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Database System Review: Chapter 12-19

My review note before final exam of ZJU Database System, 2022 Spring & Summer.

Chap12: Physical Storage Systems

1. storage

  • cache, main memory, (NVM), flash memory, magnetic disk, optical disk, magnetic tapes
  • primary/secondary(online)/tertiary(offline) storage

2. Magnetic Disks

  • head, tracks( 磁道 ), sectors( 扇区 ), extent( 盘区 )
  • Disk controller
  • move disk arm
  • compute checksum
  • Performance
  • Access time：Seek time + Rotational latency
  • Data-transfer rate
  • Sequential/Random access pattern
  • IOPS：I/O operations per second
  • MTTF：mean time to failure
  • Optimization：
  • Buffering
  • Read-ahead(Prefetch)
  • Disk-arm-scheduling
  • File organization：fragmented, defragment
  • Nonvolatile write buffer(battery backed up RAM or flash)
  • Log disk
  • Solid State Disks(SSD)：like Flash
  • Flash：erase, remapping, flash translation table, wear leveling

Chap13: Data Storage Structures

1. File organization

  • record

  • offset and length field

  • data of fixed-length attributes

  • null bitmap

  • data of variable-length attributes

  • Slotted Page Structure

  • db_review_12.1
  • header：
    • number of record entries
    • end of free space in the block
    • location and size of each record

  • record pointer points to header

  • File organization

  • Heap

  • Sequential(ordered by search key)
  • multitable clustering file organization：good for join, bad for only one
    • Table Patitioning
  • B+tree ~

  • Hashing

  • Data Dictionary

  • relations
  • User and accounting
  • Statistical/descriptive data
  • Physical file organization info

  • indices

  • Buffer-Replacement Policies

  • LRU(least recently used) strategy
  • Pinned block
  • Toss-immediate strategy
  • MRU
  • forced output(recovery)
  • Column/Row-Oriented Storage(Columnar Representation)
  • Benefits:
    • only some attributes, Reduced IO, Improved CPU cache
    • compression
    • Vector processing(modern CPU)
  • Drawbacks
    • tuple reconstruction
    • tuple deletion and update
    • decompression

Chap14: Indexing

1. Concepts

  • ordered/hash indices
  • point/range query
  • access/insertion/deletion time

2. Ordered Indices

  • primary(clustering)/secondary(non-clustering) index
  • Dense/Sparse index
  • sparse：less space, less maintenance(insert, delete), slower for locating
  • multilevel index：outer sparse, inner primary

3. B+ Tree Index

  • db_review_14.1
  • non-leaf：sparse indices
  • db_review_14.2
  • db_review_14.3
  • Non-Unique key
  • Extra storage, Simpler code, More I/O, Widely used
  • Secondary index：replace record pointer with primary-index search key
  • String：variable fanout, space utilization for split, prefix compression
  • Composite search keys

4. Write Optimized Indices

  • Log-structured merge tree (LSM-tree), Buffer tree
  • Stepped-merge index（k trees each level）
  • Bloom filter

Chap15: Query Processing

1. Basic Steps

  • Parsing and translation：Into relational algebra
  • Optimization：choose evaluation plans
  • Evaluation

explain \<query>

2. Selection Cost

block transfers and seeks

  • linear search
  • db_review_15.1
  • db_review_15.2
  • db_review_15.3
  • db_review_15.4
  • db_review_15.5
  • comparision：need index
  • conjunctive selection：one/composite index, intersection of identifiers
  • disjunctive selection by union

