cloud databases in research and practice

Felix Gessert(29.04.2014)

Slides: baqend.com/nosql.pdf

About me

PhD student (database group university of hamburg)

About me


Research Project for PhD

About me


Research Project for PhD

Cloud Database Startup

Outline

• Categories of Cloud Databases

• Properties

What are Cloud Databases?

Cloud Databases in the wild

Research Perspectives

Wrap-up andliterature

2003: GFS (Google)

2006: BigTable (Google)

2007: Dynamo (Amazon)

2007: S3 (Amazon)

2008: SimpleDB (Amazon)

2010: SQL Azure

2011: Parse

2012: DynamoDB (Amazon)

2013: Redshift (Amazon)

Context: What kinds of Cloud databases are there?

DBaaS

Infrastructure-as-a-Service

Platform-as-a-Service

…Database-as-a-ServiceCloud SQL

Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB BigQuery

EMR GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedRDBMSs

BigQuery

EMR GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedRDBMSs

Cloud-Deploymentof DBMSs

BigQuery

EMR GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs


BigQuery

EMR GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs


Cloud-onlyDBaaS-Systems

BigQuery

EMR GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs



BigQuery

EMR

Analytics-as-a-Service

GCS

S3

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs



BigQuery

EMR


GCS

S3

ObjectStores

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs

Storage APIs



BigQuery

EMR


GCS

S3

ObjectStores

DBaaS




Amazon RDS

SQL Azure

Cloudant

MongoHQ

Parse

Orestes

Google F1

DynamoDB

ManagedNoSQL DBs

ManagedRDBMSs

Backend-as-a-Service

Storage APIs



BigQuery

EMR


GCS

S3

ObjectStores

Cloud-deployeddatabase

Data-Analytics-as-a-Service

Database-as-a-Service

SQL

ManagedRDBMS

ManagedDWH

NoSQL

ManagedNoSQL DB


Proprietary or

Polyglot

Files Object Store

Cloud Databases




SQL

ManagedRDBMS

ManagedDWH

NoSQL

ManagedNoSQL DB


Proprietary or

Polyglot

Files Object Store

Cloud Databases

Standard Interface




SQL

ManagedRDBMS

ManagedDWH

NoSQL

ManagedNoSQL DB


Proprietary or

Polyglot

Files Object Store

Cloud Databases

Standard Interface

Vendor-specific

Interface

Application Architecture <-> Cloud Database Category

Architectures

Applications

Data Warehouse

Operative Database

Reporting Data MiningAnalytics

Data

Manag

emen

tData

Analy

tics


Architectures

Applications

Data Warehouse

Operative Database


Data

Manag

emen

tData

Analy

tics

DBaaS


Architectures

Applications

Data Warehouse

Operative Database


Data

Manag

emen

tData

Analy

tics

DBaaSProbaby not.

Cloud-Deployed Database

IaaS-Cloud


IaaS-Cloud

Cloud-deploy yourfavourite database system


IaaS-Cloud

Cloud-deploy yourfavourite database system

Does not solve:Provisioning, Backups, Security, Scaling, Elasticity, Performance Tuning, Failover, Replication, ...

Managed RDBMS/DWH/NoSQL DB

IaaS-Cloud

DBaaS-Provider


IaaS-Cloud

RDBMS DWH NoSQL DB

DBaaS-Provider

Provisioning, Backups, Security, Scaling, Elasticity, Performance Tuning, Failover, Replication, ...


IaaS-Cloud

RDBMS DWH NoSQL DB

DBaaS-Provider


IaaS-Cloud

RDBMS DWH NoSQL DB

DBaaS-Provider

SQL Azure

Google

Cloud SQL

RD

BM

S


IaaS-Cloud

RDBMS DWH NoSQL DB

DBaaS-Provider

SQL Azure

Google

Cloud SQL

RD

BM

SN

oSQ

LD

B


IaaS-Cloud

RDBMS DWH NoSQL DB

DBaaS-Provider

Amazon Redshift

SQL Azure

Google

Cloud SQL

RD

BM

SN

oSQ

LD

BD

WH

Proprietary DB/Object Store

Cloud

Black-Box Databaseor file system

Managed byCloud Provider

Provid

er‘sA

PI


Cloud



Provid

er‘sA

PI

Amazon

SimpleDB

Google Cloud

DatastoreAzure Tables

Database.com

BigTable, Megastore, Spanner, F1, Dynamo,

PNuts, Relational Cloud, …

Prop

rietaryD

B


Cloud



Provid

er‘sA

PI

Amazon

SimpleDB

Google Cloud

Storage

Azure Blob

Storage

Google Cloud

DatastoreAzure Tables

Openstack

Swift

Database.com

BigTable, Megastore, Spanner, F1, Dynamo,

PNuts, Relational Cloud, …

Prop

rietaryD

BO

bject

Store

Backend-as-a-Service, Polyglot PersistenceService

IaaS-Cloud

Backend API

Service-Layer

Data API


IaaS-Cloud

Backend API

Service-Layer

Data API

Authentication, Users, Validation,etc.

Maps to (different) databases


IaaS-Cloud

Backend API

Service-Layer

Data API

Realtim

e B

aaS


IaaS-Cloud

Backend API

Service-Layer

Data API

Realtim

e B

aaS

BaaS

AppCelerator

Cloud


Cloud

Analytics Cluster

Provisioning, Data Ingest


Cloud

Analytics Cluster


Azure

HDInsight

Amazon Elastic

MapReduce

Had

oo

p


Cloud

Analytics Cluster


Azure

HDInsight

Google

BigQuery

Google

Prediction API

Amazon Elastic

MapReduce

Had

oo

pC

usto

m

DBaaS: Common Aspects

#1 Metric: Total Cost

Daniela Florescu and Donald Kossmann “Rethinking cost and performance of database systems”, SIGMOD Rec. 2009.


#1 Metric: Total Cost

Maximum utilization ofavailable hardware:

Multi-Tenancy

Daniela Florescu and Donald Kossmann “Rethinking cost and performance of database systems”, SIGMOD Rec. 2009.


Multi-Tenancy - four common approaches:

T. Kiefer, W. Lehner “Private table database virtualization for dbaas” UCC, 2011

Private OS Private Process/DB Private Schema Shared Schema




Private OS

VM

Hardware Resources

Database Process

Database

Schema

Private Process/DB Private Schema Shared Schema

e.g. Amazon RDS




Private OS

VM

Hardware Resources

Database Process

Database

Schema

Private Process/DB Private Schema

VM

Hardware Resources

Database Process

Database

Schema

Shared Schema

e.g. Amazon RDS e.g. MongoHQ




Private OS

VM

Hardware Resources

Database Process

Database

Schema


VM

Hardware Resources

Database Process

Database

Schema

VM

Hardware Resources

Database Process

Database

Schema

Shared Schema

e.g. Amazon RDS e.g. MongoHQ e.g. Google DataStore




Private OS

VM

Hardware Resources

Database Process

Database

Schema


VM

Hardware Resources

Database Process

Database

Schema

VM

Hardware Resources

Database Process

Database

Schema

Shared Schema

VM

Hardware Resources

Database Process

Database

Schema

Virtual Schema

e.g. Amazon RDS e.g. MongoHQ e.g. Google DataStore Most SaaS Apps



W. Lehner, U. Sattler “Web-scale Data Management for the Cloud” Springer, 2013

Private OS

Private Process/DB

Private Schema

Shared Schema

App.indep.

