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Fundamentals

ACID Principle

  • Atomicity: the entire transaction happens at once, or doesn't happen at all

  • Consistency: database must be consistent before and after transactions (follow all rules, no violations)

  • Isoluation: multiple transactions can occur independently without inteference

  • Durability: changes of a successful transaction occurs even if the system failure occurs

  • Isolation Levels

    • Dirty Read: read the updated value in a transaction that hasn't been commited yet
    • Non-repeatable Read: during the course of a transaction, data in a row changes
    • Phantom Read: two identical queries return different results (a new row has just been added by another transaction)
    • MySQL is repeatable read, PostgreSQL is read commited
    • https://www.youtube.com/watch?v=GAe5oB742dw image-1735857313948.png

Bloom Filter

  • in situations where read is not efficient (like a LSM tree), use bloom filter can eliminate some of the non-existing keys, thus return "no, key is not there" to save read time.
  • it may produce falst positives (it says it's there but it is actually not there) because of hash collisions
  • How to choose an appropriate size for bloom filter?
    • for a chosen expected number of keys and acceptable false positive rates, use this formula:
      • m = -n * log2(p) / (log(2))^2
      • n = number of keys
      • p = acceptable false positive rates
      • m = size of bloom filter in bits
    • optimal number of hash functions (k):
      • k = (m / n) * log(2)

C10K problem?

  • the challenge of designing and implementing a server that can efficiently handle a large number of concurrent client connections, specifically 10,000.
  • solution:
    • asynchronous I/O
    • Non-Blocking I/O
    • Efficient Data Structures
    • Connection Pooling

Change Data Capture

  • captures changes made within a database
  • Implmentations:
    • Query based
    • Trigger based
    • Log based
    • Proprietary (developed by database vendor) based

Idempotent

  • some operation is idempotent if it always returns the same result.
  • Ex, x=5 is idempotent while x++ is not

Thundering Herd problem?

  • when a large number of requests try to access a resources in a small time frame due to reasons like cache eviction, hardware restart, etc.
  • Flooded backend may further cause system failures
  • To mitigate this issue
    • randomized cache expiration time
    • rate limiting
    • backend sharding
    • asynchronous cache updates
    • backoff time on client side
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