but the flush to disk operation is not performed on it. However, the flushing on the log file takes place once per second also when the value is 2. Note that the once-per-second flushing is not 100% guaranteed to happen every second, due to process scheduling issues.
The default value of 1 is the value required for ACID compliance. Better performance can be achieved by setting the value different from 1, but then you can lose at most one second worth of transactions in a crash. With a value of 0, any mysqld process crash can erase the last second of transactions. With a value of 2, then only an operating system crash or a power outage can erase the last second of transactions.
innodb_log_buffer_size=8M
The size of buffer that innodb uses to write log files on disk. A large buffer allows large transactions to run without a need to write the log to disk before the transactions commit.
innodb_log_file_size=512M
The size of each log file in a log group. The combined size of log files must be less than 4GB. Sensible values range from 1MB to 1/N-th of the size of the buffer pool, where N is the number of log files in the group. The larger the value, the less checkpoint flush activity is needed in the buffer pool, saving disk I/O. But larger log files also mean that recovery is slower in case of a crash.
innodb_log_files_in_group=2
The number of log files in the log group.
innodb_open_files=300
The maximum no of tablespaces that innodb can keep open at one time.
innodb_support_xa=false
If this is enabled, InnoDB support for two-phase commit in XA transactions is enabled, which causes an extra disk flush for transaction preparation. If you do not wish to use XA transactions, you can disable this variable to reduce the number of disk flushes and get better InnoDB performance.
innodb_thread_concurrency=8
InnoDB tries to keep the number of operating system threads concurrently inside the engine less than or equal to the limit given by this variable. Once the number of threads reaches this limit, additional threads are placed into a wait state within a FIFO queue for execution. Threads waiting for locks are not counted in the number of concurrently executing threads. By default this value should be twice the number of available cpus/cores.
Using some of the data here, you can easily setup a machine to perform better using innodb. You will always have to choose between performance and reliability as both are inversely proportional. As performance increases the reliability of data would decrease. The variables that i have set here are for high performance only.
Saturday, September 19, 2009
Tuesday, September 08, 2009
mad world - tears for fears
All around me are familiar faces
Worn out places, worn out faces
Bright and early for their daily races
Going nowhere, going nowhere
Their tears are filling up their glasses
No expression, no expression
Hide my head I want to drown my sorrow
No tommorow, no tommorow
And I find it kind of funny, I find it kind of sad
The dreams in which Im dying are the best I've ever had
I find it hard to tell you, I find it hard to take
When people run in circles its a very, very
Mad world, Mad World
Children waiting for the day they feel good
Happy birthday, happy birthday
And I feel the way that every child should
Sit and listen, sit and listen
Went to school and I was very nervous
No one knew me, no one knew me
Hello teacher tell me whats my lesson
Look right through me, look right through me
And I find it kind of funny, I find it kind of sad
The dreams in which I'm dying are the best I've ever had
I find it hard to tell you, I find it hard to take
When people run in circles its a very, very
Mad world, Mad World, enlarging your world
Mad World
Worn out places, worn out faces
Bright and early for their daily races
Going nowhere, going nowhere
Their tears are filling up their glasses
No expression, no expression
Hide my head I want to drown my sorrow
No tommorow, no tommorow
And I find it kind of funny, I find it kind of sad
The dreams in which Im dying are the best I've ever had
I find it hard to tell you, I find it hard to take
When people run in circles its a very, very
Mad world, Mad World
Children waiting for the day they feel good
Happy birthday, happy birthday
And I feel the way that every child should
Sit and listen, sit and listen
Went to school and I was very nervous
No one knew me, no one knew me
Hello teacher tell me whats my lesson
Look right through me, look right through me
And I find it kind of funny, I find it kind of sad
The dreams in which I'm dying are the best I've ever had
I find it hard to tell you, I find it hard to take
When people run in circles its a very, very
Mad world, Mad World, enlarging your world
Mad World
Monday, September 07, 2009
moniter and improve lucene search
How ?
Simple - use lucidgaze available at http://www.lucidimagination.com/Downloads/LucidGaze-for-Lucene
With lucidgaze you could analyze
So, we went ahead and downloaded the api but were unable to find any example program or source code. After some amount of tinkering around we were finally able to figure out how to use it.
You could get the code here : http://ai-cafe.blogspot.com/2009/09/lucid-gaze-tough-nut.html
Simple - use lucidgaze available at http://www.lucidimagination.com/Downloads/LucidGaze-for-Lucene
With lucidgaze you could analyze
- Read/write stats on index utilization, distribution and throughput
- Query efficiency stats - show how effectively user input is analyzed and decomposed for processing by the index
- Mapping of tokenizers, token-streams and analyzers - makes transparent how text is processed and indexed
So, we went ahead and downloaded the api but were unable to find any example program or source code. After some amount of tinkering around we were finally able to figure out how to use it.
You could get the code here : http://ai-cafe.blogspot.com/2009/09/lucid-gaze-tough-nut.html
Wednesday, September 02, 2009
DB Basics : Types of joins
Join Types
So, you have designed a normalized database design for your application. Maybe up to the 3rd normal form. And now, when you need to run queries, you would need to join the tables in the query to get the required information. There has to be some common data that allow those tables to be connected in some meaningful way. Although it’s possible to have more than one common column between two tables, most often, the join key will be the primary key of one table and a foreign key in the other.
Lets perform queries against the following table.
company : company_id (pk), company_name
model : model_id(pk), company_id(fk), model_name
Here, we have a company table and a model table in 1:n relationship. A car company can have multiple models in the market. Let us take some sample data
Inner join : An inner join is defined as a join in which rows must match in both tables in order to be included in the result set.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 INNER JOIN model t2 on t1.company_id = t2.company_id where t1.company_name = 'Audi';
+--------------+-------+
| Manufacturer | Model |
+--------------+-------+
| Audi | A4 |
| Audi | A6 |
| Audi | A8 |
| Audi | Q7 |
+--------------+-------+
Outer Join : Outer joins will return records in one table that aren’t matched in another. Outer joins can be further divided into the two types of left and right. In a left outer join, all records from the first (left-hand) table in a join that meet any conditions set in the WHERE clause are returned, whether or not there’s a match in the second (right-hand) table.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 left join model t2 on t1.company_id = t2.company_id where t1.company_name in ('Toyota','BMW');
+--------------+--------+
| Manufacturer | Model |
+--------------+--------+
| Toyota | Corola |
| Toyota | Camry |
| Toyota | Innova |
| BMW | NULL |
+--------------+--------+
Here 'BMW' is returned even when it does not have any entry in the model Table.
