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Hadoop Meetup Jan 2019 - HDFS Scalability and Consistent Reads from Standby Node
- 1. Consistent Reads from Standby Node Konstantin V Shvachko Sr. Staff Software Engineer @LinkedIn Chen Liang Senior Software Engineer @LinkedIn Chao Sun Software Engineer @Uber
- 2. Agenda HDFS CONSISTENT READ FROM STANDBY 1 • Motivation • Consistency Read from Standby • Challenges • Design and Implementation • Next steps
- 3. The Team 2 • Konstantin Shvachko (LinkedIn) • Chen Liang (LinkedIn) • Erik Krogen (LinkedIn) • Chao Sun (Uber) • Plamen Jeliazkov (Paypal)
- 4. Consistent Reads From Standby Nodes
- 5. Motivation 4 • 2x Growth/Year In Workloads and Size • Approaching active Name Node performance limits rapidly • We need a scalability solution • Key Insights: • Reads comprise 95% of all metadata operations in our practice • Another source of truth for read: Standby Nodes • Standby Nodes Serving Read Requests • Can substantially decrease active Name Node workload • Allowing cluster to scale further!
- 6. Architecture ROLE OF STANDBY NODES DataNodes Active NameNode Standby NameNodes JournalNodes Write 5 /Read• Standby nodes have same copy of all metadata (with some delay) • Standby Node syncs edits from Active NameNode • Standby nodes can potentially serve read requests • All reads can go to Standby nodes • OR, time critical applications can still choose to read from Active only
- 7. Challenges DataNodes Active NameNode Standby NameNodes JournalNodes Write Read 6 /Read • Standby Node delay • ANN write edits to JN, then SbNN applying the edits from JN • With delay at minute magnitude • Consistency • If client performs a read after a write, client would expect to see the state change
- 8. Fast Journaling DELAY REDUCTION 7 • Fast Edit Tailing HDFS-13150 • Current JN is slow: serving whole segments of edits from disk • Optimization on JN and SbNN o JN caching recent edits in memory, only applied edits are served o SbNN request only recent edits through RPC calls o Fall back to existing mechanism on error • Significantly reduce SbNN delay o Reduce from 1 minute to 2 to 50 milliseconds • Standby node delay is no more than a few ms in most cases
- 9. Consistency Model 8 • Consistency Principle: • If client c1 modifies an object state at id1 at time t1, then in any future time t2 > t1, c1 will see the state of that object at id2 >= id1 • Read-Your-Own-Write • Client writes to Active NameNode • Then read from the StandbyNode • Read should reflect the write Active NameNode Standby NameNodes JournalNodes lastSeenStateId txnid = 100 = 100 txnid = 99 100 100
- 10. txnid = 100 Consistency Model 9 • Consistency Principle: • If client c1 modifies an object state at id1 at time t1, then in any future time t2 > t1, c1 will see the state of that object at id2 >= id1 • LastSeenStateID • Monotonically increasing Id of ANN namespace state txnid • Kept on client side, client’s known most recent ANN state • Sent to SbNN, SbNN only replies after it has caught up to this state Active NameNode Standby NameNodes JournalNodes lastSeenStateId txnid = 100 = 100 100 100
- 11. Corner Case: Stale Reads 10 • Stale Read Cases • Case1: Multiple client instances • DFSClient#1 to write to ANN, DFSClient#2 to read SbNN • DFSClient#2’s state older than DFSClient#1, read is out of sync • Case2: Out-of-band communication • Client#1 writes to ANN, inform client#2 • Client#2 read from SbNN, not see the write Active NameNode DFSClient#1 Standby NameNode Write DFSClient#2 Read Read your own writes Active NameNode DFSClient#1 Standby NameNode Write DFSClient#2 Read Third-party communication
- 12. msync API 11 • Dealing with Stale Reads: FileSystem.msync() • Sync between existing client instances • Force the DFSClient to sync up to the most recent state of ANN • Multiple client instances: call msync on DFSClient#2 • Out-of-band communication: client#2 calls msync first before read • “Always msync” mode HDFS-14211 Active NameNode DFSClient#1 Standby NameNode Write DFSClient#2 Read Read your own writes Active NameNode DFSClient#1 Standby NameNode Write DFSClient#2 Read Third-party communication
- 13. Robustness Optimization: Standby Node Back-off REDIRECT WHEN TOO FAR BEHIND • In the case where a Standby node state is too far behind, client may retry another node • e.g. Standby node machine running slow • Standby Node Back-off • 1: Upon receiving request, if Standby node finds itself too far behind requested state, it rejects the request, throwing retry exception • 2: If a request has been in queue for long, and Standby still is not caught up, Standby rejects the request, throwing retry exception • Client Retry • Upon retry exception, client tries a different standby node, or simply falling back to ANN 12
