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FaceBase Data Management

FaceBase Data Management. Data Management Goals. Schema-less data format Fast search Fast update Scalability Transactions* Atomicity Consistency Isolation Durability. Having it all…. No one system has everything Brewer’s theorem (CAP) Consistency Availability Partition Tolerance

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FaceBase Data Management

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  1. FaceBase Data Management

  2. Data Management Goals • Schema-less data format • Fast search • Fast update • Scalability • Transactions* • Atomicity • Consistency • Isolation • Durability

  3. Having it all… • No one system has everything • Brewer’s theorem (CAP) • Consistency • Availability • Partition Tolerance • What can we compromise on? • What can we sacrifice?

  4. MongoDB • Document store • BSON • Search: Fast, but indexes have some restrictions • Prefixing – no true multi-dimensional indexes • Update: Fast, but indexes have to explicitly declared • Scalabilty • Designed to scale out and shard • Strong consistency and eventual consistency

  5. Solr • Document store • Each document has several key-values, no hierarchy • Web API • Search • Fast keyword and range searches • Mulit-attribute search? • Update • No updates searchable until “commit” • “commit” is time-consuming – rebuild the index • Scalability • Similar to “commit”, replication requires creating a complete image

  6. Cassandra • Document store • Indexed on primary key – key-value model • Fast single-key equality search • More complex queries not as efficient • Fast updates • Scalability • Designed for distributed environment • Emphasis of high availability, partition tolerance • Brewer’s Conjecture: consistency is compromised

  7. MySQL • Relational model • Build a document model over relational model • Fast search • Fast update • Performance concerns with large data set • Scalability • Replication, sharding • True transactions • Easy to generate sequence numbers • Transactions can incorporate Drupal and FB data w/o coupling

  8. Head to Head

  9. Possible Routes • Hybrid MongoDB and MySQL • Benefits: Less resource demand • Drawback: Recovery and durability • Keep metadata in MongoDB, use MySQL as a catalogue • In-house EAV with MySQL • Benefits: Better interoperability • Drawback: Higher resource demand • Implement EAV store over MySQL.

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