Similar to the left outer join, a right outer join returns all records from the second (right-hand) table in a join that meet any conditions set in the WHERE clause, whether or not there’s a match in the first (left-hand) table.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 right join model t2 on t1.company_id = t2.company_id where t2.model_name in ('Custom','Nano');
+--------------+--------+
| Manufacturer | Model |
+--------------+--------+
| Tata | Nano |
| NULL | Custom |
+--------------+--------+
Cross-join :
The cross-join, also referred to as a Cartesian product, returns all the rows in all the tables listed in the query. Each row in the first table is paired with all the rows in the second table. This happens when there is no relationship defined between the two tables. We do not require cross join in our general applications, so we should try to avoid it. A cross join happens when we fail to provide a filler for the join in the query.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1, model t2;
+--------------+-----------+
| Manufacturer | Model |
+--------------+-----------+
| Audi | A4 |
| GM | A4 |
| Ford | A4 |
| Toyota | A4 |
| Tata | A4 |
| BMW | A4 |
| Audi | A6 |
| GM | A6 |
.....
.....
.....
| Ford | Custom |
| Toyota | Custom |
| Tata | Custom |
| BMW | Custom |
+--------------+-----------+
So, you have designed a normalized database design for your application. Maybe up to the 3rd normal form. And now, when you need to run queries, you would need to join the tables in the query to get the required information. There has to be some common data that allow those tables to be connected in some meaningful way. Although it’s possible to have more than one common column between two tables, most often, the join key will be the primary key of one table and a foreign key in the other.
Lets perform queries against the following table.
company : company_id (pk), company_name
model : model_id(pk), company_id(fk), model_name
Here, we have a company table and a model table in 1:n relationship. A car company can have multiple models in the market. Let us take some sample data
| company_id | company_name |
| 1 | Audi |
| 2 | GM |
| 3 | Ford |
| 4 | Toyota |
| 5 | Tata |
| 6 | BMW |
| model_id | company_id | model_name |
| 1 | 1 | A4 |
| 2 | 1 | A6 |
| 3 | 1 | A8 |
| 4 | 1 | Q7 |
| 5 | 2 | Chevrolet |
| 6 | 2 | Hummer |
| 7 | 3 | Ikon |
| 8 | 3 | Endeavor |
| 9 | 3 | Fiesta |
| 10 | 4 | Corola |
| 11 | 4 | Camry |
| 12 | 4 | Innova |
| 13 | 5 | Indica |
| 14 | 5 | Nano |
| 15 | 5 | Safari |
| 16 | Null | Custom |
Inner join : An inner join is defined as a join in which rows must match in both tables in order to be included in the result set.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 INNER JOIN model t2 on t1.company_id = t2.company_id where t1.company_name = 'Audi';
+--------------+-------+
| Manufacturer | Model |
+--------------+-------+
| Audi | A4 |
| Audi | A6 |
| Audi | A8 |
| Audi | Q7 |
+--------------+-------+
Outer Join : Outer joins will return records in one table that aren’t matched in another. Outer joins can be further divided into the two types of left and right. In a left outer join, all records from the first (left-hand) table in a join that meet any conditions set in the WHERE clause are returned, whether or not there’s a match in the second (right-hand) table.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 left join model t2 on t1.company_id = t2.company_id where t1.company_name in ('Toyota','BMW');
+--------------+--------+
| Manufacturer | Model |
+--------------+--------+
| Toyota | Corola |
| Toyota | Camry |
| Toyota | Innova |
| BMW | NULL |
+--------------+--------+
Here 'BMW' is returned even when it does not have any entry in the model Table.
Similar to the left outer join, a right outer join returns all records from the second (right-hand) table in a join that meet any conditions set in the WHERE clause, whether or not there’s a match in the first (left-hand) table.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1 right join model t2 on t1.company_id = t2.company_id where t2.model_name in ('Custom','Nano');
+--------------+--------+
| Manufacturer | Model |
+--------------+--------+
| Tata | Nano |
| NULL | Custom |
+--------------+--------+
Cross-join :
The cross-join, also referred to as a Cartesian product, returns all the rows in all the tables listed in the query. Each row in the first table is paired with all the rows in the second table. This happens when there is no relationship defined between the two tables. We do not require cross join in our general applications, so we should try to avoid it. A cross join happens when we fail to provide a filler for the join in the query.
mysql> select t1.company_name as 'Manufacturer', t2.model_name as 'Model' from company t1, model t2;
+--------------+-----------+
| Manufacturer | Model |
+--------------+-----------+
| Audi | A4 |
| GM | A4 |
| Ford | A4 |
| Toyota | A4 |
| Tata | A4 |
| BMW | A4 |
| Audi | A6 |
| GM | A6 |
.....
.....
.....
| Ford | Custom |
| Toyota | Custom |
| Tata | Custom |
| BMW | Custom |
+--------------+-----------+
Monday, August 24, 2009
ext4 filesystem
ext4 is the next "version" of filesystem after ext3. It was released with linux kernel version 2.6.28 which comes with ubuntu 9.04 (Jaunty).
Benefits of ext4 over ext3
Now lets see the steps needed to convert your desktop filesystem from ext3 to ext4.
After the reboot you would be using the ext4 filesystem.
Important note : Your old files have not been converted to the ext4 technology. Only new files written to disk will use the ext4 technology. But since ext3 & ext4 are compatible, you wont face any problems accessing older ext3 files on disk. With usage the ext3 files will automatically disappear and get converted to ext4.
Benefits of ext4 over ext3
- Bigger filesystem and file sizes : ext3 supports 16TB of filesystem size and max file size of 2TB. And ext4 supports 1EB (10^18 bytes = 1024*1024 TB) of filesystem size and max file size of 16TB. Though you would never come across such huge storage system in desktop computers.
- subdirectory limitations : ext3 allows "only" 32000 subdirectories/files in a directory. ext4 allows unlimited number of subdirectories.
- Multiblock allocation : ext3 allocates 1 block (4KB) at a time. So if you write a 100 MB file you will be calling the ext3 block allocator 25600 times. Also this does not allow the block allocator to optimize the allocation policy because it does not know the total amount of data being allocated. Ext4 on the other hand used a multiblock allocator to allocate multiple blocks in a single go. This improves performance to a great extent.
- Extents : Ext3 uses indirect block mapping scheme to keep track of each block for the blocks corresponding to the data of a file. Ext4 uses extents (contiguous physical blocks) that is to say that the data lies in the next n blocks. It improves performance and reduces file fragmentation
- Delayed allocation : Ext3 allocates the blocks as soon as possible. Ext4 delays the allocation of physical blocks by as much as possible. Until then the blocks are kept in cache. This gives the block allocator the opportunity to optimize the allocation of blocks.
