Db presentation google_megastore

Department of information SystemCourse:IS533-Advanced topics in database

Done by :Alanoud Saad AlqoufiID :435920068

Supervised by : Prof.Almetwally Mohamed MostafaDate :5/3/2015

Google Megastore

Outline• Introduction• Architecture and data model• Transactions and concurrency control• Replications• Data structurs and algorithms• Failure Detection• Throughput • Limitation • Related work• Experience• Questions

What is Google Megastore

A database over Bigtable with high availability• Widely deployed in Google• Used on more than 100 application• Handle more than 3 billion write and 20 billion read• Store nearly a petabyte of primary data• Available on GAE since Jan 2011

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Motivation• Scalability• Availability • Consistency• Responsive• Rapid development

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Megastore=RDBMS+NOSQLRDBMS NOSQL

Slow performance High performanceNot scalable Scalable

Fixed data model No schemaEasier to code Complicated

Consistent Less consistent

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Megastore

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Toward Availability and Scalability

1. Data Replication Paxos Algorithm

2. Data Partitioning Entity Groups

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Entity Group Operations

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Design of Megastore• De-normalized dataData Model• The data model declared in schema• Each schema has a set of tables • Table could be Root or Child table• The root along with all child entities called Entity

Group

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Sample schema for Photo Sharing ServiceCREATE SCHEMA PhotoApp;

CREATE TABLE User { required int64 user_id;required string name;} PRIMARY KEY(user_id), ENTITY GROUP ROOT;

CREATE TABLE Photo {required int64 user_id;required int32 photo_id; required int64 time;required string full_url;optional string thumbnail_url;repeated string tag;} PRIMARY KEY(user_id, photo_id),IN TABLE User,ENTITY GROUP KEY(user_id) REFERENCES User;

CREATE LOCAL INDEX PhotosByTimeON Photo(user_id, time);CREATE GLOBAL INDEX PhotosByTagON Photo(tag) STORING (thumbnail_url); 8

IndexesSecondary indexes are supported

• Local index• Global index• Storing clause• Repeated index• Inline index

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Mapping to Big tableBigtable column name=Megastore table name+Property name

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Transactions and concurrency controlConcurrency Control

MVCC• Read consistency• Current• Snapshot• Inconsistent reads

• Write consistency

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Transactions and concurrency control

Complete transaction lifecycle in Megastore1. Read2. Application logic3. Commit4. Apply5. Clean up

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Queues• Example: Calendar application

2 Phase commit


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Paxos

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Replications

Modified Paxos

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Fast reads Fast writes

Replications

New Replica Types• Full Replicas• Witness Replicas• Read-only Replicas

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Replications

Replicated Logs

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Data structurs and algorithms

Reads

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Writes

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Failure Detection

• Chubby lock service

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Write Throughput• Sharding entity groups• Place replicas in same region• Bulk processing

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Limitations• Latency• Chain gang throttling• Not enforce policies on physical layout

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Experience

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Related Work• NoSQL Bigtable, Cassandra, Yahoo PNUTS, Amazon SimpleDB

• Data replication processHbase, CouchDB, Dynamo

• Paxos algorithmSCALARIS, Keyspace

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Questions1. Megastore is built upon2. Synchronous replication based upon3. Partitioned into a vast space of small databases each with its own

replicated4. The data model is declared in a strongly typed5. Megastore tables are either or tables6. 3 levels of read consistency: 7. Cross entity group updates are supported by:

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BigTable

Paxos

log

SchemaRoot

Child

CurrentSnapshotInconsistent

2 Phase commit

Any Questions?

Data & Analytics

Db presentation google_megastore