I am using mysql workbench for mysql databases.
I created 2 tables to store files.i.e., "database" and "reports".
I used the same collation and engine for both tables.
But when I look in the table maintenance, "data free" for table-database is always 1Mib
but for table-reports is always 0.
Consequently when I try to download the file from the database, it downloads just 10bytes. I am quite sure my code to download file from the database is correct, since I am using the same code for the table-database, and its working perfectly, and not with table-reports (I know that for table-reports I need to change some parameters and field names).
Kindly solve the issue.
Related
I have to compare two database tables and fields ( Not their DATA) .
Say Live DB and Development DB. In live DB there are 200 tables and
in development DB there are 218 Tables With some new column fields
added in old tables too.
During the final stage i have to update the Live DB by adding the new
columns and tables taken from the development DB .
I should not loss and data in the old DB.
I tried many ways to do this but it is taking a very long time for me
to finish. One of the tool i used is mysql workbench.
Is there any queries to perform this using information schema ?
To make the things easier i imported the development DB from development server and uploaded in the live db with different name . Now both the DB are in the same server but with diff name. E.g. sitedb , sitedevdb
Whenever you update the development DB with changes that will eventually be put live, you should write a .sql script that will repeat the changes on the live database.
Otherwise this operation is super-tricky.. you may have to manually figure out the added columns yourself, or you may be able to use each INFORMATION_SCHEMA.COLUMNS to compare the old tables.
For the new tables SHOW CREATE **table_name** is really useful.
A MySQL comparison tool is probably what you need for this. The company I work for, Redgate, offers MySQL Compare (for the database schema) and MySQL Data Compare (for data).
These tools are free for non-commercial use.
I am facing a problem for a task I have to do at work.
I have a MySQL database which holds the information of several clients of my company and I have to create a backup/restore procedure to backup and restore such information for any single client. To clarify, if my client A is losing his data, I have to be able to recover such data being sure I am not modifying the data of client B, C, ...
I am not a DB administrator, so I don't know if I can do this using standard mysql tools (such as mysqldump) or any other backup tools (such as Percona Xtrabackup).
To backup, my research (and my intuition) led my to this possibile solution:
create the restore insert statement using the insert-select syntax (http://dev.mysql.com/doc/refman/5.1/en/insert-select.html);
save this inserts into a sql file, either in proper order or allowing this script to temporarily disable the foreign key checks to meet foreign keys' constraint;
of course, I do this for all my clients on a daily base, using a file for each client (and day).
Then, in the case I have to restore the data for a specific client:
I delete all his data left;
I restore the correct data using his sql file I created during the backup.
This way I believe I may recover the right data of client A without touching the data of client B. Is my solution eventually working? Is there any better way to achieve the same result? Or do you need more information about my problem?
Please, forgive me if this question is not well-formed, but I am new here and this is my first question so I may be unprecise...thanks anyway for the help.
Note: we will also backup the entire database with mysqldump.
You can use the --where parameter, you could provide a condition like *client_id=N* . Of course I am making an assumption since you don't provide any information on your schema.
If you have a Star schema , then you could probably write a small script that backups all lookup tables (considering they are adequately small) by using this parameter --tables and use the --where condition for your client data table. For additional performance, perhaps you could partition the table by the client_id.
I have reached the limit of RAM in analyzing large datasets in R. I think my next step is to import these data into a MySQL database and use the RMySQL package. Largely because I don't know database lingo, I haven't been able to figure out how to get beyond installing MySQL with hours of Googling and RSeeking (I am running MySQL and MySQL Workbench on Mac OSX 10.6, but can also run Ubuntu 10.04).
Is there a good reference on how to get started with this usage? At this point I don't want to do any sort of relational databasing. I just want to import .csv files into a local MySQL database and do the subsetting in with RMySQL.
I appreciate any pointers (including "You're way off base!" as I'm new to R and newer to large datasets... this one's around 80 mb)
The documentation for RMySQL is pretty good - but it does assume that you know the basics of SQL. These are:
creating a database
creating a table
getting data into the table
getting data out of the table
Step 1 is easy: in the MySQL console, simply "create database DBNAME". Or from the command line, use mysqladmin, or there are often MySQL admin GUIs.
Step 2 is a little more difficult, since you have to specify the table fields and their type. This will depend on the contents of your CSV (or other delimited) file. A simple example would look something like:
use DBNAME;
create table mydata(
id INT(11) NOT NULL AUTO_INCREMENT PRIMARY KEY,
height FLOAT(3,2)
);
Which says create a table with 2 fields: id, which will be the primary key (so has to be unique) and will autoincrement as new records are added; and height, which here is specified as a float (a numeric type), with 3 digits total and 2 after the decimal point (e.g. 100.27). It's important that you understand data types.
Step 3 - there are various ways to import data to a table. One of the easiest is to use the mysqlimport utility. In the example above, assuming that your data are in a file with the same name as the table (mydata), the first column a tab character and the second the height variable (with no header row), this would work:
mysqlimport -u DBUSERNAME -pDBPASSWORD DBNAME mydata
Step 4 - requires that you know how to run MySQL queries. Again, a simple example:
select * from mydata where height > 50;
Means "fetch all rows (id + height) from the table mydata where height is more than 50".
