Debunking the Myths of HDFS Erasure Coding Performance
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The document discusses erasure coding as an alternative to replication in distributed storage systems like HDFS. It notes that while replication provides high durability, it has high storage overhead, and erasure coding can provide similar durability with half the storage overhead but slower recovery. The document outlines how major companies like Facebook, Windows Azure Storage, and Google use erasure coding. It then provides details on HDFS-EC, including its architecture, use of hardware acceleration, and performance evaluation showing its benefits over replication.
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Debunking the Myths of HDFS Erasure Coding Performance
- 1. Debunking the Myths of HDFS Erasure Coding Performance
- 2. HDFS inherits 3-way replication from Google File System - Simple, scalable and robust 200% storage overhead Secondary replicas rarely accessed Replication is Expensive
- 3. Erasure Coding Saves Storage Simplified Example: storing 2 bits Same data durability - can lose any 1 bit Half the storage overhead Slower recovery 1 01 0Replication: XOR Coding: 1 0⊕ 1= 2 extra bits 1 extra bit
- 4. Erasure Coding Saves Storage Facebook - f4 stores 65PB of BLOBs in EC Windows Azure Storage (WAS) - A PB of new data every 1~2 days - All “sealed” data stored in EC Google File System - Large portion of data stored in EC
- 5. Roadmap Background of EC - Redundancy Theory - EC in Distributed Storage Systems HDFS-EC architecture Hardware-accelerated Codec Framework Performance Evaluation
- 6. Durability and Efficiency Data Durability = How many simultaneous failures can be tolerated? Storage Efficiency = How much portion of storage is for useful data? useful data 3-way Replication: Data Durability = 2 Storage Efficiency = 1/3 (33%) redundant data
- 7. Durability and Efficiency Data Durability = How many simultaneous failures can be tolerated? Storage Efficiency = How much portion of storage is for useful data? XOR: Data Durability = 1 Storage Efficiency = 2/3 (67%) useful data redundant data X Y X ⊕ Y 0 0 0 0 1 1 1 0 1 1 1 0 Y = 0 ⊕ 1 = 1
- 8. Durability and Efficiency Data Durability = How many simultaneous failures can be tolerated? Storage Efficiency = How much portion of storage is for useful data? Reed-Solomon (RS): Data Durability = 2 Storage Efficiency = 4/6 (67%) Very flexible!
- 9. Durability and Efficiency Data Durability = How many simultaneous failures can be tolerated? Storage Efficiency = How much portion of storage is for useful data? Data Durability Storage Efficiency Single Replica 0 100% 3-way Replication 2 33% XOR with 6 data cells 1 86% RS (6,3) 3 67% RS (10,4) 4 71%
- 10. EC in Distributed Storage Block Layout: Data Locality 👍🏻 Small Files 👎🏻 128~256MFile 0~128M … 640~768M 0~128 M block0 DataNode 0 128~ 256M block1 DataNode 1 0~128M 128~256M … 640~ 768M block5 DataNode 5 DataNode 6 … parity Contiguous Layout:
- 11. EC in Distributed Storage Block Layout: File block0 DataNode 0 block1 DataNode 1 … block5 DataNode 5 DataNode 6 … parity Striped Layout: 0~1M 1~2M 5~6M 6~7M Data Locality 👎🏻 Small Files 👍🏻 Parallel I/O 👍🏻 0~128M 128~256M
- 12. EC in Distributed Storage Spectrum: Replication Erasure Coding Striping Contiguous Ceph Ceph Quancast File System Quancast File System HDFS Facebook f4 Windows Azure
- 13. Roadmap - - HDFS-EC architecture Hardware-accelerated Codec Framework Performance Evaluation
