Day 2 General Session Presentations RedisConf
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The document discusses new memory technologies like persistent memory and their implications. It provides latency and bandwidth numbers for different memory types and notes that heterogeneous memory systems using tiers of DRAM and NVM provide opportunities for better performance and cost. Examples are given of key-value stores and databases leveraging NVM to achieve high performance while reducing costs. The talk also discusses how new distributed data structures like CRDTs could be used across servers with shared memory.
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Day 2 General Session Presentations RedisConf
- 1. Salvatore Sanfilippo Creator of Redis
- 2. Yiftach Shoolman Co-Founder & CTO Redis Labs
- 3. 2 Hot Topics + 1 New Trend
- 4. Redis on New Memory Technologies
- 5. 0.10 250.00 0.10 500.00 0.10 1.00 10.00 100.00 1,000.00 $0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 DRAM NV-DIMM/PM NVMe SSD SATA SSD microsec $/GB 1 GB cost vs. Read/Write Performance 1 GB cost Read latency Write latency $9 $0.4 High DRAM Cost and Low Flash Performance
- 6. High DRAM Cost and Low Flash Performance 0.10 90.00 250.00 0.10 25.00 500.00 0.10 1.00 10.00 100.00 1,000.00 $0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 DRAM NV-DIMM/PM NVMe SSD SATA SSD microsec $/GB 1 GB cost vs. Read/Write Performance 1 GB cost Read latency Write latency $9 $0.4 $1
- 7. High DRAM Cost and Low Flash Performance 0.10 1.00 90.00 250.00 0.10 1.00 25.00 500.00 0.10 1.00 10.00 100.00 1,000.00 $0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 DRAM NV-DIMM/PM NVMe SSD SATA SSD microsec $/GB 1 GB cost vs. Read/Write Performance 1 GB cost Read latency Write latency $9 $0.4 $1 $2
- 8. Bring Redis Performance on Persistent Memories Close to Redis Performance on DRAM The Challenge
- 9. Before dealing with this challenge…
- 10. Is Persistent Memory Really Persistent ? NVMe NV-DIMM Cloud Instance
- 11. NVMe NV-DIMM Failed Instance Is Persistent Memory Really Persistent ?
- 12. Is Persistent Memory Really Persistent ? NVMe NV-DIMM Failed Instance NVMe NV-DIMM New Empty Instance
- 13. Data Loss Is Persistent Memory Really Persistent ? NVMe NV-DIMM Failed Instance NVMe NV-DIMM New Empty Instance
- 14. Alternative Solution Redundant Flash Array Cloud Instance
- 15. Alternative Solution New non-empty Instance Redundant Flash Array Failed Instance
- 16. Another Alternative • Requires ultra high-speed/low- latency network like Fiber-Channel or Infiniband • Latency is still at ~ 100microsec • Expensive, complex and not supported by cloud vendorsNew non-empty Instance Redundant Flash Array Failed Instance
- 17. Persistent Memory = Ephemeral Memory Persistent Storage AOF, Snapshot NVMe NV-DIMM Cloud Instance
- 18. Redis on Flash (RoF) • Flash used as a RAM extender (not as a persistent storage) • In RAM: dict & keys & hot values • On Flash: cold values • Values are either in RAM or on Flash • Multi-shard; multi-threaded; asynchronous • Pluggable storage engine (RocksDB and IBM’s ark) • 100% compatible with the OSS Redis
- 20. Behind the Scene
- 21. Increase RAM ratio Scale-out Cluster 2500 GB2500 GB 50%50%
- 22. RoF is Fast Avg: 2.04M ops/sec Max: 2.14M ops/sec Avg: 0.91msec Max: 0.98 msec % below 1msec:100% Avg: 313RMB / 9.4WMB Max: 1.71RGB / 96WMB Avg: 1.45Gbps (Tx) / 0.97Gbps (Rx) Max: 1.6Gbps (Tx) / 1.2Gbps (Rx) >2M Ops/sec, > 1GB disk bandwidth, @ <1 msec latency read write read write
- 24. Customer Perspective • Keep the speed of Redis • Maintain all Redis commands - simple and complex • Flexible configuration across regions • Any # of regions, single/multi-az/rack, any combination of nodes/shards • Fully-managed service
- 25. How it Should Work? Start Synced Not synced
- 26. How it Should Work? Write Locally Synced Not synced App App
- 27. How it Should Work? Distribute P2P Asynchronously Synced Not synced App App
- 28. Partially Converged How it Should Work? Synced Not synced App App
- 29. App App Fully Converged How it Should Work? Synced Not synced
- 30. Our Approach to Master-Master • Behaves like local Redis, performs like local Redis • Based on CRDT (Conflict-free Replication Data Type) • Supports Strong Eventual Consistency • No consensus protocol; no need for R + W > N • Any two nodes that have received the same (unordered) set of updates will be in the same state • More to come…
- 31. 1 New Trend
- 32. Foreword
- 33. If you are here …
- 34. You’re probably a geek
- 35. Or a geekette (the female geek)
- 36. • Dealing with simple problems • Writing code in shitty languages • Using silly npm packages • Not refactoring your poor old architecture because of politics You are tired of…
- 37. And you really want to solve real problems
- 38. So what are your options ?
