Hellerstein Joe

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Information about Hellerstein Joe
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Published on November 21, 2007

Author: Naples

Source: authorstream.com

A Storage Manager for Telegraph:  A Storage Manager for Telegraph Joe Hellerstein, Eric Brewer, Steve Gribble, Mohan Lakhamraju, Rob von Behren, Matt Welsh (mis-)guided by advice from Stonebraker, Carey and Franklin Telegraph Elevator Pitch:  Telegraph Elevator Pitch Global-Scale DB: Query all the info in the world It’s all connected, what are we waiting for? Must operate well under unpredictable HW and data regimes. Adaptive shared-nothing query engine (River) Continuously reoptimizing query plans (Eddy) Must handle “live” data feeds like sensors/actuators E.g. “smart dust” (MEMS) capturing reality Not unlike Wall Street apps, but scaled way, way up. Must work across administrative domains Federated query optimization/data placement Must integrate browse/query/mine Not a new data model/query language this time; a new mode of interaction. CONTROL is key: early answers, progressive refinement, user control online Storage Not in the Elevator Pitch!:  Storage Not in the Elevator Pitch! We plan to federate “legacy” storage That’s where 100% of the data is today! But: Eric Brewer et al. want to easily deploy scalable, reliable, available internet services: web search/proxy, chat, “universal inbox” Need some stuff to be xactional, some not. Can’t we do both in one place?!! And: It really is time for two more innocents to wander into the HPTS forest. Changes in: Hardware realities SW engineering tools Relative importance of price/performance and maintainability A Berkeley tradition! Postgres elevator pitch was about types & rules Outline: Storage This Time Around:  Outline: Storage This Time Around Do it in Java Yes, Java can be fast (Jaguar) Tighten the OS’s I/O layer in a cluster All I/O uniform. Asynch, 0-copy Boost the concurrent connection capacity Threads for hardware concurrency only FSMs for connection concurrency Clean Buffer Mgmt/Recovery API Abstract/Postpone all the hard TP design decisions Segments, Versions and indexing on the fly?? Back to the Burroughs B-5000 (“Regres”) No admin trumps auto-admin. Extra slides on the rest of Telegraph Lots of progress in other arenas, building on Mariposa/Cohera, NOW/River, CONTROL, etc. Decision One: Java has Arrived:  Decision One: Java has Arrived Java development time 3x shorter than C Strict typing Enforced exception handling No pointers Many of Java’s “problems” have disappeared in new prototypes Straight user-level computations can be compiled to >90% of C’s speed Garbage collection maturing, control becoming available The last, best battle: efficient memory and device management Remember:We’re building a system not a 0-cost porting story, 100% Java not our problem Linking in pure Java extension code no problem Jaguar:  Jaguar Two Basic Features in a New JIT: Rather than JNI, map certain bytecodes to inlined assembly code Do this judiciously, maintaining type safety! Pre-Serialized Objects (PSOs) Can lay down a Java object “container” over an arbitrary VM range outside Java’s heap. With these, you have everything you need Inlining and PSOs allow direct user-level access to network buffers, disk device drivers, etc. PSOs allow buffer pool to be pre-allocated, and tuples I the pool to be “pointed at” Matt Welsh Some Jaguar Numbers:  Some Jaguar Numbers Datamation Disk-to-Disk sort on Jaguar 450 MHz Pentium IIs w/Linux, Myrinet running JaguarVIA peak b/w 488 Mbit/sec One disk/node Nodes split evenly between readers and writers No raw I/O in Linux yet, scale up appropriately Some Jaguar Numbers:  Some Jaguar Numbers Decision Two: Redo I/O API in OS:  Decision Two: Redo I/O API in OS Berkeley produced the best user-level networking Active Messages. NOW-Sort built on this. VIA a standard, similar functionality. Leverage for both nets and disks! Cluster environment: I/O flows to peers and to disks these two endpoints look similar - formalize in I/O API reads/writes of large data segments to disk or network peer drop message in “sink”, completion event later appears on application-specified completion queue disk and network “sinks” have identical APIs throw “shunts” in to compose sinks and filter data as it flows reads also asynchronously flow into completion queues Steve Gribble and Rob von Behren Decision Two: Redo I/O API in OS:  Decision Two: Redo I/O API in OS Two implementations of I/O layer: files, sockets, VM buffers portable, not efficient, FS and VM buffer caches get in way raw disk I/O, user-level network (VIA), pinned memory non-portable, efficient, eliminates double buffering/copies Finite State Machines:  Finite State Machines We can always build synch interfaces over asynch And we get to choose where in the SW stack to do so: apply your fave threads/synchronization wherever you see fit Below that, use FSMs Web servers/proxies, cache consistency HW use FSMs Want order 100x-1000x more concurrent clients than threads allow One thread per concurrent HW activity FSMs for multiplexing threads on connections Thesis: we can do all of query execution in FSMs Optimization = composition of FSMs We only hand-code FSMs for a handful of executor modules PS: FSMs