![]() This means that after changing the MySQL setting, your database queries like "SELECT * FROM collections" will still work 100% although the actual table name would appear as "Collections" in your database. Still, this will NOT affect the case sensitive behavior of MySQL as long as your filesystem is running in case insensitive mode. This also means that MySQL will store the table names according to the filesystem settings and you will end up with lower case table names.įortunately, there is a setting option in MySQL with which you can override the default case insensitive behavior and force MySQL to store the table names in case sensitive mode. ![]() That can be overridden with a registry setting but most Windows machines are running in case insensitive mode. The reason for this is that Windows uses case insensitive filesystem by default. Background on the Problem (for people interested in the topic) More information on its usage can be found from the add-on documentation. Here's a free add-on that provides you a migration from case insensitive database to a case sensitive database in concrete5: There's no reason to panic if you have used case insensitive mode in your database and are about to move your concrete5 site to a case sensitive environment. If you have moved your site and it's not working because of the case sensitivity issue, you probably get a blank page with one ADODB exception visible on top of the page stating either: If you have already installed concrete5 after doing the settings change, this setting will not affect the old table names that you have generated although your installation will still work (if you want to know why, read the background). This setting will bypass the default behavior and store your table names in case sensitive mode. For example, your MySQL installation can be found from C:\Program Files\MySQL\ but not guaranteed that it will be in that location. ![]() In Windows, you can find your MySQL configuration in a file called my.ini from your MySQL installation dir. You can fix this BEFORE installing concrete5 by adding this line to your MySQL configuration file: lower_case_table_names=0 This also applies to some variants of Unix-based systems that use case insensitive file system. However, if you are running on top of Windows with default registry settings and installed MySQL database server with default settings, you do not see names as such in your database. For example, you might find table names as AttributeKeys, Collections, CollectionsVersions, etc. This is another example of how Chartio is helping to put the power of data in everybody’s hands, regardless of SQL knowledge.Concrete5 uses case sensitive naming conventions for its database table names. Instead all it takes is a basic understanding of the principles involved. While it may take a few more clicks and steps than in SQL Mode, the resulting line chart done in Interactive Mode requires no knowledge of SQL Syntax. Then after hiding the original ‘Provider’ column and using a REORDER COLUMNS step and a PIVOT DATA step we’ll get the same table arrangement we got in SQL Mode and can present the same table we did in SQL Mode. This will effectively build everything we need in an underlying query to create the CASE STATEMENT we did above, in Chartio’s Data Pipeline.Īdding a CASE STATEMENT pipeline step allows us to set the conditions for the WHEN and the ELSE just like we did before, without having to type in the entire SQL syntax. Then drag ‘Created Date’ and ‘Provider’ to the dimensions box and re-label them ‘Date’ and ‘Email Provider.’ After that, using the ‘Created Date’ column you can set the date span (or build your WHERE clause) to be everything after. First, let’s build the query.ĭrag the ‘Clicks Column’ to the measures box and aggregate it by TOTAL SUM of the Column Clicks, then re-label it ‘CLICKS.’ ![]() After building our underlying query to pull in all the columns we’re going to need SUM OF CLICKS, DATE and EMAIL ADDRESS we can use the Data Pipeline to manipulate this data post-SQL. In using Chartio, we can do all of the above without writing any SQL but leveraging the Data Explorer and the Data Pipeline features. ![]() Then after adding a PIVOT DATA step into the Data Pipeline, we’ll get a table properly arranged in the proper format to set up a line chart showing how clicks are compared over time. When you piece all three of those columns for one SELECT STATEMENT and throw in the rest of the necessary pieces to build a SQL query, it all take shape below. The resulting table of this CASE STATEMENT with corresponding emails alone. "Provider" = 'Gmail' THEN 'Gmail'Īnd, the else statement would be ‘Other’ for every other email address provider. ![]()
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