A Storage Manager for Telegraph

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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

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Telegraph Elevator Pitch

• Global-Scale DB Query all the info in the world
• Its 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

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Storage Not in the Elevator Pitch!

• We plan to federate legacy storage
• Thats 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.
• Cant 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

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Outline Storage This Time Around

• Do it in Java
• Yes, Java can be fast (Jaguar)
• Tighten the OSs 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.

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Decision One Java has Arrived

• Java development time 3x shorter than C
• Strict typing
• Enforced exception handling
• No pointers
• Many of Javas problems have disappeared in new
  prototypes
• Straight user-level computations can be compiled
  to 90 of Cs speed
• Garbage collection maturing, control becoming
  available
• The last, best battle efficient memory and
  device management
• RememberWere building a system
• not a 0-cost porting story, 100 Java not our
  problem
• Linking in pure Java extension code no problem

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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 Javas 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

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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

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Some Jaguar Numbers
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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

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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

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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

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Decision 3 A Non-Decision
Read
Lock
Update
Unlock
Scan
Begin
Commit
Abort
Deadlock Detect
Pin
Readaction Updateaction
Unpin
Recoveryaction
Flush
Commit/Abort-action

• The New Mohan (Lakhamraju), Rob von Behren

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Tech Trends for I/O

• Bandwidth potential of seek growing
  exponentially
• Memory and CPU speed growing by Moores Law

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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
  Gribbles 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

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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

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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?

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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.

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River, Cont.
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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)

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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)

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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

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Federation Optimization Options
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More Telegraph CONTROL UIs

• None of the paradigms for web search work
• Formal queries (SQL) too hard to formulate
• Keywords too fuzzy
• Browsing doesnt scale
• Mining doesnt 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

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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

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Scalable Spreadsheet picture
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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

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More?

• http//db.cs.berkeley.edu/telegraph
• jmh,joey_at_cs.berkeley.edu