IsolationRessource

Util.Maintenance,Provisioning

Billing Models:


Usage

Account

Billing Models:


Usage

Account

Pay-per-useParameters: Network, Bandwidth, Storage, CPU, Requests, etc.Payment: Pre-Paid, Post-PaidVariants: On-Demand, Auction, Reserved

e.g. DynamoDB

Billing Models:


Usage

Account

End ofmonth

Plan-basedParameters: Allocated Plan (e.g. 2 instances + X GB storage)

e.g. MongoHQ

Billing Models:


Usage

Account

End ofmonth

Plan-basedParameters: Allocated Plan (e.g. 2 instances + X GB storage)

Free Tier: free plan or free initial account credit

e.g. MongoHQ


Database-a-

a-Service

Authentication

Authorization

API


Database-a-

a-Service

Authentication

Authorization

API

Authenticate


Internal Schemes External IdentityProvider

Federated Identity (SSO)

e.g. Amazon IAM e.g. OpenID e.g. SAML

Database-a-

a-Service

Authentication

Authorization

API

Authenticate





Used extensively

Database-a-

a-Service

Authentication

Authorization

API

Authenticate





Used extensively

Database-a-

a-Service

Authentication

Authorization

API

Authenticate

Token





Used extensively

Database-a-

a-Service

Authentication

Authorization

API

Authenticate

Token

Authenticated Request





Used extensively

User-based Access Control

Role-based Access Control

Policies

e.g. Amazon S3 ACLs e.g. Amazon IAM e.g. XACML

Database-a-

a-Service

Authentication

Authorization

API

Authenticate

Token






Used extensively



Policies


Database-a-

a-Service

Authentication

Authorization

API

Authenticate

Token


Response





Used extensively



Policies


Database-a-

a-Service

Authentication

Authorization

API

Authenticate

Token


Response

Federated ACLs

M. Decat, B. Lagaisse, et al. “Toward efficient and confidentiality-aware federation of access control policies “, DOA Trusted Cloud 2013

• Imagine ACL: „Patient data can only beaccessed by treating physician“

• Idea: decompose policy and evaluate parts neardata owner

Service Level Agreements


SLA



SLA

Legal Part1. Fees2. Penalties

Technical Part1. SLO2. SLO3. SLO



SLA

Legal Part1. Fees2. Penalties

Technical Part1. SLO2. SLO3. SLO

Service Level Objectives:• Availability• Durability• Consistency/Staleness• Query Response Time

SLAs – achieved through Workload Management






Maximize:




QOS for NoSQL DBs

Y. Zhu et al. “Scheduling with Freshness and Performance Guarantees for Web Applications in the Cloud“, CRPIT

Old: Workload management in RDBMs (DB2 andOracle)

New:Use well-known scheduling algorithms for queriesin replicated DBs

Resource Provisionig

Goal: Resources ⇔ SLAs


T. Lorido-Botran, J. Miguel-Alonso et al.: “Auto-scaling Techniques forElastic Applications in Cloud Environments”. Technical Report, 2013

Resources

Time





Resources

Time

Expected Load





Resources

Time

Expected Load

Provisioned Resources:• #No of Shard- or Replica

servers• Computing, Storage,

Network Capacities





Resources

Time

Actual Load





Resources

Time

Actual Load

Overprovisioning:• SLAs met• Excess Capacities





Resources

Time

Actual Load


Underprovisioning:• SLAs violated• Usage maximized





Resources

Time

Actual Load


Underprovisioning:• SLAs violated• Usage maximized

SmartSLA

P. Xiong: “Intelligent management of virtualized resources for database systems in cloud environment”, ICDE 2011

Solution: machine learning (regression + boosting) for prediction choose allocation that minimizesSLA penalties

Resourceallocation

Databaseperformance

LearnMapping

Functional Requirements

Scan-Querys

Conditional Updates

Transactions

Query by Example

Joins

Analytics

Elasticity

Consistency

Read-Latency

Write-Latency

Write-Throughput

Scalability of Data Volume

Read Scalability

Read-Availability

Write-Availability

Non-Functional Requirements

Durability

Write Scalability

DBaaS: General Considerations


Scan-Querys

Conditional Updates

Transactions

Query by Example

Joins

Analytics

Elasticity

Consistency

Read-Latency

Write-Latency

Write-Throughput


Read Scalability

Read-Availability

Write-Availability


Durability

Write Scalability


aaS


Scan-Querys

Conditional Updates

Transactions

Query by Example

Joins

Analytics

Elasticity

Consistency

Read-Latency

Write-Latency

Write-Throughput


Read Scalability

Read-Availability

Write-Availability


Durability

Write Scalability


aaS

Questions to ask: • Which requirements are met by the DB?• Which are met by the provider? SLAs

Outline

Examples of different clouddatabase systems:• Cloud-deployed• Managed DBMS

• SQL• NoSQL

• Proprietary• BaaS





Idea: Run (mostly) unmodified DB on IaaS

Cloud-Deployed DB

Method I: DIY

Method II: Deployment Tools

Method III: Marketplaces


Cloud-Deployed DB

Method I: DIY



1. Provision VM(s)


Cloud-Deployed DB

Method I: DIY



1. Provision VM(s) 2. Install DBMS (manual, script, Chef, Puppet)


Cloud-Deployed DB

Method I: DIY



> whirr launch-cluster --confighbase.properties

Login, cluster-size etc. Amazon EC2

1. Provision VM(s) 2. Install DBMS (manual, script, Chef, Puppet)

Idea: Run preconfigured DB on IaaS

AWS Marketplace AWS

Marketplace

Model:

Cloud-Deployed

Pricing:

Instance +

Volume +

License

Underlying DB:

Choosable

API:

DB-specific

Idea: Run preconfigured DB on IaaS

AWS Marketplace AWS

Marketplace

Model:

Cloud-Deployed

Pricing:

Instance +

Volume +

License

Underlying DB:

Choosable

API:

DB-specific

Bad: • No Clusters• Not managed (automatic Updates, Snapshots, etc.)• Private OS Multi-Tenancy Bad Resource Usage

Good:

• Easy to get started

Amazon Elastic MapReduce EMR

Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce



Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce

Provisions



Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce

Job Tracker

Task Tracker + HDFS Data Node

Task Tracker

Provisions



Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce

Data Source and Sink

Job Tracker


Task Tracker

Provisions



Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce

Submits Hadoop Jobs as:• JAR• Streaming• Cascading• Pig• Hive• Impala


Job Tracker


Task Tracker

Provisions



Model:

Analytics-aaS

Pricing:

Infrastructure

API:

