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Flexible Transactional Storage. Russell Sears sears@cs.berkeley.edu HPTS 2005. Outline. Introduction Problems with existing systems A modular approach Composable on-disk data structures Application control of low-level primitives Microbenchmarks The next steps

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Flexible transactional storage

Flexible Transactional Storage

Russell Sears


HPTS 2005


  • Introduction

  • Problems with existing systems

  • A modular approach

    • Composable on-disk data structures

    • Application control of low-level primitives

    • Microbenchmarks

  • The next steps

    • Library optimization during application compilation

    • Verification of application-specific extensions

  • Conclusion


  • New applications introduce new demands for storage infrastructure

    • Database implementations eventually adapt

      • Continuous queries, database file systems, XML, OLAP

    • But not always

      • Web search, GMail, P2P

  • Either way, custom storage solutions fill in the cracks

    • Expensive; little reuse of existing infrastructure

    • Subtle bugs lead to data corruption

Selective reuse of storage system components
Selective Reuse of Storage System Components

  • Expose the RSS to allow greater reuse

    • Berkeley DB / Sleepy Cat

    • Layered Databases

  • Proven real-world improvements in performance and code complexity

  • Why not provide lower level interfaces?

Our focus

Query Optimizer

Query Evaluator

Storage System




Physical Access Methods

Recovery / Durability



Page File

Log File

Our Focus

Allow applications to directly customize and reuse underlying storage primitives

Design goals
Design Goals

  • Let applications build upon or replace modules

    • Allocation strategies

    • Page layout

    • On disk data structures

    • Concurrency control

    • Log (format, durability and reordering)

    • Recovery

  • Improved usability and performance

    • Application specific data structure organization

    • Program specific optimizations

Lladd s storage interface
LLADD’s Storage Interface

(Lightweight Library for Atomicity and Data Durability)

  • Focus on simplifying the APIs within the RSS

    • “redo()” and “undo()” (there is no “do()”)

    • Subcomponents implement flexible APIs

write log


Log Manager

Data Structure Plugin


invoke REDO

Wrapper Function(s)

log entries


Recovery / Abort

Operation Implementation

UNDO/REDO requests

Read-only Access Methods


page updates

read memory

Page File

Write ahead logging implementation

(Arrows point in the direction of application data flow)

Reusable data structures

Poor locality / High overhead?

Index Page

Pages contain fixed length records

Internal Fragmentation?

Reusable data structures


Linked Lists

  • Familiar object oriented design patterns allow data structure reuse

  • Nested Top Actions can be used to provide atomicity

  • Easy to specialize data structures

Linear Hash Table


Bucket List

Hash table bulk load time
Hash Table Bulk Load Time

  • Layered version’s performance is competitive

  • Also benchmarked optimized version

    • No nested top actions à Temporary inconsistency

    • Saves log bandwidth, roughly doubles throughput

    • Complex, monolithic code

Object serialization

System Memory

File system cache

DB page cache

Application Data

(Live objects)


Object serialization

  • Persistent objects are often triple buffered

  • Turning off OS cache removes one copy

  • We can remove a second copy

The problem with the page cache
The Problem with the Page Cache

  • Approach #1: Reduce the number of live objects

    • Need to repeatedly serialize and deserialize objects

    • CPU intensive

  • Approach #2: Reduce the size of the page cache

    • Object updates force a write to the page cache

    • Two extra disk accesses (1 read, 1 write) to update an object in cache!

Specialized page caching
Specialized Page Caching

  • Defer page update until object is evicted from application memory

    • Issue log writes immediately

    • Application cache manipulates page cache directly

Object serialization performance
Object serialization performance

Roughly doubled throughput while reducing memory requirements.

Access locality and object serialization
Access Locality and Object Serialization

Under heavy memory pressure, the optimization allows the cache to be utilized efficiently

Language based tools
Language Based Tools

  • Modern programming techniques provide some interesting opportunities

    • Software verification

    • Optimization

  • High level interfaces make it difficult to take advantage of some of these tools

  • How much do we gain by moving to lower level interfaces?


