RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach Shoolman
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This document summarizes RedisConf 2017, covering several topics: 1. Running Redis on Flash in a DBaaS model for improved performance and cost savings compared to other NoSQL databases. 2. Redis modules gaining momentum with over 50 created so far, and the importance of multi-threading for high performance. Useful modules highlighted include RediSearch, ReJSON, Redis-ML, and Redis-Graph. 3. Using Redis for IoT applications, with challenges around small edge devices and clusters, high throughput from thousands of devices, and varied functionality needs addressed through Redis modules.
![Query: Friends Who’ve Visited Places I’ve Been To
MATCH (:person {name:“Roi Lipman”})-
[:visited]->(c:country)<-[:visited]-
(f:person)<-[:friend]-
(:person {name:“Roi Lipman”})
RETURN f, c](https://image.slidesharecdn.com/nl9spqrhsh2idur27dn5-signature-63eb679a51d9f2635aab57d30239359e45bcafc9b7592f195361637136b881e8-poli-170619224802/85/RedisConf17-Doing-More-With-Redis-Ofer-Bengal-and-Yiftach-Shoolman-23-320.jpg)
![Query: Who’s Gone on a Business Trip
MATCH (p:person)-
[v:visited {purpose:business}]->(c:country)
RETURN p, v, c](https://image.slidesharecdn.com/nl9spqrhsh2idur27dn5-signature-63eb679a51d9f2635aab57d30239359e45bcafc9b7592f195361637136b881e8-poli-170619224802/85/RedisConf17-Doing-More-With-Redis-Ofer-Bengal-and-Yiftach-Shoolman-25-320.jpg)
![This Leads To:
"...any database will eventually store
[some data in] JSON"](https://image.slidesharecdn.com/nl9spqrhsh2idur27dn5-signature-63eb679a51d9f2635aab57d30239359e45bcafc9b7592f195361637136b881e8-poli-170619224802/85/RedisConf17-Doing-More-With-Redis-Ofer-Bengal-and-Yiftach-Shoolman-42-320.jpg)
RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach Shoolman
- 1. #redisconf17 DOING MORE WITH REDIS OFER BENGAL YIFTACH SHOOLMAN CO-FOUNDERS, REDIS LABS
- 2. #1 – Running Redise Flash in a DBaaS Model
- 3. Redise Cloud Private Fully managed, serverless scaling Redise service in VPCs on: AWS, MS Azure, GCP & IBM Softlayer
- 4. Redise Flash – Built for a Tiered Memory Architecture Persistent Storage: Entire Dataset AOF, Snapshot SSD: Cold Values DRAM: Keys & Hot Values Cluster Node
- 5. Flexible RAM : Flash Ratios
- 6. New Memory Technology is Evolving Fast
- 7. DRAM vs Flash : Performance vs Cost $9 $0.4 $1 $2
- 8. Flash Memory - What Has Changed During Last Year New NVMe based local SSD that can be attached to a Cloud Instance
- 9. Flash Memory - What Has Changed During Last Year New NVMe based local SSD that can be attached to a Cloud Instance New NVMe based SSD I3 Instances
- 10. I3 (NVMe) are x2.6 Faster and 80% Cheaper than I2 (SATA) Single node @ <1msec latency
- 11. Flash Memory - What Has Changed During Last Year New NVMe based local SSD that can be attached to a Cloud Instance New NVMe based SSD I3 Instances New Flash-GT card with multiple NVMes integrated
- 12. Flash Memory - What Has Changed During Last Year New NVMe based local SSD that can be attached to a Cloud Instance New NVMe based SSD I3 Instances New Flash-GT card with multiple NVMes integrated New 3D XPoint chipset on NVME Devices
- 13. Redise Flash + Intel® Optane™ SSD 2040 1380 590 728 142 64 0 500 1000 1500 2000 2500 95% 85% 50% KOps/sec RAM hit ratio item size = 1000B; read/write = 50%/50% Optane P3700 Up to x9 Similar to what runs on:
- 14. DBaaS Price Comparison $2,200,162/yr $1,772,606/yr $766,096/yr $232,875/yr $0/yr $500,000/yr $1,000,000/yr $1,500,000/yr $2,000,000/yr $2,500,000/yr Other Redis Provider RCP RAM Cloud-Based NoSQL RCP Flash 2TB Dataset with HA @ 100K ops/sec (on-demand pricing) <1msec <1msec <10msec <1msec Up to 89% saving!