Hadoop

Amazon Elastic

MapReduce

Submits Hadoop Jobs as:• JAR• Streaming• Cascading• Pig• Hive• Impala


Job Tracker


Task Tracker

Provisions


• No data locality with S3• AWS Import/Export: send your HDD• HBase Integration• Compatible with Spot and Reserved Instances• Similar: Azure HDInsight

Idea: Web-scale analysis of nested data

Google BigQuery BigQuery

Model:

Analytics-aaS

Pricing:

Storage + GBs

Processed

API:

REST

Google

BigQuery



Model:

Analytics-aaS

Pricing:

Storage + GBs

Processed

API:

REST

Google

BigQuery

Dremel

Melnik et al. “Dremel: Interactive analysis of web-scale datasets”, VLDB 2010

Idea:Multi-Level execution tree on nested columnar data format(≥100 nodes)



Model:

Analytics-aaS

Pricing:

Storage + GBs

Processed

API:

REST

Google

BigQuery

Dremel

Melnik et al. “Dremel: Interactive analysis of web-scale datasets”, VLDB 2010

Idea:Multi-Level execution tree on nested columnar data format(≥100 nodes)

• SLA: 99.9% uptime / month• Fundamentally different from relational DWHs

and MapReduce• Design copied by Apache Drill, Impala, Shark

Relational Database Service

Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific

• Synchronous Replication• Automatic Failover


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific


99,95% uptime SLA


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific


99,95% uptime SLA

Provisioned IOPS: access to EBS volumes network-optimized (up to 4000 IOPS)


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific

EC2 instances: Up to 32 Cores, 244 GB RAM, 10 GbE


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific

EC2 instances: Up to 32 Cores, 244 GB RAM, 10 GbE

Minor Version Upgrades are performed without downtime


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific

Backups are automated and scheduled


Amazon RDS RDS

Model:

Managed RDBMS

Pricing:

Instance + Volume

+ License

Underlying DB:

MySQL, Postgres,

MSSQL, Oracle

API:

DB-specific

Backups are automated and scheduled

• Support for (asynchronous) Read Replicas• Administration: Web-based or SDKs• Only RDBMSs• “Analytic Brother“ of RDS: RedShift (PDWH)

Similar to RDS

Microsoft SQL Azure SQL Azure

Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure

Similar to RDS


Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure

Cloud SQL Server

P. Bernstein et al. “Adapting Microsoft SQL server for cloud computing”, ICDE 2011

• Multi-Tenant MSSQL• Paxos-like commit protocol for

consistent replication

Similar to RDS


Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure

Keyless Table Group: regular databaseKeyed Table Group: partitioned by row key

Cloud SQL Server




Similar to RDS


Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure


Consistency unit (ACID boundary)

Cloud SQL Server




Similar to RDS


Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure



Automatic Partitioning for Keyed Table Groups

Cloud SQL Server




Similar to RDS


Model:

Managed RDBMS

Pricing:

Database size

Underlying DB:

MSSQL Server

API:

T-SQL/TDS

SQL Azure



Automatic Partitioning for Keyed Table Groups

Cloud SQL Server




• SLA: 99.9% uptime / month• Usually Cheaper than RDS (Multi-Tenancy)• Smaller Databases (max. 150 GB)• Rich MSSQL server tooling• Keyed Table Group internal feature only

MySQL for Google App Engine PaaS

Support for: patching, replication, backup

SLA: 99,95 % uptime / month

Other RDBMS servicesGoogle

Cloud SQL Google Cloud SQL

Pricing:

Database size

Underlying DB:

MySQL




Postgres for Heroku PaaS

Hosted on EC2

No SLAs



Pricing:

Database size

Underlying DB:

MySQL

Heroku Postgres

Pricing:

Plan based

Underlying DB:

Postgres





Hosted on EC2

No SLAs

MySQL for OpenStack (Icehouse)

Under development (HP driven)

VM ⇔ Tenant



Pricing:

Database size

Underlying DB:

MySQL

Heroku Postgres

Pricing:

Plan based

Underlying DB:

Postgres

Trove

Pricing:

Own Hardware

Underlying DB:

MySQL

Trove





Hosted on EC2

No SLAs

MySQL for OpenStack (Icehouse)

Under development (HP driven)

VM ⇔ Tenant



Pricing:

Database size

Underlying DB:

MySQL

Heroku Postgres

Pricing:

Plan based

Underlying DB:

Postgres

Trove

Pricing:

Own Hardware

Underlying DB:

MySQL

Trove

Evaluation of Cloud RDBMSs

D. Kossmann,T. Kraska: An evaluation of alternative architectures for transaction processing in the cloud”, Sigmod 2010

TPC-W Benchmark (Online Shop), 2010, Emulated Browsers / RPS:

HBase Wide-Column

CP Over Row Key

~700 1/4 Apache

(EMR)

MongoDB Doc-ument

CP yes >100<500

4/4 GPL

Riak Key-Value

AP ~60 3/4 Apache

(Softlayer)

Cassandra Wide-Column

AP WithComp. Index

>300<1000

2/4 Apache

Redis Key-Value

CA Through Lists, etc.

manual N/A 4/4 BSD

Managed NoSQL services

Model CAP ScansSec.

IndicesLargestCluster

Lic.Lear-ning DBaaS

HBase Wide-Column

CP Over Row Key

~700 1/4 Apache

(EMR)

MongoDB Doc-ument

CP yes >100<500

4/4 GPL

Riak Key-Value

AP ~60 3/4 Apache

(Softlayer)

Cassandra Wide-Column

AP WithComp. Index

>300<1000

2/4 Apache

Redis Key-Value

CA Through Lists, etc.

manual N/A 4/4 BSD

Managed NoSQL services

Model CAP ScansSec.

IndicesLargestCluster

Lic.Lear-ning DBaaS

And there are many more:• CouchDB (e.g. Cloudant)• CouchBase (e.g. KuroBase Beta)• ElasticSearch(e.g. Bonsai)• Solr (e.g. WebSolr)• …

MongoHQ MongoHQ

Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

MongoDB

API:

Mongo, REST (beta)

EC2-based MongoDB-as-a-Serivce

MongoHQ MongoHQ

Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

MongoDB

API:

Mongo, REST (beta)


Private Process Multi-TenancyScale-Up Strategy: RAM, IOPs and CPU increased at runtimeMaximum Size: 1TB

Free Tier

MongoHQ MongoHQ

Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

MongoDB

API:

Mongo, REST (beta)


Private Process Multi-TenancyScale-Up Strategy: RAM, IOPs and CPU increased at runtimeMaximum Size: 1TB

Free Tier

VM-Deployment (EC2):M1.large on EC2: $128.10on EC2 with 1-yr-Res.: $30With MongoHQ: $637

MongoHQ MongoHQ

Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

MongoDB

API:

Mongo, REST (beta)


#1 thing that should never happen:

MongoHQ

Problem: Scalability

Client

Client

configconfigconfig

mongos

Replica Set

Master

Slave

Slave

mongos

MongoHQ


Client

Client

configconfigconfig

mongos

Replica Set

Master

Slave

Slave

mongos

What if Writes / second or data volume become bottleneck?

