WiredTiger & What's New in 3.0

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I'm Excited

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AgendaMongoDB 3.0Storage Engine APIWiredTiger Architecture

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Benefits

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Performance!

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Compression in Action

(Flights database, ht Asya)

But 3.0 is more than WiredTiger

http://www.livingincebuforums.com/ipb/uploads/monthly_10_2011/post-198-0-67871200-1318223706.jpg

Pluggable Storage Engine API

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MongoDB Architecture

Content Repo

IoT Sensor Backend Ad Service Customer

Analytics Archive

MongoDB Query Language (MQL) + Native Drivers

MongoDB Document Data Model

MMAP V1 WT In-Memory ? ?

Supported in MongoDB 3.0 Future Possible Storage Engines

Man

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Example Future State

Experimental

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Storage Engine API• Allows to "plug-in" different storage engines

– Different work sets require different performance characteristics – mmapv1 is not ideal for all workloads– More flexibility

• Can mix storage engines on same replica set/sharded cluster• Opportunity to integrate further ( HDFS, native encrypted, hardware

optimized …)

Storage Engines

2 Storage Engines Available

… and more in the making!MMAPv1 WiredTiger

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MongoDB Architecture

Content Repo

IoT Sensor Backend Ad Service Customer

Analytics Archive

MongoDB Query Language (MQL) + Native Drivers

MongoDB Document Data Model

MMAP V1 WT In-Memory ? ?

Supported in MongoDB 3.0 Future Possible Storage Engines

Man

agem

ent

Sec

urity

Example Future State

Experimental

mongod --storageEngine wiredTiger

MMAPv1

https://angrytechnician.files.wordpress.com/2009/05/memory.jpg

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MMAPv1

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MMAPv1

• Improved concurrency control

• Great performance on read-heavy workloads

• Data & Indexes memory mapped into virtual address space

• Data access is paged into RAM

• OS evicts using LRU

• More frequently used pages stay in RAM

3.0 Default

http://cdn.theatlantic.com/static/infocus/ngt051713/n10_00203194.jpg

WiredTiger

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What is WiredTiger?• Storage engine company founded by BerkeleyDB alums• Recently acquired by MongoDB• Available as a storage engine option in MongoDB 3.0

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Motivation for WiredTiger• Take advantage of modern hardware:

– many CPU cores– lots of RAM

• Minimize contention between threads– lock-free algorithms, e.g., hazard pointers– eliminate blocking due to concurrency control

• Hotter cache and more work per I/O– compact file formats– compression

WiredTiger Architecture

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WiredTiger Architecture

All operations will be handled atomically through the transaction mechanism

For data access methods MongoDB is

using the Row storage, although we are working on more options for future

versions

In-memory Performance

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Traditional B+tree (ht wikipedia)

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Trees in cache

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Hazard Pointers

1 memory flush

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Pages in cache

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In-memory performance• Trees in cache are optimized for in-memory access• Follow pointers to traverse a tree

– no locking to access pages in cache• Keep updates separate from clean data• Do structural changes (eviction, splits) in background threads

Document-level Concurrency

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What is Concurrency Control?

• Computers have– multiple CPU cores– multiple I/O paths

• To make the most of the hardware, software has to execute multiple operations in parallel

• Concurrency control has to keep data consistent• Common approaches:

– locking– keeping multiple versions of data (MVCC)

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WiredTiger Concurrency Control

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Pages in cacheskiplist

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Multiversion Concurrency Control (MVCC)

• Multiple versions of records kept in cache• Readers see the committed version before the transaction started

– MongoDB “yields” turn large operations into small transactions• Writers can create new versions concurrent with readers• Concurrent updates to a single record cause write conflicts

– MongoDB retries with back-off

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MVCC In Action

updateupdate

Compression

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WiredTiger Page I/O

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Trees in cache

read required

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Pages in cache

reconciled during write

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Checksums• A checksum is stored with every page• Checksums are validated during page read

– detects filesystem corruption, random bitflips• WiredTiger stores the checksum with the page address (typically

in a parent page)– extra safety against reading a stale page image

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Compression• WiredTiger uses snappy compression by default in MongoDB• Supported compression algorithms:

– snappy [default]: good compression, low overhead– zlib: better compression, more CPU– none

• Indexes also use prefix compression– stays compressed in memory

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Compression in Action

(Flights database, ht Asya)

What's Next

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WiredTiger LSM support

• Out-of-order insert workloads• Data set much larger than cache• Query performance is not (as) important• Resource overhead of background maintenance

acceptable

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What’s next for WiredTiger?

• Tune for (many) more workloads– avoid stalls during checkpoints with 100GB+ caches– make capped collections (including oplog) more efficient– Mark Callaghan seeing ~2x speedup in 3.1 snapshots:

http://smalldatum.blogspot.com/ • Make Log Structured Merge (LSM) trees work well with MongoDB

– out-of-cache, write-heavy workloads• Adding encryption• More advanced transactional semantics in the storage engine API

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Updates and Upgrades

• Can not– Can't copy database files– Can't just restart w/ same dbpath

• Yes we can!– Initial sync from replica set works perfectly!– mongodump/restore

• Rolling upgrade of replica set to WT:– Shutdown secondary – Relaunch w/ --storageEngine=wiredTiger– Rollover

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Gotchas

• No 32-bit Support– WT is 64bit only

• system.indexes & system.namespaces deprecated– Explicit commands: db.getIndexes() db.getCollectionNames()

https://tingbudongchine.files.wordpress.com/2012/08/lemonde1.jpeg

Take Aways

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MongoDB 3.0

• Pluggable Storage Engine API• Storage Engines• Large Replica Sets• Big Polygon• Security Enhancements – SCRAM • Audit Trail• Simplified Operations – Ops Manager• Tools Rewrite

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Benefits

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Performance!

http://www.mongodb.com/lp/white-paper/benchmark-report

http://cl.jroo.me/z3/v/D/C/e/a.baa-Too-many-bicycles-on-the-van.jpg

Norberto LeiteTechnical Evangelistnorberto@mongodb.com@nleite

Obrigado!