- 15. #2 – Modules Are Gaining Momentum
- 16. Modules Status • 50+ created • C, C++, Go, Python supported • Multi-threaded infrastructure
- 17. Why Multi-Threading is Crucial for Modules 100msec (module) 0.1msec (core) 0.1msec (core) 0.1msec (core) A Single-Threaded Module
- 18. Why Multi-Threading is Crucial for Modules 100msec (module) 0.1msec (core) 0.1msec (core) 0.1msec (core) A Single-Threaded Module 100.3msec 100.2msec 100.1msec 100msec Average latency - 100.15msec Module latency - 100msec Average core latency - 100.2msec
- 19. Why Multi-Threading is Crucial for Modules 100msec (module) 0.1msec (core) 0.1msec (core) 0.1msec (core) A Multi-Threaded Module 0.3msec 0.2msec 0.1msec 100msec Average latency - 25.15msec Module latency - 100msec Average core latency - 0.2msec 100msec (module) 0.1msec (core) 0.1msec (core) 0.1msec (core) A Single-Threaded Module 100.3msec 100.2msec 100.1msec 100msec Average latency - 100.15msec Module latency - 100msec Average core latency - 100.2msec
- 22. Friends, Friends of Friends and Visited Countries
- 23. Query: Friends Who’ve Visited Places I’ve Been To MATCH (:person {name:“Roi Lipman”})- [:visited]->(c:country)<-[:visited]- (f:person)<-[:friend]- (:person {name:“Roi Lipman”}) RETURN f, c
- 24. Result: Friends Who’ve Visited Places I’ve Been To
- 25. Query: Who’s Gone on a Business Trip MATCH (p:person)- [v:visited {purpose:business}]->(c:country) RETURN p, v, c
- 26. Result: Who’s Gone on a Business Trip
- 27. Benchmarks Per shard • 150K inserts / sec • 15K queries / sec
- 28. A Deep Dive on Redis-Graph: Salon 12 @ 2:30 pm
- 30. The Machine / Deep Learning (ML/DL) World (1) Training (2) Creating a model (3) Serving the model
- 31. (1) Training (2) Creating a model (3) Serving the model Homegrown The Machine / Deep Learning (ML/DL) World
- 32. Challenge #1 - Model Accuracy
- 33. Challenge #1 - Model Accuracy Just avoid…
- 34. Real World Example • Ads serving company • Need to serve 20,000 ads/sec @ 50msec data-center latency • Runs 1k campaigns
- 35. Real World Example • Runs 1k campaigns 1K random forest • Each forest has 15K trees • On average each tree has 7 levels (depth) 20K x 1K x 15K x 3.5 = 1.05 trillion ops/sec
- 36. Challenge #2 - Accurate Models are Expensive to Serve! Item Calculation Total Max ops/sec on the strongest AWS instance vcore 2.6Ghz x 0.9 (OS overhead) x 0.1 (10 lines of code per ops) x 0.1 (Java overhead) 23.4 million # of vcores needed 1.05 trillion / 23.4 million 44,872 # of c4.8xlarge instances needed 44,872 / 36 1,247 Total cost reserved instances 1,247 x 9213 ~$11.5M/yr
- 37. Ads Serving Use-Case w/ and w/o Redise + ML Homegrown 1,247 x c4.8xlarge 35 x c4.8xlarge Cut computing infrastructure by 97%
- 38. How ML/DL Should be Severed (1) Training (2) Creating a model (3) Serving the model
- 39. A Deep Dive on Redis-ML: Salon 8 @ 1:45 pm
- 41. Jeff Atwood's Law (Stack Overflow, Stack Exchange) : "any application that can be written in JavaScript, will eventually be written in JavaScript"
- 42. This Leads To: "...any database will eventually store [some data in] JSON"
- 43. What We Found
- 44. ReJSON (= Redis + JSON) in 5 Words • Intuitive • Integrated • Any client • Fast
- 45. Performance: 39491 bytes, 3 nesting levels ReJSON Raw JSON & Lua MessagePack & Lua
- 47. The Fastest Search in Town x5 - x10 Faster
- 48. The Feature List is Long 1. Full-Text indexing of multiple fields in documents 2. Incremental indexing without performance loss 3. Document ranking (provided manually by the user at index time) 4. Complex Boolean queries with AND, OR, NOT operators between sub-queries 5. Optional query clauses 6. Prefix based searches 7. Field weights 8. Auto-complete suggestions (with fuzzy prefix suggestions) 9. Exact Phrase Search, Slop based search 10. Stemming based query expansion in many languages (using Snowball) 11. Support for custom functions for query expansion and scoring (see Extensions) 12. Limiting searches to specific document fields (up to 8 fields supported) 13. Numeric filters and ranges 14. Geo filtering using Redis' own Geo-commands 15. Supports any utf-8 encoded text 16. Retrieve full document content or just ids 17. Automatically index existing HASH keys as documents 18. Document deletion and updating with index garbage collection.
