If you save bounch of html files with table in directory with .html extension, they will
we all read as single data set, allowing easy analysys of search results, for example.
+CSV files with .csv extension are parsed using , as delimiter. Encoding is utf8
+and can be specified in filename, before extension like this: data.encoding.csv
-Start with:
+CouchDB data can be imported using files which contain full url to CouchDB database
+or url to CouchDB view to import. URL's filename should end in *.couchdb
- LANG=hr_HR.utf8 ./script/mojo_facets daemon --reload
+lsblk .pairs format is basically shell variables in form NAME="value"
-Data replication:
+Start with:
+
+ LANG=hr_HR.utf8 ./script/mojo_facets daemon --reload
+
- # master
- ./script/mojo_facets daemon --listen 'http://*:4444' --reload
+Changing tabular data:
- # slave
- MASTER=http://localhost:4444 ./script/mojo_facets daemon --reload
+Just double click on any table cell and click outside or focus out to save change.
-Turning actions into changes:
+Data action and changes:
There are two kinds of audit log in MojoFacets:
-1. actions stored in /tmp/actions are clicks on user interface with parameters
+1. actions stored in /tmp/actions are clicks on user interface with parameters,
+ they will probably be erased on next reboot since they are in /tmp
-2. changes are more structured, including old value and unique values from
- edit of one value in dataset
+2. changes in data/database.changes are more structured, including old value
+ and unique identifiers for that row
+
+Changes can be applied on any dataset currently in memory.
+
+Whole idea of changes is to create audit log which is detailed enough to recostruct
+current state of dataset from source file and list of changes. However, to speed
+up operations, you can periodically save your in-memory data to /tmp/ in perl
+storeable format using save link in interface.
+
+
+Data replication:
-If you want to create changes which can be applied again on original dataset or
-any other dataset which has same unique values you can use helper script:
+Actions can be replicated to other hosts using MASTER enviroment variable
+or config menu
- $ ./script/actions-to-changes /data/mojo_facets/*
+ # slave
+ MASTER=http://localhost:4444 ./script/mojo_facets daemon --reload
-Modify your data using perl snippets
+Code console to modify data using perl snippets:
Experimental REPL console supports perl snippets which get $row hash which is one
-element from your dataset. All values are repetable, so you always have to create
-array of values if you are creating new columns, even for single value.
+element from your dataset.
+
+If you want to create or update values, you will have to use $update hash to set
+new values.
+
+If you want to report something from your dataset (also called reduce in map/reduce
+terminology) you can use $out hash to store values which will be used to generate
+new dataset using $key and $value for column names.
+
+All values are repetable, but if you create just a scalar, magic(tm) inside MojoFacets
+will try to upgrade it to [ $scalar ] so you don't have to do it explicitly.
+
+Code examples are stored in public/code
+
+They use column1,column2.description.pl notatition so only snippets which have applicable
+column will be shown.
+
+
+Facet code eval:
+
+Code snippet will be executed for each facet $value and will report $count and $checked state.
+You can also update $checked to programatically select part of facet values.
+
+
+Export data:
-* generate new columns with number of elements in some other column
+All exported data is stored in public/export/database/
- $row->{count} = [ $#{ $row->{original} } + 1 ];
+There you can find saved filters and items generated with export checkbox
-* generate century column from year
+ filter.column_name.optional_description
+ items.column1.column2.column3
- foreach my $year ( @{ $row->{Year} } ) {
- push @{ $row->{century} }, int($year/100)+1;
- }
projects / MojoFacets.git / blobdiff