- fast fsck : The total fsck time improves from 2 to 20 times,
- journal checksumming : Ext4 checksums the journal data to know if the journal blocks are corrupted. Journal checksumming allows one to convert the two-phase commit system of Ext3's journaling to a single phase, speeding the filesystem operation up to 20% in some cases - so reliability and performance are improved at the same time.
- "No Journaling" mode : in Ext4 depending on your requirements, the journaling feature can be disabled.
- Online defragmentation : Ext4 supports online defragmentation. There is a e4defrag tool which can defrag individual files or even complete filesystems.
- Inode related features : Larger inodes, nanosecond timestamps, fast extended attributes, inodes reservation
- Persistent preallocation : Applications tell the filesystem to preallocate the space, and the filesystem preallocates the necessary blocks and data structures, but there is no data on it until the application really needs to write the data in the future.
- Barriers on by default : This option improves the integrity of the filesystem at the cost of some performance. A barrier forbids the writing of any blocks after the barrier until all blocks written before the barrier are committed to the media. By using barriers, filesystems can make sure that their on-disk structures remain consistent at all times.
Now lets see the steps needed to convert your desktop filesystem from ext3 to ext4.
- check the version of linux kernel. It should be > 2.6.28-11.
jayant@gamegeek:~$ uname -a
Linux gamegeek 2.6.28-15-generic #49-Ubuntu SMP Tue Aug 18 19:25:34 UTC 2009 x86_64 GNU/Linux - Just in case - take a backup of your important data
- boot from live cd and run the following commands for converting the partition /dev/sda6 to ext4 from ext3.
$ sudo bash
$ tune2fs -O extents,uninit_bg,dir_index /dev/sda6
$ e2fsck -pf /dev/sda6 - Mount the partition and change its type entry in /etc/fstab
$ mount -t ext4 /dev/sda6 /mnt
$ vim /mnt/etc/fstab
Change
# /dev/sda6
UUID=XXXXXX / ext3 relatime,errors=remount-ro 0 1
To
# /dev/sda6
UUID=XXXXXX / ext4 relatime,errors=remount-ro 0 1
Save the changes - Reinstall grub - this is optional. If you do not do this and you get a fatal error 13 while booting the machine, just boot using the live cd and run these commands.
$ sudo bash
$ mkdir /mnt/boot
$ mount /dev/sda6 /mnt/boot
$ grub-install /dev/sda --root-directory=/mnt --recheck
After the reboot you would be using the ext4 filesystem.
Important note : Your old files have not been converted to the ext4 technology. Only new files written to disk will use the ext4 technology. But since ext3 & ext4 are compatible, you wont face any problems accessing older ext3 files on disk. With usage the ext3 files will automatically disappear and get converted to ext4.
Saturday, August 15, 2009
Eyes!!!
Jai parked his old yellow bajaj priya scooter in the college's parking lot and took out his polythene containing his books. Since college, he had abandoned taking his books in bags. Bags are for school boys, not for college going big boys. He used to carry a single notebook and a pen in his polybag - simply to scribble during the lectures. He looked at his watch - it was already 11 AM and the first class was almost getting over. He was again late. He thought, why did he have to sit late in the night and read novels on his home PC. It was the same story everyday. Always missing the first lecture.
He walked into the college building and towards his class, and met some of the other late comers in the lobby. They were all waiting for the lecture to finish so that they could go and take their seats in the lecture room. Adi, one of his good friends said `The lecture is not that important`. Jai said `Ya, you would say that. You were also late`. Few other people were also strolling around. Others were sitting on the window slab and discussing girls. Girls tend to be the ususal topic during college times.
The topic of the new batch came in. Someone said `Heard that the cutoff for admission to medical has gone up to 97%`. In those days the admissions in Gujarat based universities used to happen on merit basis. So whatever your score was in the 10+2 exams could decide your future. People used to go to tutions after school for extra coaching. Jai asked `What was the cutoff for computer science?`. That was the branch we were all in. Adi answered `I think it was 95%`. The percentage marks was calculated only for four subjects - Physics, Maths, Biology and Chemistry. Medical seats were the first to get filled up, followed by Chemical - due to the fact that there were a lot of chemical jobs in Gujarat. And then there were Electronics and Computers which got filled up next.
And the topic shifted to the new batch which came in last year. Jai and his friends were now in the third year of their graduation. After feasting their eyes on the girls in their own batch they were now tired and were craving for something new. There was this saying that the girls in the batch one year junior to them were good.
'Have you heard about the girl nikita' Adi asked.
'No, what about her?' Jai answered.
'She is supposed to be a bombshell' Adi answered.
'What yaar? Every good looking girl is a bombshell. What is so special about her?' Jai asked again.
'Man you got to see her to believe her beauty' Adi said.
'Now you have got me interested. What is so good about her - figure, hair ?' Jai presses again to get a vulgar description of the girl.
But it seems that even Adi had not looked at that girl. He had just heard about her. Else he would have gone ahead and gave a complete inch by inch description of the girl.
The bell rang and we went up the stairs to stay out of site of the teacher whose class we had bunked (or missed). We saw the professor go out followed by a bunch of 'intelligent' students asking him questions.
'Trying to impress the prof' - Adi commented
'Well, they make marks by impressing the prof. We should follow in their footsteps' - Jai said.
'To hell with marks. What they want are better recomendations, so they could pursue their MS in US? Do you want to go to US?' - Adi said
'Firstly man, i dont want to study further. I am tired of studying. And secondly, I think it is a waste of money to go to US? You might be at the same level if you have 2 years experience. And lastly, my family does not have enough money to send me to US and i do not want to take a loan for studying' Jai explained.
'I know, i know, lets get into class' - Adi said.
And we got into the class. The last benches were the best. Teachers rarely saw what was happening on the last benches. Some were finishing off their practical journals. Others were simply making doodles. It was the attendance that mattered. Attendance was required to be eligible to give exams.
So how did those guys make the exams when they did not actually "Attend" the class. Mostly simply got the syllabus and studied on their own. They used to get a one month leave for preparing for their exams. And that was enough to sail through the exams.
After 2 boring lectures, there was a half hour lunch break. And we all moved down to the canteen. Jai and Adi went from the back entrance - which was actually a thin wall. After 2 years of trying to cross it without losing their balance, they had now perfected their skill. Adi ran over the wall and Jai followed. Both landed in the Canteen.
