The World of Structured Storage System

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The World of Structured Storage System

  1. 1. The World of Structured Storage System one size does not fit all Schubert Zhang, Nov.2009
  2. 2. Summary <ul><li>Structured Storage, differentiate from file stores and blob stores. </li></ul><ul><li>Both relational and non-relational structured storage systems are important. </li></ul><ul><li>No single solution is appropriate for all applications. </li></ul><ul><li>Application-Intent Taxonomy * </li></ul><ul><ul><li>Features-first stores </li></ul></ul><ul><ul><li>Scale-first stores </li></ul></ul><ul><ul><li>Simple structure stores </li></ul></ul><ul><ul><li>Batch-analytic stores </li></ul></ul><ul><ul><li>Purpose-optimized stores </li></ul></ul>
  3. 3. Features-first Stores (main stream: RDBMS) <ul><li>Non-sharded RDBMS </li></ul><ul><li>For feature-rich applications </li></ul><ul><li>Usually OLTP </li></ul><ul><li>Low latency (real time) </li></ul><ul><li>Some in-database calculations, indexing (primary, secondary), strong relation model (1,2,3). </li></ul><ul><li>Examples of common workloads </li></ul><ul><ul><li>enterprise financial systems </li></ul></ul><ul><ul><li>human resources systems </li></ul></ul><ul><ul><li>customer relationship management systems </li></ul></ul><ul><ul><li>etc. </li></ul></ul><ul><li>Examples of products </li></ul><ul><ul><li>Oracle, SQL Server, DB2, MySQL, PostgreSQL, … </li></ul></ul><ul><ul><li>Cloud Solution: Amazon Relational Database Service (Amazon RDS) </li></ul></ul>
  4. 4. Scale-first Stores (main stream: key-value store) <ul><li>Scale is more important than features. </li></ul><ul><li>Must scale without bound and being able to do this without restriction. </li></ul><ul><li>Impossible to run on a single RDBMS. </li></ul><ul><li>Usually OLTP </li></ul><ul><li>Low latency (real time) </li></ul><ul><li>Less in-database calculations, less indexing, less relations. </li></ul><ul><li>Tow solutions </li></ul><ul><ul><li>Shard the application data over a large number of RDBMS instances. </li></ul></ul><ul><ul><ul><li>The data is sharded over 10s or even 100s of independent database instances. </li></ul></ul></ul><ul><ul><ul><li>Not expect cross-database joins, aggregations, global secondary indexes, global stored procedures, and all the other relational database features that are incredibly hard to scale. </li></ul></ul></ul><ul><ul><ul><li>Example: Windows Live Messenger. </li></ul></ul></ul><ul><ul><ul><li>DB2 Parallel Edition, Oracle RAC: support full relational model, but still not good. </li></ul></ul></ul><ul><ul><li>Use a highly scalable key-value store. </li></ul></ul><ul><ul><ul><li>Some key-value store product examples include: Project Voldemort , Ringo , Scalaris , Kai , Dynomite , MemcacheDB , ThruDB , CouchDB , Cassandra , HBase and Hypertable </li></ul></ul></ul><ul><ul><ul><li>Cloud Solution: Amazon SimpleDB. </li></ul></ul></ul><ul><ul><ul><li>Simple primary indexing (distributed B+Tree, hash, partition, etc.). </li></ul></ul></ul><ul><li>Examples of applications </li></ul><ul><ul><li>Very high scale web sites such as Facebook, MySpace, Gmail, Yahoo, and Amazon.com </li></ul></ul><ul><ul><li>Some of these sites actually do make use of relational databases but many do not. </li></ul></ul>
  5. 5. Simple Structured Stores (main stream: key-value store) <ul><li>Many applications don’t need the features, cost, or complexity of an RDBMS, nor the high scalability. </li></ul><ul><li>Just need a simple key-value store. </li></ul><ul><li>Simple query </li></ul><ul><li>Simple Index access </li></ul><ul><li>Simple, cheap, fast, and low operational burden </li></ul><ul><li>Examples </li></ul><ul><ul><li>Low-end: BerkeleyDB </li></ul></ul><ul><ul><li>High-end: Cassandra, Project-Voldemort, Dynamo, etc. </li></ul></ul><ul><ul><li>Cloud: Amazon SimpleDB </li></ul></ul>