Once you have mastered those basics, you can move to more complex examples such as creating 2 or more tables and running queries that join data from each.
Then - you can turn to the RMySQL manual. In RMySQL, you set up the database connection, then use SQL query syntax to return rows from the table as a data frame. So it really is important that you get the SQL part - the RMySQL part is easy.
There are heaps of MySQL and SQL tutorials on the web, including the "official" tutorial at the MySQL website. Just Google search "mysql tutorial".
Personally, I don't consider 80 Mb to be a large dataset at all; I'm surprised that this is causing a RAM issue and I'm sure that native R functions can handle it quite easily. But it's good to learn new skill such as SQL, even if you don't need them for this problem.
I have a pretty good suggestion. For 80MB use SQLite. SQLite is a super public domain, lightweight, super fast file-based database that works (almost) just like a SQL database.
http://www.sqlite.org/index.html
You don't have to worry about running any kind of server or permissions, your database handle is just a file.
Also, it stores all data as a string, so you don't even have to worry about storing the data as types (since all you need to do is emulate a single text table anyway).
Someone else mentioned sqldf:
http://code.google.com/p/sqldf/
which does interact with SQLite:
http://code.google.com/p/sqldf/#9._How_do_I_examine_the_layout_that_SQLite_uses_for_a_table?_whi
So your SQL create statement would be like this
create table tablename (
id INT(11) INTEGER PRIMARY KEY,
first_column_name TEXT,
second_column_name TEXT,
third_column_name TEXT
);
Otherwise, neilfws' explanation is a pretty good one.
P.S. I'm also a little surprised that your script is choking on 80mb. It's not possible in R to just seek through the file in chunks without opening it all up in memory?
The sqldf package might give you an easier way to do what you need: http://code.google.com/p/sqldf/. Especially if you are the only person using the database.
Edit: Here is why I think it would be useful in this case (from the website):
With sqldf the user is freed from having to do the following, all of which are automatically done:
database setup
writing the create table statement which defines each table
importing and exporting to and from the database
coercing of the returned columns to the appropriate class in common cases
See also here: Quickly reading very large tables as dataframes in R
I agree with what's been said so far. Though I guess getting started with MySQL (databases) in general is not a bad idea for the long if you are going to deal with data. I mean I checked your profile which says finance PhD student. I don't know if that means quant. finance, but it is likely that you will come across really large datasets in your career. I you can afford some time, I would recommend to learn something about databases. It just helps.
The documentation of MySQL itself is pretty solid and you can a lot of additional (specific) help here at SO.
I run MySQL with MySQL workbench on Mac OS X Snow Leopard too. So here´s what helped me to get it done comparatively easy.
I installed MAMP , which gives my an local Apache webserver with PHP, MySQL and the MySQL tool PHPmyadmin, which can be used as a nice webbased alternative for MySQL workbench (which is not always super stable on a Mac :) . You will have a little widget to start and stop servers and can access some basic configuration settings (such as ports through your browser) . It´s really one-click install here.
Install the Rpackage RMySQL . I will put my connection string here, maybe that helps:
Create your databases with MySQL workbench. INT and VARCHAR (for categorical variables that contain characters) should be the field types you basically need at the beginning.
Try to find the import routine that works best for you. I don't know if you are a shell / terminal guy – if so you'll like what was suggested by neilfws. You could also use LOAD DATA INFILE which is I prefer since it's only one query as opposed to INSERT INTO (line by line)
If you specify the problems that you have more accurately, you'll get some more specific help – so feel free to ask ;)
I assume you have to work a lot with time series data – there is a project (TSMySQL) around that use R and relational databases (such as MySQL, but also available for other DBMS) to store time series data. Besides you can even connect R to FAME (which is popular among financers, but expensive). The last paragraph is certainly nothing basic, but I thought it might help you to consider if it´s worth the hustle to dive into it a little deeper.
Practical Computing for Biologists as a nice (though subject-specific) introduction to SQLite
Chapter 15. Data Organization and Databases
Just some pointers here.
I am making fairly extensive modifications to a site, including the MySQL database.
My plan is to do everything on my development server, export the new MySQL structure for the db and import it onto the clients server.
Basically I need to know that performing a structure only import will not overwrite/delete existing data. I am not making changes to the data type or field length.
In my experience, when you export a database (through phpMyAdmin for instance), part of the SQL script that is created includes a "DROP TABLE IF EXISTS 'table_name';" before doing a "CREATE TABLE 'table_name'...;" to build the new table.
My guess is that this is not what you want to do! Certainly use the dev system to alter the structure in order to make everything correct, but then look around for a database synchronisation routine where you can provide the old structure, the new structure, and the software will create the appropriate "ALTER TABLE 'table_name'...;" scripts to make the required changes.
You should then really examine these change files before executing them on the live database, and of course BACKUP the live database, and ensure you are able to fully recover from the backup before starting any of the alterations!