- 14. Configuration and Startup Process 13 • Configuring NameNodes • Configure namenodes via haadmin • Observer mode is similar to Standby, but serves read and does not perform checkpointing • All NameNodes start as check pointing Standby, Standby can be transitioned to Active or Observer • Configuring Client • Configure to use ObserverReadProxyProvider • If not, client still works but only talks to ANN • ObserverReadProxy will discover the state of all NNs Active Standby Observer Check Pointing Standby Read Serving Standby Active
- 15. Current Status 14 • Test and benchmark • With YARN application, e.g. TeraSort • With HDFS benchmarks, e.g. DFSIO • Run on a cluster with >100 nodes and with Kerberos and delegation token enabled • Merged to trunk (3.3.0) • Being backported to branch-2 • Active work on further improvement/optimization • Has been running at Uber in production
- 16. Background ● Back in 2017, Uber’s HDFS clusters were in a bad shape ○ Rapid growth in # of jobs accessing HDFS ○ Ingestion & adhoc jobs co-locate on the same cluster ○ Lots of listing calls on very large directories (esp. Hudi) ● HDFS traffic composition: 96% reads, 4% writes ● Presto is very sensitive to HDFS latency ○ Occupies ~20% of HDFS traffic ○ Only reads from HDFS, no write
- 17. Implementation & Timeline ● Implementation (compare to open source version) ○ No msync or fast edit log tailing ■ Only eventual consistency with max staleness of 10s ○ Observer was NOT eligible to NN failover ○ Batched edits loading to reduce NN locktime when tailing edits ● Timeline ○ 08/2017 - finished the POC and basic testing in dev clusters ○ 12/2017 - started collaborating with HDFS open source community (e.g., Linkedin, Paypal) ○ 12/2018 - fully rolled out to Presto in production ○ Tool multiple retries along the process ■ Disable access time (dfs.namenode.accesstime.precision) ■ HDFS-13898, HDFS-13924
- 18. Impact Comparing to traffic goes to active NameNode, Observer NameNode improves the overall throughput by ~20% (roughly the same throughput from Presto), while RPC queue time has dropped ~30X.
- 19. Impact (cont.) Presto listing status call latency has dropped 8-10X after migrating to Observer
- 20. Next Steps
- 21. Three-Stage Scalability Plan 2X GROWTH / YEAR IN WORKLOADS AND SIZE • Stage I. Consistent reads from standby • Optimize for reads: 95% of all operations • Consistent reading is a coordination problem • Stage II. In-memory Partitioned Namespace • Optimize write operations • Eliminate NameNode’s global lock – fine-grained locking • Stage III. Dynamically Distributed Namespace Service • Linear scaling to accommodate increases in RPC load and metadata growth HDFS-12943 20
- 22. NameNode Current State NAMENODE’S GLOBAL LOCK – PERFORMANCE BOTTLENECK • Three main data structures • INodeMap: id -> INode • BlocksMap: key -> BlockInfo • DatanodeMap: don’t split • GSet – an efficient HashMap implementation • Hash(key) -> Value • Global lock to update INodes and blocks 21 NameNode – FSNamesystem INodeMap – Directory Tree GSet: Id -> INode BlocksMap – Block Manager GSet: Block -> BlockInfo DataNode Manager
- 23. Stage II. In-memory Partitioned Namespace ELIMINATE NAMENODE’S GLOBAL LOCK • PartitionedGSet: • two level mapping 1. RangeMap: keyRange -> GSet 2. RangeGSet: key -> INode • Fine-grained locking • Individual locks per range • Different ranges are accessed in parallel 22 NameNode GSet-1 DataNode Manager GSet-2 GSet-n GSet-1 GSet-2 GSet-n INodeMap - Partitioned GSet BlocksMap - Partitioned GSet
- 24. Stage II. In-memory Partitioned Namespace EARLY POC RESULTS 23 • PartitionedGSet: two level mapping • LatchLock: swap RangeMap lock for GSet locks corresponding to inode keys • Run NNTroughputBenchmark creating 10 million directories • 30% throughput gain • Large batches of edits • Why not 100%? • Key is inodeId – incremental number generator • Contention on the last partition • Expect MORE
- 25. Stage III. Dynamically Distributed Namespace SCALABLE DATA AND METADATA • Split NameNode state into multiple servers based on ranges • Each NameNode • Serves a designate range of INode keys • Metadata in PartitionedGSet • Can reassign certain subranges to adjacent nodes • Coordination Service (Ratis) • Change ranges served by NNs • Renames / moves, Quotas 24 NameNode 1 INodeMap Part-GSet DataNode Manager BlocksMap Part-GSet NameNode 2 NameNode n INodeMap Part-GSet DataNode Manager BlocksMap Part-GSet INodeMap Part-GSet DataNode Manager BlocksMap Part-GSet
- 26. Thank You! Konstantin V Shvachko Chen Liang Chao Sun Sr. Staff Software Engineer @LinkedIn Software Engineer @Uber Senior Software Engineer @LinkedIn 25 Consistent Reads from Standby Node
Hinweis der Redaktion
- State transition diagram
- Winter is coming!
- See Appendix. The SlideShare version will have more details about the Satellite Cluster configuration and operational solutions