EAN-130000000000079   EAN-13 barcode 0000000000079
UPC-A000000000079   UPC-A barcode 000000000079
UPC-E00000709   UPC-E barcode 00000709
Product NameHolster Work Trouser & Knee Pads 34in
CategoryHome
Amazon.comA Buy on Amazon ~ B00014328W
SKU8300-SR1-20041
Width13.78 inches    (convert)
Height0.71 inches    (convert)
Length21.65 inches    (convert)
Weight41.6 ounces    (convert)
BindingKitchen
Long DescriptionThe Roughneck Black & Grey Holster Work Trouser have been designed to meet the needs of everyday use. They are made from 280gsm polyester cotton twill, with Cordura reinforcement on the knee pad pockets, back of the trouser hem and holster pockets. Cordura is a hardwearing fabric designed to withstand everyday use. The trousers have a with reinforced crutch and YKK zip and zip pull for easy operation. The Holster Trousers have many pockets including: Two holster pockets.Two front pockets. Two hook and loop rear pockets. A multi left leg side pocket with hook and loop pocket. Separate pocket for mobile phone storage secured by hook and loopRight leg pocket with hook and loop flap. Supplied with Roughneck Knee pads are made form 100% Polyethylene with an ergonomic shape for comfort and flexibility. They are the perfect fit for the Roughneck Holster trousers and can be used with Roughneck work trousers.These Holster Trousers have the following specifications: Waist: 34 inches.Leg: 31 inches. Holster Work Trouser & Knee Pads 34in
Created01-10-2013 9:03:48am
Modified11-20-2017 1:48:26am
MD5be2612f55f1054a6a7ffad6de4882417
SHA25632733fd24209a9370d1d4ff312b80b31ed9bb01969cce5bb4af661a45c2679d3
Search Googleby EAN or by Title
Query Time0.0007970

Here we will demonstrate the most basic example of importing the CSV data files that we produce on this site into your MySQL database.

For information about various databases you can use and how to import CSV files into them, please view the overview article "Importing CSV data into your database".

For this example, we are going to import the product data CSV file out of the sample_ean_data.zip but this same process will work on the full data download file. We will also be executing the commands in the MySQL Workbench but you can also use the command line tool with the same commands if you like.

First, start by creating a blank table. Use the table layout described in the read_me file for the most up-to-date table layout. It is suggested that you not use any indexing at this point. You can add indexes later. It is most likely that you will have your own tables where you want to store your data so importing the CSV files can be done into temporary tables and then later copied over to your tables. Leaving off the indexes and constraints on these import tables reduces the risk of import errors. Here is an example:

create table ean_product
(
    EAN13             varchar(13),
    UPCA              varchar(12),
    UPCE              varchar(8),
    SKU               varchar(200),
    PriceNew          numeric(15,2),
    PriceUsed         numeric(15,2),
    PriceDate         date,
    company           varchar(13),
    product           varchar(100),
    description       varchar(100),
    category          int,
    url               varchar(500),
    created           datetime,
    modified          datetime
);

Next we perform the import using the LOAD DATA INFILE command. The path to the file depends on where you saved the data and which operating system you are on. For Windows users you might find your file on the C: drive and Linux users may find your date in your home (~) folder. This example shows a Linux import. Only the path would be different between the operating systems.

LOAD DATA LOCAL
    INFILE '~/sample_ean_data/sample_ean_product.csv' 
    INTO TABLE ean_product
    FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY '\\'
    LINES TERMINATED BY '\r\n'
    IGNORE 1 LINES;

Finally, lets look at the data that we just imported.

SELECT * FROM EAN_PRODUCT;

You may have seen some warnings after the import command. If you are concerned about these warnings, examine the data. It could be that some data has grown beyond the size specified in the read_me file. If you are worried, make the fields larger and try the process again after deleting all of the data out of the table.

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