Flink Forward Berlin 2017: Stefan Richter - A look at Flink's internal data structures and algorithms for efficient checkpointing
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The document discusses Flink's use of internal data structures to efficiently support checkpointing. It describes how Flink uses RocksDB, a log-structured merge tree database, as a backend to support asynchronous and incremental checkpoints. RocksDB allows checkpoints to be taken with low overhead by creating immutable snapshots of the on-disk data structures. It also facilitates incremental checkpoints by efficiently detecting state changes between checkpoints based on the creation and deletion of immutable sorted string tables. The document provides an example to illustrate how RocksDB integrates with the distributed filesystem and job manager to support incremental checkpointing. It also discusses how Flink uses a copy-on-write hash map approach with the heap state backend to support asynchronous checkpoints while detecting state
![Heap Backend: Chained Hash Map
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![34
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![36
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![37
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„Structural
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![Copy-on-Write Hash Map
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Map<K, S> {
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int mapVersion;
int requiredVersion;
OrderedSet<Integer> snapshots;
}
Entry<K, S> {
final K key;
S state;
Entry next;
int stateVersion;
int entryVersion;
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![Copy-on-Write Hash Map - Snapshots
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Map<K, S> {
Entry<K, S>[] table;
int mapVersion;
int requiredVersion;
OrderedSet<Integer> snapshots;
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Entry<K, S> {
final K key;
S state;
Entry next;
int stateVersion;
int entryVersion;
}
Create Snapshot:
1. Flat array-copy of table array
2. snapshots.add(mapVersion);
3. ++mapVersion;
4. requiredVersion = mapVersion;
Release Snapshot:
1. snapshots.remove(snapVersion);
2. requiredVersion = snapshots.getMax();](https://image.slidesharecdn.com/ff17berlin-170914104549/85/Flink-Forward-Berlin-2017-Stefan-Richter-A-look-at-Flink-s-internal-data-structures-and-algorithms-for-efficient-checkpointing-39-320.jpg)
Flink Forward Berlin 2017: Stefan Richter - A look at Flink's internal data structures and algorithms for efficient checkpointing
- 1. 1 Stefan Richter @StefanRRichter September 13, 2017 A Look at Flink’s Internal Data Structures for Efficient Checkpointing
- 2. Flink State and Distributed Snapshots 2 State Backend Stateful Operation Source Event
- 3. 3 Trigger checkpoint Inject checkpoint barrier Stateful Operation Source Flink State and Distributed Snapshots „Asynchronous Barrier Snapshotting“
- 4. 4 Take state snapshot Synchronously trigger state snapshot (e.g. copy-on-write) Flink State and Distributed Snapshots Stateful Operation Source „Asynchronous Barrier Snapshotting“
- 5. 5 DFS Processing pipeline continues Durably persist full snapshots asynchronously For each state backend: one writer, n reader Flink State and Distributed Snapshots Stateful Operation Source
- 6. Challenges for Data Structures ▪ Asynchronous Checkpoints: ▪ Minimize pipeline stall time while taking the snapshot. ▪ Keep overhead (memory, CPU,…) as low as possible while writing the snapshot. ▪ Support multiple parallel checkpoints. 6
- 7. 7 DFS Durably persist incremental snapshots asynchronously Processing pipeline continues ΔΔ (differences from previous snapshots) Flink State and Distributed Snapshots Stateful Operation Source
- 8. Full Snapshots 8 K S 2 B 4 W 6 N K S 2 B 3 K 4 L 6 N K S 2 Q 3 K 6 N 9 S K S 2 B 4 W 6 N K S 2 B 3 K 4 L 6 N K S 2 Q 3 K 6 N 9 S Checkpoint 1 Checkpoint 2 Checkpoint 3 time State Backend (Task-local) Checkpoint Directory (DFS) Updates Updates
- 9. Checkpoint Directory (DFS) Incremental Snapshots 9 K S 2 B 4 W 6 N K S 2 B 3 K 4 L 6 N K S 2 Q 3 K 6 N 9 S K S 2 B 4 W 6 N K S 3 K 4 L K S 2 Q 4 - 9 S iCheckpoint 1 iCheckpoint 2 iCheckpoint 3 Δ(-, icp1) Δ(icp1, icp2) Δ(icp2, icp3) time State Backend (Task-local) Updates Updates
- 10. 5 TB 10 TB 15 TB Incremental Full Incremental vs Full Snapshots 10 Checkpoint Duration State Size
- 11. State Backend (Task-local) Incremental Recovery 11 K S 2 B 4 W 6 N K S 2 B 3 K 4 L 6 N K S 2 Q 3 K 6 N 9 S K S 2 B 4 W 6 N K S 3 K 4 L K S 2 Q 4 - 9 S time Recovery? + + iCheckpoint 1 iCheckpoint 2 iCheckpoint 3 Δ(-, icp1) Δ(icp1, icp2) Δ(icp2, icp3) Checkpoint Directory (DFS)
- 12. Challenges for Data Structures ▪ Incremental checkpoints: ▪ Efficiently detect the (minimal) set of state changes between two checkpoints. ▪ Avoid unbounded checkpoint history. 12
- 13. Two Keyed State Backends 13 HeapKeyedStateBackend RocksDBKeyedStateBackend • State lives in memory, on Java heap. • Operates on objects. • Think of a hash map {key obj -> state obj}. • Goes through ser/de during snapshot/restore. • Async snapshots supported. • State lives in off-heap memory and on disk. • Operates on serialized bytes. • Think of K/V store {key bytes -> state bytes}. • Based on log-structured-merge (LSM) tree. • Goes through ser/de for each state access. • Async and incremental snapshots.