MongoHQ


Client

Client

configconfigconfig

mongos

Replica Set

Replica Set

Master

Slave

Slave

Master

Slave

Slave

mongos

Sharding (Scale Out)• Dynamic Scaling: Tenant adds

Replica Set• Elastic Scaling: Provider

adds/removes Replica SetMongoHQ: Only manual shardingon “contact us” basis

MongoHQ


Client

Client

configconfigconfig

mongos

Replica Set

Replica Set

Master

Slave

Slave

Master

Slave

Slave

mongos

Sharding (Scale Out)• Dynamic Scaling: Tenant adds

Replica Set• Elastic Scaling: Provider

adds/removes Replica SetMongoHQ: Only manual shardingon “contact us” basis

• Bad: no SLAs, no horizontal scaling

• Good: Solid Dashboard, Backups, Replication, integration with Heroku

• Competitors: MongoLab, ObjectRocket, MongoSoup

ElastiCache ElastiCache

Model:

Managed NoSQL

Pricing:

Infrastructure

Underlying DB:

Memcache, Redis

API:

DB, REST

(management)

„RDS for Memcache and Redis“


Model:

Managed NoSQL

Pricing:

Infrastructure

Underlying DB:

Memcache, Redis

API:

DB, REST

(management)


Memcache or RedisMemcache can be run as a cluster (client-side sharding)

Limited choice of instance types


Model:

Managed NoSQL

Pricing:

Infrastructure

Underlying DB:

Memcache, Redis

API:

DB, REST

(management)

„RDS for Memcache and Redis“Announced last Saturday: Snapshots


Model:

Managed NoSQL

Pricing:

Infrastructure

Underlying DB:

Memcache, Redis

API:

DB, REST

(management)



Model:

Managed NoSQL

Pricing:

Infrastructure

Underlying DB:

Memcache, Redis

API:

DB, REST

(management)


$ elasticache-modify-cache-parameter-group xy

AWS CLI tools and SDKsoffer a superset of Dashboard functions

Many Hosted NoSQLDbaaS Providers represented

Heroku DBaaS Addons

Many Hosted NoSQLDbaaS Providers represented

And Search

Heroku DBaaS Addons

Heroku Redis2Go example Redis2Go

Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

Redis

API:

Redis

Create Heroku App:


Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

Redis

API:

Redis

Create Heroku App:

Add Redis2Go Addon:


Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

Redis

API:

Redis

Create Heroku App:

Add Redis2Go Addon:

Use Connection URL (environment variable):


Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

Redis

API:

Redis

Create Heroku App:

Add Redis2Go Addon:


Deploy:


Model:

Managed NoSQL

Pricing:

Plan-based

Underlying DB:

Redis

API:

Redis

Create Heroku App:

Add Redis2Go Addon:


Deploy:• Very simple• Only suited for small to medium

applications (no SLAs, limited control)

SimpleDB Table-Store

CP Yes (asqueries)

Auto-matic

SQL-like(no joins, groups, …)

REST + SDKs

Dynamo-DB

Table-Store

CP By rangekey / index

Local Sec.Global Sec.

Key+Cond. On Range Key(s)

REST + SDKs

Automaticover Prim. Key

AzureTables

Table-Store

CP By rangekey

Key+Cond. On Range Key

REST + SDKs

Automaticover Part. Key

99.9% uptime

AE/CloudDataStore

Entity-Group

CP Yes (asqueries)

Auto-matic

Conjunct.of Eq. Predicates

REST/SDK, JDO,JPA

Automaticover EntityGroups

S3, Az. Blob, GCS

Blob-Store

AP REST + SDKs

Automaticover key

99.9% uptime(S3)

Proprietary Database services

Model CAP ScansSec.

IndicesQueries API SLA

Scale-out

SimpleDB Table-Store

CP Yes (asqueries)

Auto-matic

SQL-like(no joins, groups, …)

REST + SDKs

Dynamo-DB

Table-Store

CP By rangekey / index

Local Sec.Global Sec.

Key+Cond. On Range Key(s)

REST + SDKs

Automaticover Prim. Key

AzureTables

Table-Store

CP By rangekey

Key+Cond. On Range Key

REST + SDKs

Automaticover Part. Key

99.9% uptime

AE/CloudDataStore

Entity-Group

CP Yes (asqueries)

Auto-matic

Conjunct.of Eq. Predicates

REST/SDK, JDO,JPA

Automaticover EntityGroups

S3, Az. Blob, GCS

Blob-Store

AP REST + SDKs

Automaticover key

99.9% uptime(S3)

Proprietary Database services

Model CAP ScansSec.

IndicesQueries API SLA

Scale-out

There are many more object stores (HP, Rackspace, etc.)…but no comparable Table Stores

Azure Storage

Load-Balancing-System

Application

HTTP/HTTPS

Worker-Role

Web-Role

IIS-Webserver

Virtual Machines

Call

OtherServices

Not Allowed

Virtual Machines

Azure Storage

Load-Balancing-System

Application

HTTP/HTTPS

Worker-Role

Web-Role

IIS-Webserver

Virtual Machines

Call

OtherServices

Not Allowed

Virtual Machines

Tables

Blobs

Queues

Table Service example: Azure Tables

Partition Key

Row Key (sortiert)

Timestamp(autom.)

Property1 Propertyn

intro.pdf v1.1 14/6/2013 … …

intro.pdf v1.2 15/6/2013 …

präs.pptx v0.0 11/6/2013 …

Partition

Partition

RES

T A

PI


Partition Key

Row Key (sortiert)

Timestamp(autom.)

Property1 Propertyn

intro.pdf v1.1 14/6/2013 … …

intro.pdf v1.2 15/6/2013 …

präs.pptx v0.0 11/6/2013 …

Partition

Partition

RES

T A

PI

SparseHash-distributed toparition servers

No Index: Lookup only (!) by full table scanAtomic "Entity-Group Batch Transaction" possible

Similar to Amazon SimpleDB and DynamoDB


Partition Key

Row Key (sortiert)

Timestamp(autom.)

Property1 Propertyn

intro.pdf v1.1 14/6/2013 … …

intro.pdf v1.2 15/6/2013 …

präs.pptx v0.0 11/6/2013 …

Partition

Partition

RES

T A

PI

• Indexes all attributes• Rich(er) queries• Many Limits (size, RPS, etc.)

