1.现代存储架构下的系统优化实践-卢冕

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1.现代存储架构下的系统优化实践-卢冕

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#信息技术

随着存储技术的发展，现代存储架构呈现出了前所未有的分级复杂性和功能上的革命（比如内存数据持久化），因此也对上层软件如何优化提出了新的挑战。本次分享基于我们的实践经验，演示如何在现代存储架构下，通过创新性的技术，比如持久化内存数据结构、分级存储、冷热数据分离等技术进行系统优化，以利用现代存储架构的特性。

卢冕，第四范式系统架构师

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4 .代表性存储技术发展时间线磁带机械硬盘 CD-ROM 软盘 NAND Flash DVD SD Card 1951 1956 1965 1971 1989 1996 1999 外存傲腾持久内存 B+tree Oracle NTFS 3D XPoint 2018 1973 1979 1993 2015 非易失性内存 LRU algorithm PMDK 1993 2014 磁芯存储器 SRAM DRAM DDR/GDDR DDR 4 HBM2 GDDR 6 1947 1961 1966 1998 2011 2016 2018 内存 Copyright ©2021 4Paradigm All Rights Reserved. CUDA 4 2007

9 .第四范式在现代存储架构上的技术实践 • 对内：面向 AI 全流程的优化 – 持久内存 Memory Mode，低成本内存扩容 – HyperPS: 针对推理的参数服务器 • 对内 + 对外：开源工作 memark.io: 现代存储架构技术社区 – 【已开源】PmemStore: 针对人工智能负载优化的存储引擎 - VLDB 2021, 持久化跳表即将合入 PMDK 核心库 libpmemobj-cpp - https://github.com/4paradigm/pmemstore – 【已开源】Pafka v0.1.1: Kafka 优化版本 - 10x 性能提升，0 代码修改 - https://github.com/4paradigm/pafka – 【9月开源】OpenEmbedding: 训练参数服务器，和 TensorFlow 整合 – 【2021 Q4 开源】HyperVec: 基于PMem的向量搜索库 – 【2021 Q4 开源】Elasticsearch 的分级存储优化版本 – 【2022 H1 开源】 PmemStore 驱动的内存工程数据库 OpenMLDB - OpenMLDB (DRAM version)： https://github.com/4paradigm/OpenMLDB/ Copyright ©2021 4Paradigm All Rights Reserved. 9

10 . 面向 AI 全流程的异构存储优化持久内存 AD MODE 优化模块持久内存 MEM MODE 优化模块在线推理流程 User/APP/Web 离线探索/自学习流程预测结果预测请求行为反馈 HyperPS：参数服务器 OpenMLDB：特征数据库 Pafka：消息队列在线推理实时特征抽取消息队列（模型）（特征工程脚本）在线推理离线探索/自学习自动模型训练自动特征工程内存扩容内存扩容离线探索 Copyright ©2021 4Paradigm All Rights Reserved. 10

12 .What is On-line Decision Augmentation (OLDA) Finance Media Retail OLDA has been widely used in many applications: • Real-time fraud detection • Personalized recommendation Medical Internet Energy • Production forecast ... Copyright ©2021 4Paradigm All Rights Reserved. 12

13 . Strict time constraint OLDA Workflow History Data Response Applications Trained Prediction Model / Score Data Warehouse Train Original Record Feature Validate Feature Get Extraction Structured Engineering Test Parameter Data In-memory Selected Database Model Features Matrix Features Vector Structured Transactional Database Raw Data OpenMLDB Off-line Training On-line Inference Copyright ©2021 4Paradigm All Rights Reserved. 13

14 . The Characteristics of the Feature Extraction Card ID Date Time Amount Types of Currency POS Info ... A New Transaction 9527xxxxxx 20200702 13:26 124.8 USD 8880xxxxxx ... /Purchasing Record Card Info: Card Level, Activation Date . . . Shop Info: Shop ID, Type, City, Country, . . . Basic Account Info: Card Number, Current Balance . . . Features (hundreds of) Pattern of the Transaction Time: Pattern of Visited Shops: • The top 3 most frequent transactions • The top 3 shops that most frequently occurred in the past 10s, 1 min, 5 mins and 10 mins. appear in the last 10s, 1 min, 5 min and 10 mins. Real-time Features . . . . . . (thousands of) Copyright ©2021 4Paradigm All Rights Reserved. 14

15 . The Challenges of the Feature Extraction Existing in-memory database can not meet the time constraint 1. Most of the real-time features can not be pre- extracted. Strict timing constraint 2. Most of the real-time features are computed over multiple time windows. (<tens of milliseconds) 3. A large number of real-time features extracted for each online inference prediction. Copyright ©2021 4Paradigm All Rights Reserved. 15

16 .Feature Engineering Database (OpenMDLB) • FEQL Engine • FEQL • a SQL-like language • LLVM-based execution engine • Storage Engine • High availability • Double-layered skiplist: optimized data structure for “time-windowed real- time feature extraction” * OpenMLDB is renamed from FEBD, which is the original name used in the paper Copyright ©2021 4Paradigm All Rights Reserved. 16

19 .Comparing DRAM-based OpenMLDB with Existing Databases Micro Benchmark: Performance of Extracting Real-time Features over Varied Number of Time Windows Copyright ©2021 4Paradigm All Rights Reserved. 19

20 .Limitation of DRAM-based OpenMLDB 1. Rapid data growth (~10 TB) + limited capacity of the DRAM High hardware cost 2. Sync data from volatile DRAM to HDDs/SSDs Long tail latency 3. Reload data from backup storage to DRAM after failure Long recovery time Copyright ©2021 4Paradigm All Rights Reserved. 20

22 .Challenge of Compare-And-Swap on PMem Power OFF Thread 1 CAS (ADD T1) Flush(T1) Thread 2 Compute Features Flush(F1) F1 based on T1 Time Data inconsistency after recovery T1 not exist F1 exist Copyright ©2021 4Paradigm All Rights Reserved. 22

23 .In-memory Double-layered Persistent Skiplist PCAS Persistent-Compare-And-Swap Low overhead consistency guarantee without lock: 1. Flush-on-read 2. Smart pointer Copyright ©2021 4Paradigm All Rights Reserved. 23

24 .Evaluating FEDB on Real-world Fraud Detection Real-world Workload Latency under Real-world Workloads On Different DBMS 37X ~ 610X faster DBMS Configurations Copyright ©2021 4Paradigm All Rights Reserved. 24

27 .消息队列系统：Kafka ➔ Pafka Kafka • 广泛的应用场景：消息传输，日志搜集，流处理…… • 高性能、高可扩展、高可用 • 性能受限于存储设备（HDD/SSD） Pafka (PMem Accelerated Kafka) • 基于 Kafka 架构和 APIs, 业务代码迁移零成本 • 使用持久内存打破 Kafka 性能瓶颈 • 大幅提升单节点吞吐，降低硬件成本达 10 倍 Copyright ©2021 4Paradigm All Rights Reserved. 27

28 .Pafka 架构 • 基于 Kafka 高可扩展性架构 • 通过 PMDK ，将 segment 的存储从 HDD/SSD 赋能为可在 PMem 上持久化 • 引入 MixChannel 概念，实现 PMem 和次级外存的分级持久化 • 引入后台数据迁移策略，冷热数据分开存储（v0.2.0 开发中） Copyright ©2021 4Paradigm All Rights Reserved. 28

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