Image
EAN-13	0786936826623
UPC-A	786936826623
Product Name	Arachnophobia
Category	Electronics / Photography: A/V Media: Movie / TV
Amazon.com	Buy on Amazon ~ B0088EDONK
SKU	A786936826623
Model	25546808
Price New	9.15 US Dollars    (curriencies)
Price Used	7.98 US Dollars    (curriencies)
Rating	PG-13 - Parents Strongly Cautioned
IMDb
Trailer	Watch The Trailer
Run Time	103 minutes
Aspect Ratio	1.85:1
Cast	Jeff Daniels, Harley Jane Kozak, John Goodman, Julian Sands
Director	Frank Marshall
Genre	COMEDY,FANTASY,HORROR
Run Time	105 minutes
Width	5.3 inches    (convert)
Height	0.35 inches    (convert)
Length	6.75 inches    (convert)
Weight	15 hundredths pounds    (convert)
Binding	Blu-ray
Release Year	1990
Format	Blu-ray, Enhanced, NTSC, Restored, Subtitled, Widescreen
Published	02/24/2013
Run Time	105 minutes
Features	Shrink-wrapped
Long Description	A large spider from the jungles of South America is accidently transported in a crate with a dead body to America where it mates with a local spider. Soon after, the residents of a small California town disappear as the result of spider bites from the deadly spider offspring. It's up to a couple of doctors with the help of an insect exterminator to annihilate these eight legged freaks before they take over the entire town.
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Created	03-05-2013 4:49:06am
Modified	06-05-2019 5:14:07pm
MD5	055bf0c78f5dada8cd234cbf008d1142
SHA256	c34523bba194ad8b501fc958afac04ef58872ba0dde7e7afd4f939ba4133525c
Search Google	by EAN or by Title
Query Time	0.0180461

Article of interest

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.