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Large Table Performance Problem With MYSQL Performance


I am doing some benchmarking of an application using MYSQL under Windows and I confused about the results.

Environment
Emachine 6417 with amd3200+ 64 bit processor 3.3GB ram

mysql 4.1 with odbc 3.51 MYISAM
windows 2003 server std edition
date, account and invoice number are indexed
Database size 18 million rows

I am querying (selecting) columns of a date and an account

our tester program that opens a socket to the Mysql database and does a select for the above n times
each time the date and the account is randomized to minimize hits on records closeby.
This program will perform over 1000 queries per second.
At the end, the Mysql socket will be closed
Even when the socket is closed each time, I still get 400 queries per second.

When I enter a similar query manually a web interface, I get about 3-9 second response time. This program opens/closes a socket for each query when using EMS I get similar 9 second results.

Does anyone have any suggestions Also in production, this table will be accesse for both read and write will I have problems. My testing showed that Innodb is much slower.




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mysql 4.1 with odbc 3.51 MYISAM
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country

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(2) Germany
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city

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And I have one of these tables below.


message1

(id) (city) message
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(3) (4) I was born in France.
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message2

(id) (city) (country) message
(1) (2) (1) I love New York.
(2) (5) (7) Kumkang mountains are beautiful.
(3) (4) (8) I was born in France.
(4) (6) (4) Great Wall is great.
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code1

select city, country
from message1
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code2

select city, country
from message2
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I have setup a detail page which displays the various fields out of the products table and then looks for all reviews in the reviews table which match the products key and display the average. All good so far.
I want to have a page which displays a whole list of the products and show their rating next to them but I'm wondering if making it search the reviews table and calculate the average for 50 products in one go is going to cause a bit of a strain on the server?
Is there another way I can do this, perhaps storing the precalculated average in the products table but how would I go about doing that?

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How To Check Performance?
I have a web application which uses MySQL and I wanted to check out the performance of the database.

I have been trying to use JMeter to test the performance of it but I was wondering if there is anything MySQL specific which can give me statistics on performance etc.

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SELECT WHERE Performance On LIKE Vs =
I have a table:

CREATE TABLE file
(id VARCHAR(10) PRIMARY KEY,
image MEDIUMBLOB);

The table is filled with 4073 rows with images of 50-100k each.

When I do a SELECT query using a WHERE-clause I get a huge difference
in time before the result is delivered:

SELECT image FROM file WHERE id=1234567801;
1 row in set (3.73 sec)

SELECT image FROM file WHERE id LIKE 1234567801;
1 row in set (0.02 sec)

Why is this difference in times?

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(View) Performance
I have a huge table with product data from different shops in my database. There is a flag column 'import' witch flagt products with 1 for my PHP script.

To select from one shop all product individually, work with it, and then update these product import flag to 0 in the database, seems to be the slowest way. I thougt about a view about all products of a shop with flag '1'. Code:

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Performance (speed)
I have implemented a simple web crawler which uses a mySQL database to save a list of URLs to crawl. Obviously I dont wish to crawl the same URL twice so before adding a URL to the database I search the database first to ensure it hasn't already been added. When I add URLs to the database it is usually done it batches of 150 URLs, this is the process I use to search for an existing URL:

I use a PreparedStatement called 'findUrlStatement' which consists of: SELECT Url FROM urlTable WHERE Url = ?

Then I use this code to determine if a URL is already in he database:

findUrlStatement.setString(1, url);
ResultSet urlFound = findUrlStatement.executeQuery();
if (!urlFound.next()) {
// The URL is NEW, so add it to the database
}

As you can see the above routine is repeated for each of the 150 URLs. This typically takes anything from 4-8 seconds to complete.

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