Split Snapshots and Skippy Indexing: Long Live the Past! Ross Shaull Liuba Shrira Brandeis...

Split Snapshots and Skippy Indexing:Long Live the Past!

Ross Shaull <rshaull@cs.brandeis.edu>

Liuba Shrira <liuba@cs.brandeis.edu>

Brandeis University

Our Idea of a Snapshot

• A window to the past in a storage system• Access data as it was at time snapshot was

requested• System-wide• Snapshots may be kept forever

– I.e., “long-lived” snapshots

• Snapshots are consistent– Whatever that means…

• High frequency (up to CDP)

Why Take Snapshots?

• Fix operator errors• Auditing

– When did Bob’s salary change, and who made the changes?

• Analysis– How much capital was tied up in blue shirts at the

beginning of this fiscal year?

• We don’t necessarily know now what will be interesting in the future

BITE

• Give the storage system a new capability: Back-in-Time Execution

• Run read-only code against current state and any snapshot

• After issuing request for BITE, no special code required for accessing data in the snapshot

Other Approaches: Databases

• ImmortalDB, Time-Split BTree (Lomet) – Reorganizes current state– Complex

• Snapshot isolation (PostgreSQL, Oracle)– Extension to transactions– Only for recent past

• Oracle FlashBack– Page-level copy of recent past (not forever)– Interface seems similar to BITE

Other Approaches: FS

• WAFL (Hitz), ext3cow (Peterson)– Limited on-disk locality– Application-level consistency a challenge

• VSS (Sankaran)– Blocks disk requests– Suitable for backup-type frequency

A Different Approach

• Goals:– Avoid declustering current state– Don’t change how current state is accessed– Application requests snapshot– Snapshots are “on-line” (not in warehouse)

• Split Snapshots– Copy past out incrementally– Snapshots available through virtualized buffer

manager

Our Storage System Model

• A “database”– Has transactions– Has recovery log– Organizes data in pages on disk

Our Consistency Model

• Crash consistency– Imagine that a snapshot is declared, but

then before any modifications can be made, the system crashes

– After restart, recovery kicks in and the current state is restored to *some* consistent point

– All snapshots will have this same consistency guarantee after a crash

I want record R

Our Storage System Model

P1

P3

…

Application

Cache

Disk

P1 … Pn

AccessMethods

Database

Snapshot Now

Find Table

Find Root

Search for R

Return R

P1 Address XP2 Address Y…

Page Table

Retaining the Past

Versus

Copy-on-Write (COW)

P1 P2 P1

P1

P2

P1

P2

PageTable

PageTable

Snapshot PageTable “S”

Operations:

Snapshot “S”

Modify P1

The old page table became the Snapshot

page table

P1P1

Split-COW

Expensive to update P2 in both

page tables

P1 P2

P1 P1 P2

P1

P2

PageTable

P1

P2

SPT(S)

P1P1

P2

P2

SPT(S+1)

What’s next

1. How to manage the metadata?2. How will snapshot pages be accessed?3. Can we be non-disruptive?

Metadata Solution

• Metadata (page tables) created incrementally

• Keeping many SPTs costly

• Instead, write “mappings” into log

• Materialize SPT on-demand

Maplog

Start

Maplog• Mappings created incrementally• Added to append-only log• Start points to first mapping created

after a snapshot is declared

P1 P1 P2 P1 P1 P1 P2 P1P2

Sna

p 1

Sna

p 2

Sna

p 3

Sna

p 4

Sna

p 5

Sna

p 6

P3

P1 P1 P2 P1 P1 P1 P2 P1P2

Sna

p 1

Sna

p 2

Sna

p 3

Sna

p 4

Sna

p 5

Sna

p 6

Maplog

Start

Maplog• Materialize SPT with scan• Scan for SPT(S) begins at Start(S)• Notice that we read some mappings

that we do not need

P3

Cost of Scanning Maplog

• Let overwrite cycle length L be the number of page updates required to overwrite entire database

• Maplog scan cannot be longer than overwrite cycle

• Let N be the number of pages in the database

• For a uniformly random workload, L N ln N (by the “coupon collector’s waiting time” problem)

• Skew in the update workload lengthens overwrite cycle

• Skew of 80/20 (80% of updates to 20% of pages) increases L by a factor of 4

Skew hurts

Skippy

P1 P2 P1 P1P2Skippy Level 1

Maplog

Start

• Copy first-encountered mapping (FEM) within node to next level

P1 P1 P2 P1 P1 P1 P2 P1P2

Sna

p 1

Sna

p 2

Sna

p 3

Sna

p 4

Sna

p 5

Sna

p 6

P3

P3

Pointers

Copies

Skippy

P1 P2 P1 P1P2

Maplog

Start

P1 P1 P2 P1 P1 P1 P2 P1P2

Sna

p 1

Sna

p 2

Sna

p 3

Sna

p 4

Sna

p 5

Sna

p 6

P3

P3Skippy Level 1

Cut redundant mapping count in

half

K-Level Skippy• Can eliminate effect of skew — or more• Enables ad-hoc, on-line access to snapshots,

whether they are old or young

Skew # Skippy Levels Time to Materialize SPT (s)

50/50 0 13.8

80/20 0 19.0

1 15.8

2 14.7

3 13.9

99/1 0 33.3

1 6.69

Read Current StateBITE

Accessing Snapshots• Transparent to layers above cache• Indirection layer to redirect page requests

from a BITE transaction into the snapstore

P1 P2

P1 P1 P2

Cache

P1

P2

P2

Non-Disruptiveness

• Can we create Skippy and COW pre-states without disrupting the current state?

• Key idea:– Leverage recovery to defer all snapshot-

related writes– Write snapshot data in background to

secondary disk

Implementation• BDB 4.6.21• Page cache augmented

– COWs write-locked pages– Trickle COW’d pages out over time

• Leverage recovery– Metadata created in-memory at transaction

commit time, but only written at checkpoint time– After crash, snapshot pages and metadata can be

recovered in one log pass

• Costs– Snapshot log record– Extra memory– Longer checkpoints

Early Disruptiveness Results

• Single-threaded updating workload of 100,000 transactions

• 66M database • We can retain a

snapshot after every transaction for a 6–8% penalty to writers

• Tests with readers show little impact on sequential scans (not depicted)

631

575

472

656

593

508

674

613

511

0

100

200

300

400

500

600

700

800

50/50 80/20 99/1

Skew

Time (s)

No Snapshots

Snapshots Every Other Transaction

Snapshots Every Transaction

Paper Trail

• Upcoming poster and short paper at ICDE08

• “Skippy: a New Snapshot Indexing Method for Time Travel in the Storage Manager” to appear in SIGMOD08

• Poster and workshop talks– NEDBDay08, SYSTOR08

Questions?

Backups…

Recovery Sketch 1

• Snapshots are crash consistent• Must recover data and metadata for all

snapshots since last checkpoint• Pages might have been trickled, so must

truncate snapstore back to last mapping before previous checkpoint

• We require only that a snapshot log record be forced into the log with a group commit, no other data/metadata must be logged until checkpoint.

Recovery Sketch 2

• Walk backward through WAL, applying UNDOs

• When snapshot record is encountered, copy the “dirty” pages and create a mapping

• Trouble is that snapshots can be concurrent with transactions

• Cope with this by “COWing” a page when an UNDO for a different transaction is applied to that page

The Future

• Sometimes we want to scrub the past– Running out of space?– Retention windows for SOX-compliance

• Change past state representation– Deduplication– Compression