3. Sorting Cost

  • merge sort：simple(\(b_b=1\))->advanced
  • runs=\(\lceil b_r/M\rceil\)
  • passes=\(\lceil\log_{M-1}(b_r/M)\rceil\)->\(\lceil\log_{\lfloor M/b_b\rfloor-1}(b_r/M)\rceil\)
  • block transfers=\(2b_r\lceil\log_{M-1}(b_r/M)\rceil+b_r\)->\(2b_r\lceil\log_{\lfloor M/b_b\rfloor-1}(b_r/M)\rceil+b_r\)
  • seeks=\(2\lceil b_r/M\rceil+b_r(2\lceil \log_{M-1}(b_r/M)\rceil-1)\)
  • ->\(2\lceil b_r/M\rceil+\lceil b_r/b_b\rceil(2\lceil \log_{\lfloor M/b_b\rfloor-1}(b_r/M)\rceil-1)\)

4. Join Cost

outer relation: r, inner relation: s

  • Nested-Loop Join
  • block transfer = \(n_r\times b_s+b_r\)
  • seeks = \(n_r+b_r\)
  • Block Nested-Loop Join：M=2->M
  • block transfer = \(b_r\times b_s+b_r\) -> \(\lceil b_r/(M-2)\rceil\times b_s+b_r\)

  • seeks = \(2b_r\) -> \(2\lceil b_r/(M-2)\rceil\)

  • Indexed Nested-Loop Join

  • db_review_15.6
  • Merge-Join(Assume already sorted)
  • block transfer = \(b_r+b_s\)
  • seeks = \(\lceil b_r/x_r\rceil+\lceil b_s/x_s\rceil\)(\(x_r+x_s=M\))
  • \(x_r=\frac{M\sqrt{b_r}}{\sqrt{b_r}+\sqrt{b_s}}\), \(x_s=\frac{M\sqrt{b_s}}{\sqrt{b_r}+\sqrt{b_s}}\)
  • Hash-Join(for equi-join and natural joins)
  • r: probe input, s: build input
  • h {0, ..., n}
  • n = \(\lceil b_s/M\rceil*f\), f: fudge factor(around 1.2)
  • Recursive partitioning: n = M-1
  • if \(M>b_s/M+1\)->\(M>\sqrt{b_s}\), needn't recursive partitioning
  • block transfer = \(3(b_r+b_s)+4n_h\)(needn't recursive partitioning)
  • need: \(2(b_r+b_s)\lceil\log_{M-1}(b_s/M)\rceil+b_r+b_s\)
  • seeks = \(2(\lceil b_r/b_b\rceil+\lceil b_s/b_b\rceil)\lceil\log_{M-1}(b_s/M)\rceil\)

5. Evaluation

  • Materialization
  • double buffering
  • Pipelining
  • demand driven(lazy, pull)
    • open(), next(), close()
  • producer-driven(eager, push)
  • Query Processing in Memory
  • Compilation
  • Column-oriented storage(vector)
  • Cache conscious algorithm

Chap16: Query Optimization

1. Generating Equivalent Expressions

db_review_16.1
db_review_16.2
db_review_16.3
db_review_16.4
db_review_16.5
db_review_16.6
  • Performing the selection/projection as early as possible
  • Join Order
  • Space requirements：sharing common sub-experssions
  • Time requirements：Dynamic programming

2. Statistics for Cost Estimation

\(b_r=\lceil n_r/f_r\rceil\)

Selection

满足条件的数量

db_review_16.7

缺信息：\(n_r/2\)

中选率 selectivity=\(s_i/n_r\)

db_review_16.8

Join

db_review_16.9
db_review_16.10
db_review_16.11

Other

db_review_16.12
db_review_16.13
db_review_16.14
db_review_16.15
db_review_16.16

3. Choice of Evaluation Plans

Join-Order

  • (2(n-1))!/(n-1)!
  • DP, Time O(3^n), Space O(2^n)
  • left-deep join tree, Time O(3^n)

Heuristic Optimization

  • Perform selection/projection early
  • Perform most restrictive selection and join operations
  • Perform left-deep join order

