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Transforming the Database: Critical Innovations for Performance at Scale
The document is a presentation overview by Benny Halevy and Tzach Livyatan regarding ScyllaDB, emphasizing its innovative database architecture designed for high performance and scalability. It highlights performance benchmarks showing ScyllaDB's superiority over legacy NoSQL databases, offering significant improvements in throughput and latency at a lower total cost of ownership. Additionally, it discusses technical aspects, historical context, and use cases demonstrating ScyllaDB's capabilities across various industries.
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Transforming the Database: Critical Innovations for Performance at Scale
- 1. Transforming the Database: CriticalInnovations for Performance at Scale Benny Halevy, Director Software Engineering, ScyllaDB Tzach Livyatan, VP Product, ScyllaDB
- 2. Brought to youby VIRTUAL EVENT | OCTOBER 19 + 20 P99 Conf: All Things Performance The event for developers who care about P99 percentiles and high-performance, low-latency applications. Register at p99conf.io
- 3. Poll Where are youin your NoSQL adoption?
- 4. Tzach Livyatan VP ofProduct, ScyllaDB + Lead the product team in ScyllaDB + Appreciate distributed system testing + Lives in Tel Aviv, father of two
- 5. + Leading thestorage software development team at ScyllaDB + Has been working on operating systems and distributed file systems for over 20 years + Most recently, Benny led software development for GSI Technology, and previously co-founded Tonian (later acquired by Primary Data) and led it as CTO + Before Tonian, Benny was the lead architect in Panasas of the pNFS protocol Director, Software Engineering, ScyllaDB Benny Halevy
- 6. Agenda + Howdid we get here? Quick history of HW + Shard Per Core Architecture + IO Scheduler revisit + I4i results + Benchmark a PB cluster + Even More Optimizations + QA
- 7. + Infoworld 2020Technology of the Year! + Founded by designers of KVM Hypervisor The Database Built for Gamechangers 7 “ScyllaDB stands apart...It’s the rare product that exceeds my expectations.” – Martin Heller, InfoWorld contributing editor and reviewer “For 99.9% of applications, ScyllaDB delivers all the power a customer will ever need, on workloads that other databases can’t touch – and at a fraction of the cost of an in-memory solution.” – Adrian Bridgewater, Forbes senior contributor + Resolves challenges of legacy NoSQL databases + >5x higher throughput + >20x lower latency + >75% TCO savings + DBaaS/Cloud, Enterprise and Open Source solutions + Proven globally at scale
- 8. Why Scylla? On-Prem Cloud Hosted ScyllaCloud Best High Availability in the industry Best Disaster Recovery in the industry Best Scalability in the industry Best Performance in the industry Auto-tune — out of the box performance Fully compatible with Cassandra & DynamoDB The power of Cassandra at the speed of Redis and more
- 9. 9 +400 Gamechangers LeverageScyllaDB Seamless experiences across content + devices Fast computation of flight pricing Corporate fleet management Real-time analytics 2,000,000 SKU -commerce management Real-time location tracking for friends/family Video recommendation management IoT for industrial machines Synchronize browser properties for millions Threat intelligence service using JanusGraph Real time fraud detection across 6M transactions/day Uber scale, mission critical chat & messaging app Network security threat detection Power ~50M X1 DVRs with billions of reqs/day Precision healthcare via Edison AI Inventory hub for retail operations Property listings and updates Unified ML feature store across the business Cryptocurrency exchange app Geography-based recommendations Distributed storage for distributed ledger tech Global operations- Avon, Body Shop + more Predictable performance for on sale surges GPS-based exercise tracking
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- 11. Quick history ofHW and DBs
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- 13. Non Uniform MemoryAccess (NUMA) 13
- 14. What happened? 14 + Perthread performance plateaued + Cores: 1 ⟶ 256, NUMA + RAM: 2GB ⟶ 2TB + Disk space: 10GB ⟶ 10TB + Disk seek time: 10-20ms ⟶ 20µs + Network throughput: 1Gbps ⟶ 100Gbps This year: 64/128 cores/threads/cpu, 400Gbps NIC, Disk 10µs latency, 1.5TB/device, DDR5 2TB/DIMM AWS u-24tb1.metal: 224 cores, 448 threads, 24TB RAM
- 15. 15 A Brief Historyof Databases 15 1970s Mainframes: inception of the relational model 1990s LAN age: replication, external caching, ORMs SQL 1980s SQL, relational databases become de-facto standard 2000s WEB 2.0: NoSQL databases for scale 2010s Cloud age: commoditization of NoSQL, NewSQL inception 1996 1995 1978 2008 2015 2014
- 16. Cloud Infrastructure: TheLast ~10 Years 16 SSD: $2500/TB Performance improvement 2008 2012 Typical instance 4 cores SSD $100/TB - 1000x faster, 10x cheaper 96 core VMs - 20x more cores 100Gbps NICs - 100x more throughput 2015 2022 2000 CPU core systems and beyond
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- 18. Shard Per Core Sharenothing, block nothing 18
- 19. Sharding/Partitioning + Common conceptin distributed databases + Break the system to N non-interacting parts + Usually done by hash(partition_key) % N + Data/load may be unbalanced + Fact of life in distributed databases 🤷 + Logical mapping of data shards to core shards 19
- 20. Sharding All TheWay Down 20 Node ID Shard ID
- 21. Shard per Core Cassandra TCP/IP Scheduler queue queue queue queue queue Threads NIC Queues Kernel TraditionalStack Seastar’s Sharded Stack Memory Lock contention Cache contention NUMA unfriendly TCP/IP Task Scheduler queue queue queue queue queue smp queue NIC Queue DPDK Kernel (isn’t involved) Userspace TCP/IP Task Scheduler queue queue queue queue queue smp queue NIC Queue DPDK Kernel (isn’t involved) Userspace TCP/IP queue queue queue queue queue smp queue NIC Queue Kernel (isn’t involved) Userspace No contention Linear scaling NUMA friendly Core Database Task Scheduler queue queue queue queue smp queue Userspace NIC Queue 21 vs.