• Provisioned Throughput• On SSDs („single digit latency“)• Optional Indexes

Azure Table Storage Azure Tables

Model:

Propriertary

Pricing:

Requests + Storage

+ Network

Underlying DB:

Custom System

API:

REST

Challenges:

Single partition and range key (modelling) Very “basic“ queries

Azure Tables


Model:

Propriertary

Pricing:

Requests + Storage

+ Network

Underlying DB:

Custom System

API:

REST

Challenges:


Good:Automatic DistributionReplicated 3x locally + 1x async. geo-replica

Azure Tables


Model:

Propriertary

Pricing:

Requests + Storage

+ Network

Underlying DB:

Custom System

API:

REST

Challenges:



Very good:SLA (99.9% uptime)Internal architecture published

Azure Tables


Model:

Propriertary

Pricing:

Requests + Storage

+ Network

Underlying DB:

Custom System

API:

REST

Challenges:




Azure Tables

Windows Azure Storage

B. Calder, et al. "Windows Azure Storage: a highly available cloud storage service with strong consistency." , SOSP 2011

Idea:• Layered storage infrastructure for Blobs and Tables• Use research results (GFS, BigTable, Paxos, LSM, Erasure Coding)


Model:

Propriertary

Pricing:

Requests + Storage

+ Network

Underlying DB:

Custom System

API:

REST

Challenges:




Azure Tables

Windows Azure Storage

B. Calder, et al. "Windows Azure Storage: a highly available cloud storage service with strong consistency." , SOSP 2011

Idea:• Layered storage infrastructure for Blobs and Tables• Use research results (GFS, BigTable, Paxos, LSM, Erasure Coding)

Think: GFS/HDFSThink: BigTable/HBase

Think: Chubby/ZooKeeper

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST

Successor to SimpleDB

Limitations: • Slow (~50-100 RPS)• 10 GB per Domain• Query result max. 2500 records or

1 MB• Max. 1K-sized attributes

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST


dom:com.cnn content : "<html>…"

Primary Key Attribute (scalar or set)

page:index

Range Key

Item:

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST




Querying Options:

• GetItem: Key Lookup

• Query: Primary Key + Condition on Range Key

• Scan: Full Table Scan with filter

• EMR: Hive queries (for analytics)

page:index

Range Key

Item:

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST


Consistency: Strongly (2x price) or Eventually Consistent Reads

Atomic (Conditional) Updates per Item

Indexing Options:◦ Local Sec. Index: consistent additional Range Key

◦ Global Sec. Index: eventually consistent index-table (Primary Key)



page:index

Range Key

Item:

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST


DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST


Unit of Billing

DynamoDB DynamoDB

Model:

Propriertary

Pricing:

Provisioned

Throughput +

Network

Underlying DB:

Custom System

API:

REST


Unit of BillingGood:• Low Latency (SSD)• Data partitioning and AZ-replicationBad:• Scaling not elastic (Capacity Units)• No SLAs, no internals published (≠ Dynamo!)• No built-in backups ( AWS data pipeline)• Vendor Lock-in

AE/Cloud DataStore DataStore

Model:

Propriertary

Pricing:

CPU + Storage +

Network

Underlying DB:

MegaStore

API:

SDK, JPA, JDO

Google Cloud

Datastore

Structured Storage System for App Engine

Based on:◦ Megastore BigTable Colossus


Model:

Propriertary

Pricing:

CPU + Storage +

Network

Underlying DB:

MegaStore

API:

SDK, JPA, JDO

Google Cloud

Datastore



Schemafree Entity Group (EG) data model:

User

IDName

Photo

IDUserURL

Root Table Child Table

1

n


Model:

Propriertary

Pricing:

CPU + Storage +

Network

Underlying DB:

MegaStore

API:

SDK, JPA, JDO

Google Cloud

Datastore




User

IDName

Photo

IDUserURL


1

n

EG: User + n Photos• Unit of ACID transactions/

consistency• Fields autoindexed

(eventually consistent)


Model:

Propriertary

Pricing:

CPU + Storage +

Network

Underlying DB:

MegaStore

API:

SDK, JPA, JDO

Google Cloud

Datastore




User

IDName

Photo

IDUserURL


1

n

EG: User + n Photos• Unit of ACID transactions/

consistency• Fields autoindexed

(eventually consistent)

SELECT * FROM photos

WHERE ANCESTOR IS :34 AND name = „sunset“

ORDER BY date ASC

LIMIT 10

OFFSET 10

AE/Cloud DataStore

Internally:

Entity Groups define partitions

SynchronousPaxos-based

replication

ACID per EG. Maximum of1 Write/s to an EG.

Eventual Consistencyacross groups

Stored in BigTable

AE/Cloud DataStore

Internally:

MegaStore

J. Baker, et al. "Megastore: Providing Scalable, Highly Available Storage for Interactive Services." CIDR 2011.

• Paxos-based replication andtransactions

• 100 Google applicationsProblems: Slow Writes, PredefinedEntity Groups



replication



Stored in BigTable

AE/Cloud DataStore

Internally:

MegaStore






replication



Stored in BigTable

Spanner

J. Corbett et al. "Spanner: Google’s globally distributed database." TOCS 2013

Idea:• Autosharded Entity Groups• Not based on BigTableImplementation:• TrueTime API (GPS + atomic

clocks) commit timestampsof 2PL-SI transactions

• Paxos-replication per Shard

AE/Cloud DataStore

Internally:

MegaStore






replication



Stored in BigTable

Spanner





F1

J. Shute, et al. "F1: A distributed SQL database that scales.“, VLDB 2013

Idea:• Full SQL relational database built on

Spanner, powers AdWordsImplementation:• 5-way replication• Data Model: relational + hierarchy

(customercampaignAdGroup)• Transactions: Snapshot-Read-Only,

Pessimistic (Spanner), optimistic• Distributed SQL Engine

AE/Cloud DataStore

Internally:

MegaStore






replication



Stored in BigTable

Spanner





F1

J. Shute, et al. "F1: A distributed SQL database that scales.“, VLDB 2013

Idea:• Full SQL relational database built on

Spanner, powers AdWordsImplementation:• 5-way replication• Data Model: relational + hierarchy

(customercampaignAdGroup)• Transactions: Snapshot-Read-Only,

Pessimistic (Spanner), optimistic• Distributed SQL Engine

Good:• Transactions (though limited)• Good scalability of data volumeBad:• Entity Groups hard to define• Bad Scale-Out for write and reads (Kossmann et

al.) no advantage over RDBMS for small datavolumes

A Spanner/F1 based DBaaS?

Idea: Blobs with RESTful CRUD Interface

Distribution: 𝐵𝑢𝑐𝑘𝑒𝑡, 𝐾𝑒𝑦 → 𝑆𝑒𝑟𝑣𝑒𝑟 + 𝑅𝑒𝑝𝑙𝑖𝑐𝑎𝑠

CDN Integration (Azure CDN, Cloudfront)

S3: > 2 Trillion (2 ⋅ 1012) objects

Azure Blobs, Amazon S3, Google Cloud Storage

Container

Blobcontains

Block 1 (up to 4 MB)

Block 2

Block n

REST-Request

Azure Blobs:

Block Blob: 4MB blocks large filesPage Blob: 512B blocks random IO

Automatic Versioning

Pricing: per request (10.000 ~ 1c) and storage(1GB/month ~ 3c) and network (1GB ~ 10c)


S3 BlobDELETE /puppy.jpg HTTP/1.1

Host: mybucket.s3.amazonaws.comAuthorization: AWS AKIAIO...