4. Optimizing Nested Subqueries

correlation variables/evaluation

Materialized Views

  • incremental view maintenance

Chap17: Transactions

1. ACID

Atomicity, Consistency, Isolation, Durability

2. Transaction State

db_review_17.1
  • concurrency advantages
  • increased processor and disk utilization
  • reduced average response time
  • Anomalies in Concurrent Executions
  • Lost Update（丢失修改）
  • Dirty Read（读脏数据）
  • Unrepeatable Read （不可重复读）
  • Phantom Problem（幽灵问题 ）
  • Serializability
  • conflict serializability(冲突可串行化 )
    • 冲突操作对顺序相同
  • view serializability（视图可串行化）
    • 读的东西相同，最终写的东西相同（弱于冲突可串行化）
  • Precedence graph（前驱图 ）
  • serializability order by topological sorting
  • Recoverable schedule：T2 读 T1 所写，则 T1 需在 T2 前 commit
  • Cascading/Cascadeless rollback：read committed
  • Transaction Isolation Levels
  • Serializable
  • Repeatable read
  • Read committed
  • Read uncommitted

Chap18: Concurrency Control

1. Lock-Based Protocols

  • concurrency-control manager
  • exclusive(X), shared(S)
  • db_review_18.1
  • enforce serializability , 但不能避免 deadlock

  • 某事务等待被授予 X 锁，然而有一系列的事务在申请 S 锁，该等待 X 锁的事务迟迟得不到 X 锁，于是被饿死了 (starvation)

  • Growing Phase ( 增长阶段 ), Shrinking Phase( 缩减阶段 )

  • basic/strict/rigorous two-phase locking
  • Deadlock Handling
  • Deadlock prevention：封锁所有数据项，迫使偏序顺序执行
  • Timeout-Based Schemes：starvation 仍可能发生，合适 timeout interval 困难
  • wait-die scheme — non-preemptive：旧等新
  • wound-wait scheme — preemptive：新等旧

2. Graph-Based Protocols

  • Tree Protocol
  • advantage
  • unlock early
  • deadlock-free
  • pitfall
  • not guarantee recoverability
  • lock more data items

3. Multiple Granularity

  • fine/coarse granularity
  • intention-shared (IS)/exclusive(IX):
  • shared and intention-exclusive(SIX)
  • 过程
  • 先锁根
  • 锁 S, IS，则父 IX, IS
  • 锁 X, SIX, IX，则父 IX, SIX
  • 先 unlock 子，再 unlock 父
  • 插入，删除加 X 锁；select 加 S 锁
  • 并发度低，Index locking protocols 更好

Chap19: Recovery System

0. Outline

  • 故障分类 Failure Classification
  • 存储结构 Storage Structure
  • 数据访问 Data Access
  • 恢复与原子性 Recovery and Atomicity
  • 基于日志的恢复 Log-Based Recovery
  • 远程备份系统 Remote Backup Systems
  • 使用提早锁释放和逻辑撤销操作进行恢复 Recovery with Early Lock Release and Logical Undo Operations
  • ARIES 恢复算法 ARIES Recovery Algorithm

1. Failure Classification

  • 事务故障Transaction failure :
  • 逻辑错误Logical errors: 内部错误条件
  • 系统错误System errors: deadlock 等
  • 系统故障System crash: 电源错误 power failure，软硬件错误 hardware or software failure
  • 一旦故障必然停止的假设Fail-stop assumption: 假定非易失性 non-volatile 存储内容不会因系统崩溃而损坏 corrupted
  • DBS 完整性检查：防止磁盘数据损坏
  • 磁盘错误Disk failure: 磁头崩溃等
  • 假设破坏是可检测的 detectable：磁盘驱动器使用校验和 checksums 来检测故障
db_review_19.1

2. Storage Structure

  • Volatile storage:
  • main memory, cache memory
  • Nonvolatile storage:
  • disk, tape, flash memory, non-volatile (battery backed up) RAM
  • Stable storage:
  • 通过在不同的非易失性介质上保持多个拷贝来近似

