inexpensive scalable information access many internet applications need to access data for millions...

Cloud Data Management

Inexpensive Scalable Information Access

• Many Internet applications need to access data for millions of concurrent users

• Relational DBMS technology cannot scale to these workloads using commodity hardware

• Need for low cost scalable DBMSs resulted in the advent of the key-value stores (e.g., Google’s Bigtable, Yahoo!’s PNUTS, and Amazon’s Dynamo)

Key-value Stores

Scalability and availability is more important than rich functionality

• Scalability: Scale out to thousands of commodity servers

• Availability: Data replicated across data centers to ensure high availability of user data in the presence of failures

Key-value Data Model• Primary abstraction is a table of rows or key-value pair

• Each row is identified by a unique key, and the value can vary in its structure– Keys are arbitrary strings which can be up to 64K bytes– Arbitrary number of columns per row– Arbitrary data type for each column (i.e., data validation

done by applications)• An interpreted binary string , i.e., a Blob

• Columns with their own attribute as in relational DBMSs

• Multiple versions of each row can be maintained and accessed through timestamps

From Needs to Constraints

• Retrieval– (row, column, timestamp) lookup only– In some systems, simple relational operations

are supported such as selection and projection

• Update– Updates and deletes need to specify the primary

key

• Atomicity– Atomic Read and write only possible at row level

Scalability & Fault Tolerance Consideration

• Logical entity can be effectively represented as a single row

• Each row typically resides in a single server, and data access is restricted to a single key Application-level data manipulation is restricted to

a single computer obviating the need for multi-server coordination and synchronization

• Rationale: (1) requests generally distributed throughout the data set, (2) impact of failure limited to the rows served by the failed server

Cluster Management – Master-based

• A centralized master server keeps track of all data servers using a highly fault-tolerant (FT) service

• This FT service keeps track of the data stored at the different servers

• When a data server fails, FT service reports this failure and the master can reassign the data to other servers

• If the master fails, a new master is elected to take over

Cluster Management – Decentralized

• Typically based on gossip messages exchanged among the servers continuously

• These messages contain relevant performance measurements

• The failure of a server is detected when a gossip message from that server is missing

• This approach is more fault tolerant; but it incurs message overhead

Google’s Bigtable

Master Chubby node

Tablet Server i Tablet Server j

Tablet 1 Tablet 2 Tablet 3

GFS Chunk Server

SSTable1

SSTable2

SSTable3

SSTable 4(replica)


GFS Chunk Server

SSTable4

SSTable5

SSTable6

SSTable 2(replica)

• A table is a set of tablets • A master server allocates

tablets among tabet servers and is responsible for load balancing

Logicalview

Physicallayout

A tablet is stored as a collection of

SSTable files Tablet, logically represented as a key range, is the unit of distribution and load balancing

Distributed file system

Tablets• A logical table is divided into multiple tablets,

each hold an interval of table rows

• Each tablet is stored in one or more SSTable files

• When a tablet grows beyond a certain size, it is split into two new tablets

Google’s Bigtable - Chubby

Master Chubby node



GFS Chunk Server

SSTable1

SSTable2

SSTable3

SSTable 4(replica)


GFS Chunk Server

SSTable4

SSTable5

SSTable6

SSTable 2(replica)

Logicalview

Physicallayout

A tablet is stored as a collection of

SSTables

• Highly fault tolerant - consisting of five active replicas. Service is live when majority of replicas are running

• It is used for managing the tablet servers

Replication is handled by GFS

Determines which server to

hold a tablet

Google’s Bigtable - Column Families

• Related columns stored in fixed number of families (the unit for data colocation and access at the storage layer)

• Permissions can be applied at family level to grant access to different applications

Google’s Bigtable - Chubby

• The master and every tablet server obtains a timed lease with Chubby that must be periodically renewed

• A server can carry out its responsibilities only if it has an active lease

• Every tablet server periodically reports to the master using heartbeat messages (that also contain the load statistics)

• Master detects failures based on the heartbeat messages and uses the statistics for load balancing

Google’s Bigtable – Server Failure

Master Chubby node



GFS Chunk Server

SSTable1

SSTable2

SSTable3

SSTable 4(replica)


GFS Chunk Server

SSTable4

SSTable5

SSTable6

SSTable 2(replica)

Logicalview

Physicallayout

If this server fails

Tablet 4

Informs Server i to take over Tablet 4

inexpensive scalable information access many internet applications need to access data for millions...

Documents