The canteen was a huge shed covered with tin. There were cemented tables where one could simply stand and have their food. The food was ok. One can get anything ranging from pakodas to rice and dal here. Jai and Adi were deciding what to have, when someone said 'Look there's nikita'. And we all turned around. She was ordering something from the counter. 'Let me get pakodas and coke' Jai said. And rushed over to the counter.
What Jai saw, amazed him. Nikita was just like a common girl. Nothing great about her. Ofcourse she had a okie figure - nothing great or out of proportion. Hair to her shoulders. She was wearing normal stuff - nothing too revealing or fashionable. Her hair was tied in a ponie.
'One plate pakodas and a coke' - Jai ordered.
And she turned around and looked directly at Jai. Jai stopped breathing for a couple of seconds - while she looked at him. And then she took her order and left. Jai was mesmerized.
'20 Rs.' - Said the canteen boy and shook Jai.
'Wow!!' - said Jai and rushed back with pakodas and coke to Adi.
'Oh God!!, she is beautiful' said Jai, 'Lets move closer to her table'.
And they moved closer to her table to overhear what she was saying.
'So,' Adi said, 'What is so good about her?'
'Her eyes man' - Jai answered, 'I have never seen eyes like that. She has brown eyes and they are huge and deep. You could stare at them for a couple of seconds and you could get lost. It is not that i have not seen good eyes. I have seen blue eyes, black eyes and even foggy eyes, really big eyes. But, she has the eyes that could beat all of them - even the best one of them. I dont know where she got them. Otherwise she is a normal girl'.
'Oh!' - Adi shows his realization.
'But man you have to see them to believe them. I am sure i could never forget those eyes. I have never seen eyes like that, and I am sure i would never ever see eyes like those' - Jai repeats.
'Maybe she is wearing contacts' Adi says.
'No yaar, those eyes were real. Even colored contacts could not be that beautiful' - Jayant says.
'But man, do you want ahead and try to be friends with her? I think she has a boyfriend though.' - Adi asks.
'I think she sounds quite dumb.' Said Jai after overhearing to Nitika's conversation for some time. 'I would prefer intelligence over beauty'
'Well, then you have a long way to look - cause beauty and brains and compatibility is a very rare match' - Adi explains.
They finish their lunch and head back to the classes for more lectures. The eyes that Jai had seen stays embedded within his mind - and he may dream about them.
He walked into the college building and towards his class, and met some of the other late comers in the lobby. They were all waiting for the lecture to finish so that they could go and take their seats in the lecture room. Adi, one of his good friends said `The lecture is not that important`. Jai said `Ya, you would say that. You were also late`. Few other people were also strolling around. Others were sitting on the window slab and discussing girls. Girls tend to be the ususal topic during college times.
The topic of the new batch came in. Someone said `Heard that the cutoff for admission to medical has gone up to 97%`. In those days the admissions in Gujarat based universities used to happen on merit basis. So whatever your score was in the 10+2 exams could decide your future. People used to go to tutions after school for extra coaching. Jai asked `What was the cutoff for computer science?`. That was the branch we were all in. Adi answered `I think it was 95%`. The percentage marks was calculated only for four subjects - Physics, Maths, Biology and Chemistry. Medical seats were the first to get filled up, followed by Chemical - due to the fact that there were a lot of chemical jobs in Gujarat. And then there were Electronics and Computers which got filled up next.
And the topic shifted to the new batch which came in last year. Jai and his friends were now in the third year of their graduation. After feasting their eyes on the girls in their own batch they were now tired and were craving for something new. There was this saying that the girls in the batch one year junior to them were good.
'Have you heard about the girl nikita' Adi asked.
'No, what about her?' Jai answered.
'She is supposed to be a bombshell' Adi answered.
'What yaar? Every good looking girl is a bombshell. What is so special about her?' Jai asked again.
'Man you got to see her to believe her beauty' Adi said.
'Now you have got me interested. What is so good about her - figure, hair ?' Jai presses again to get a vulgar description of the girl.
But it seems that even Adi had not looked at that girl. He had just heard about her. Else he would have gone ahead and gave a complete inch by inch description of the girl.
The bell rang and we went up the stairs to stay out of site of the teacher whose class we had bunked (or missed). We saw the professor go out followed by a bunch of 'intelligent' students asking him questions.
'Trying to impress the prof' - Adi commented
'Well, they make marks by impressing the prof. We should follow in their footsteps' - Jai said.
'To hell with marks. What they want are better recomendations, so they could pursue their MS in US? Do you want to go to US?' - Adi said
'Firstly man, i dont want to study further. I am tired of studying. And secondly, I think it is a waste of money to go to US? You might be at the same level if you have 2 years experience. And lastly, my family does not have enough money to send me to US and i do not want to take a loan for studying' Jai explained.
'I know, i know, lets get into class' - Adi said.
And we got into the class. The last benches were the best. Teachers rarely saw what was happening on the last benches. Some were finishing off their practical journals. Others were simply making doodles. It was the attendance that mattered. Attendance was required to be eligible to give exams.
So how did those guys make the exams when they did not actually "Attend" the class. Mostly simply got the syllabus and studied on their own. They used to get a one month leave for preparing for their exams. And that was enough to sail through the exams.
After 2 boring lectures, there was a half hour lunch break. And we all moved down to the canteen. Jai and Adi went from the back entrance - which was actually a thin wall. After 2 years of trying to cross it without losing their balance, they had now perfected their skill. Adi ran over the wall and Jai followed. Both landed in the Canteen.
The canteen was a huge shed covered with tin. There were cemented tables where one could simply stand and have their food. The food was ok. One can get anything ranging from pakodas to rice and dal here. Jai and Adi were deciding what to have, when someone said 'Look there's nikita'. And we all turned around. She was ordering something from the counter. 'Let me get pakodas and coke' Jai said. And rushed over to the counter.
What Jai saw, amazed him. Nikita was just like a common girl. Nothing great about her. Ofcourse she had a okie figure - nothing great or out of proportion. Hair to her shoulders. She was wearing normal stuff - nothing too revealing or fashionable. Her hair was tied in a ponie.
'One plate pakodas and a coke' - Jai ordered.
And she turned around and looked directly at Jai. Jai stopped breathing for a couple of seconds - while she looked at him. And then she took her order and left. Jai was mesmerized.
'20 Rs.' - Said the canteen boy and shook Jai.
'Wow!!' - said Jai and rushed back with pakodas and coke to Adi.
'Oh God!!, she is beautiful' said Jai, 'Lets move closer to her table'.
And they moved closer to her table to overhear what she was saying.
'So,' Adi said, 'What is so good about her?'