  6. 6. Batch-analytic stores/Data Warehouses (main stream: with MapReduce) <ul><li>Traditional Data Warehouse </li></ul><ul><ul><li>Based on RDBMS </li></ul></ul><ul><ul><li>Multidimensional data model, Data Cubes. (Stars, Snowflakes Schemas) </li></ul></ul><ul><ul><li>OLAP </li></ul></ul><ul><ul><li>Big, but not large enough for modern data scale. Hard to scale </li></ul></ul><ul><li>Fashional Data Warehouse </li></ul><ul><ul><li>Sharded/Partitioned data storage (by RDBMS, MPP Database, or proprietary SQL stores) </li></ul></ul><ul><ul><li>Enhanced by MapReduce for queries </li></ul></ul><ul><ul><li>Large, but not unbounded. Not easy to scale, not real distributed. </li></ul></ul><ul><ul><li>Examples: </li></ul></ul><ul><ul><ul><li>Greenplum (SQL+MapReduce+PostgreSQL) </li></ul></ul></ul><ul><ul><ul><li>Aster Data (SQL+MapReduce+MPP RDBMS) </li></ul></ul></ul><ul><ul><ul><li>HadoopDB (SQL+Hive+Hadoop MapReduce+RDBMS) </li></ul></ul></ul>
  7. 7. Purpose-Optimized Stores <ul><li>Columnar DB/DW </li></ul><ul><ul><li>Vertica (analytic, columnar, aggressive compression, shared nothing, hybrid data store, fast write, fast read) </li></ul></ul><ul><li>DW Appliances (Special hardware and solutions) </li></ul><ul><ul><li>Teradata </li></ul></ul><ul><ul><li>Netezza </li></ul></ul><ul><li>Special DB </li></ul><ul><ul><li>StreamBase, etc. </li></ul></ul><ul><ul><li>… </li></ul></ul>
  8. 8. Future DBMS/DW? <ul><li>????? Mixed / Hybrid ???? </li></ul><ul><li>Stores raw data + Calculated informational data. </li></ul><ul><li>Raw data are collected, historic data. Be stored in distributed data storage system </li></ul><ul><ul><li>Shared nothing </li></ul></ul><ul><ul><li>Large scale. </li></ul></ul><ul><ul><li>Commodity Hardware </li></ul></ul><ul><li>Informational data be stored in RDBMS (small size) or distributed key-value stores. </li></ul><ul><li>MapReduce </li></ul><ul><li>Examples: </li></ul><ul><ul><li>Hive (more need to be completed) </li></ul></ul><ul><ul><li>To be created. </li></ul></ul>
  9. 9. Amazon AWS Cloud Structured Storage Solutions <ul><li>For feature-first applications </li></ul><ul><ul><li>RDS (Cloud based Relational Database service, MySQL) </li></ul></ul><ul><ul><li>Amazon EC2- RDMBS AMI (+ EBS) </li></ul></ul><ul><li>For scale-first applications </li></ul><ul><ul><li>SimpleDB (Cloud based simple key-value store, no relation model) </li></ul></ul><ul><ul><li>Amazon EC2- KeyValue AMI (+EBS) </li></ul></ul><ul><li>The guess for Amazon’s future structured storage solution. </li></ul><ul><ul><li>Enhanced SimpleDB (enhanced scalability, etc. May take some ideas from BigTable, Dynamo, etc.) </li></ul></ul><ul><ul><li>New high scalable key-value stores, such as HBase (when it become stronger). </li></ul></ul>
  10. 10. There is no one-size-fits-all solution! <ul><li>There are too many contradictory requirements in the structured data world. </li></ul><ul><li>The contradiction of data processing </li></ul><ul><ul><li>Real-time or near-real-tome data availability, support “up-to-now” data measurement. </li></ul></ul><ul><ul><li>Batch processing for large size of data, such as aggregation. </li></ul></ul><ul><li>The contradiction of data access: </li></ul><ul><ul><li>Low-latency fast query response, like Lookup. </li></ul></ul><ul><ul><li>High-latency ad-hoc analytic query for historical data. </li></ul></ul><ul><li>But, there is no one-size-fits-all answer for above contradictory requirements. </li></ul><ul><li>“ Important not to try to be all things to all people!” – Jeff Dean, Keynote at LADIS’09 </li></ul>
  11. 11. References <ul><li>One Size Fits All: An Idea Whose Time Has Come and Gone: http:// www.cs.brown.edu/~ugur/fits_all.pdf </li></ul><ul><li>James Hamilton's Blog: http://perspectives.mvdirona.com/2009/11/03/OneSizeDoesNotFitAll.aspx </li></ul>

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