- 39. Option #1 – Create your own startup
- 40. 50% of startups fail in the first year and 95% percent fail within five years
- 41. Option #2 – Create a Redis Module
- 42. Get respect from the community
- 43. And (hopefully) get many stars on GitHub
- 44. How do you make $$$ ?
- 45. • A marketplace for OSS and closed source modules • All modules are certified by Redis Labs for these platforms: Module Hub 1M+ instances 350K+ instances 100+ F500 Companies www.redismodules.com
- 46. Geo Clustering
- 47. Geo Clustering
- 48. Adding a Module to Module Hub Upload A few clicks
- 49. Adding a Module to Module Hub Upload Certification Process One monthA few clicks
- 50. Adding a Module to Module Hub Upload Alpha Certification Process One monthA few clicks Still not shown in the marketplace. Test by 10 selected customers
- 51. Adding a Module to Module Hub Upload Alpha Beta Certification Process One month At least 1 month free A few clicks Still not shown in the marketplace. Test by 10 selected customers
- 52. Adding a Module to Module Hub Upload Alpha Beta GACertification Process One month Still not shown in the marketplace. Test by 10 selected customers At least 1 month free A few clicks
- 53. So – can you make $$$ here ?
- 54. 3.15 2.40 21.00 8.70 24.57 10.61 0.00 5.00 10.00 15.00 20.00 25.00 30.00 Full text search Prefix search Average Latency (msec) RLEC Elasticsearch Solr 20,045 6,831 690 3,686 621 3,133 0 5,000 10,000 15,000 20,000 25,000 Full text search Prefix search Ops/sec RLEC Elasticsearch Solr 85% higher 32x higher 7.8x faster 4.1x faster redisearch The world fastest text search engine
- 55. For only $0.114/hr redisearch – the world fastest text search engine 3.15 2.40 21.00 8.70 24.57 10.61 0.00 5.00 10.00 15.00 20.00 25.00 30.00 Full text search Prefix search Average Latency (msec) RLEC Elasticsearch Solr 20,045 6,831 690 3,686 621 3,133 0 5,000 10,000 15,000 20,000 25,000 Full text search Prefix search Ops/sec RLEC Elasticsearch Solr 85% higher 32x higher 7.8x faster 4.1x faster
- 56. The Possibilities are Truly Endless Thank You @yiftachsh
- 57. Salvatore Sanfilippo Creator of Redis
- 58. Adrian Colyer Venture Partner at Accel Author of the Morning Paper Blog Former CTO, VMWare, Pivotal and Spring Source
- 59. As I get older, my memory just keeps getting better and better!
- 61. A new era of hardware (and what it might mean for Redis) A new era of data structures? Talk outline a b
- 62. Human computers at Dryden by NACA (NASA) - Dryden Flight Research Center Photo Collection http://www.dfrc.nasa.gov/Gallery/Photo/Places/HT ML/E49-54.html. Licensed under Public Domain via Commons - https://commons.wikimedia.org/wiki/File:Human_c omputers_- _Dryden.jpg#/media/File:Human_computers_- _Dryden.jpg
- 63. Computing on a human scale 63 10ns 70ns 10ms 10s 1:10s 116d Registers & L1-L3 File on desk Main memory Office filing cabinet HDDTrip to the warehouse
- 64. Compute HTM Persistent Memory NI FPGA GPUs Memory NVDIMMs Persistent Memory Networking 100GbE RDMA Storage NVMe Next-gen NVM Next generation hardware All change please 64
- 65. 2-10m Computing on a human scale 65 10s 1:10s 116d File on desk Office filing cabinet Trip to the warehouse 4x capacity fireproof local filing cabinets 23-40m Phone another office (RDMA) 3h20m Next-gen warehouse
- 66. The new ~numbers everyone should know 66 Latency Bandwidth Capacity/IOPS Register 0.25ns L1 cache 1ns L2 cache 3ns 8MB L3 cache 11ns 45MB DRAM 62ns 120GBs 6TB - 4 socket NVRAM’ DIMM 620ns 60GBs 24TB - 4 socket 1-sided RDMA in Data Center 1.4us 100GbE ~700K IOPS RPC in Data Center 2.4us 100GbE ~400K IOPS NVRAM’ NVMe 12us 6GBs 16TB/disk,~2M/600K NVRAM’ NVMf 90us 5GBs 16TB/disk, ~700/600K
- 67. NVMe Example: Redis on Flash 67 • 2M IOPS • Sub-ms latency • 1:4 DRAM:NVM split (40x SATA SSD)
- 68. NVMf Example: E8 Storage 68 $12M Series B led by Accel • 10M IOPS in single appliance, accessed via RDMA • 100us/50us read/write latency • ...and this is with NAND Flash!
