majorsilence_reporting

Installation

No gem is published to RubyGems yet. Load from source using $LOAD_PATH or require_relative:

# Add the lib directory to the load path
$LOAD_PATH.unshift '/path/to/reporting-ruby/lib'

# Load all three classes at once
require 'majorsilence_reporting'

# Or load individual classes
require 'majorsilence_reporting/report'
require 'majorsilence_reporting/report_aot'
require 'majorsilence_reporting/report_native'

No runtime gem dependencies. The native FFI mode uses Ruby's built-in Fiddle standard library.

Integration modes

Report — subprocess (dotnet)

require 'majorsilence_reporting/report'

rpt = Report.new('/path/to/report.rdl', '/path/to/RdlCmd.dll')
rpt.set_connection_string('Data Source=/path/to/data.db')
rpt.set_parameter('StartDate', '2026-01-01')

# Save to disk
rpt.export('pdf', '/tmp/output.pdf')

# Or get bytes in memory (e.g. for a Rails controller)
data = rpt.export_to_memory('pdf')
send_data data, type: 'application/pdf', disposition: 'inline'

ReportAot — AOT binary

Same API as Report but targets a self-contained binary. No .NET runtime required. Unlocks additional export formats (xlsx_table, tifb, mht).

require 'majorsilence_reporting/report_aot'

rpt = ReportAot.new('/path/to/report.rdl', '/path/to/RdlCmd')
rpt.set_connection_string('Data Source=/path/to/data.db')

data = rpt.export_to_memory('xlsx_table')

# In a Rack/Sinatra response:
[200, { 'Content-Type' => 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' }, [data]]

ReportNative — Fiddle FFI

Load librdlnative.so directly into the Ruby process via the built-in Fiddle library. No child process is spawned.

require 'majorsilence_reporting/report_native'

# Load once at startup. Pre-loads sibling .so/.dylib files and sets RDLNATIVE_LIB_DIR.
lib = RdlLibrary.load('/path/to/librdlnative.so')

rpt = ReportNative.new(lib, '/path/to/report.rdl')
rpt.set_connection_string('Data Source=/path/to/data.db')

data = rpt.export_to_memory('pdf')   # returns binary String

Core API

All three classes share the same interface:

In-memory datasets

ReportNative supports supplying data entirely in memory via add_data() — no database connection required. Pass a dataset name and an array of hashes where every value is a string.

rpt.add_data('SalesData', [
  { 'Product' => 'PDF Library Pro',  'Revenue' => '1200.00', 'Units' => '3' },
  { 'Product' => 'Report Designer',   'Revenue' => '250.00',  'Units' => '1' },
  { 'Product' => 'Support (12 mo.)',  'Revenue' => '500.00',  'Units' => '1' },
])

data = rpt.export_to_memory('pdf')

Export formats