AWS Ireland DC

Replicas

Reduced Redundancy: only 1 replicaGlacier: tape-disk archivalAmazon S3:






AWS Ireland DC

Replicas


S3 Consistency

D. Bermbach,, S. Tai. "Eventual consistency: How soon is eventual?”, MW4SOC 11

Findings:• Inconsistency window varies

from 2-11 seconds• Monotonic Read Consistency

is often violated






AWS Ireland DC

Replicas


S3 Consistency

D. Bermbach,, S. Tai. "Eventual consistency: How soon is eventual?”, MW4SOC 11

Findings:• Inconsistency window varies

from 2-11 seconds• Monotonic Read Consistency

is often violated

Building a database on S3

M. Brantner, et al. "Building a database on S3." Sigmod 2008

Idea:• Use S3 as the persistent storage of a

database

Implementation:• Buffer Pool and Log Manager on S3• No transaction or query support

Founded June, 2011

Acquired by Facebook April, 2013

Pricing: ◦ Free

◦ Pro (199$)

◦ Enterprise

Parse - MBaaS Parse

Model:

Backend-aas

Pricing:

Plan-based

Underlying DB:

Mainly MongoDB

API:

SDKs, REST

Founded June, 2011


Pricing: ◦ Free

◦ Pro (199$)

◦ Enterprise

Parse - MBaaS Parse

Model:

Backend-aas

Pricing:

Plan-based

Underlying DB:

Mainly MongoDB

API:

SDKs, REST

Parse Core

Founded June, 2011


Pricing: ◦ Free

◦ Pro (199$)

◦ Enterprise

Parse - MBaaS Parse

Model:

Backend-aas

Pricing:

Plan-based

Underlying DB:

Mainly MongoDB

API:

SDKs, REST

Parse Core Parse Analytics

Founded June, 2011


Pricing: ◦ Free

◦ Pro (199$)

◦ Enterprise

Parse - MBaaS Parse

Model:

Backend-aas

Pricing:

Plan-based

Underlying DB:

Mainly MongoDB

API:

SDKs, REST

Parse Core Parse Analytics Parse Push

Parse - MBaaS

AuthenticationUser + PasswordOAuth: Facebook, Twitter

Parse - MBaaS


Parse - MBaaS

Query Cinemas(new Parse.Query('Cinemas'))

.withinKilometers(...)

.fetch()


Parse - MBaaS

Query Cinemas(new Parse.Query('Cinemas'))

.withinKilometers(...)

.fetch()

Query Movies(new Parse.Query('Movies'))

.greaterThan('startAt‘, now)

.notEqualTo('cinemas', cId)

.fetch()

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets

Idea: Full-Stack JavaScript with Node.js, MongoDB and WebSockets

Web Browser Node.js + MongoDB(Single Server)

WebSocket

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets


<div class="player {{selected}}"><span class="name">{{name}}</span><span class="score">{{score}}</span>

</div>


WebSocket

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets


Players = new Meteor.

Collection("players");

if (Meteor.isServer) {

//…


</div>


WebSocket

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets





//…

if (Meteor.isClient) {

Template.leaderboard.players = function () {

return Players.find({},

{sort: {score: -1, name: 1}});

};


</div>


WebSocket

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets





//…





};


</div>


WebSocket

$ meteor deploy abc.meteor.com

Meteor Meteor

Model:

Backend-aaS

Pricing:

No yet revealed

Underlying DB:

MongoDB

API:

WebSockets





//…





};


</div>


WebSocket

$ meteor deploy abc.meteor.com

Very productive for very small projects

Fundamentally limited scalability:• Server tails Mongo‘s oplog• And holds the fetched data state of

every client

Outline

• Hot Topics• Orestes: a scalable, low-

latency architecture• Baqend: putting it into

practice





Example: CryptDB

Idea: Only decrypt as much as neccessary

Encrypted Databases: Research

RDBMS

SQL-Proxy

Encrypts and decrypts, rewrites queries

Example: CryptDB



RDBMS

SQL-Proxy


Relational Cloud

C. Curino, et al. "Relational cloud: A database-as-a-service for the cloud.“, CIDR 2011

DBaaS Architecture:• Encrypted with CryptDB• Multi-Tenancy through live

migration• Workload-aware partitioning

(graph-based)

Example: CryptDB



RDBMS

SQL-Proxy


Relational Cloud

C. Curino, et al. "Relational cloud: A database-as-a-service for the cloud.“, CIDR 2011

DBaaS Architecture:• Encrypted with CryptDB• Multi-Tenancy through live

migration• Workload-aware partitioning

(graph-based)

• Early approach• Not adopted in practice, yet

Dream solution:Full Homorphic Encryption

Transactions/Consistency: Research

Dynamo Eventual None 1 RT -

Yahoo PNuts Timeline per key Single Key 1 RT possible

COPS Causality Multi-Record 1 RT possible

MySQL (async) Serializable Static Partition 1 RT possible

Megastore Serializable Static Partition 2 RT -

Spanner/F1 Snapshot Isolation Partition 2 RT -

MDCC Read-Commited Multi-Record 1 RT -

Consistency Transactional UnitCommit Latency

Data Loss?










Data Loss?

Multi-Data Center Consistency

T. Kraska et al. "MDCC: Multi-data center consistency." EuroSys, 2013.

Idea:• Multi-Data center commit protocol with

single round-trip

Implementation:• Optimistic Commit Protocol• Fast, Generalized Multi-Paxos

Result: almost as fast as Dynamo-style










Data Loss?

Multi-Data Center Consistency

T. Kraska et al. "MDCC: Multi-data center consistency." EuroSys, 2013.

Idea:• Multi-Data center commit protocol with

single round-trip

Implementation:• Optimistic Commit Protocol• Fast, Generalized Multi-Paxos

Result: almost as fast as Dynamo-style

Currently no NoSQL DB implementsconsistent Multi-DC replication

YCSB (Yahoo Cloud Serving Benchmark)

Benchmarking: Research

Data Store



Client

Wo

rkload

Gen

erator

Plu

ggable

DB

interface

Data Store

Threads

Stats



Client

Wo

rkload

Gen

erator

Plu

ggable

DB

interface

Workload:

1. Operation Mix

2. Record Size

3. Popularity Distribution

Runtime Parameters:

DB host name,

threads, etc.

Data Store

Threads

Stats



Client

Wo

rkload

Gen

erator

Plu

ggable

DB

interface

Workload:

1. Operation Mix

2. Record Size


Runtime Parameters:

DB host name,

threads, etc.

Read()Insert()Update()Delete()Scan()

Data Store

Threads

Stats

DB protocol



Client

Wo

rkload

Gen

erator

Plu

ggable

DB

interface

Workload:

1. Operation Mix

2. Record Size


Runtime Parameters:

DB host name,

threads, etc.