- 69. Data tiering in heterogeneous memory systems DDR-DRAM NVM Capacity per CPU 100s of GBs Terabytes Read latency 1x 2x to 4x Write bandwidth 1x ⅛ x to ¼ x Estimated cost 5x 1x Endurance 1016 106 to 108 Dulloor et al. 2016 (EuroSys)
- 70. What will it mean? 70 this! All DRAM (baseline) All NVM tiered memory, with increasing NVM proportion Impact on cost? Impact on performance? Impact on performance/$?
- 71. Key-value store results - performance impact Proportion of DRAM Performance relative to all DRAM NVM only 1.15 - 1.45x 1/8 1.14 - 1.4x 1/4 1.06 - 1.13x 1/2 1.05x beware!
- 72. Key-value store results - cost impact Proportion of DRAM Performance/$ relative to all DRAM NVM only 3.45 - 4.5x 1/8 3.1x 1/4 2.3x 1/2 1.6x
- 73. And don’t forget about capacity! Proportion of DRAM Performance relative to all DRAM Performance/$ relative to all DRAM Capacity relative to all DRAM NVM only 1.15 - 1.45x 3.45 - 4.5x 4 - 10x 1/8 1.14 - 1.4x 3.1x 3.6 -8.9x 1/4 1.06 - 1.13x 2.3x 3.3 - 7.8x 1/2 1.05x 1.6x 2.5 - 5.5x
- 74. 74 Ultra low latency, PBs of data • The RAMCloud storage system - Ousterhout et al. 2015 • Implementing linearizability at large scale and low latency - Lee et al. 2015 1000+ Servers PB+ Memory RDMA RIFL
- 75. 75 RAMCloud Performance Operation End-to-end Latency median (99.9%-ile) Read 4.7 (9.2)μs Write (durable) 13.4 (148)μs Transaction 20μs 5-object transaction 27μs TPC-C throughput (10 nodes) 35K tps
- 76. 76 No compromises Distributed transactions with consistency, availability, and performance Dragojević et al. 2015 “This paper demonstrates that new software in modern data centers can eliminate the need to compromise. It describes the transaction, replication, and recovery protocols in FaRM, a main memory distributed computing platform. FaRM provides distributed ACID transactions with strict serializability, high availability, high throughput and low latency. These protocols were designed from first principles to leverage two hardware trends appearing in data centers: fast commodity networks with RDMA and an inexpensive approach to providing non- volatile DRAM.”
- 77. 77 FaRM Performance Performance TPC-C tps (90 nodes) 4.5M 99%-ile latency 1.9ms K-V Store* qps (20 nodes) 146M latency (at peak throughput) 35μs (* FaRM: Fast Remote Memory - Dragojević et al. 2014)
- 78. 78 Exploiting HTM... Persist. Memory RDMA HTM DrTM Fast in-memory transaction processing using RDMA & HTM - Wei et al. 2015 5.5M tps (TPC-C, 6 node cluster)
- 79. Redis is way more than just a ‘K-V store’... b A new era of data structures?
