1. Telegraph Endeavour Retreat 2000 Joe Hellerstein

2. 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?

3. 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?

4. 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

5. 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

6. 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

7. 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

8. Static Query Plans Volatile environments like sensors need to adapt at a much finer grain

9. 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

10. 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?

11. 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.

12. 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.

13. 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?

14. 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

15. Extra slides for backup

16. 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

17. CONTROL Continuous 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

18. Performance Regime for CONTROL New “Greedy” Performance Regime –Maximize 1 st derivative of the user-happiness function Time 100% CONTROL Traditional

19. CONTROL Continuous Output and Navigation Technology with Refinement On Line

21. 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

22. 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”

23. Static Query Plans Volatile environments like sensors need to adapt at a much finer grain

24. 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

25. Competitive Eddies Eddy R2R1 R3 S1S2 S3  hash blockindex1  index2

27. Potter’s Wheel Anomaly Detection

28. The Data Flood is Real Source: J. Porter, Disk/Trend, Inc. http://www.disktrend.com/pdf/portrpkg.pdf

29. 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!

30. 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 only get more complex! –Both technically and ethically Temperature, light, humidity, pressure, accelerometer, magnetics

31. Explaining Disk Appetite Areal density increases 60%/yr Yet Mb/$ rises much faster! Source: J. Porter, Disk/Trend, Inc. http://www.disktrend.com/pdf/portrpkg.pdf