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Schema Design
The document discusses the importance of schema design in big data, particularly with non-relational databases like HBase, which operates on HDFS due to its fault tolerance. It highlights the design principles, data models, and distribution strategies for scalability, along with insights on key design considerations to avoid performance issues. It emphasizes the suitability of NoSQL technologies for systems managing hundreds of millions to billions of rows without requiring advanced query languages.
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Schema Design
- 1. Big Data SchemaDesign Deepak
- 2. Overview • Schema design is vital for performance. • Keywords : Non-relational, NOSQL, Distributed • Underlying File system : GFS, HDFS • Examples : Hadoop, GFS, Hbase, Big Tables etc • Example implementations : Facebook, Wallmart etc.
- 3. When to use •Typically with systems having >=100’s of millions/billions rows • Records of the order of 100’s or 1000’s of TB’s • No advanced Query Language needed • Typed columns or other RDBMS features not needed
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- 7. Overview • HBase runson top of HDFS • HDFS was chosen because of its fault tolerance, check summing, failover properties • Java Native client or REST API • Manager manages cluster, Region Servers manages data
- 8. HBase Data Model •Table: design-time namespace, has many rows. • Row: atomic key/value container, with one row key • Column Family: divide columns into physical files • Column: a key in the k/v container inside a row • Timestamp: long milliseconds, sorted descending • Value: a time-versioned value in the k/v container
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- 11. Thoughts on thelogical view • Unit of scalability is Region. • The rows are not tied to a server. They maybe moved around for load balancing. • Add nodes so that we do not have too many regions per node • Too many regions per node will work against distribution
- 12. Column Family • EachColumn Family represents a Physical storage unit ( A Directory) • Data that are queried together should be stored together. • Features such as compression can be enabled per Column Family
- 13. Bloom Filter • Generatedautomatically when an HFile is flushed to disk • Available in primary memory • Contains Row keys • CK can be stored as part of RK, but that might overload the memory. • Can filter based on what is stored.
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- 16. Tall vs FatTables • Fat tables with large amounts of data in each column. • Tall tables with large amounts of rows. • Tall is good for search or scans • Fat is good for fetches or gets • Rows don’t split • Atomicity is only at row level, having compound keys, atomicity is not guaranteed
- 17. Key Design • Sequentialkeys : Example timestamp as key • With Sequential keys you keep hot spotting on a region. • Salting to distribute the records • Field promotion • Random keys
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- 19. Summary • Think twicebefore you decide on NOSQL technologies • Avoid hotspots • Store values at appropriate places • Choose the right keys • Store inferences into RDBMS if necessary
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