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CDR-Stats : VoIP Analytics Solution for Asterisk and FreeSWITCH with MongoDB

CDR-Stats is a free and open source call detail record analysis and reporting software for Freeswitch, Asterisk and other types of VoIP Switch. It allows you to interrogate CDR to provide reports and statistics via a simple to use powerful web interface. It is based on the Django Python Framework, Celery, SocketIO, Gevent and MongoDB.

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Call Data Analysis
for Asterisk & FreeSWITCH
      with MongoDB

     Arezqui Belaid @areskib
     <info@star2billing.com>
Problems to solve

             - Millions of Call records
             - Multiple sources
             - Multiple data formats
             - Replication
             - Fast Analytics
             - Multi-Tenant
             - Realtime
             - Fraud detection
Why MongoDB
- NoSQL - Schema-Less
- Capacity / Sharding
- Upserts
- Replication : Increase read capacity
- Async writes : Millions of entries / acceptable losses
- Compared to CouchDB - native drivers
What does it look like?   Dashboard
Hourly / Daily / Monthly reporting
Compare call traffic

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CDR-Stats : VoIP Analytics Solution for Asterisk and FreeSWITCH with MongoDB

  • 1. Call Data Analysis for Asterisk & FreeSWITCH with MongoDB Arezqui Belaid @areskib <info@star2billing.com>
  • 2. Problems to solve - Millions of Call records - Multiple sources - Multiple data formats - Replication - Fast Analytics - Multi-Tenant - Realtime - Fraud detection
  • 3. Why MongoDB - NoSQL - Schema-Less - Capacity / Sharding - Upserts - Replication : Increase read capacity - Async writes : Millions of entries / acceptable losses - Compared to CouchDB - native drivers
  • 4. What does it look like? Dashboard
  • 5. Hourly / Daily / Monthly reporting
  • 9. Under the hood - FreeSWITCH (freeswitch.org) - Asterisk (asterisk.org) - Django (djangoproject.com) - Celery (celeryproject.org) - RabbitMQ (rabbitmq.com) - Socket.IO (socket.io) - MongoDB (mongo.org) - PyMongo (api.mongo.org) - and more...
  • 10. Our Data - Call Detail Record (CDR) 1) Call info : 2) BSON : CDR = { 'hangup_cause_q850':'20', ... 'hangup_cause':'NORMAL_CLEARING', 'callflow':{ 'sip_received_ip':'192.168.1.21', 'caller_profile':{ 'sip_from_host':'127.0.0.1', 'tts_voice':'kal',7', 'username':'1000', 'accountcode':'1000', 'destination_number':'5578193435', 'sip_user_agent':'Blink 0.2.8 (Linux)', 'ani':'71737224', 'answerusec':'0', 'caller_id_name':'71737224', 'caller_id':'71737224', ... 'call_uuid':'adee0934-a51b-11e1-a18c- }, 00231470a30c', ... 'answer_stamp':'2012-05-23 15:45:09.856463', }, 'outbound_caller_id_name':'FreeSWITCH', 'variables':{ 'billsec':'66', 'mduration':'12960', 'progress_uepoch':'0', 'effective_caller_id_name':'Extension 1000', 'answermsec':'0', 'sip_via_rport':'60536', 'outbound_caller_id_number':'0000000000', 'uduration':'12959984', 'duration':'3', 'sip_local_sdp_str':'v=0no=FreeSWITCH 'end_stamp':'2012-05-23 15:45:12.856527', 1327491731n' 'answer_uepoch':'1327521953952257', }, 'billmsec':'12960', ... ... 3) Insert Mongo : db.cdr.insert(CDR);
  • 12. Pre-Aggregate - Daily Collection Produce data easier to manipulate : current_y_m_d = datetime.strptime(str(start_uepoch)[:10], "%Y-%m-%d") CDR_DAILY.update({ 'date_y_m_d': current_y_m_d, 'destination_number': destination_number, 'hangup_cause_id': hangup_cause_id, 'accountcode': accountcode, 'switch_id': switch.id, },{ '$inc': {'calls': 1, 'duration': int(cdr['variables']['duration']) } }, upsert=True) Output db.CDR_DAILY.find() : { "_id" : ..., "date_y_m_d" : ISODate("2012-04-30T00:00:00Z"), "accountcode" : "1000", "calls" : 1, "destination_number" : "0045277522", "duration" : 23, "hangup_cause_id" :9, "switch_id" :1 } ... - Faster to query pre-aggregate data - Upsert is your friend / update if exists - insert if not
  • 13. Map-Reduce - Emit Step - MapReduce is a batch processing of data - Applying to previous pre-aggregate collection (Faster / Less data) map = mark_safe(u''' function(){ emit( { a_Year: this.date_y_m_d.getFullYear(), b_Month: this.date_y_m_d.getMonth() + 1, c_Day: this.date_y_m_d.getDate(), f_Switch: this.switch_id }, {calldate__count: 1, duration__sum: this.duration} ) }''')
  • 14. Map-Reduce - Reduce Step Reduce Step is trivial, it simply sums up and counts : reduce = mark_safe(u''' function(key,vals) { var ret = { calldate__count : 0, duration__sum: 0, duration__avg: 0 }; for (var i=0; i < vals.length; i++){ ret.calldate__count += parseInt(vals[i].calldate__count); ret.duration__sum += parseInt(vals[i].duration__sum); } return ret; } ''')
  • 15. Map-Reduce Query : out = 'aggregate_cdr_daily' calls_in_day = daily_data.map_reduce(map, reduce, out, query=query_var) Output db.aggregate_cdr_daily.find() : { "_id" : { "a_Year" : 2012, "b_Month" : 5, "c_Day" : 13, "f_Switch" :1 }, "value" : { "calldate__count" : 91, "duration__sum" : 5559, "duration__avg" : 0 } } { "_id" : { "a_Year" : 2012, "b_Month" : 5, "c_Day" : 14, "f_Switch" :1 }, "value" : { "calldate__count" : 284, "duration__sum" : 13318, "duration__avg" : 0 } } ...
  • 16. Roadmap - Quality monitoring - Audio recording - Add support for other telecoms switches - Improve - refactor (Beta) - Testing - Listen and Learn
  • 17. WAT else...? - Website : http://www.cdr-stats.org - Code : github.com/star2billing/cdr-stats - FOSS / Licensed MPLv2 - Get started : Install script Try it, it's easy!!!
  • 18. Questions ? Twitter : @areskib Email : areski@gmail.com