anatomy

CMSC828K: Anatomy of a Database System: 

CMSC828K: Anatomy of a Database System Instructor: Amol Deshpande amol@cs.umd.edu

Anatomy of a Database System: 

Anatomy of a Database System How is it implemented ? Issues: Process models Parallelism Storage models Buffer manager Query processing architecture Transaction processing Etc…

Overview: 

Overview

Process Models: 

Process Models Processes Heavyweight, context switch expensive Costly to create, limits on how many Large address space, OS support from the beginning Threads lightweight, more complicated to program no OS support till recently In theory, can have very large numbers, in practice, not lightweight enough Huge implications on performance Many DBMS wrote their own operating systems, their own thread packages etc…

Process Models: 

Process Models Assume: Uniprocessors + OS support for efficient threads Option 1: “Process per connection” Not scalable (1000 Xion/s?), Shared data structures OS manages time-sharing, easy to implement

Process Models: 

Process Models Assume: Uniprocessors + OS support for efficient threads Option 2: “Server Process Model” Single multi-threaded server. Efficient. Difficult to port/debug, no OS protection. Requires asynchronous I/O.

Process Models: 

Process Models Assume: Uniprocessors + OS support for efficient threads Option 3: “Server Process + I/O processes” Use I/O processes for handling disks. One process per device.

Process Models: 

Process Models Passing data across threads Disk I/O buffers and Client communication buffers DBMS threads, OS processes, OS Threads etc… Earlier OSs did not support: Buffering control, asynchronous I/O, high-performance threads Many DBMSs implemented their own thread packages Much replication of functionality How to map DBMS threads on OS processes/threads ? One or more processes/threads to host SQL processing threads One or more “dispatcher processes/threads” One proces/thread per disk, one process/thread per log disk One coordinator agent process/thread per session Processes/threads for background tools/utilities

Parallelism: 

Parallelism

Parallelism: 

Parallelism Shared memory Direct mapping from uni-processor Shared nothing Horizontal data partitioning, partial failure Query processing, optimization challenging Shared disk Distributed lock managers, cache-coherency etc..

Storage Models: 

Storage Models Spatial control Sequential vs random Seeks not improving that fast Controlling spatial locality Directly access to the disk (if possible) Allocate a large file, and address using the offsets

Storage Models: 

Storage Models Buffer management DBMS need control – why ? Correctness (WAL), performance (read-ahead) Typical installations not I/O-bound Allocate a large memory region Maintain a page table with: disk location, dirty bit, replacement policy stats, pin count Page replacement policy LRU-2 “double buffering” issues Memory-mapping: mmap

Query Processing: 

Query Processing Assume single-user, single-threaded Concurrency managed by lower layers Steps: Parsing: attritube references, syntax etc… Catalog stored as “denormalized” tables Rewriting: Views, constants, logical rewrites (transitive predicates, true/false predicates), semantic (using constraints), subquery flattening

Query Processing: 

Query Processing Steps: Optimizer Block-by-block Machine code vs interpretable Compile-time vs run-time Selinger ++: Larger plan space, selectivity estimation Top-down (SQLServer), auto-tuning, expensive fns “Hints”

Query Processing: 

Query Processing Steps: Executor “get_next()” iterator model Narrow interface between iterators Can be implemented independently Assumes no-blocking-I/O Some low-level details Tuple-descriptors Very carefully allocated memory slots “avoid in-memory copies” Pin and unpin

Query Processing: 

Query Processing SQL Update/Delete “Halloween” problem Access Methods B+-Tree and heap files Multi-dimensional indexes not common init(SARG) “avoid too many back-and-forth function calls” Allow access by RID

Transactions: 

Transactions Monolithic (why?) Lock manager, log manager, buffer pool, access methods ACID Typically: “I” – locking, “D” – logging “A” – locking + logging, “C” – runtime checks BASE ? (Eric Brewer) Basically Available Soft-state Eventually consistent

Transactions: 

Transactions Locks Strict 2PL most common Uses a dynamic hash table-based “lock table” Contains: lock mode, holding Xion, waiting Xions etc Also, a way to start the Xion when a lock is obtained Latches Quick-duration Mostly for internal data structures, internal logic Can’t have deadlocks or other consistency issues

Isolation Levels: 

Isolation Levels Degrees of consistency (Gray et al) Read uncommitted, read committed, repeatable read, serializable “Phantom” tuples ANSI SQL Isolation levels Not fully well-defined

Log manager: 

Log manager Required for atomicity and durability Allows recovery and transaction aborts Why a problem ? “STEAL” and “NO FORCE” Concepts: Write-ahead logging, in-order flushes etc Undo/redo, checkpoints ARIES

Locking/Logging and Indexes: 

Locking/Logging and Indexes Locking: Can’t use 2PL on indexes Solutions: “Crabbing”, Right-link schemes Logging: No need to “undo” a index page split Phantom problem: 1. Use predicate locking 2. “next-key” locking

Shared Components: 

Shared Components Memory allocations Usually “context”-based Allocate a large context, and do everything within it Why ? Disk management subsystems Dealing with RAID etc Replication services Copy, trigger-based or replay-log Statistics gathering, reorganization/index construction, backup/export