I've had to do this a lot, and it always goes like this:
Make a backup of the live database, complete with data.
Make a backup of the live database schema only.
Calculate the differences between the old (live) schema and the new (devel) schema.
Create all of the 'ALTER TABLE ...' DDL statements necessary to upgrade from the old schema to the new one. Keep in mind that if you rename a field, you probably won't be able to just rename it -- you'll need to create the new field, copy the data from the old field, and then drop the old field.
If you changed relationships between tables, you'll probably need to drop indexes and foreign key relationships first, and then add them back afterwards.
You'll need to populate any new fields based upon their default values, if any.
Once you've got all the pieces working, you'll need to combine them into one large script, and then run it on a copy of the live database.
Dump the schema and compare it against the desired new schema -- if they don't match, go back to step 3 and repeat.
Dump the data and compare it against the expected changes -- again, if they don't match, go back to step 3 and repeat.
You're going to learn a lot more about SQL DDL/DML during this process than you ever thought you'd learn. (For one project, where we were switching from natural keys to UUID keys for 50+ tables, I ended up writing programs to generate all of the DDL/DML.)
Good luck, and make frequent backups.
I'd recommend to prepare a sql script for every change you do on development server, so you will be able to reproduce it on development. You shouldn't get to the point where you need to calculate differences between database structures
This is how I do it, all changes are reflected in sql scripts, and I can reconstruct the history of my database running all these files if needed.
Test the final release version on a "staging" mysql server. Make a copy of your production server on another machine and test your script to make sure everything's ok.
Of course, preliminary database backup is a must.
I need to convert data that already exists in a MySQL database, to a SQL Server database.
The caveat here is that the old database was poorly designed, but the new one is in a proper 3N form. Does any one have any tips on how to go about doing this? I have SSMS 2005.
Can I use this to connect to the MySQL DB and create a DTS? Or do I need to use SSIS?
Do I need to script out the MySQL DB and alter every statement to "insert" into the SQL Server DB?
Has anyone gone through this before? Please HELP!!!
See this link. The idea is to add your MySQL database as a linked server in SQL Server via the MySQL ODBC driver. Then you can perform any operations you like on the MySQL database via SSMS, including copying data into SQL Server.
Congrats on moving up in the RDBMS world!
SSIS is designed to do this kind of thing. The first step is to map out manually where each piece of data will go in the new structure. So your old table had four fields, in your new structure fileds1 and 2 go to table a and field three and four go to table b, but you also need to have the autogenerated id from table a. Make notes as to where data types have changed and you may need to make adjustments or where you have required fileds where the data was not required before etc.
What I usually do is create staging tables. Put the data in the denormalized form in one staging table and then move to normalized staging tables and do the clean up there and add the new ids as soon as you have them to the staging tables. One thing you will need to do if you are moving from a denormalized database to a normalized one is that you will need to eliminate the duplicates from the parent tables before inserting them into the actual production tables. You may also need to do dataclean up as there may be required fileds in the new structure that were not required in the old or data converstion issues becasue of moving to better datatypes (for instance if you stored dates in the old database in varchar fields but properly move to datetime in the new db, you may have some records which don't have valid dates.
ANother issue you need to think about is how you will convert from the old record ids to the new ones.
This is not a an easy task, but it is doable if you take your time and work methodically. Now is not the time to try shortcuts.
What you need is an ETL (extract, transform, load) tool.
http://en.wikipedia.org/wiki/Extract,_transform,_load#Tools
I don't really know how far an 'ETL' tool will get you depending on the original and new database designs. In my career I've had to do more than a few data migrations and we usually always had to design a special utility which would update a fresh database with records from the old database, and yes we coded it complete with all the update/insert statements that would transform data.
I don't know how many tables your database has, but if they are not too many then you could consider going the grunt root. That's one technique that's guaranteed to work after all.
If you go to your database in SSMS and right-click, under tasks should be an option for "Import Data". You can try to use that. It's basically just a wizard that creates an SSIS package for you, which it can then either run for you automatically or which you can save and then alter as needed.
The big issue is how you need to transform the data. This goes into a lot of specifics which you don't include (and which are probably too numerous for you to include here anyway).
I'm certain that SSIS can handle whatever transformations you need to do to change it from the old format to the new. An alternative though would be to just import the tables into MS SQL as-is into staging tables, then use SQL code to transform the data into the 3NF tables. It's all a matter of what your most comfortable with. If you go the second route, then the import process that I mentioned above in SSMS could be used. It will even create the destination tables for you. Just be sure that you give them unique names, maybe prefixing them with "STG_" or something.
Davud mentioned linked servers. That's definitely another way that you can go (and got my upvote). Personally, I prefer to copy the tables over into MS SQL first since linked servers can sometimes have weirdness, especially when it comes to data types not mapping between different providers. Having the tables all in MS SQL will also probably be a bit faster and saves time if you have to rerun or correct portions of the data. As I said though, the linked server method would probably be fine too.
I have done this going the other direction and SSIS works fine, although I might have needed to use a script task to deal with slight data type weirdness. SSIS does ETL.