  • Servicing a cache hit is expensive compared to a pointer traversal

  • Programs typically access the same page repeatedly

  • Simple solution: Keep a pointer to the last value returned by the page cache

  • Problem: Unrelated, interleaved calls

    • Multi-threaded code

    • Layered APIs


  • Consider this application code:

    for(int i = 0; i < len; i++) {

    value = hash_lookup(recordid, key[i]);


  • hash_lookup() probably looks something like this:

    hash_lookup(…) {

    Page * p = pin(recordid.page);

    // Read hashtable header


    … // pin and unpin bucket, data pages


  • Memoize header by storing values in the application’s stack frame

Dynamic checks
Dynamic Checks

  • Insert memoization logic into application code, and store memoized values on the stack.

    • Preserves access locality within each thread

    • Handles “special cases” (B-Tree roots, iterators, etc)

    • Simplifies application/library source code

  • Implemented using CIL, a C source to source transformation library.

  • ~2x speedup on read-only CPU-bound hash table workload

Static analysis work in progress
Static analysis (work in progress)

  • Dynamic checks are expensive

  • Use BLAST to remove redundant checks at compile time

    • Tentatively remove check and call to pin()

    • Ask BLAST to prove the memoized value is correct at pin()’s call site.

  • Assumed the original program is “well behaved” C by removing problematic constructs

Verification of invariants future work
Verification of Invariants (future work)

  • Extensions to the library must follow a number of invariants

    • Using nested top actions correctly

    • Updating the LSN of altered pages

    • Not relying upon transient data in redo()/undo()

    • and so on

  • Want to check application code’s adherence to invariants

  • Hopefully, this will allow us to guarantee high level properties are met

  • Similar in spirit to the use of SLAM to verify Windows drivers


  • Presented a simple storage architecture that supports a wide variety of applications

  • The architecture brings up a number of interesting research questions

  • A preliminary implementation is available

    • Ready for researchers, not for important data

    • http://lladd.sourceforge.net/


Eric Brewer

Jimmy Kittiyachavalit

Jim Blomo

Jason Bayer

Mike Demmer

Bowei Du

Gilad Arnold

Amir Kamil

Colleen Lewis


Database systems take control away from developers
Database Systems Take Control Away from Developers

  • Great solution for established classes of applications

  • Leads to serious problems in unanticipated situations

  • A DBMS implementation can only support a finite set of semantics and must make decisions regarding

    • Data layout / programming model

    • Concurrency / consistency

    • Recovery / durability

    • Replication / scalability

One solution
One Solution

  • Give application developers more choices

    • Relational / Cube / XML data models

    • Optimistic / pessimistic concurrency control

    • Serializable / Repeatable Read / Read Committed / Read Uncommitted

    • Disable media recovery, partial logging, no logging

    • 2PC, merge replication, master / slave, partitioning

    • and so on…

  • Leads to complex DBMS implementations

  • It takes a long time to get this right!

Editing dbms source code is difficult
Editing DBMS Source Code is Difficult

  • Requires knowledge of complex DB internals

  • Easy to get the extensions wrong

  • Difficult to test or debug

  • Breaks existing functionality

  • Leads to incompatible DB versions.

    Are these all just artifacts of conventional

    database design?


  • It must be easy to add new extensions, and hard to (accidentally) break existing ones.

  • Low level changes should not alter high level functionality in unexpected ways

  • Bugs in recovery logic should be obvious

  • In ‘interesting’ cases, should see ‘significant’ performance improvement.

Multiple page formats

Generic page layout:

Fixed length record layout:


Fixed Length Data


Record Count

Page type specific


Page Type



Multiple page formats

  • Record id’s are of the form: (page, slot, length)

  • ‘slot’ is interpreted by the appropriate page format implementation; ‘length’ is for the application’s benefit.

  • Page Type 0 is reserved (allows lazy page initialization)

Dynamic check example

Original Code

foo(int i, record r) {

Page *p;

while(i--) {


p = pin(r.page);


if(...) {

r.page++; r.slot = 0;




Optimized Code

foo(int i, record r) {

Page *p = null;

while(i--) {


if(!p ||

p->page != r.page) {


p = pin(r.page);


if(...) {

r.page++; r.slot = 0;



if(p) unpin(p);


Dynamic Check Example

Static analysis example

Original Code + Dynamic Checks

foo(int i, record r) {

Page *p = pin(r.page);

while(i--) {


if(!p ||

p->page != r.page) {


p = pin(r.page);





Optimized Code

foo(int i, record r) {

Page *p = pin(r.page);

while(i--) {





Static Analysis Example

Potential applications
Potential applications

  • Tool for future database research

  • Improved performance from better compiler / language based optimization

  • New programming language primitives seek to abstract SQL away. In some cases legacy declarative interfaces may simply be getting in the way

Lock manager api
Lock Manager API

  • Page level locking can be supported by the buffer manager, but requires solid error handling.