3. Data Access

db_review_19.2

4. Database recovery

  • Recovery algorithms：ACID 中的 ACD
  • 2 部分：normal transaction processing 中留信息，failure 后 recover
  • 假定严格 (strict) 两阶段封锁协议，保证 no dirty read
  • 幂等性 Idempotent：再执行同果

5. Log-Based Recovery

log

  • log：on SS
  • 先写日志原则 WAL(Write-Ahead Logging)
  • Log to SS before data to db

concurrency control

  • 所有事务共享一个 disk buffer 和一个 log file
  • buffer 是 write back、allocate 的
  • 严格两阶段封锁协议：commit 或 abort 后的被写数据才能被读 / 写
  • 使用 logical undo logging 可以支持提早释放锁 early lock release

Transaction Commit

  • transaction commit：commit log 被输出到 SS
  • 确保先前的 log 都已经被输出到 SS
  • 事务的写项可能仍在 buffer 中

Undo and Redo

  • undo：写补偿日志 compensation log；redo 不写日志
  • repeating history - undo

Checkpoint

  • All logs：main memory->SS
  • All modified buffer blocks->disk
  • \->SS, L: active transactions
  • 停机

Log Record Buffering

  • Log 存于 Buffer，当 (1)buffer 满 (2)log force 时写入 SS
  • log force：transaction commit 时，将其全部 log 写入 SS
  • Group commit：单个输出操作输出多个日志记录，从而降低 I/O 成本。
  • 对于 Log Record Buffering，需遵循如下规则：
  • 按创建顺序将 log record 输出到 SS
  • \<T commit> 先入 SS，T 再进入 commit 状态
  • WAL (strictly, only undo information required)

DB buffering

recovery algorithm 可能有以下策略：

  • 非强制策略no-force policy：commit 而不入 disk
  • 强制策略force policy：commit 必入 disk (expensive commit)
  • 窃取策略steal policy：commit 前就入 disk

Fuzzy Checkpointing

  • 避免 long interruption 于 checkpointing
  • 过程
  • 停机
  • 写 \<checkpoint L>
  • 列脏块
  • 停机结束
  • 脏块 ->disk（脏块不更新，依旧 WAL）
  • 更新 last_checkpoint(on disk)

Dump(recovery of non-volatile storage)

  • 类似于 checkpoint，DB to SS
  • all log to SS-> all buffer to disk->DB to SS-> 写 \<dump> to SS
  • recover from disk failure
  • 从最新 dump 恢复，log-based recover
  • fuzzy/online dump

6. Recovery with Early Lock Release and Logical Undo Operations

  • logical/physical undo logging, logical operations
  • redo：physically
  • <Ti, Oj, operation-begin>
  • <Ti, Oj, operation-end, U>
  • operation-end 之前 crash/rollback，则物理 undo；反之，逻辑 undo。
  • rollback：<Ti, Oj, operation-abort>.

7. ARIES Recovery Algorithm

Data Structure

  • log sequence number(LSN)：in pages
  • Page LSN
  • Log records(many types)
  • Dirty page table

Page LSN

  • 最后一条反应于该页的 LSN
  • update
  • 加 X 锁 (X-latch the page)，写 log
  • 写 page
  • 更新 PageLSN
  • 放锁
  • flush：S 锁
  • 保证 consistency，可物理 redo
  • PageLSN 避免重复 redo，从而保证幂等性 idempotence

Log Record

  • PrevLSN：同一事务前一 log 的 LSN
db_review_19.3
  • UndoNextLSN：undo 时的下一 LSN（减少 undo）
db_review_19.4

DirtyPageTable

PageLSN, RecLSN

3 passes

  • Analysis pass: undo-list, dirty pages, RedoLSN
  • start：last complete checkpoint log record
  • RedoLSN = min of RecLSN / checkpoint
  • Redo pass: RedoLSN, using RecLSN、PageLSNs
  • 不在 dirty page table，或 LSN < RecLSN，skip
  • 否则，fetch page：pageLSN < LSN, redo
  • Undo pass:

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