- 49. RediSearch Shard Redis Search Engine Coordinator + + = RediSearch Shard
- 50. RediSearch Cluster Node 1 Node 2 Node 3
- 51. RediSearch Cluster Node 1 Node 2 Node 3 ClientFT.SEARCH idx “foo”
- 52. RediSearch Cluster Fully-Connected Fan-Out/In Node 1 Node 3 “foo” {3} “foo” {4} “foo” {5} “foo” {6} “foo” {2} “foo” {1} ClientFT.SEARCH idx “foo” Node 2
- 53. When RediSearch Cluster Becomes Larger Node 1 Node 2 Node 3
- 54. RediSearch Cluster Tree-Based Fan-Out/In Node 1 Node 2 Node 3 “foo” {5},{6},{7},{8} “foo” {9},{10},{11},{12} ClientFT.SEARCH idx “foo”
- 55. RediSearch Cluster Tree-Based Fan-Out/In Node 1 Node 2 Node 3 “foo” {5},{6},{7},{8} “foo” {9},{10},{11},{12} ClientFT.SEARCH idx “foo” “foo” {5} “foo” {7} “foo” {6} “foo” {8} “foo” {1} “foo” {3} “foo” {2} “foo” {4} “foo” {9} “foo” {11} “foo” {10} “foo” {12}
- 56. Let’s See it in Action… Preview
- 57. #3 – Redis for IoT
- 63. The Evolution of an IoT Device Simple Sensor
- 64. The Evolution of an IoT Device Simple Sensor Complex Device More Features
- 65. The Evolution of an IoT Device Simple Sensor Complex Device Complex Device with Memory More Features Online/ Offline Memory
- 66. Redis-Based IoT Device • Runs on Raspberry Pi • Single OSS Redis instance (3MB footprint) • Redis Stream client • Persistence • Low cost
- 67. IoT Edge Challenges Small Form Factor Cluster (Per node: 4 cores, 1-2GB DRAM, 50GB Flash)
- 68. IoT Edge Challenges Small Form Factor Cluster (Per node: 4 cores, 1-2GB DRAM, 50GB Flash) Thousands of Devices (20K+ devices, 10 updates/sec/device 200K+ updates/sec)
- 69. IoT Edge Challenges Small Form Factor Cluster (Per node: 4 cores, 1-2GB DRAM, 50GB Flash) Thousands of Devices (20K+ devices, 10 updates/sec/device 200K+ updates/sec) Varied Functionality (Time series, Search, Graph, ML, Geo)
- 70. IoT Edge Challenges Simple & Reliable (Integrated, short failover time) Small Form Factor Cluster (Per node: 4 cores, 1-2GB DRAM, 50GB Flash) Thousands of Devices (20K+ devices, 10 updates/sec/device 200K+ updates/sec) Varied Functionality (Time series, Search, Graph, ML, Geo)
- 71. Redise Cluster for the Edge • Redise Cluster on RPI/x86 • Redise Flash • Persistent & HA • Redis Streams Server + Client • Module options: Search, JSON, Graph, Time-Series, ML, Neural, RQL, Enhanced GEO Stream API Streams Data-Structure Multi-Function 1 2 3 Time-series JSON ML Graph Search GEO RQL
- 72. Redise Blueprint for IoT Devices • Raspberry Pi support • A single OSS Redis instance (3MB footprint) • Persistent • Redis Streams Client • RPi/x86 nodes (4 cores/2GB RAM /50GB SSD) • Redise cluster • Persistent & HA • Redise on Flash • Redis Streams Server & Client • Modules: ‒ Search, JSON, Graph, Time-Series, ML, Nueral, RQL, Enhanced GEO Edge • Large Redise cluster(s) • Multi-cloud/DBaaS/Self-managed • Multi-master geo-replication • Persistent & HA (multi-az) • Redise on Flash • Redis Streams Server • Modules: ‒ Search, JSON, Graph, Time-Series, ML, RQL, Enhanced GEO Cloud
- 73. #4 – Multi-Master Geo-Distributed Redis
- 74. Multi-Master Geo-Distributed Workloads BIDDING/POLLING/LEADERBOARD ₤ $ $ ₱ € PROFILES/SESSION MANAGEMENT