- 80. Evolution of the humble data structure 80 Collection classes & stdlibs ? single thread/core Concurrent data types multi- thread/core Distributed data types multi-server Shared concurrent data types shared-server PACELC
- 81. 81 Libraries, Servers, & PACELC App Instance App InstanceApp Instance CRDT Library DS Server DS Server Client Client Client Client Client CRDT Server
- 82. 82 CRDT Evolution Operation Based ● prepare and effect ● smaller messages ● require reliable exactly-once causal broadcast ● membership management ● no batching State Based ● state shipping and idempotent join ● larger messages ● support unreliable communication channels ● accommodate batching Delta-State Based ● local execution, ship representation of the effect & idempotently join ● smaller messages ● support unreliable communication channels ● accommodate batching
- 83. Data Structures! E.g. GCounter and PNCounter { replica_id value } 83 What are CRDTs made of? {“A”->3, “B”->5} HINCRBY gcounter A 1 {“A”->4, “B” -> 5} HVALS gcounter… 4+5 = 9 {“A” -> 4, “B” -> 7} {“A”->3, “B”->5} HINCRBY gcounter B 1 HINCRBY gcounter B 1 {“A”->3, “B” -> 7} {“A” -> 4, “B” -> 7} delta B -> 7 delta A -> 4 A B
- 84. Causal Context X Dot Store 84 Causal ∂-CRDTs version vector (Redis hash) Dot.Event (Redis set or hash) Dot = replica_id:sequence_no INCR + a delta queue for propagation... WATCH my_crdt:seq_no next = INCR my_crdt:seq_no MULTI HSET my_crdt:ccontext replica_id next SADD my_crdt:dotstore “replica_id:next:val” RPUSH my_crdt:deltaq delta EXEC
- 85. A new era of hardware (and what it might mean for Redis) A new era of data structures Summary a b
- 87. Salvatore Sanfilippo Creator of Redis
- 88. Laura Merling Former VP and GM of IoT Initiative at SAP
- 89. The Internet of Things Real Time Reality
- 90. The IoT Landscape PLATFORMS AND ENABLEMENT SOFTWARE, FULL-STACK, DEVELOPER, ANALYTICS, OPEN SOURCE
- 91. Big Data with an IOT View Volume Velocity Data streams and near-real time; RFID tags, sensors and smart metering. Variety Structured and unstructured. Variability Inconsistent; event triggered. Complexity Multiple sources; connect and correlate relationships, hierarchies and multiple data linkages. Volume Billions of sensors and machine to machine transactions.
- 92. Analytics Continuum DiagnosticDescriptive PrescriptivePredictive What Happened Why Did it Happen What Will Happen Make it Happen In Real Time
- 93. Geospatial The Redis IOT Opportunity PubSub DatabaseCaching Time Series
- 94. Challenges • Latency • Automation • Complexity Oil and Gas
- 95. Challenges • Transformer • Distribution Electric Vehicle Charging
- 96. Manufacturing Challenges • Unpredictable • Adherence • Unavailable
- 98. Connected Home Challenges • Latency • Interaction • Simplification
- 99. Retail Challenges • Latency • Interaction/Engagement • Inventory
- 100. The Data MINING IoT FOR THE UNFILTERED TRUTH
- 101. Thank You @magicmerl
- 102. Salvatore Sanfilippo Creator of Redis
- 103. Reynold Xin Chief Architect Spark at Databricks
- 104. Spark and Redis: In-situ analytics beyond Hadoop Reynold Xin @rxin 2016-05-11 RedisConf
- 105. About Databricks Founded by creators of Spark in 2013 Cloud enterprise data platform - Managed Spark clusters - Interactive data science - Production pipelines - Data governance, security, …
- 106. Please put up your hand if you know what Spark is?
- 107. Please put up your hand if you think your significant other knows what Spark is?
- 108. open source data processing engine built around speed, ease of use, and sophisticated analytics largest open source data project with 1000+ contributors
- 113. “Spark is the Taylor Swift of big data software.” - Derrick Harris, Fortune
- 114. Analytics: a traditional approach Hadoop is complex to operate Data consistency is difficult to maintain
- 116. Analytics in-situ SQL Streaming MLEnable SQL analytics over Redis Use Redis to store streaming data Use Redis to serve results generated by Spark
- 119. Thank You @rxin
- 120. Salvatore Sanfilippo Creator of Redis
- 121. Enjoy RedisConf Updates • Your morning break is located downstairs in the Sponsor Showcase • Three concurrent Breakout Sessions will begin at 11:15am • The Developer Workshop begins at 1:00pm in the Developer Café • Save your Redis Community Raffle ticket for today’s drawing for a chance to win a Phantom Drone
- 122. 11:15am-12:00pm New Redis Capabilities: Own, Make, Share 1:00pm-1:45pm SSH I/O Streaming Via Redis-based Persistent Message Queue 2:00pm-2:45pm Walmart and IBM Revisit the Linear Road Benchmark 3:15pm-4:00pm Back your App with MySQL & Redis, the Cloud Foundry Way 4:15pm-5:00pm Postgres and Redis Sitting in a Tree 122 BREAKOUTS: WEDNESDAY, MAY 11
- 123. Salvatore Sanfilippo Creator of Redis