Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Mongo for aadhaar

3,252 views

Published on

  • Be the first to comment

Mongo for aadhaar

  1. 1. Search data store for the worlds largest biometric identity system Regunath Balasubramanian Shashikant Soni regunathb@gmail.com soni.shashikant@gmail.com twitter @regunathbCONFIDENTIAL: For limited circulation only Slide 1
  2. 2. India● 1.2 billion residents ● 640,000 villages, ~60% lives under $2/day ● ~75% literacy, <3% pays Income Tax, <20% banking ● ~800 million mobile, ~200-300 mn migrant workers● Govt. spends about $25-40B on direct subsidies ● Residents have no standard identity document ● Most programs plagued with ghost and multiple identities causing leakage of 30-40% Slide 2
  3. 3. Aadhaar● Create a common ‘national identity’ for every ‘resident’ ●Biometric backed identity to eliminate duplicates ●‘Verifiable online identity’ for portability● Applications ecosystem using open APIs ●Aadhaar enabled bank account and payment platform ●Aadhaar enabled electronic, paperless KYC (Know Your Customer) Slide 3
  4. 4. Search Requirements● Multi-attribute query like: name contains ‘regunath’ AND city = ‘bangalore’ AND address contains ‘J P Nagar’ AND YearOfBirth = ……● Search 1.2B resident data with photo, history ●35Kb - Average record size● Response times in milliseconds● Open scale out Slide 4
  5. 5. Why MongoDB● Auto-sharding● Replication● Failover … Essentially an AP (slaveOk) data store in CAP parlance● Evolving schema● Map-Reduce for analysis● Full text search ●Compound (or) multi-keys Slide 5
  6. 6. Design { _id:123456789, name: ‘abcde’, year:1980, ….. } MongoDB 2 Search API Client App Name=‘abcde’ Solr 1 Address=‘some place’ Indexes Name: ‘abcde’ Year= 1980 Address: ‘some place’ year: 1980● Read/Search ●Sharded Solr indexes for search ●Keyed document read from MongoDB● Write ●Eventual consistency (across data sources) driven by application ●Composite MongodDB-Solr app persistence handler Slide 6
  7. 7. Implementation and Deployment ● Start - 4M records in 2 shards Current - 250M records in 8 shards ( 8 x ~2 TB x 3 replicas) ● Performance , Reliability & Durability ●SlaveOk ●getLastError, Write Concern: availability vs durability  j = journaling  w = nodes-to-write ● Replica-sets / Shards – how? RS 1 RS 1 RS 1 Rs 2 RS 2 RS 2Primary Config 1 Config 2 Config 3SecondaryArbiter Router Router Router Slide 7
  8. 8. Monitoring and Troubleshooting● Monitoring tools evaluated ●MMS ●munin● Manual approach - daily ritual ●RS, DB, config, router - health and stats● Problem analysis stats ●mongostat, iostat, currentOps, logs ●Client connections● Stats for storage, shards addition ●Data file size ●Shard data distribution ●Replication Slide 8
  9. 9. Key Learnings on MongoDB● Indexing 32 fields ●Compound indexes ●Multi-keys indexes  {…"indexes" : [{ "email":"john.doe@email.com", "phone":"123456789“ }] }  db.coll.find ({ "indexes.email" : "john.doe@email.com" }) ●Indexes use b-tree ●Many fields to index ●Performs well upto 1-2M documents ●Best if index fits in memory● Data replication, RS failover ●Rollback when RS goes out of sync  Manual restore (physical data copy)  Restarting a very stale node Slide 9
  10. 10. Questions? Regunath Balasubramanian Shashikant Soni regunathb@gmail.com soni.shashikant@gmail.com twitter @regunathbCONFIDENTIAL: For limited circulation only Slide 10

×