TelegraphEndeavour Retreat 2000

Joe Hellerstein

Roadmap

• Motivation & Goals• Application Scenarios• Quickie core technology overview

– Adaptive dataflow– Event-based storage manager– Come hear more about these

tonight/tomorrow!• Status and Plans

– Dataflow infrastructure & apps– Storage manager?

Motivations

• Global Data Federation– All the data is online – what are we waiting for?– The plumbing is coming

• XML/HTTP, XML/WAP, etc. give LCD communication• but how do you flow, summarize, query and analyze data

robustly over many sources in the wide area?• Ubiquitous computing: more than clients

– sensors and their data feeds are key• smart dust, biomedical (MEMS sensors)• each consumer good records (mis)use

– disposable computing• video from surveillance cameras, broadcasts, etc.

• Huge Data flood a’comin’!– will it capsize the good ship Endeavour?

Initial Telegraph Goals• Unify data access & dataflow apps

– Commercial wrappers for most infosources– Most info-centric apps can be cast as dataflow– The data flood needs a big dataflow manager!– Goal: a robust, adaptive dataflow engine

• Unify storage– Currently lots of disparate data stores

• Databases, Files, Email servers (and http access on these)– Goal: A single, clean storage manager that can serve:

• DB records & semantics• Files and “semantics”• Email folders, calendars, etc. and semantics

Challenge for Dataflow: Volatility!• Federated query processors

– A la Cohera, IBM DataJoiner– No control over stats, performance, administration

• Large Cluster Systems “Scaling Out”– No control over “system balance”

• User “CONTROL” of running dataflows– Long-running dataflow apps are interactive– No control over user interaction

• Sensor Nets– No control over anything!

• Telegraph– Dataflow Engine for these environments

The Data Flood: Main Features

• What does it look like?– Never ends: interactivity required

• Online, controllable algorithms for all tasks!

– Big: data reduction/aggregation is key– Volatile: this scale of devices and nets will

not behave nicely

The Telegraph Dataflow Engine

• Key technologies– Interactive Control

• interactivity with early answers and examples• online aggregation for data reduction

– Dataflow programming via paths/iterators • Elevate query processing frameworks out of DBMSs• Long tradition of static optimization here

– Suggestive, but not sufficient for volatile environments

– Continuously adaptive flow optimization• massively parallel, adaptive dataflow• Rivers and Eddies

Static Query Plans

• Volatile environments like sensors need to adapt at a much finer grain

Continuous Adaptivity: Eddies

• How to order and reorder operators over time– based on performance, economic/admin feedback

• Vs.River:– River optimizes each operator “horizontally”– Eddies optimize a pipeline “vertically”

Eddy

Unifying Storage

• Storage management buried inside specific systems

• Elevate and expose the core services & semantic options– Layout/indexing– Concurrent access/modification– Recovery

• Design for clustered environments– Replicate for reliability (tie-ins with Ninja)– Cluster options: your RAM vs. my disk– Events & State Machines for scalability

• Unify eventflow and dataflow?• Share optimization lessons?

Status: Adaptive Dataflow• Initial Eddy results promising, well received (SIGMOD 2K)• Finishing Telegraph v0 in Java/Jaguar

– Prototype now running• Demo service to go live on web this summer

– Analysis queries over web sites• We’ve picked a provocative app to go live with (stay tuned!)• Incorporates Ninja “path” project for caching

– Goal: Telegraph is to “facts and figures” as search engines are to “documents”

• Longer-term goals:– Formalize & optimize Eddy/River scheduling policies– Study HCI/systems/stats issues for interaction– Crawl “Dark Matter” on the web– Attack streams from sensors

• Sequence queries and mining, data reduction, browsing, etc.

Status: Unified Storage Manager

• Prototype implementation in Java/Jaguar– ACID transactions + (non-ACID) Java file access– Robust enough to get TPC-W numbers– Events/states vs. threads

• Echoes Gribble/Welsh results: better than threaded under load, but Java complicates detailed mesurement

• Time to re-evaluate importance of this part– Interest? More mindshare in dataflow infrastructure.– Vs. tuning an off-the-shelf solution (e.g. Berkeley

DB)?– Goal? unified lessons about dataflow/eventflow

optimization on clusters.

Integration with Rest of Endeavour• Give

– Be dataflow backbone for diverse “clients”• Our own Telegraph apps (federated dataflow, sensors)• Replication/delivery dataflow engine for OceanStore• Scalable infrastructure for tacit info mining algorithms?• Pipes for next version of Iceberg?

– Telegraph Storage Manager provides storage (xactional/otherwise) for OceanStore? Ninja?