'Her eyes man' - Jai answered, 'I have never seen eyes like that. She has brown eyes and they are huge and deep. You could stare at them for a couple of seconds and you could get lost. It is not that i have not seen good eyes. I have seen blue eyes, black eyes and even foggy eyes, really big eyes. But, she has the eyes that could beat all of them - even the best one of them. I dont know where she got them. Otherwise she is a normal girl'.
'Oh!' - Adi shows his realization.
'But man you have to see them to believe them. I am sure i could never forget those eyes. I have never seen eyes like that, and I am sure i would never ever see eyes like those' - Jai repeats.
'Maybe she is wearing contacts' Adi says.
'No yaar, those eyes were real. Even colored contacts could not be that beautiful' - Jayant says.
'But man, do you want ahead and try to be friends with her? I think she has a boyfriend though.' - Adi asks.
'I think she sounds quite dumb.' Said Jai after overhearing to Nitika's conversation for some time. 'I would prefer intelligence over beauty'
'Well, then you have a long way to look - cause beauty and brains and compatibility is a very rare match' - Adi explains.
They finish their lunch and head back to the classes for more lectures. The eyes that Jai had seen stays embedded within his mind - and he may dream about them.
Tuesday, August 11, 2009
Document oriented data stores
A document oriented database or data store does not use tables for storing data. It stores each record as a document with certain characteristics. So a multiple documents in this type of store can have different characteristics - which means different number of fields per record and different fields per row. The benefit would be that if you are using a document oriented database for storing a large number of records in a huge database, any change in the number or type of row does not need an alter on the table. All you need to do is insert new documents with new structure and it is automatically inserted to the current datastore.
I went ahead and tried comparing some document-oriented data stores - TokyoTyrant, MongoDb and CouchDb and i compared them to Mysql-5.4 as well to get an idea about the performance advantage.
I created 3 scripts in all - 2 for insert and 1 for select. And created a 50,00,000 record table with a very normal record structure
field1 -> string, indexed
field2 -> string, indexed
date -> date, not indexed
n -> integer, indexed
The size of the original mysql table was 214 MB data & 205 MB index - total 419 MB for 50,00,000 records. The 3 scripts could be described as follows
Test Machine configuration:
CPU : Intel Xeon 1.6 GHz - Quad Core [4 MB L2 cache] 64 Bit
Memory : 8 GB RAM
OS : Centos 5.2 - Kernel 2.6.18 64 bit
Mysql
Used mysql version 5.4
Load during execution : 3.5
Time taken by Insert script 1 : 19682.673708916 sec for 2501000 records
Time taken by Insert script 2 : 19608.902073145 sec for 2499000 records
Time taken by Select script : 20465.380266905 sec for 8099782 records
Table engine : MyISAM
Total database size in mysql 5.4 : 215+233 = 248 MB
Ofcourse i used MyISAM which resulted in table locks, and i had indexes on three fields which resulted in increased locking times on these tables during inserts & selects.
MongoDB - www.mongodb.org
Used version for testing : 0.9.1
The current version available is 0.9.7 which has a lot of bug fixes and performance improvement features.MongoDB is written in C++ and stores data in BSON format which is Binary Serialization of JSON documents. It has a console (similar to mysql) which can be used to fire queries - so unlike bdb, you dont have to go and write programs to fire queries. MongoDB has full indexing support for different columns. You can also do query profiling (like explain in mysql) to check the execution path of the query and then make optimizations if possible. It provides replication support. It can be used to store large binary data like videos very efficiently.
The one good thing about the future of mongodb is that it would be providing auto sharding of data for cloud level scalability. This feature is in alpha stage now, but it should be mature in some time and could be used then.
Mongodb provides drivers/apis for a lot of languages including python, php, java, ruby, c++ and perl. I downloaded the php driver and compiled it and installed the extension mongo.so in the php extentions directory. And then i ran the same tests to check out the speed of mongodb.
Load during execution : 2.5
Time taken by Insert script 1 : 1006.6038348675 sec for 2501000 records
Time taken by Insert script 2 : 1435.0536739826 sec for 2499000 records
Time taken by Select script : 2942.2539789677 sec - 9999914 records
Total database size in mongodb : 4 GB (both data & index)
Wow, so Mongodb turns to be approximately 16 times faster than simple mysql - MyISAM tables
But it takes up a huge amount of space. Why is that? Well, Mongodb creates data files with predefined sizes so that it does not have to increase or decrease the file size as per requirements. The first file it creates is of 64MB, the next file is 128MB etc. upto 2GB. After 2GB the remaining files are of 2GB only. So, even if we exceed a byte above the current file of 2GB, another file of 2GB will be created - even if it is 98% empty.
Another important fact about mongodb is that mongodb's storage engine uses memory-mapped files for performance. This limits the data size on a 32 bit machine to around 2GB. I had hit this limitation earlier so i used a 64 bit machine for testing. But this architecture of data files in
mongodb storage engine allows the code to be much plain and simple and open to embrace the 64 bit world. Mongodb does not support traditional locking which is another reason it is fast.
More info / References :
32 bit limitation => blog.mongodb.org/post/137788967/32-bit-limitations
performance testing => www.mongodb.org/display/DOCS/Performance+Testing
regarding mongodb => www.mongodb.org/display/DOCS/Home
Quickstart => www.mongodb.org/display/DOCS/Quickstart
Production deployments => www.mongodb.org/display/DOCS/Production+Deployments
Locking in mongodb => www.mongodb.org/display/DOCS/Atomic+Operations
Tokyo Cabinet/Tyrant
Tokyo Tyrant is a set of 3 applications
- tokyo cabinet : the embedded data store
Tokyo Cabinet => tokyocabinet.sourceforge.net/spex-en.html
- tokyo tyrant : network api
Tokyo Tyrant => tokyocabinet.sourceforge.net/tyrantdoc/
- tokyo dystopia : fulltext search system
Tokyo Dystopia => tokyocabinet.sourceforge.net/dystopiadoc/
I explored tokyo cabinet-1.4.29 & tokyo tyrant-1.1.30. So after installing tokyo cabinet, I had to go ahead and install tokyo tyrant on top of tokyo cabinet for the networking support. Tokyo tyrant provides a set of binaries for talking to the tokyo cabinet embedded database. So you can use tokyo tyrant to insert and select data from a tokyo cabinet server.
I got an api for php to interact with the tokyo tyrant server. It is known as Php Tyrant and it can be obtained from mamasam.indefero.net/p/tyrant/. It is written completely in php and is not a C extension.
If you go through the documentation, you would see that Tokyo cabinet supports majorly 4 types of databases.