- 75. Challenge #1 – Geo-Distributed Latency Redis is fast!: > 1,000,000 ops/sec, < 1msec latency Light is slower > 20msec RTT Network is even slower > 70msec RTT
- 76. Consistency Consensus Slowness App App App ~100msec for a ‘write’ operation
- 77. Challenge #2 – Conflict Resolution for Complex Data-Types • Application level solution too complex to write • LWW (Last Write Wins) doesn’t work for many of the Redis use cases, e.g.: • Counters • Sets • Sorted Sets • Lists • Modules’ new datatypes
- 78. Hello CRDT Conflict-free Replicated Data Type
- 79. CRDT • Years of academic research • Based on consensus free protocol • Strong eventual consistency • Built to resolve conflicts with complex data types
- 80. Consensus Free with Strong Eventual Consistency App App App Write Locally
- 81. Consensus Free with Strong Eventual Consistency App App App Write Locally, Converge Asynchronously
- 82. Solving Conflicts – Counters Replica A: C = 500 Replica B: C = 500 Replica C: C = 500
- 83. Solving Conflicts – Counters Replica A: C = 500 INCRBY 200 Replica B: C = 500 Replica C: C = 500
- 84. Solving Conflicts – Counters Replica A: C = 500 INCRBY 200 Replica B: C = 500 DECRBY 300 Replica C: C = 500
- 85. Solving Conflicts – Counters Replica A: C = 500 INCRBY 200 Replica B: C = 500 DECRBY 300 Replica C: C = 500 INCRBY 1000
- 86. Solving Conflicts – Counters Replica A: C = 500 INCRBY 200 Replica B: C = 500 DECRBY 300 Replica C: C = 500 INCRBY 1000 Convergence Function (commutative): 500 + ΣC(i) = = 500 +200 -300 +1000 = 1400
- 87. Solving Conflicts – Sets Replica A: S = {A, B, C} Replica B: S = {A, B, C} Replica C: S = {A, B, C}
- 88. Solving Conflicts – Sets Replica A: S = {A, B, C} SADD D Replica B: S = {A, B, C} Replica C: S = {A, B, C}
- 89. Solving Conflicts – Sets Replica A: S = {A, B, C} SADD D Replica B: S = {A, B, C} SADD A Replica C: S = {A, B, C}
- 90. Solving Conflicts – Sets Replica A: S = {A, B, C} SADD D Replica B: S = {A, B, C} SADD A Replica C: S = {A, B, C} SREM A
- 91. Solving Conflicts – Sets Replica A: S = {A, B, C} SADD D Replica B: S = {A, B, C} SADD A Replica C: S = {A, B, C} SREM A Convergence Function (associative): • S = S + D + A - A = {A, B, C, D} • Observed Removed + Add wins
- 92. Solving Conflicts – Sorted Sets Replica A: SS = {A:35, B:23, C:4} Replica B: SS = {A:35, B:23, C:4} Replica C: SS = {A:35, B:23, C:4}
- 93. Solving Conflicts – Sorted Sets Replica A: SS = {A:35, B:23, C:4} Replica B: SS = {A:35, B:23, C:4} Replica C: SS = {A:35, B:23, C:4} Convergence Function (associative & commutative): Counters + Sets
- 94. Solving Conflicts – Lists Replica A: L= ABC Replica B: L= ABC Replica C: L= ABC
- 95. Solving Conflicts – Lists Replica A: L= ABC Replica B: L= ABC Replica C: L= ABC Convergence Function: Based on Directed Acyclic Graphs (DAG) Too complex to explain!!!
- 96. Multi-Master Redis is Here Preview
- 97. What’s Unique About Redis-CRDT • Based on a Redis module (CRDB) • Support bi-direction replication • Multiple topologies • Solves a new set of problems • Counters deletion • Expiry • Cluster configuration • Etc. RingFully-Connected
- 98. A Deep Dive on Multi-Master: Salon 8 @ 3:15 pm
- 99. Thank You