- 22. Seastar + Open sourceframework, powering ScyllaDB, Redpanda, ValuStor + A “mini operating system in userspace” + Task scheduler, I/O scheduler + Fully asynchronous - userspace coroutines + Direct I/O, self managed cache (bypass pagecache) + One thread per core, one shard per core 22
- 23. ScyllaDB is Different 23 +Multi queue + Poll mode + Userspace + TCP/IP + Thread per core + lock-free + Task scheduler + Reactor programing + C++14 + NUMA friendly + Log structured allocator + Zero copy + DMA + Log structured + merge tree + DBaware cache + Userspace I/O + scheduler
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- 25. Why Scheduling AtAll + Different components compete for limited resources (Reads, Writes, Admin) + They have different priorities + They have no idea how not to over-consume the resource
- 26. How Does ItWork? Flush sched-group Compaction sched-group Query sched-group
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- 28. Diskplorer 3 (AWSi3en.3xlarge)
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- 30. The New I/OScheduler + Collect information about disks + Build a more accurate mathematical disk model + Embody the model into the I/O scheduler
- 31. Latency While Replacinga Node
- 32. Replace a Nodein ScyllaDB 4.6 New node added Streaming completed P99 Latency
- 33. Replace a nodein ScyllaDB 5.1 New node added Streaming completed P99 Latency
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- 36. Latest Results I3vs I4 - One Node I3.16xlarge vs i4.16xlarge (64 vCPU servers) 50% Reads / 50% Writes Latency tests with 50% of the max throughput
- 37. Latest Results I3vs I4 - 3 Node Cluster Big thanks to Michał Chojnowski for benchmarking all the new AWS instances types! I3.16xlarge vs i4.16xlarge (64 vCPU servers) 50% Reads / 50% Writes Latency tests with 50% of the max throughput 67% better price/performance!
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- 39. Bill of Materials +ScyllaDB cluster: 20 x i3en.metal AWS instances, each having: + 96 vCPUs + 768 GiB RAM + 60 TB NVMe disk space + 100 Gbps network bandwidth + Load Generators: 50 x c5n.9xlarge AWS instances, each having: + 36 vCPUs + 96 GiB RAM + 50 Gbps network bandwidth
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- 42. Concurrent Workloads: R/W+ 80/20 (1) Throughput is in transactions/second (2) Latency is in milliseconds (1) Workload: Application: 200K R/W User: 5M 80/20 R/W (2) Write latency 0.682 P50 2.454 P99 0.326 P50 1.252 P99 Read latency 1.195 P50 4.555 P99 0.744 P50 3.709 P99 + 5Mi user 80/20 R/W ops/sec + 200K application 50/50 R/W ops/sec + Added user write workload increases app workload latency.
- 43. Making Conflicting LoadsCoexist with Workload Prioritization https://www.scylladb.com/2019/05/23/workload-prioritization-running-oltp-and-olap-traffic-on-t he-same-superhighway/
- 44. Workload Prioritization ina Glance placeholder
- 45. Concurrent workloads: R/W+ 80/20 (1) Throughput is in transactions/second (2) Latency is in milliseconds With Workload Prioritization + As the 80/20 user workload interfered with the application latency, let’s reduce its relative priority to better share the system resources. (1) Workload: Application: 200K R/W User: 5M 80/20 R/W before: 1000 shares after: 1000 shares before: 1000 shares after: 500 shares (2) Write latency 0.682 2.454 0.354 P50 1.184 P99 0.326 1.252 0.440 P50 3.244 P99 Read latency 1.195 4.555 0.855 P50 3.731 P99 0.744 3.709 1.043 P50 6.455 P99
- 46. + Each servicelevel has its own per-shard queue for consuming cpu and I/O Service Levels in Action Application workload (200K ops/sec) User workload (5M ops/sec)
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- 49. Removed Large PartitionPenalty RAM Disk
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- 52. Higher Throughput -Lower Cost ScyllaDB vs Google Bigtable ScyllaDB vs DynamoDB ScyllaDB vs Cassandra 1/7th the cost 26x performance in real-life scenario 4 ScyllaDB nodes vs 40 Cassandra nodes 2.5X less expensive up to 22x better latencies 1/5th cost 20x better latencies in real-life scenario
- 53. Poll How much datado you under management of your transactional database?
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- 55. Thank you for joiningus today. @scylladb scylladb/ slack.scylladb.com @scylladb company/scylladb/ scylladb/