Hash database - a key value store which uses hash algorithm to retrieve records. The time complexity in this case is constant [ O(1) ].
B+ tree database - a B+Tree is slower than the hash database. Records of a B+Tree are sorted and arranged in logical pages. So the time complex
ity of record retrival is O(log n). But the size of a B+Tree database is half of a hash database.
Fixed length database - It is faster than a hash database. But it has a restriction that each key has to be a natural number and the length of
each value is limited. The whole region of the database is mapped to memory by the mmap call - which reduces overhead related to file I/O.
Table database - It is close to a document oriented database than a key value pair. You can also form indexes on columns in the table to improve search and sorting performance. Indices are implemented as different files of B+Tree database.
So, i created a simple table database with the structure i wanted and created indexes on the required columns. And started running my scripts. But i found that tokyo cabinet is fast in the beginning, but it suffers from file locks. So any operation happening on the table locks the comp
lete file. This caused a lot of problem with simultaneous inserts & selects. In fact i had cases where the server stopped responding totally. And it took 1 hr 17 minutes to push in just 661 inserts & fire 270 selects. So i stopped running the select script and focused on running multiple insert scripts. And then ran the select script separately.
This says that if you go ahead implementing tokyo tyrant on a huge table, you should be aware of the file locks and hence should implement the selects on a slave of the main database. A master can handle multiple inserts while a slave can handle multiple selects. Or you form a queue for firing queries sequentially instead of simultaneously.
I ran 2 insert scripts simultaneously and the results were even worse then mysql.
Load during execution : 2.9
Time taken by Insert script 1 : 65671.21945715 sec for 2501000 records
Time taken by Insert script 2 : 63807.51564312 sec for 2499000 records
Total database size : 1095 MB [715 MB data + 380 MB index]
And i ran 2 select scripts simultaneously and the results were comparable to that of mysql.
Load during execution : 2.1
Time taken by Select script 1 : 20269.342120886 sec for 9999914 records
Time taken by Select script 2 : 20115.848437071 sec for 9999914 records
What could be said about tokyo tyrant is that it should be used as a key-value store - maybe by using a hash database or a fixed length database for persistent session storage.
Couchdb
couchdb.apache.org/
The third database i looked into was couchdb. Which i had heard a lot about. The good thing about couchdb is that it provides a RESTful JSON API that can be accessed from any environment that allows HTTP requests. Couchdb is written in erlang and is quite fast. Again it does not have a c extention. There is a php api known as phpillow which wraps all functionalities of couchdb in its function calls.
You can download phpillow at arbitracker.org/phpillow.html
Also note that it works only with php 5.3. With php 5.2.x it still has a lot of bugs. Firstly there is no proper documentation, so it took me some time to figure out how to write insertion and select scripts and how to go about creating views (couchdb calls indexes as views). When i ra
n the scripts i found that it was crashing a lot. So, i did not go ahead with a proper testing of couchdb. Couchdb also supports master master replication (with developer supplied conflict resolution).
You could go ahead and read the comparison of couchdb & mongodb at mongodb's page
www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
www.mongodb.org/display/DOCS/MongoDB%2C+CouchDB%2C+MySQL+Compare+Grid
I went ahead and tried comparing some document-oriented data stores - TokyoTyrant, MongoDb and CouchDb and i compared them to Mysql-5.4 as well to get an idea about the performance advantage.
I created 3 scripts in all - 2 for insert and 1 for select. And created a 50,00,000 record table with a very normal record structure
field1 -> string, indexed
field2 -> string, indexed
date -> date, not indexed
n -> integer, indexed
The size of the original mysql table was 214 MB data & 205 MB index - total 419 MB for 50,00,000 records. The 3 scripts could be described as follows
- insert script 1 : went from 1 to 25,00,000 pulling 1000 records in a single query from the source database and inserting the records one by one to the target data store.
- insert script 2 : went from 50,00,000 to 24,99,999 pulling 1000 records in a single query from source database and inserting the records one by one to the target data store.
- select script : picks records from the source database and makes 2 queries for both field1 & field2 and fires both the queries on the target data store.
Test Machine configuration:
CPU : Intel Xeon 1.6 GHz - Quad Core [4 MB L2 cache] 64 Bit
Memory : 8 GB RAM
OS : Centos 5.2 - Kernel 2.6.18 64 bit
Mysql
Used mysql version 5.4
Load during execution : 3.5
Time taken by Insert script 1 : 19682.673708916 sec for 2501000 records
Time taken by Insert script 2 : 19608.902073145 sec for 2499000 records
Time taken by Select script : 20465.380266905 sec for 8099782 records
Table engine : MyISAM
Total database size in mysql 5.4 : 215+233 = 248 MB
Ofcourse i used MyISAM which resulted in table locks, and i had indexes on three fields which resulted in increased locking times on these tables during inserts & selects.
MongoDB - www.mongodb.org
Used version for testing : 0.9.1
The current version available is 0.9.7 which has a lot of bug fixes and performance improvement features.MongoDB is written in C++ and stores data in BSON format which is Binary Serialization of JSON documents. It has a console (similar to mysql) which can be used to fire queries - so unlike bdb, you dont have to go and write programs to fire queries. MongoDB has full indexing support for different columns. You can also do query profiling (like explain in mysql) to check the execution path of the query and then make optimizations if possible. It provides replication support. It can be used to store large binary data like videos very efficiently.
The one good thing about the future of mongodb is that it would be providing auto sharding of data for cloud level scalability. This feature is in alpha stage now, but it should be mature in some time and could be used then.
Mongodb provides drivers/apis for a lot of languages including python, php, java, ruby, c++ and perl. I downloaded the php driver and compiled it and installed the extension mongo.so in the php extentions directory. And then i ran the same tests to check out the speed of mongodb.
Load during execution : 2.5
Time taken by Insert script 1 : 1006.6038348675 sec for 2501000 records
Time taken by Insert script 2 : 1435.0536739826 sec for 2499000 records
Time taken by Select script : 2942.2539789677 sec - 9999914 records
Total database size in mongodb : 4 GB (both data & index)
Wow, so Mongodb turns to be approximately 16 times faster than simple mysql - MyISAM tables
But it takes up a huge amount of space. Why is that? Well, Mongodb creates data files with predefined sizes so that it does not have to increase or decrease the file size as per requirements. The first file it creates is of 64MB, the next file is 128MB etc. upto 2GB. After 2GB the remaining files are of 2GB only. So, even if we exceed a byte above the current file of 2GB, another file of 2GB will be created - even if it is 98% empty.