a theme in OS research at Berkeley The road to manageable mini-OSes for tiny devices Compiler support really needed Decision 3: A Non-Decision:  Decision 3: A Non-Decision “The New” Mohan (Lakhamraju), Rob von Behren Lock Unlock Deadlock Detect Recoveryaction Readaction Updateaction Begin Commit Abort Read Update Scan Pin Unpin Flush Commit/Abort-action Tech Trends for I/O:  Tech Trends for I/O “Bandwidth potential” of seek growing exponentially Memory and CPU speed growing by Moore’s Law Undecision 4: Segments?:  Undecision 4: Segments? Advantages of variable-length segments: Dynamic auto-index: Can stuff main-mem indexes in segment Can delete those indexes on the fly CPU fast enough to re-index, re-sort during read: Gribble’s “shunts” Physical clustering specifiable logically “I know these records belong together” Akin to horizontal partitioning Seeks deserve treatment once reserved for cross-canister decisions! Plenty of messy details, though Esp. memory allocation, segment split/coalesce Stonebraker & the Burroughs B-5000: “Regres” Undecision 5: Recovery Plan A:  Undecision 5: Recovery Plan A Tuple-shadows live in-segment I.e. physical undo log records in-segment Segments forced at commit VIA force to remote memory is cheap, cheap, cheap 20x sequential disk speed group commits still available can narrow the interface to the memory (RAM disk) When will you start trusting battery-backed RAM? Do we need to do a MTTF study vs. disks? Replication (Mirroring or Parity-style Coding) for media failure Everybody does replication anyhow SW Engineering win ARIES Log->Sybase Rep Server->SQL? YUCK!! Recovery = Copy. A little flavor of Postgres to decide which version in-segment is live. “The New” Mohan, Rob von Behren Undecision 5’: Recovery Plan B:  Undecision 5’: Recovery Plan B Fixed-len segments (a/k/a blocks) ARIES to some degree of complexity Performance study vs. Plan A Is this worth our while? More Cool Telegraph Stuff: River:  More Cool Telegraph Stuff: River Shared-Nothing Query Engine “Performance Availability”: Take fail-over ideas but convert from binary (master or mirror) to continuous (share the work at the appropriate fraction): provides robust performance in the presence of performance variability Key to a global-scale system: hetero hardware, changing workloads over time. Remzi Arpaci-Dusseau and pals Came out of NOW-Sort work Remzi off to be abused by DeWitt & co. River, Cont.:  River, Cont. More Cool Telegraph Stuff: Eddy:  More Cool Telegraph Stuff: Eddy How to order and reorder operators over time key complement to River: adapt not only to the hardware, but to the processing rates Two scheduling tricks: Back-pressure on queues Weighted lottery Ron Avnur (now at Cohera) More Cool Telegraph Stuff: Eddy:  More Cool Telegraph Stuff: Eddy How to order and reorder operators over time key complement to River: adapt not only to the hardware, but to the processing rates Two scheduling tricks: Back-pressure on queues Weighted lottery Ron Avnur (now at Cohera) More Telegraph Stuff: Federation:  More Telegraph Stuff: Federation We buy the Cohera pitch Federation = negotiation + incentives economics is a reasonable metaphor Mariposa studied federated optimization inches deep Way better than 1st-generation distributed DBs And their “federation” follow-ons (Data Joiner, etc.) But economic model assumes worst-case bid fluctuation Two-phase optimization a tried-and-true heuristic from a radically different domain We want to think hard about architecture, and tradeoff between cost guesses and a requests for bid Amol Deshpande Federation Optimization Options:  Federation Optimization Options More Telegraph: CONTROL UIs:  More Telegraph: CONTROL UIs None of the paradigms for web search work Formal queries (SQL) too hard to formulate Keywords too fuzzy Browsing doesn’t scale Mining doesn’t work well on its own Our belief: need a synergy of these with a person driving Interaction key! Interactive Browsing/Mining feed into query composition process And loop with it Step 1: Scalable Spreadsheet:  Step 1: Scalable Spreadsheet Working today: Query building while seeing records Transformation (cleaning) ops built in Split/merge/fold/re-format columns Note that records could be from a DB, a web page (unstructured or semi), or could be the result of a search engine query Merging browse/query Interactively build up complex queries even over weird data This is detail data. Apply mining for roll up clustering/classification/associations Shankar Raman Scalable Spreadsheet picture:  Scalable Spreadsheet picture Now Imagine Many of These:  Now Imagine Many of These Enter a free-text query, get schemas and web page listings back Fire off a thread to start digging into DB behind a relevant schema Fire off a thread to drill down into relevant web pages Cluster results in various ways to minimize info overload, highlight the wild stuff User can in principle control all pieces of this Degree of rollup/drill Thread weighting (a la online agg group scheduling) Which leads to pursue Relevance feedback All data flow, natural to run on River/Eddy! How to do CONTROL in economic federation Pay as you go actually nicer than “bid curves” More?:  More? http://db.cs.berkeley.edu/telegraph {jmh,joey}@cs.berkeley.edu

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