- 14. Choosing Block Layout Medium: 1~6 blocksSmall files: < 1 blockAssuming (6,3) coding Large: > 6 blocks (1 group) 96.29% 1.86% 1.85% 26.06% 9.33% 64.61% small medium large file count space usage Top 2% files occupy ~65% space Cluster A Profile 86.59% 11.38% 2.03% 23.89% 36.03% 40.08% file count space usage Top 2% files occupy ~40% space small medium large Cluster B Profile 99.64% 0.36% 0.00% 76.05% 20.75% 3.20% file count space usage Dominated by small files small medium large Cluster C Profile
- 16. Generalizing Block NameNode Mapping Logical and Storage Blocks Too Many Storage Blocks? Hierarchical Naming Protocol:
- 17. Client Parallel Writing streamer queue streamer … streamer Coordinator
- 19. Reconstruction on DataNode Important to avoid delay on the critical path - Especially if original data is lost Integrated with Replication Monitor - Under-protected EC blocks scheduled together with under-replicated blocks - New priority algorithms New ErasureCodingWorker component on DataNode
- 20. Data Checksum Support Supports getFileChecksum for EC striped mode files - Comparable checksums for same content striped files - Can’t compare the checksums for contiguous file and striped file - Can reconstruct on the fly if found block misses while computing Planning to introduce new version of getFileChecksum - To achieve comparable checksums between contiguous and striped file
- 21. Roadmap - - Hardware-accelerated Codec Framework Performance Evaluation
- 22. Acceleration with Intel ISA-L 1 legacy coder - From Facebook’s HDFS-RAID project 2 new coders - Pure Java — code improvement over HDFS-RAID - Native coder with Intel’s Intelligent Storage Acceleration Library (ISA-L)
- 23. Why is ISA-L Fast? pre-computed and reused parallel operation Direct ByteBuffer
- 30. Hive-on-MR — locality sensitive
- 31. Hive-on-Spark — locality sensitive
- 32. Conclusion Erasure coding expands effective storage space by ~50%! HDFS-EC phase I implements erasure coding in striped block layout Upstream effort (HDFS-7285): - Design finalized Nov. 2014 - Development started Jan. 2015 - 218 commits, ~25k LoC change - Broad collaboration: Cloudera, Intel, Hortonworks, Huawei, Yahoo, LinkedIn Phase II will support contiguous block layout for better locality
- 33. Acknowledgements Cloudera - Andrew Wang, Aaron T. Myers, Colin McCabe, Todd Lipcon, Silvius Rus Intel - Kai Zheng, Rakesh R, Yi Liu, Weihua Jiang, Rui Li Hortonworks - Jing Zhao, Tsz Wo Nicholas Sze Huawei - Vinayakumar B, Walter Su, Xinwei Qin Yahoo (Japan) - Gao Rui, Kai Sasaki, Takuya Fukudome, Hui Zheng
- 34. Questions? Zhe Zhang, LinkedIn zhz@apache.org | @oldcap http://zhe-thoughts.github.io/ Uma Gangumalla, Intel umamahesh@apache.org @UmaMaheswaraG http://blog.cloudera.com/blog/2016/02/progress-report-bringing-erasure-coding-to-apache-hadoop/
- 35. Come See us at Intel - Booth 305 “Amazing Analytics from Silicon to Software” • Intel powers analytics solutions that are optimized for performance and security from silicon to software • Intel unleashes the potential of Big Data to enable advancement in healthcare/ life sciences, retail, manufacturing, telecom and financial services • Intel accelerates advanced analytics and machine learning solutions Twitter #HS16SJ
- 36. LinkedIn Hadoop Dali: LinkedIn’s Logical Data Access Layer for Hadoop Meetup Thu 6/30 6~9PM @LinkedIn 2nd floor, Unite room 2025 Stierlin Ct Mountain View Dr. Elephant: performance monitoring and tuning. SFHUG in Aug
- 37. Backup
Notes de l'éditeur
- Simply put, it doubles the storage capacity of your cluster. This talk explains how it happens. Blog post link.
- When the GFS paper was published more than a decade ago, the objective was to store massive amount of data on a large number of cheap commodity machines. A breakthrough design was to rely on machine-level replication to protect against machine failures, instead of xxx.