Another important fact about mongodb is that mongodb's storage engine uses memory-mapped files for performance. This limits the data size on a 32 bit machine to around 2GB. I had hit this limitation earlier so i used a 64 bit machine for testing. But this architecture of data files in
mongodb storage engine allows the code to be much plain and simple and open to embrace the 64 bit world. Mongodb does not support traditional locking which is another reason it is fast.
More info / References :
32 bit limitation => blog.mongodb.org/post/137788967/32-bit-limitations
performance testing => www.mongodb.org/display/DOCS/Performance+Testing
regarding mongodb => www.mongodb.org/display/DOCS/Home
Quickstart => www.mongodb.org/display/DOCS/Quickstart
Production deployments => www.mongodb.org/display/DOCS/Production+Deployments
Locking in mongodb => www.mongodb.org/display/DOCS/Atomic+Operations
Tokyo Cabinet/Tyrant
Tokyo Tyrant is a set of 3 applications
- tokyo cabinet : the embedded data store
Tokyo Cabinet => tokyocabinet.sourceforge.net/spex-en.html
- tokyo tyrant : network api
Tokyo Tyrant => tokyocabinet.sourceforge.net/tyrantdoc/
- tokyo dystopia : fulltext search system
Tokyo Dystopia => tokyocabinet.sourceforge.net/dystopiadoc/
I explored tokyo cabinet-1.4.29 & tokyo tyrant-1.1.30. So after installing tokyo cabinet, I had to go ahead and install tokyo tyrant on top of tokyo cabinet for the networking support. Tokyo tyrant provides a set of binaries for talking to the tokyo cabinet embedded database. So you can use tokyo tyrant to insert and select data from a tokyo cabinet server.
I got an api for php to interact with the tokyo tyrant server. It is known as Php Tyrant and it can be obtained from mamasam.indefero.net/p/tyrant/. It is written completely in php and is not a C extension.
If you go through the documentation, you would see that Tokyo cabinet supports majorly 4 types of databases.
Hash database - a key value store which uses hash algorithm to retrieve records. The time complexity in this case is constant [ O(1) ].
B+ tree database - a B+Tree is slower than the hash database. Records of a B+Tree are sorted and arranged in logical pages. So the time complex
ity of record retrival is O(log n). But the size of a B+Tree database is half of a hash database.
Fixed length database - It is faster than a hash database. But it has a restriction that each key has to be a natural number and the length of
each value is limited. The whole region of the database is mapped to memory by the mmap call - which reduces overhead related to file I/O.
Table database - It is close to a document oriented database than a key value pair. You can also form indexes on columns in the table to improve search and sorting performance. Indices are implemented as different files of B+Tree database.
So, i created a simple table database with the structure i wanted and created indexes on the required columns. And started running my scripts. But i found that tokyo cabinet is fast in the beginning, but it suffers from file locks. So any operation happening on the table locks the comp
lete file. This caused a lot of problem with simultaneous inserts & selects. In fact i had cases where the server stopped responding totally. And it took 1 hr 17 minutes to push in just 661 inserts & fire 270 selects. So i stopped running the select script and focused on running multiple insert scripts. And then ran the select script separately.
This says that if you go ahead implementing tokyo tyrant on a huge table, you should be aware of the file locks and hence should implement the selects on a slave of the main database. A master can handle multiple inserts while a slave can handle multiple selects. Or you form a queue for firing queries sequentially instead of simultaneously.
I ran 2 insert scripts simultaneously and the results were even worse then mysql.
Load during execution : 2.9
Time taken by Insert script 1 : 65671.21945715 sec for 2501000 records
Time taken by Insert script 2 : 63807.51564312 sec for 2499000 records
Total database size : 1095 MB [715 MB data + 380 MB index]
And i ran 2 select scripts simultaneously and the results were comparable to that of mysql.
Load during execution : 2.1
Time taken by Select script 1 : 20269.342120886 sec for 9999914 records
Time taken by Select script 2 : 20115.848437071 sec for 9999914 records
What could be said about tokyo tyrant is that it should be used as a key-value store - maybe by using a hash database or a fixed length database for persistent session storage.
Couchdb
couchdb.apache.org/
The third database i looked into was couchdb. Which i had heard a lot about. The good thing about couchdb is that it provides a RESTful JSON API that can be accessed from any environment that allows HTTP requests. Couchdb is written in erlang and is quite fast. Again it does not have a c extention. There is a php api known as phpillow which wraps all functionalities of couchdb in its function calls.
You can download phpillow at arbitracker.org/phpillow.html
Also note that it works only with php 5.3. With php 5.2.x it still has a lot of bugs. Firstly there is no proper documentation, so it took me some time to figure out how to write insertion and select scripts and how to go about creating views (couchdb calls indexes as views). When i ra
n the scripts i found that it was crashing a lot. So, i did not go ahead with a proper testing of couchdb. Couchdb also supports master master replication (with developer supplied conflict resolution).
You could go ahead and read the comparison of couchdb & mongodb at mongodb's page
www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
www.mongodb.org/display/DOCS/MongoDB%2C+CouchDB%2C+MySQL+Compare+Grid
Saturday, August 08, 2009
Why should we watch movies at home
- Because movies@home are free - thanks to piracy
- You do not have to adjust your schedule according to the movie timings. The movie timings adjusts to your schedule
- You do not have to drive all the way to the movie hall
- You do not have to search for parking and even pay for it
- You do not have to stand in queue for tickets
- In case you had not booked the tickets in advance, you do not have to get disappointed because the tickets are not available
- You do not have to stand in queue for entry and get frisked by the guard
- You do not have to wait outside the movie hall - in heat and croud and without a place to sit
- You do not have to walk slowly with the crowd and take your seats - seats which were previously occupied by someone else - who had left popcorn and god knows what else on it
- You do not have to pay for popcorn - which might not be good. Home made popcorns are really good
- At home you do not have to watch the complete movie if it is crap.
- You can leave the movie incomplete - and still feel no guilt about it - cause it was free
- You can fast-forward the crappy scenes and time-pass songs. You don't have to bear them if you do not want to.
- And you can pause the movie if you need to attend a nature's call. You do not have to wait for an interval to attend nature's call or risk missing an important scene.
- And eventually you can avoid swine flu - by avoiding contact with so many people at the movie hall
- And also save yourself from any terrorist attack - which cannot happen at your home
I am still unsure why people go to halls to watch movies. Today's halls have screens which resemble a huge LCD TV. In the past the halls had the charm of huge screens and balcony seats. It made more sense to watch movies at movie halls at those times.
Wednesday, August 05, 2009
12 Angry Men
I am referring to the movie 12 Angry Men [imdb rating 8.9].