- 14. Asynchronous and Incremental Checkpoints with the RocksDB Keyed-State Backend 14
- 15. What is RocksDB ▪ Ordered Key/Value store (bytes). ▪ Based on LSM trees. ▪ Embedded, written in C++, used via JNI. ▪ Write optimized (all bulk sequential), with acceptable read performance. 15
- 16. RocksDB Architecture (simplified) 16 Memory Local Disk K: 2 V: T K: 8 V: W Memtable - Mutable Memory Buffer for K/V Pairs (bytes) - All reads / writes go here first - Aggregating (overrides same unique keys) - Asynchronously flushed to disk when full
- 17. RocksDB Architecture (simplified) 17 Memory Local Disk K: 4 V: Q Memtable K: 2 V: T K: 8 V: WIdx SSTable-(1) BF-1 - Flushed Memtable becomes immutable - Sorted By Key (unique) - Read: first check Memtable, then SSTable - Bloomfilter & Index as optimisations
- 18. RocksDB Architecture (simplified) 18 Memory Local Disk K: 2 V: A K: 5 V: N Memtable K: 4 V: Q K: 7 V: SIdx SSTable-(2) K: 2 V: T K: 8 V: WIdx SSTable-(1) K: 2 V: C K: 7 -Idx SSTable-(3) BF-1 BF-2 BF-3 - Deletes are explicit entries - Natural support for snapshots - Iteration: online merge - SSTables can accumulate
- 19. RocksDB Compaction (simplified) 19 K: 2 V: C K: 7 - K: 4 V: Q K: 7 V: S K: 2 V: T K: 8 V: W K: 2 V: C K: 4 V: Q K: 8 V: W multiway sorted merge „Log Compaction“ SSTable-(3) SSTable-(1)SSTable-(2) SSTable-(1, 2, 3) (rm)
- 20. RocksDB Asynchronous Checkpoint ▪ Flush Memtable. (Simplified) ▪ Then create iterator over all current SSTables (they are immutable). ▪ New changes go to Memtable and future SSTables and are not considered by iterator. 20
- 21. RocksDB Incremental Checkpoint ▪ Flush Memtable. ▪ For all SSTable files in local working directory: upload new files since last checkpoint to DFS, re-reference other files. ▪ Do reference counting on JobManager for uploaded files. 21
- 22. Incremental Checkpoints - Example ▪ Illustrate interactions (simplified) between: ▪ Local RocksDB instance ▪ Remote Checkpoint Directory (DFS) ▪ Job Manager (Checkpoint Coordinator) ▪ In this example: 2 latest checkpoints retained 22
- 23. sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 Incremental Checkpoints - Example TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 24. sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 Incremental Checkpoints - Example TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 25. sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 Incremental Checkpoints - Example TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 26. Incremental Checkpoints - Example sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 27. Incremental Checkpoints - Example sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 28. Incremental Checkpoints - Example sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 29. Incremental Checkpoints - Example 29 sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 30. Incremental Checkpoints - Example 30 sstable-(1,2,3) sstable-(1) sstable-(2) sstable-(1) sstable-(2) sstable-(3) sstable-(4) sstable-(4) sstable-(5) CP 1 CP 2 CP 3 merge sstable-(1,2,3) sstable-(4,5,6)CP 4 merge +sstable-(6) sstable-(3) sstable-(4) sstable-(1) sstable-(2) sstable-(1,2,3) sstable-(5) sstable-(4,5,6) CP 1 CP 2CP 2 CP 3 CP 4 TaskManager Local RocksDB Working Directory DFS Upload Job Manager
- 31. Summary RocksDB ▪ Asynchronous snapshots „for free“, immutable copy is already there. ▪ Trivially supports multiple concurrent snapshots. ▪ Detection of incremental changes easy: observe creation and deletion of SSTables. ▪ Bounded checkpoint history through compaction. 31
