6.033 | Spring 2018 | Undergraduate

Computer System Engineering

Week 9: Distributed Systems Part II

Lecture 16 Outline

  1. Introduction
    • Currently: Building reliable systems out of unreliable components. We’re working on implementing transactions which provide:
      • Atomicity
      • Isolation
    • So far: Have a poorly-performing version of atomicity via shadow copies.
    • Today: Logging, which will give us reasonable performance for atomicity. Logging also works when we have multiple concurrent transactions, even though for today we’re not thinking about concurrency.
  2. Motivating Example
    • begin // T1
      A = 100  
      B = 50  
      commit // At commit: A=100; B=50
      begin // T2
      A = A - 20  
      B = B + 20  
      commit // At commit: A=80; B=70
      begin // T3
      A = A + 30  
    • Problem: A = 110, but T3 didn’t commit. We need to revert.

  3. Basic Idea
    • Keep a log of all changes and whether a transaction commits or aborts.
      • Every transaction gets a unique ID.
      • UPDATE records include old an new values of a variable.
      • COMMIT records specify that transaction committed..
      • ABORT records specify that transaction aborted.
        • Not always needed.
    • (See Lecture 16 slides (PDF) for the log for this example.)
    • Nice: Updates are small appends.
  4. How to Use a Log for Transactions
    • On begin: Allocate new transaction ID (TID).
    • On write: Append entry to log.
    • On read: Scan log to find last committed value.
    • On commit: Write commit record.
      • This is the commit point.
      • Atomic because we can assume it’s a single-sector write.
      • Another way to do it would be to put checksums on each record and ignore partially-written records.
    • On abort: Nothing (could write an ABORT record but not strictly needed).
    • On recover: Nothing.
    • (see Lecture 16 slides (PDF) for code.)
  5. Performance of Log
    • Writes: Good. Sequential = fast.
    • Reads: Terrible. Must scan entire log.
    • Recovery: Instantaneous.
  6. Cell Storage
    • Improve read performance with cell storage.
      • (For us) stored on disk, i.e., non-volatile storage.
      • Updates go to log and cell storage.
      • Read from cell storage.
    • “Log” = write to log. “Install” = write to cell storage.
    • How to recover:
      • Scan the log backwards, determine what actions aborted, and undo them.
      • (see Lecture 16 slides (PDF) for code.)
      • What if we crash during recovery? No worries; recover() is idempotent. Can do it repeatedly.
    • How to write:
      • Log before install, not the other way; otherwise, can’t recover from a crash in between the two writes.
      • This is write-ahead logging.
  7. Performance of Log + Cell Storage
    • Writes: Okay, but now we write to disk twice instead of once.
    • Reads: Fast.
    • Recovery: Bad. Have to scan the entire log.
  8. Improving Performance
    • Improve writes: Use a (volatile) cache.
      • Reads go to cache first, writes go to cache and are eventually flushed to cell storage.
      • Problem: After crash, there may be updates that didn’t make it to cell storage (were in cache but not flushed).
        • Also could be updates in cell storage that need to be undone, but we had that problem before.
      • Solution: We need a redo phase in addition to an undo phase in our recovery.
    • Improving recovery:
      • Problem: Recovery takes longer and longer as the log grows.
      • Solution: Truncate the log.
      • How?
        • Assuming no pending actions:
          • Flush all cached updates to cell storage.
          • Write a CHECKPOINT record.
          • Truncate the log prior to the CHECKPOINT record.
            • Usually amounts to deleting a file.
        • With pending actions, delete before the checkpoint and earliest undecided record.
    • ABORT records
      • Can be used to help recovery and skip undo-ing aborted transaction. Not necessary for correctness—can always just pretend we crashed—but can help.
  9. What about Un-undo-able Actions?
    • What if our transaction fires a missile and then aborts?
    • Typically: Wait for software that controls the action to commit and then take the action, but have a special way to detect whether the action has/will happened.
  10. Summary * Logging is a general technique for achieving atomicity.
    • Writes are fast, reads can be fast with cell storage.
    • Need to log before installing (write-ahead), and need a recovery process. * Tomorrow is recitation: Logging for file systems. * Now: We’re good with atomicity.
    • In fact, logging will work fine with concurrent transactions; the problem will be figuring out which steps we can actually run in parallel. * Wednesday: Isolation. * Next week: Distributed transactions.

Course Info

As Taught In
Spring 2018
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Lecture Notes
Written Assignments
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