• Take– OceanStore to manage distributed metadata, security– Leverage protocols out of TinyOS for sensors– Partner with Ninja to manage local metadata?– Work with GUIR on interacting with streams?

More Info

• People:– Joe Hellerstein, Mike Franklin, Eric Brewer, Christos

Papadimitriou– Sirish Chandrasekaran, Amol Deshpande, Kris

Hildrum, Sam Madden, Vijayshankar Raman, Mehul Shah

• Software– http://telegraph.cs.berkeley.edu coming soon– ABC interactive data anlysis/cleansing at

http://control.cs.berkeley.edu• Papers:

– See http://db.cs.berkeley.edu/telegraph

Extra slides for backup

Connectivity & Heterogeneity

• Lots of folks working on data format translation, parsing– we will borrow, not build– currently using JDBC & Cohera Net Query

• commercial tool, donated by Cohera Corp. • gateways XML/HTML (via http) to ODBC/JDBC

– we may write “Teletalk” gateways from sensors• Heterogeneity

– never a simple problem– Control project developed interactive, online data

transformation tool: ABC

CONTROLContinuous Output and Navigation Technology with Refinement On Line

• Data-intensive jobs are long-running. How to give early answers and interactivity?– online interactivity over feeds

• pipelining “online” operators, data “juggle”– online data correlation algs: ripple joins, online

mining and aggregation– statistical estimators, and their performance

implications• Deliver data to satisfy statistical goals

• Appreciate interplay of massive data processing, stats, and HCI

“Of all men's miseries, the bitterest is this: to know so much and have control over nothing”

–Herodotus

Performance Regime for CONTROL

• New “Greedy” Performance Regime– Maximize 1st derivative of the user-

happiness function

Time

100%

CONTROLTraditional

CONTROLContinuous Output and Navigation Technology with Refinement On Line

CONTROLContinuous Output and Navigation Technology with Refinement On Line

River

• We built the world’s fastest sorting machine– On the “NOW”: 100 Sun workstations + SAN– But it only beat the record under ideal

conditions!• River: performance adaptivity for data

flows on clusters– simplifies management and programming– perfect for sensor-based streams

Declarative Dataflow: NOT new• Database Systems have been doing this for years

– Xlate declarative queries into an efficient dataflow plan– “query optimization” considers:

• Alternate data sources (“access methods”)• Alternate implementations of operators• Multiple orders of operators• A space of alternatives defined by transformation rules• Estimate costs and “data rates”, then search space

• But in a very static way!– Gather statistics once a week– Optimize query at submission time– Run a fixed plan for the life of the query

• And these ideas are ripe to elevate out of DBMSs– And outside of DBMSs, the world is very volatile– There are surely going to be lessons “outside the box”

Static Query Plans

• Volatile environments like sensors need to adapt at a much finer grain

Continuous Adaptivity: Eddies

• How to order and reorder operators over time– based on performance, economic/admin feedback

• Vs.River:– River optimizes each operator “horizontally”– Eddies optimize a pipeline “vertically”

Eddy

Competitive Eddies

Eddy

R2R1 R3 S1 S2 S3

hash

block index1 index2

Potter’s Wheel Anomaly Detection

The Data Flood is Real

Source: J. Porter, Disk/Trend, Inc.http://www.disktrend.com/pdf/portrpkg.pdf

0

500

1000

1500

2000

2500

3000

3500

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Year

Petabytes

Sales

Moore'sLaw

Disk Appetite, cont.

• Greg Papadopoulos, CTO Sun:– Disk sales doubling every 9 months

• Note: only counts the data we’re saving!

• Translate:– Time to process all your data doubles every 18 months– MOORE’S LAW INVERTED!

• (and Moore’s Law may run out in the next couple decades?)

• Big challenge (opportunity?) for SW systems research– Traditional scalability research won’t help

• “Ideal” linear scaleup is NOT NEARLY ENOUGH!

Data Volume: Prognostications

• Today– SwipeStream

• E.g. Wal-Mart 24 Tb Data Warehouse– ClickStream– Web

• Internet Archive: ?? Tb– Replicated OS/Apps

• Tomorrow– Sensors Galore– DARPA/Berkeley “Smart Dust”

• Note: the privacy issues onlyget more complex!– Both technically and ethically

Temperature, light, humidity, pressure, accelerometer,magnetics

Explaining Disk Appetite• Areal density increases 60%/yr • Yet Mb/$ rises much faster!

0

20

40

60

80

100

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Year

MB/$ Mb/$

Moore's Law

Source: J. Porter, Disk/Trend, Inc.http://www.disktrend.com/pdf/portrpkg.pdf