Downloaded 623 times
![51
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](https://image.slidesharecdn.com/wiredtiger-151209083622-lva1-app6892/85/MongoDB-WiredTiger-Internals-51-320.jpg)
Uploaded byNorberto Leite
MongoDB WiredTiger Internals
The document discusses the WiredTiger storage engine, introduced in MongoDB 3.0, highlighting its benefits such as improved performance, concurrency control, and support for multiple compression algorithms. It details the architecture, functions, and capabilities of WiredTiger, including how it optimizes data access and management for various workloads. The content emphasizes future enhancements and the transition of existing infrastructure to utilize WiredTiger effectively.
More Related Content
Similar to MongoDB WiredTiger Internals
![Lect 1 Number systems and base conversions. [Autosaved].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/lect1numbersystemsandbaseconversions-260111134109-67c2d865-thumbnail.jpg?width=640&height=640&fit=bounds)
MongoDB WiredTiger Internals
- 1.
- 2.
- 3.
- 4.
- 5. 5 Compression in Action (Flightsdatabase, ht Asya)
- 6. 6 Agenda Storage Engine API WiredTigerArchitecture Overall Benefits
- 7.
- 8. 8 MongoDB Architecture Content Repo IoT Sensor Backend AdService 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 Management Security Example Future State Beta
- 9. 9 Storage Engine API • Allows to "plug-in" different storage engines – Different use cases 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 …)
- 10.
- 11. 2 Storage EnginesAvailable … and more in the making! MMAPv1 WiredTiger
- 12. 12 MongoDB Architecture Content Repo IoT Sensor Backend AdService 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 Management Security Example Future State Beta
- 13.
- 14.
- 15. 15 MMAPv1 • Improved concurrencycontrol • 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
- 16.
- 17.
- 18. 18 What is WiredTiger? • Storage engine company founded by BerkeleyDB alums • Recently acquired by MongoDB • Available as a storage engine option in MongoDB 3.0
- 19. 19 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
- 20.
- 21. WiredTiger Architecture WiredTiger Engine Schema& Cursors Python API C API Java API Database Files Transactions Page read/write Logging Column storage Block management Row storage Snapshots Log Files Cache
- 22. WiredTiger Sessions Document DataModel WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal Your App Driver connection (mongod) Session Cursor Session Session Cursor Cursor
- 23. WiredTiger Sessions Document DataModel WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal Your App Driver • 3 main purposes • Schema Operations • Transaction Management • Cursor Creation
- 24. WiredTiger Schema &Cursors Document Data Model WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal db.createCollection("somecollection") db.somecollection.drop() db.somecollection.find({...}) db.somecollection.remove({_id:111}) db.somecollection.stats() db.somecollection.explain().find({_id:111})
- 25. WiredTiger Schema &Cursors Document Data Model WiredTiger Engine Transactions Snapshosts Page read/write Logging Cache Schema & Cursors Row Storage Block Management DB Files Log Files • Schema Operations • create, drop, truncate, verify … • Cursors • CRUD • Data iteration • Statistics
- 26. Collection WiredTiger Transactions Document DataModel WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal COMMAND INSERT { "_id": 111, "name": "Spock", "message": "Live Long and Prosper!" } Index {_id: 1} Index {name: 1} Index {message: "text"}
- 27. WiredTiger Transactions Document DataModel WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal • Transaction per Session close, open, commit, rollback • Ensures the atomicity/consistency of MongoDB operations • Updates / Writes documents • Updates indexes This is not multi-document transaction support!
- 28.
