How Voron works: Insight into the new RavenDB storage engine
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Voron is a low-level key-value store that is transactional and uses MVCC. It uses a multi-layered structure with a root tree containing references to named trees, which can themselves contain multiple additional trees. This tree-of-trees structure allows for optimizations like storing single items directly in the parent tree. Voron focuses on very fast reads and writes through its use of memory mapping, journaling only committed changes, and handling I/O asynchronously. It learns from other stores like LevelDB but is optimized for performance through its novel tree architecture and low-level implementation.
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How Voron works: Insight into the new RavenDB storage engine
- 1. Level 400: Diving into Voron Oren Eini ayende@ayende.com ayende.com/blog Hibernating Rhinos
- 2. Voron is… Low level key / value store Transactional / ACID MVCC Multi layers
- 3. WHY?!
- 4. background LevelDB LMDB Esent
- 5. Seeks are slow 0.01 ms – Compress 1kb with Zippy 0.25 ms – Read 1 MB from memory 0.50 ms – Ping inside data center 10.0 ms – Disk seek 10.0 ms – Read 1 MB from network 30.0 ms – Read 1 MB from disk
- 7. B+ Trees
- 8. Implementation 4KB Pages B+ Tree Page translation table MVCC Journal file Scratch file Memory mapped
- 9. Modifying the tree Find appropriate #to modify. Get a scratch page, copy #to scratch page. Register scratch #with the old ## in #translation table (PTT). Modify the #as you wish. On commit, the PTT becomes publicly visible. All changed pages are written to journal file. If rollback, revert to previous PTT, release scratch pages, done.
- 10. #0 -> #3 #1 -> #1 #0 -> #3 #1 -> #5
- 11. Background Find pages in scratch that have no one looking at older versions of them. Copy to data file. Clear the scratch space.
- 12. How it works Only I/O during commits is a single write through, compressed, of data to journal. Moving data to data file is done in async. No need to call fsync(). Full & incremental backups.
- 13. Missing the forest Voron isn’t a B+ Tree system. It doesn’t have a tree, it has trees. Plural. <blink>Important</blink>
- 14. Falling trees Single root tree Contain many additional trees. Tree is similar to a table. Operations on tree: Add(key, value) Del(key, value) Find(key) : value Iterate() (Seek,Next, Prev)
- 15. How it works?
- 16. With indexes
- 17. Finding stuff * Not the most efficient method
- 18. So, Voron has trees… Root tree Free Space tree Contains references to named trees Enough? Tree of trees MultiAdd, MultiDelete, MultiRead
- 19. Why multi trees? Optimization – if has just 1 item (and no value) can directly use the parent tree store. Store multiple items for a single value.
- 21. What voron does? Opens up a lot of interesting scenarios. We have far better control over persistence now. Very low level (bits & bytes). Very fast! Concurrency benefits: Reads Writes* * Yet Voron allows only a single writer!
- 22. What it does not? It isn’t about Linux. It can’t run on Linux*. Need to implment: PosixPureMemoryPager PosixPageFileBackedMemoryMappedPager PosixMemoryMapPager Waiting for big Linux push post 3.0 release.
- 23. the cloud story… Scratch / temp usage Utilize fast local drives that can go away. Slow I/O only hold us for tx commit (and we optimized that).
- 24. Summary Voron learned from LevelDB, LMDB, Esent. Journal for Atomicity, Consistency & Durability. MVCC for Consistency & Isolation. Root tree, named tress, multi trees.
- 25. Questions?