- 32. Asynchronous Checkpoints with the Heap Keyed-State Backend 32
- 33. Heap Backend: Chained Hash Map 33 S1 K1 Entry S2 K2 Entry next Map<K, S> { Entry<K, S>[] table; } Entry<K, S> { final K key; S state; Entry next; } Map<K, S>
- 34. 34 S1 K1 Entry S2 K2 Entry next Map<K, S> { Entry<K, S>[] table; } Entry<K, S> { final K key; S state; Entry next; } Map<K, S> Heap Backend: Chained Hash Map
- 35. 35 S1 K1 Entry S2 K2 Entry next Map<K, S> { Entry<K, S>[] table; } Entry<K, S> { final K key; S state; Entry next; } Map<K, S> Heap Backend: Chained Hash Map
- 36. 36 S1 K1 Entry S2 K2 Entry next Map<K, S> { Entry<K, S>[] table; } Entry<K, S> { final K key; S state; Entry next; } Map<K, S> Heap Backend: Chained Hash Map
- 37. 37 S1 K1 Entry S2 K2 Entry next Map<K, S> { Entry<K, S>[] table; } Entry<K, S> { final K key; S state; Entry next; } Map<K, S> „Structural changes to map“ (easy to detect) „User modifies state objects“ (hard to detect) Heap Backend: Chained Hash Map
- 38. Copy-on-Write Hash Map 38 S1 K1 Entry S2 K2 Entry next Map<K, S> „Structural changes to map“ (easy to detect) „User modifies state objects“ (hard to detect) Map<K, S> { Entry<K, S>[] table; int mapVersion; int requiredVersion; OrderedSet<Integer> snapshots; } Entry<K, S> { final K key; S state; Entry next; int stateVersion; int entryVersion; }
- 39. Copy-on-Write Hash Map - Snapshots 39 Map<K, S> { Entry<K, S>[] table; int mapVersion; int requiredVersion; OrderedSet<Integer> snapshots; } Entry<K, S> { final K key; S state; Entry next; int stateVersion; int entryVersion; } Create Snapshot: 1. Flat array-copy of table array 2. snapshots.add(mapVersion); 3. ++mapVersion; 4. requiredVersion = mapVersion; Release Snapshot: 1. snapshots.remove(snapVersion); 2. requiredVersion = snapshots.getMax();
- 40. Copy-on-Write Hash Map - 2 Golden Rules 1.Whenever a map entry e is modified and e.entryVersion < map.requiredVersion, first copy the entry and redirect pointers that are reachable through snapshot to the copy. Set e.entryVersion = map.mapVersion. Pointer redirection can trigger recursive application of rule 1 to other entries. 2.Before returning the state object s of entry e to a caller, if e.stateVersion < map.requiredVersion, create a deep copy of s and redirect the pointer in e to the copy. Set e.stateVersion = map.mapVersion. Then return the copy to the caller. Applying rule 2 can trigger rule 1. 40
- 41. Copy-on-Write Hash Map - Example 41 sv:0 ev:0 sv:0 ev:0 K:42 S:7 K:23 S:3 mapVersion: 0 requiredVersion: 0
- 42. Copy-on-Write Hash Map - Example 42 sv:0 ev:0 sv:0 ev:0 K:42 S:7 K:23 S:3 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 snapshot() -> 0
- 43. sv:0 ev:0 sv:0 ev:0 S:7 S:3 Copy-on-Write Hash Map - Example 43 K:42 K:23 sv:1 ev:1 K:13 S:2 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 put(13, 2)
- 44. sv:0 ev:0 sv:0 ev:0 S:7 S:3 Copy-on-Write Hash Map - Example 44 K:42 K:23 K:13 sv:1 ev:1 S:2 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23)
- 45. sv:0 ev:0 sv:0 ev:0 S:7 S:3 Copy-on-Write Hash Map - Example 45 K:42 K:23 K:13 sv:1 ev:1 S:2 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23) Caller could modify state object!
- 46. sv:0 ev:0 sv:0 ev:0 S:7 S:3 Copy-on-Write Hash Map - Example 46 K:42 K:23 S:3 K:13 sv:1 ev:1 S:2 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23) Trigger copy. How to connect? ?