Read()Insert()Update()Delete()Scan()

Data Store

Threads

Stats

DB protocol

Workload Operation Mix Distribution Example

A – Update Heavy Read: 50%Update: 50%

Zipfian Session Store

B – Read Heavy Read: 95%Update: 5%

Zipfian Photo Tagging

C – Read Only Read: 100% Zipfian User Profile Cache

D – Read Latest Read: 95%Insert: 5%

Latest User Status Updates

E – Short Ranges Scan: 95%Insert: 5%

Zipfian/Uniform

Threaded Conversations

Example Result

(Read Heavy):


Example Result

(Read Heavy):


Weaknesses:• Single client can be a

bottleneck• No consistency &

availability measurement

Example Result

(Read Heavy):

Benchmarking: ResearchYCSB++

S. Patil, M. Polte, et al.„Ycsb++: benchmarking and performance debugging advanced features in scalable table stores“, SOCC 2011

• Clients coordinate throughZookeeper

• Simple Read-After-Write Checks• Evaluation: Hbase & Accumulo




Example Result

(Read Heavy):








• No Transaction Support

YCSB+T

A. Dey et al. “YCSB+T: Benchmarking Web-Scale Transactional Databases”, CloudDB 2014

• New workload: TransactionalBank Account

• Simple anomaly detection forLost Updates

• No comparison of systems

Example Result

(Read Heavy):








• No Transaction Support

YCSB+T

A. Dey et al. “YCSB+T: Benchmarking Web-Scale Transactional Databases”, CloudDB 2014

• New workload: TransactionalBank Account

• Simple anomaly detection forLost Updates

• No comparison of systems

No specific applicationCloudStone, CARE, TPC

extensions?

Benchmarking: state of the art


Tests latency and throughputof IaaS-Providers, CDNs and

Object Stores


Tests latency and throughputof IaaS-Providers, CDNs and

Object Stores

Does not test: Cloud Databases

VisionYCSB Harmony

Idea:

VisionYCSB Harmony

YCSBClient

YCSBClient YCSB

Client

YCSBClient

Idea:1. Periodically

launch clients

VisionYCSB Harmony

YCSBClient

YCSBClient YCSB

Client

YCSBClient

Idea:1. Periodically

launch clients2. Benchmark

Cloud DB3. Publish results

VisionYCSB Harmony

System Availablity Reads/s Writes/s Avg. Latency 95th perc. Latency

Plot

DynamoDB 99.9% 23411 34534 3.2 ms 9 ms

RDS 99.8% 2342 2455 30 ms 80 ms

Azure Table 99.5% 22343 23442 12 ms 20 ms

GoogleDataStore

99.5% 3000 2000 30 ms 300 ms

Database research at theUniversity of Hamburg

Motivation

Classic 3-Tier-ArchitectureThree-Tier Architecture

Motivation

ClientApplicationDatabase

Web Server

Web Server


Motivation


Web Server

Web Server

High Latency


Motivation


Web Server

Web Server

Average (2014):90 HTTP Requests per

page load

High Latency


Motivation


Web Server

Web Server


page load

High Latency

Classic 3-Tier-ArchitectureThree-Tier ArchitectureWith every 100ms of additional page load time, revenue decreases by 1%.

Study by Amazon

Motivation


Web Server

Web Server


page load

High Latency

Classic 3-Tier-ArchitectureThree-Tier ArchitectureWith every 100ms of additional page load time, revenue decreases by 1%.

Study by Amazon

When increasing load time of search results by 500ms, traffic decreases by 20%.

Study by Google

SPAs

Rich ClientApplicationDatabase

Web Server

Web Server

Single-Page Applications

High Latency

(Rich Client, Smart Client)

Data (e.g. JSON)

(AngularJS, Backbone,

Ember.JS, etc.)

ORESTES

Rich ClientOrestesDB-Cluster

RESTServer

Low LatencyCloud

RESTServer

ORESTES


RESTServer

Web-Caches Low Latency

Cloud

RESTServer

CacheableDatabase Objects

ORESTES


RESTServer


ScalableNoSQL DBs

Cloud

RESTServer


ORESTES


RESTServer


ScalableNoSQL DBs

Cloud

Exposes the DB via REST API and handles:• Scaling• Cache Consistency• Transactions• Schema

RESTServer


Middleware:◦ Scalable REST API for aggregate-oriented

persistence, queries, schema management and transactions.

Caching:◦ Consistent web caching of database objects for

read scalability and latency reduction.

Transactions:◦ Optimistic cache-aware transaction model.

ORESTES: Components

Java/JDO

persist

find

createQueryJavaScript/JPA Port

REST/HTTP API

others

Application

Server

Browser or

Mobile Device

ApplicationLayer

PersistenceAPI

DataStore

Java/JDO

persist

find


REST/HTTP API

others

Application

Server

Browser or

Mobile Device

ApplicationLayer

PersistenceAPI

DataStore

HTTP Server

Trans-

actionsQuerys

Object

Persist.Schema

Key-

Value

Doc-

uments

DBaaS & BaaS Layer

Transaction Validation

HTTP Server

Config-

urationPartial

Updates

Access Control

Multi-Tenancy Schema Management

Workload Management

Cache Coherence

Autoscaling

Database-independentConcerns

Database -specific Wrappers

SLAs

HTTP Server

Java/JDO

persist

find


REST/HTTP API

others

Application

Server

Browser or

Mobile Device

ApplicationLayer

PersistenceAPI

DataStore

ISP

Forward-Proxy Caches

ISP Caches

Reverse-Proxy Caches and

Load Balancers

CDN Caches

Content DeliveryNetworks

Purge

Scale

HTTP Server

Trans-

actionsQuerys

Object

Persist.Schema

Key-

Value

Doc-

uments

DBaaS & BaaS Layer


HTTP Server

Config-

urationPartial

Updates

Access Control


Workload Management

Cache Coherence

Autoscaling



SLAs

HTTP Server

Java/JDO

persist

find


REST/HTTP API

others

Application

Server

Browser or

Mobile Device

ApplicationLayer

PersistenceAPI

DataStore

ISP


ISP Caches


Load Balancers

CDN Caches


Purge

Scale

HTTP Server

Trans-

actionsQuerys

Object

Persist.Schema

Key-

Value

Doc-

uments

DBaaS & BaaS Layer


HTTP Server

Config-

urationPartial

Updates

Access Control


Workload Management

Cache Coherence

Autoscaling



SLAs

HTTP Server

Redis (Replicated)

10201040

10101010Counting

Bloom Filter

add

delete

Node.JS (local to Server)

Stored Procedures

Custom Validation

Java/JDO

persist

find


REST/HTTP API

others

Application

Server

Browser or

Mobile Device

ApplicationLayer

PersistenceAPI

DataStore

ISP


ISP Caches


Load Balancers

CDN Caches


Purge

Scale

HTTP Server

Trans-

actionsQuerys

Object

Persist.Schema

Key-

Value

Doc-

uments

DBaaS & BaaS Layer


HTTP Server

Config-

urationPartial

Updates

Access Control


Workload Management

Cache Coherence

Autoscaling



SLAs

HTTP Server

Redis (Replicated)

10201040

10101010Counting

Bloom Filter

add

delete

Node.JS (local to Server)

Stored Procedures

Custom Validation

GET /db/{bucket}/{class}/{id}

200 OKCache-Control: public, max-age=6000ETag: "3"JSON Object

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

em.find(id) JavaScript

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

GET /db/posts/{id} HTTP

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

Cache-Hit: Return ObjectCache-Miss: Forward Request

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

Fetch object from DB and returnit with caching information

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

Scalability and Cache-Hits

Caching

Orestes

Client-

(Browser-)

Cache

Proxy

CachesISP

CachesCDN

Caches

Reverse-

Proxy

Caches

Miss

Hit

MissMiss

MissMiss

100%

50%

0%

P(Cache-Hit)

0 ms 1 ms 10 ms 20 ms 50-500 ms 50-500 ms

Scalability and Cache-Hits

Latency Benefit

App

App

BFB: Bloom Filter Bounded Staleness

Cache

1 4 020

App

App


Cache

1 4 020

How to prevent stalereads (inconsistency)?