  • Record level / index locking is tricky

    • Needs to understand built in and third party extensions

    • Plan to implement Hierarchical 2PL in a way that allows reuse by index implementations

    • Index implementations can simply lock the entire index if performance is not an issue.

In memory vs on disk semantics
In memory vs. on disk semantics

  • Holy grail: Application data acts like persistent data

    • But we still want a bunch of database features

  • One solution: Map a custom declarative interface into SQL.

    • Don’t we still need an optimizer, etc for the in memory data?

    • Transactional pages look a lot like RAM, especially if you provide a library of persistent data structures that match the ones the application uses

Sample operation implementation 1 3
Sample Operation Implementation (1/3)

// Operation Implementation

// p is the bufferPool’s current copy of the page.

int operateIncrement(int xid, Page *p, lsn_t lsn,

recordid rid, const void * d) {

inc_dec_t * arg = (inc_dec_t*)d;

int i;

latchRecord(p, rid);

readRecord(xid, p, rid, &i); // read current value

i += arg->amount;

// write new value, update LSN

writeRecord(xid, p, lsn, rid, &i);

unlatchRecord(p, rid);

return 0; // no error


Sample operation implementation 2 3
Sample Operation Implementation (2/3)

// register the operation

ops[OP_INCREMENT].implementation= &operateIncrement;

ops[OP_INCREMENT].argumentSize = sizeof(inc_dec_t);

// set the REDO to be the same as normal operation

ops[OP_INCREMENT].redoOperation = OP_INCREMENT;

// UNDO is the inverse of REDO

ops[OP_INCREMENT].undoOperation = OP_DECREMENT;

// Define inc_dec_t

typedef struct {int amount } inc_dec_t;

Sample operation implementation 3 3
Sample Operation Implementation (3/3)

// User friendly wrapper function

int Tincrement(int xid, recordid rid, int amount) {

// rec will be serialized to the log

int_dec_t rec;

rec.amount = amount;

// write a log entry, then execute it

Tupdate(xid, rid, &rec, OP_INCREMENT);

// return the incremented value

int new_value

// wrappers can call other wrappers

Tread(xid, rid, &new_value);

return new_value;


What if the database is missing a crucial feature
What if the database is missing a crucial feature?

  • An application could use the database anyway

    • Convoluted data and/or programming model

    • Performance problems

  • Or it could implement what it needs from scratch

    • Reinventing the wheel

    • Subtle problems with data loss and corruption

Modularity of storage implementation
Modularity of storage implementation

  • Focus on simplifying the APIs within the RSS

    • Operation implementations consist of two callbacks, “redo()” and “undo()” (there is no “do()”)

    • Subcomponents implement flexible APIs

Page File

read memory

page updates

Read-only Access Methods

UNDO/REDO requests


Recovery / Abort

Operation Implementation

Wrapper Function(s)

log entries



invoke REDO

App-specific extensions


Log Manager

write log

(Arrows point in the direction of application data flow)

Language based optimization
Language Based Optimization

  • Applications often use storage libraries in limited, predictable ways

  • Storage infrastructure must support all legal access patterns

  • Could add calls to the API to optimize special cases

    • Difficult to use correctly

    • Library contains multiple implementations of each function

Longer introduction
Longer Introduction

  • Conventional databases are not appropriate for some applications

    • It takes time to add support for new classes of applications

    • Niche applications may not warrant added complexity

    • Sometimes declarative interfaces are overkill

  • Low level API’s can be difficult to use

    • Expose intricately connected subsystems

    • Bugs in recovery logic

    • Applications must implement high-level functionality

  • Modern programming techniques can address these problems

Flexible transactional storage

  • Relational databases force some decisions upon application developers:

    • Data model / layout

    • Concurrency model

    • Consistency model

    • Recovery and durability semantics

    • Replication system

    • Declarative programming models

    • and so on…

Review of write ahead logging
Review of Write Ahead Logging

Begin T1

P1ß …

Abort T1

P2ß …

P3ß …

Begin T2

P3ß …

Abort T1

Problem 1: Physical undo, concurrent transactions and non-atomic operations interact poorly

Solution: Logical undo

Problem 2: If the tree is inconsistent during recovery, logical undo fails

Solution: (Nested Top Actions) Use physical undo until consistency is restored then atomically switch to logical undo