I keep on searching and downloading movies which suit my fancy - mostly horror, comedy, action, thrillers. Lots of times when i had been to the top 250 list on imdb, i had seen this movie at the top. Also when i had searched for top movies, this was on the list.
But when i read about the plot and the synopsis of the movie - it told me that there are 12 men in a room discussing talking to each other about some murder trial - it seemed quite boring. And on top of it - the complete movie is in black & white (not even eastman color).
But some days ago a friend of mine highly recommended this movie to me. So, i thought what the heck, we should watch old B/W movies as well - they are good - even if it is about 12 men sitting in a room and talking to each other - no action whatever...
So, with a "lets get it over with" attitude, i sat down to watch the movie. And it turned out to be very good. 12 men inside a room can make wonders happen. The concept is very simple, but the way it is portrayed is just wow!!!.
A must watch...
I keep on searching and downloading movies which suit my fancy - mostly horror, comedy, action, thrillers. Lots of times when i had been to the top 250 list on imdb, i had seen this movie at the top. Also when i had searched for top movies, this was on the list.
But when i read about the plot and the synopsis of the movie - it told me that there are 12 men in a room discussing talking to each other about some murder trial - it seemed quite boring. And on top of it - the complete movie is in black & white (not even eastman color).
But some days ago a friend of mine highly recommended this movie to me. So, i thought what the heck, we should watch old B/W movies as well - they are good - even if it is about 12 men sitting in a room and talking to each other - no action whatever...
So, with a "lets get it over with" attitude, i sat down to watch the movie. And it turned out to be very good. 12 men inside a room can make wonders happen. The concept is very simple, but the way it is portrayed is just wow!!!.
A must watch...
Tuesday, July 28, 2009
Brain vs GF
BRAIN Vs GF No prices for guessing who won at last. Nice one..........*
Akash was waiting for his love ....
"30 minutes late!!", his brain shouted at him, "Last time you were 5 mins late and she had literally gobbled u up ... remember??"
"Yeah yeah", he said to his brain, "You know her ... all moody and stuff .... oh there she is"
"Scold her OK?", his brain adviced.
"OK I will try"
Sweet Sheetal comes with the cutest smile and says "Im sorry honey .. I was shopping for shoes .. totally forgot about you"
"What if you had said that line buddy?", shouted his brain ... "she would have had a nervous breakdown"
Akash ignored his brain .. "Its OK honey .. its only half an hour . no problem"
She smiled once again .. held his hand and asked "Hope you remember what occasion is today"
"OMG!!!", thought Akash .....
"Brain ... search database for reminders, anniversaries, silly anniversaries, birthdays and birthdays of people I dont care about"
Brain got into action ... he started delegating work to different parts ..
parallel processing .. multiple search .......... complete memory scan.
Sheetal stared at Akash .... "Hello!! u have been staring at me for 2 minutes now ... u OK?"
"Huh!!!", he said, "Oh ... nothing's wrong .. was lost in thought"
"No records found", said the brain ...
"Da**mn!!", thought Akash
"So what say ... how do we celebrate this day?", she asked.
Akash is all confused ... "Ask her ...dumbo?".. said the brain
"OK OK ...stop pushing me"
"Honey .. U know my lousy memory .. I guess I cant recall what today is"
"WHAAAAAAAAAAAAAAATT!!!!!!!!!!!!!!!!", she shouted ... and started crying.
"How could you forget!! ..... its my doggy's birthday"
"!!!!!!!!!!!!!!!!!"
A moment of silence.
His entire brain staff was laughing at him.
Akash was dumbfounded.
"What the hell am I supposed to do know?", he asked his brain.
"Damage control sequence initialized ... dont worry our specialist will comeback with the perfect line to make everything all right"
"Better do it fast ..brainy"
The brain was working at 90% capacity .... gathering and analyzing all data on 'How to handle women?'
Finally an answer was computed and communicated to Akash.
He looked up to her, and said "Of Course I remember your doggy's bday .. how can I forget that sweet mutt's special day"
She looked up with utter surprise ...
"HUH!!!!!!! ........ Doggy is the name of my cat you je**rk"
She stood up angrily and left.
Akash and his brain were left there clueless .
...........
Ever!!
Akash was waiting for his love ....
"30 minutes late!!", his brain shouted at him, "Last time you were 5 mins late and she had literally gobbled u up ... remember??"
"Yeah yeah", he said to his brain, "You know her ... all moody and stuff .... oh there she is"
"Scold her OK?", his brain adviced.
"OK I will try"
Sweet Sheetal comes with the cutest smile and says "Im sorry honey .. I was shopping for shoes .. totally forgot about you"
"What if you had said that line buddy?", shouted his brain ... "she would have had a nervous breakdown"
Akash ignored his brain .. "Its OK honey .. its only half an hour . no problem"
She smiled once again .. held his hand and asked "Hope you remember what occasion is today"
"OMG!!!", thought Akash .....
"Brain ... search database for reminders, anniversaries, silly anniversaries, birthdays and birthdays of people I dont care about"
Brain got into action ... he started delegating work to different parts ..
parallel processing .. multiple search .......... complete memory scan.
Sheetal stared at Akash .... "Hello!! u have been staring at me for 2 minutes now ... u OK?"
"Huh!!!", he said, "Oh ... nothing's wrong .. was lost in thought"
"No records found", said the brain ...
"Da**mn!!", thought Akash
"So what say ... how do we celebrate this day?", she asked.
Akash is all confused ... "Ask her ...dumbo?".. said the brain
"OK OK ...stop pushing me"
"Honey .. U know my lousy memory .. I guess I cant recall what today is"
"WHAAAAAAAAAAAAAAATT!!!!!!!!!!!!!!!!", she shouted ... and started crying.
"How could you forget!! ..... its my doggy's birthday"
"!!!!!!!!!!!!!!!!!"
A moment of silence.
His entire brain staff was laughing at him.
Akash was dumbfounded.
"What the hell am I supposed to do know?", he asked his brain.
"Damage control sequence initialized ... dont worry our specialist will comeback with the perfect line to make everything all right"
"Better do it fast ..brainy"
The brain was working at 90% capacity .... gathering and analyzing all data on 'How to handle women?'
Finally an answer was computed and communicated to Akash.
He looked up to her, and said "Of Course I remember your doggy's bday .. how can I forget that sweet mutt's special day"
She looked up with utter surprise ...
"HUH!!!!!!! ........ Doggy is the name of my cat you je**rk"
She stood up angrily and left.
Akash and his brain were left there clueless .
...........
Ever!!
Subscribe to:
Posts (Atom)