App

App


Cache

1 4 020

App

App


Cache

1 4 020

purge(obj)

hashB(oid)hashA(oid)

13

App

App


Cache

1 4 020

purge(obj)

131 1 110Flat(Counting Bloomfilter)

App

App


Cache

1 4 020

purge(obj)

131 1 110


App

App


Cache

1 4 020


1 1 110

App

App


Cache

1 4 020


1 1 110

𝑓 ≈ 1 − 𝑒−𝑘𝑛𝑚

𝑘

𝑘 = ln 2 ⋅ (𝑛

𝑚)

False-Positive

Rate:

Hash-

Functions:

With 10.000 Updates per 10 minutes and 1% error rate: 12 KByte

App

App


Cache

1 4 020


1 1 110

SCOT: Scalable Cache-Aware Optimistic Transactions

Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1

Reads

Writes2

Writes

(Hidden)


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1

Reads

Writes2

Commit: read- & write-set versions

Committed OR aborted + stale objects3

Writes

(Hidden)


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1

Reads

Writes2



Writes

(Hidden)

validation 4

E.g. Redis or ZooKeeper

prevent conflicting

validations


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1

Reads

Writes2



Writes

(Hidden)

validation 4


5Writes (Public)

Read all

prevent conflicting

validations


Cache

Cache

Cache

REST-Server

REST-Server

REST-Server

DB

Coordinator

Client

Begin Transaction

Bloom Filter1

Reads

Writes2



Writes

(Hidden)

validation 4


5Writes (Public)

Read all

prevent conflicting

validations

Caching → Shorter transactionduration → less aborts

PolyglotPersistence

application

Orestes servers

REST/HTTPprotocol

Redis MongoDB db4o

meta data contains SLA

parse SLA & route data

manage materialisation

resolve mapping

Polyglot Persistence Mediator

PolyglotPersistence

application

Orestes servers

REST/HTTPprotocol

Redis MongoDB db4o

meta data contains SLA

parse SLA & route data

manage materialisation

resolve mapping

Polyglot Persistence Mediator

Results:Article-Objects with Impression Count

Article

IDTitle…

Imp.

Imp.ID

MongoDB Redis Sorted Set

Speedup with PPM:• 50-1000%• 66% performance of Varnish

Cloud Evaluation of ORESTES

Client Machine

50 ...

Web

CacheOrestes

Server

Versant

DB

Amazon EC2 Ireland EC2 USA165 ms

Client Machine

Client Machine


Client Machine

50 ...

Web

CacheOrestes

Server

Versant

DB


Client Machine

Client Machine

30 000 Objekte

500 Anfragen/

Client

30 000 Objects500 Req./Clients10/1 Read/Write


Client Machine

50 ...

Web

CacheOrestes

Server

Versant

DB


Client Machine

Client Machine

Stateless Scale-out REST middleware for DBaaS

Generic Schema, Authentication, Multi-Tenancy, etc.

SCOT (Scalable Optimistic Cache-Aware Transactions):◦ Optimistic Database-indepdent ACID transactions

BFB (Bloom Filter Bounded Staleness):◦ Allows static caching with tunable consistency guarantee

PPM (Polyglot Persistence Mediator):◦ SLAs appropriate database

Orestes: Summary

From Research toPractice

Orestes as a startup

Baqend


Baqend

Internet

Seoxy

REST-API Transactions Schema Management Cache Consistency

Auto-Scaling Multi-Tenancy Security and Access Control Provisioning


Baqend

Baqend in Action

Baqend in ActionGET /app.html


db.find(Menu, 'main').done(...);

db.find(Page, 'hero').done(...);

db.query(Page, 'top3').done(...);


db.find(Menu, 'main').done(...);

db.find(Page, 'hero').done(...);

db.query(Page, 'top3').done(...);

GET /img/pic005.jpgGET /img/pic017.jpgGET /img/pic022.jpg

More: Baqend.com

Wrap-up & book recommendations

How to choose a cloud database:

Wrap-up

Managed RDBMS

Managed DWH

Managed NoSQL DB


Proprietary Serivce

Object Store


Wrap-up

Define your functionalrequirements

Define your non-functionalrequirements

Managed RDBMS

Managed DWH

Managed NoSQL DB


Proprietary Serivce

Object Store


Wrap-up



Managed RDBMS

Managed DWH

Managed NoSQL DB


Proprietary Serivce

Object Store

1. Underyling DB2. Docs & books3. Your own tests

Evaluate by:


Wrap-up



Managed RDBMS

Managed DWH

Managed NoSQL DB


Proprietary Serivce

Object Store


4. SLAs5. Docs & books6. Experience Reports

Evaluate by:


Wrap-up



Managed RDBMS

Managed DWH

Managed NoSQL DB


Proprietary Serivce

Object Store


4. SLAs5. Docs & books6. Experience Reports

Evaluate by:

Try it

Book recommendations

Book recommendations

• (Non-scientific) literature is rare• Some books cover specific DBaaS and

cloud platforms

Blogs

http://nosql.mypopescu.com/

http://www.dzone.com/mz/nosql

http://www.dbms2.com/

http://hackingdistributed.com/

http://highscalability.com/

http://www.nosqlweekly.com/

VLDB (Very Large Databases)

SIGMOD (Special Interest Group on Management of Data)

ICDE (International Conference on Data Engineering)

CIDR (Conference on Innovative Data Systems Research)

SOCC (Symposium on Cloud Computing)

OSDI/SOSP (Operating Systems Design and

Implementation/ Symposium on Operating System Principles)

EuroSys

Top Scientific ConferencesD

atabase

Research

Distrib

uted

System

s R

esearch

VLDB (Very Large Databases)

SIGMOD (Special Interest Group on Management of Data)

ICDE (International Conference on Data Engineering)

CIDR (Conference on Innovative Data Systems Research)

SOCC (Symposium on Cloud Computing)

OSDI/SOSP (Operating Systems Design and

Implementation/ Symposium on Operating System Principles)

EuroSys

Top Scientific ConferencesD

atabase

Research

Distrib

uted

System

s R

esearch

This year probably in Washington D.C.Learn more: scdm2013.com

Thank you. Queries?

Contact:

[email protected]

http://baqend.comhttp://orestes.infohttp://scdm2013.com

cloud databases in research and practice

Science