- 47. sv:0 ev:0 sv:0 ev:0 S:7 S:3 Copy-on-Write Hash Map - Example 47 K:42 K:23 S:3 sv:1 ev:1 K:13 sv:1 ev:1 S:2 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23) Copy entry. How to connect? ?
- 48. sv:0 ev:0 sv:0 ev:0 S:7 S:3K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 48 K:42K:13 sv:1 ev:1 S:2 sv:0 ev:1 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23) Copy entry (recursive). Connect?
- 49. sv:0 ev:0 sv:0 ev:0 S:7 S:3 S:3 sv:1 ev:1 K:23 Copy-on-Write Hash Map - Example 49 K:42K:13 sv:1 ev:1 S:2 sv:0 ev:1 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(23)
- 50. S:3 sv:1 ev:1 sv:0 ev:0 sv:0 ev:0 S:7 S:3K:23 Copy-on-Write Hash Map - Example 50 K:42K:13 sv:1 ev:1 S:2 sv:0 ev:1 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(42)
- 51. S:3 sv:1 ev:1 sv:0 ev:0 sv:0 ev:0 S:7 S:3K:23 Copy-on-Write Hash Map - Example 51 K:42K:13 sv:1 ev:1 S:2 sv:0 ev:1 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(42) Copy required!
- 52. S:3 sv:1 ev:1 sv:0 ev:0 sv:0 ev:0 S:7 S:3K:23 Copy-on-Write Hash Map - Example 52 K:42 S:7 K:13 sv:1 ev:1 S:2 sv:1 ev:1 snapshotVersion: 0 mapVersion: 1 requiredVersion: 1 get(42)
- 53. K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 53 K:42 sv:1 ev:1 S:7 K:13 sv:1 ev:1 S:2 mapVersion: 1 requiredVersion: 0 release(0)
- 54. K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 54 sv:1 ev:1 S:2 K:42 sv:1 ev:1 S:7 K:13 snapshotVersion: 1 mapVersion: 2 requiredVersion: 2 snapshot() -> 1
- 55. K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 55 sv:1 ev:1 S:2 K:42 sv:1 ev:1 S:7 K:13 snapshotVersion: 1 mapVersion: 2 requiredVersion: 2 remove(23) Change next-pointer?
- 56. K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 56 sv:1 ev:1 S:2 K:42 sv:1 ev:1 S:7 K:13 snapshotVersion: 1 mapVersion: 2 requiredVersion: 2 remove(23) Change next-pointer?
- 57. sv:1 ev:2 K:23 S:3 sv:1 ev:1 Copy-on-Write Hash Map - Example 57 sv:1 ev:1 S:2 K:42 sv:1 ev:1 S:7 K:13 snapshotVersion: 1 mapVersion: 2 requiredVersion: 2 remove(23)
- 58. sv:1 ev:2 Copy-on-Write Hash Map - Example 58 K:42 S:7 K:13 sv:1 ev:1 S:2 mapVersion: 2 requiredVersion: 0 release(1)
- 59. Copy-on-Write Hash Map Benefits ▪ Fine granular, lazy copy-on-write. ▪ At most one copy per object, per snapshot. ▪ No thread synchronisation between readers and writer after array-copy step. ▪ Supports multiple concurrent snapshots. ▪ Also implements efficient incremental rehashing. ▪ Future: Could serve as basis for incremental snapshots. 59
- 60. Questions? 60
Editor's Notes
- #2: I would like to start with a very brief recap how state and checkpoints work in Flink to motivate the challenges the impose on the data structures of our backends.
- #5: State backend is local to operator so that we can operate up to the speed of local memory variables. But how can we get a consistent snapshot of the distributed state?
- #11: Users want to go to 10TB or even up to 50TB state! significant overhead! Beauty: self contained, can be easily deleted or recovered
- #75: RocksDB is an embeddable persistent k/v store for fast storage. If user modifies state under key 42 3 times, only the latest change is reflected in memtable.
- #77: Checkpoint coordinator lives in the JobMaster State backend working area is local memory / local disk Checkpointing area is DFS (stable, remote, replicated)
- #81: FLUSH memtable as first step, so that all deltas are on disk SST files are immutable, their creation and deletion is the delta. we have a shared folder for the deltas (sstables) that belong to the operator instance
- #86: Important new step, we can only reference successful incremental checkpoints in future checkpoints!
- #88: New set of sst tables, 225 is missing, so is 227… we can assume the got merged and consolidated in 228 or 229.
- #98: Think of events of user interactions, key is a user id and we store a click count for each user. We talk about keyed state.