- A more efficient approach to reliably store data is erasure coding. Here’s a simplified example
- In this talk I will introduce how we implemented erasure coding in HDFS.
- RS uses more sophisticated linear algebra operations to generate multiple parity cells, and thus can tolerate multiple failures per group. It works by multiplying a vector of k data cells with a Generator Matrix (GT) to generate an extended codeword vector with k data cells and m parity cells. In this particular example, it combines the strong durability of replication and high efficiency of simple XOR. More importantly, flexible.
- To manage potentially very large files, distributed storage systems usually divide files into fixed-size logical byte ranges called logical blocks. These logical blocks are then mapped to storage blocks on the cluster, which reflect the physical layout of data on the cluster. The simplest mapping between logical and storage blocks is a contiguous block layout, which maps each logical block one-to-one to a storage block. Reading a file with a contiguous block layout is as easy as reading each storage block linearly in sequence.
- To manage potentially very large files, distributed storage systems usually divide files into fixed-size logical byte ranges called logical blocks. These logical blocks are then mapped to storage blocks on the cluster, which reflect the physical layout of data on the cluster. The simplest mapping between logical and storage blocks is a contiguous block layout, which maps each logical block one-to-one to a storage block. Reading a file with a contiguous block layout is as easy as reading each storage block linearly in sequence.
- Non-trivial trade-offs between x and x, y and y.
- In all cases, the saving from EC will be significantly lower if only applied on large files. In some cases, no savings at all.
- The former represents a logical byte range in a file, while the latter is the basic unit of data chunks stored on a DataNode. In the example, the file /tmp/foo is logically divided into 13 striping cells (cell_0 through cell_12). Logical block 0 represents the logical byte range of cells 0~8, and logical block 1 represents cells 9~12. Cells 0, 3, 6 form a storage block, which will be stored as a single chunk of data on a DataNode. To reduce this overhead we have introduced a new hierarchical block naming protocol. Currently HDFS allocates block IDs sequentially based on block creation time. This protocol instead divides each block ID into 2~3 sections, as illustrated in Figure 7. Each block ID starts with a flag indicating its layout (contiguous=0, striped=1). For striped blocks, the rest of the ID consists of two parts: the middle section with ID of the logical block and the tail section representing the index of a storage block in the logical block. This allows the NameNode to manage a logical block as a summary of its storage blocks. Storage block IDs can be mapped to their logical block by masking the index; this is required when the NameNode processes DataNode block reports.
- Figure 8 first shows results from an in-memory encoding/decoding micro benchmark. The ISA-L implementation outperforms the HDFS-EC Java implementation by more than 4x, and the Facebook HDFS-RAID coder by ~20x. Based on the results, we strongly recommend the ISA-L accelerated implementation for all production deployments.
- Figure 8 first shows results from an in-memory encoding/decoding micro benchmark. The ISA-L implementation outperforms the HDFS-EC Java implementation by more than 4x, and the Facebook HDFS-RAID coder by ~20x. Based on the results, we strongly recommend the ISA-L accelerated implementation for all production deployments.
- We also compared end-to-end HDFS I/O performance with these different coders against HDFS’s default scheme of three-way replication. The tests were performed on a cluster with 11 nodes (1 NameNode, 9 DataNodes, 1 client node) interconnected with 10 GigE network. Figure 9 shows the throughput results of 1) client writing a 12GB file to HDFS; and 2) client reading a 12GB file from HDFS. In the reading tests we manually killed two DataNodes so the results include decoding overhead. As shown in Figure 9, in both sequential write and read and read benchmarks, throughput is greatly constrained by the pure Java coders (HDFS-RAID and our own implementation). The ISA-L implementation is much faster than the pure Java coders because of its excellent CPU efficiency. It also outperforms replication by 2-3x because the striped layout allows the client to perform I/O with multiple DataNodes in parallel, leveraging the aggregate bandwidth of their disk drives. We have also tested read performance without any DataNode failure: HDFS-EC is roughly 5x faster than three-way replication.
- Phase 2 backup slide
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