- 29. Traditional B+tree (htwikipedia)
- 30. Trees in cache non-resident child ordinarypointer root page internal page internal page root page leaf page leaf page leaf page leaf page
- 31. Cache Page in Disk rootpage leaf page Disk Page not in memory File Read
- 32. Cache Page in Memory rootpage Internal Page Internal Page Internal Page Internal Page Leaf Page Leaf Page Leaf Page Leaf Page
- 33. Traditional B-tree root page InternalPage Internal Page Internal Page Internal Page Leaf Page Leaf Page Leaf Page Leaf Page
- 34. Deadlock Avoidance root page InternalPage Internal Page Internal Page Internal Page Leaf Page Leaf Page Leaf Page Leaf Page
- 35. Cache Page in Memory rootpage Internal Page Internal Page Internal Page Internal Page Leaf Page Leaf Page Leaf Page Leaf Page Doc1Doc0 Doc5Doc2 Doc3 Doc4 Writer Reader Standard pointers between tree levels instead of traditional files system offset pointers
- 36. Page in Cache Disk Pageimages Page images Page images on-disk Page Image index Update on-disk Page Image index Updates db.collection.insert( {"_id": 123}) Clean Page Dirty Page Eviction Thread
- 37. Page in Cache Disk Pageimages Page images Page images on-disk Page Image index Update on-disk Page Image index Updates db.collection.insert( {"_id": 123}) Clean Page Dirty Page Index is build during read
- 38. Page in Cache Disk Pageimages Page images Page images on-disk Page Image index Updates' db.collection.insert( {"_id": 123}) Dirty Page Updates are held in a skip list Updates'' db.collection.insert( {"_id": 125}) Updates''' db.collection.insert( {"_id": 124}) Time
- 39. Reconciliation Disk Page images Page images Pageimages on-disk Page Image index Updates' Dirty Page Eviction Thread Updates'' Updates''' Reconciliation Cache full After X operations On a checkpoint
- 40. 40 In-memory performance • Treesin 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
- 41. 41 Takeaways • Page andtree access is optimized • Allows concurrent operations – updates skip lists • Tuning options that you should be aware of: – storage.wiredTiger.engineConfig.cacheSizeGB • You can determine the space allocated for your cache • Makes your deployment and sizing more predictable https://docs.mongodb.org/manual/reference/configuration-options/#storage.wiredTiger.engineConfig.cacheSizeGB Wiredtiger cache! MongoDB uses more memory than just the Storage Engine needs!
- 42.
- 43. 43 What is ConcurrencyControl? • 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)
- 44. MVCC on-disk Page Image index WRITE Btxn(1) Updates'''Updates' WRITE A txn(2) Updates'' Time WRITE A txn(3) Conflict Detection!
- 45. 45 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
- 46. MVCC on-disk Page Image index Updates' WRITE Btxn(1) Updates'' Updates''' WRITE A txn(2) Time WRITE A txn(3) READ A
- 47.
- 48. 48 WiredTiger Page IO DocumentData Model WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal on-disk Page Image index Updates''' Disk Page Reconciliation Page Allocation Splitting
- 49. 49 WiredTiger Page IO DocumentData Model WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal on-disk Page Image index Updates''' Disk Page' Reconciliation Page Allocation Splitting Page''
- 50. 50 WiredTiger Page IO DocumentData Model WiredTiger Engine Transactions Snapshosts Page read/write WAL Cache Schema & Cursors Row Storage Block Management DB Files Journal on-disk Page Image index Updates''' Disk Reconciliation Page Allocation Splitting Page Compression - snappy (default) - zlib - none
- 51. 51 Compression • WiredTiger usessnappy 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
- 52. 52 Checksums • A checksumis stored with every uncompressed 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
- 53. 53 Compression in Action (Flightsdatabase, ht Asya)
- 54. 54 Takeaway • Main featurethat impacts the vast majority of MongoDB projects / use cases • CPU bound • Different algorithms for different workloads – Collection level compression tuning • Smaller Data Faster IO – Not only improves the disk space footprint – also IO – and index traversing
- 55. Other Gems ofWiredTiger
- 56. 56 Index per Directory • Use different drives for collections and indexes – Parallelization of write operations – MMAPv1 only allows directoryPerDatabase mongod Collection A Index "name " Collection B Index "_id"
- 57. 57 Consistency without Journaling • MMAPv1 uses write-ahead log (journal) to guarantee consistency • WT doesn't have this need: no in-place updates – Write-ahead log committed at checkpoints and with j:true – Better for insert-heavy workloads – By default journaling is enabled! • Replication guarantees the durability
- 58.
- 59.
- 60. 60 What’s next forWiredTiger? • Tune for (many) more workloads – avoid stalls during checkpoints with 100GB+ caches – make capped collections (including oplog) more efficient • Adding encryption • More advanced transactional semantics in the storage engine API
- 61. 61 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 – Delete dbpath – Relaunch w/ --storageEngine=wiredTiger – Wait for resync – Rollover
- 62.
- 63. 63 MongoDB 3.0 • PluggableStorage Engine API • Storage Engines • Large Replica Sets • Big Polygon • Security Enhancements – SCRAM • Audit Trail • Simplified Operations – Ops Manager • Tools Rewrite
- 64.
- 65.
- 66.