DistOS 2015W Session 11

BigTable

• Google System used for storing data of various Google Products, for instance Google Analytics, Google Finance, Orkut, Personalized Search, Writely, Google Earth and many more
• Big table is
  • Sparse
  • Persistant
  • Muti dimensional Sorted Map
• It is indexed by
  • Row Key: Every read or write of data under single row key is atomic. Each row range is called Tablet. Select Row key to get good locality for data access.
  • Column Key: Grouped into sets called Column Families. Forms basic unit of Access Control.All data stored is of same type.Syntax used: family:qualifier
  • Time Stamp:Each cell consists of multiple versions of same data which are indexed by Timestamps.In order to avoid collisions, Timestamps need to be generated by applications.
• Big Table API: Provides functions for
  • Creating and Deleting
    • Tables
    • Column Families
  • Changing Cluster
  • Changing Table
  • Column Family metadata like Access Control Rights.
  • Set of wrappers which allow Big Data to be used both as
    • Input source
    • Output Target
• The timestamp mechanism in BIG table helps clients to access recent versions of data with simple accessing aspects of using row and column.
• Parallel computation and cluster management system makes BIG table flexible and highly scalable.

Dynamo

• Amazon's Key Value Store
• Availability is the buzz word for Dynamo. Dynamo=Availability
• Shifted Computer Science paradigm from caring about the consistency to availability.
• Sacrifices consistency under certain failure scenarios.
• Treats failure handling as normal case without impact on availability and performance.
• Data is partitioned and replicated using consistent hashing and consistency is facilitated by use of object versioning.
• This system has certain requirements such as:
  • Query Model: Simple read and write operations to data item that are uniquely identified by a key.
  • ACID properties: Atomicity, Consistency, Isolation, Durability.
  • Efficiency: System needs to function on a commodity hardware infrastructure.
• Service Level Agreements(SLA): They are a negotiated contract between a client and a service regarding characteristics related to systems. They are used in order to guarantee that in a bounded time period, an application can deliver it's functionality.
• System Architecture: It consists of System Interface, Partitioning Algorithm, Replication,Data Versioning.
• Successfully handles
  • Server Failure
  • Data Centre Failure
  • Network Partitions
• Allows service owners to customize their own storage systems according to their storage systems to meet the desired performance, durability and consistency SLAs.
• Building block for highly available applications.

Cassandra

• Facebook's storage system to fulfil needs of the Inbox Search Problem
• Partitions data across the cluster using consistent hashing.
• Distributed multi dimensional map indexed by a key
• In it's data model:
  • Columns grouped together into sets called column families. Column Families further of 2 types:
    • Simple column families
    • Super column families
• API consists of :
  • Insert
  • Get
  • Delete
• System Architecture consists of :
  • Partitioning: Takes place using consistent hashing
  • Replication: Each item replicated at n hosts where "n" is the replication factor configured per system.
  • Membership: Cluster membership is based on Scuttle butt which is a highly efficient anti-entropy Gossip based mechanism.The Membership further has sub part such as:
    • Failure Detection
  • Bootstrapping
  • Scaling the cluster
• It can run cheap commodity hardware and handle high throughput
• Its multiple usable structure makes it very scalable

Spanner

• Google's scalable, multi version, globally distributed database.
• Has been built on top of the Google's Big table.
• Provided data consistency and Supports SQL like Interface.
• Uses a separate high-reliability time service to guarantee the correctness properties around concurrency control.
  • The timestamps are utilized.
• It shares data across machines and migrates data automatically across machines
• Data Control Functions in spanner controls latency and performance