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Big Table Indexing, session 9 CS6200: Information Retrieval Slides - PowerPoint PPT Presentation

May 24, 2023 •308 likes •376 views

Big Table Indexing, session 9 CS6200: Information Retrieval Slides by: Jesse Anderton Distributed Storage BigTable was developed by Google to manage their storage needs. It is a distributed storage system designed to scale across hundreds of

  1. Big Table Indexing, session 9 CS6200: Information Retrieval Slides by: Jesse Anderton

  2. Distributed Storage BigTable was developed by Google to manage their storage needs. It is a distributed storage system designed to scale across hundreds of thousands of machines, and to gracefully continue service as machines fail and are replaced. Storage systems such as BigTable are natural fits for processes distributed with MapReduce. “A Bigtable is a sparse, distributed, persistent multidimensional sorted map.” –Chang et al, 2006.

  3. BigTable Rows The data in BigTable is logically organized into rows. For instance, the inverted list for a term can be stored in a single row. A single cell is identified by its row key, column, and timestamp. Efficient methods exist for fetching or updating particular groups of cells. Only populated cells consume filesystem space: the storage is inherently sparse.

  4. BigTable Tablets BigTable rows reside within logical tables, which have pre-defined columns and group records of a particular type. The rows are subdivided into ~200MB tablets, which are the fundamental underlying filesystem blocks. Tablets and transaction logs are replicated to several machines in case of failure. If a machine fails, another server can immediately read the tablet data and transaction log with virtually no downtime.

  5. BigTable Operations All operations on a BigTable are row-based operations. Most SQL operations are impossible here: no joins or other structured queries. BigTable rows can have massive numbers of columns, and individual cells can contain large amounts of data. For instance, it’s no problem to store a translation of a document into many languages, each in its own column of the same row.

  6. Wrapping Up Storage systems such as BigTable are natural fits for distributed algorithm execution. Google invented BigTable to handle its index, document cache, and most of its other massive storage needs. This has produced a whole generation of distributed storage systems, called NoSQL systems. Some examples include MongoDB, Couchbase, etc. Next, we’ll consider how to run queries efficiently on an index.

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