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How @twitterhadoop chose google cloud
- 1. How @twitterhadoop chose Google Cloud Joep Rottinghuis & Lohit VijayaRenu Twitter Hadoop Team (@twitterhadoop) 1
- 2. 1. Twitter infrastructure 2. Hadoop evaluation 3. Evaluation outcomes 4. Recommendations and conclusions 5. Q&A Credit to presentation at GoogleNext 2019 by Derek Lyon & Dave Beckett (https://youtu.be/4FLFcWgZdo4) 2
- 4. Twitter’s infrastructure ● Twitter founded in 2006 ● Global-scale application ● Unique scale and performance characteristics ● Real-time ● Built to purpose and well optimized ● Large data centers 4
- 5. Strategic questions 1. What is the long-term mix of cloud versus datacenter? 2. Which cloud provider(s) should we use? 3. How can we be confident in this type of decision? 4. Why should we evaluate this now (2016)? 5
- 6. Tactical questions 1. What is the feasibility and cost of large-scale adoption? 2. Which workloads are best-suited for the cloud and are they separable? 3. How would our architecture change on the cloud? 4. How do we get to an actionable plan? 6
- 7. Evaluation process ● Started evaluation in 2016 ● Were able to make a patient, rigorous decision ● Defined baseline workload requirements ● Engaged major providers ● Analyzed clouds for each major workload ● Built overall cloud plan ● Iterated and optimized choices 7
- 8. Evaluation Timeline Considering Moving ● PoC’s Completed & Results Delivered ● Legal Agreement with T&C’s ratified ● Kickoff dataproc, bigquery, dataflow experimentation ● Security and Platform Review ● v1 Hadoop on GCP Architecture Ratified ● Begin build for migration plan ● Consensus built with Product, Revenue, Eng ● Migration Kickoff ● Proposal to migrate Hadoop to GCP formally accepted June ‘16 ● Initial Cloud RfP release ● 27 Synthetic PoC’s on GCP begin ● Testing Projects / Network established Sept ‘16 Mar ‘17 July ‘17 Nov ‘17 Jan ‘18 Apr ‘18 June ‘18 8
- 9. Built overall cloud plan ● Created a series of candidate architectures for each platform with their resource requirements ● Developed a migration project plan & timeline ● Created financial projections ● With some other business considerations 9
- 10. Financial modeling ● 10-year time horizon to avoid timing artifacts ● Compared on premise and multiple cloud scenarios ● Costs of migration and long-term ● Long-term price/performance curves (e.g. Moore’s Law, historical pricing) ● Two independent models to avoid model errors 10
- 11. ● An immediate all-in migration at Twitter scale is: expensive, distracting, and risky ● More value from new architectures and transformation, so start smaller and learn as we go ● Hadoop offered several important, specific benefits with lower risk ● We gained confidence in our investments in both cloud projects and data centers What we found 11
- 12. >1.4T Messages Per Day >500K Compute Cores >300PB Logical Storage Hadoop@Twitter scale >12,500 Peak Cluster Size 12
- 13. Type Use Compute % Real-time Critical performance production jobs with dedicated capacity 10% Processing Regularly scheduled production jobs with dedicated capacity 60% Ad-hoc One off / ad-hoc queries and analysis 30% Cold Dense storage clusters, not for compute minimal Twitter Hadoop cluster types 13
- 14. Twitter Hadoop challenges 1. Scaling: Significant YoY Compute & Storage growth 2. Hardware: Designing, building, maintaining & operating 3. Capacity Planning: Hard to predict for adhoc especially 4. Agility: Must respond fast especially for adhoc compute 5. Deployment: Must deploy at scale and in-flight 6. Network: Both cross-DC and cross-cluster 7. Disaster Recovery: Durable copies needed in 2+ DCs 14
- 15. Twitter Hadoop requirements ● Network sustained bandwidth per core ● Disk (data) sustained bandwidth per core ● Large sequential reads & writes ● Throughput not latency ● Capacity ● CPU / RAM not usually the bottleneck ● Consistency of datasets (set of HDFS files) 15
- 16. Twitter Hadoop on premise hardware numbers Clusters: 10 to 10K nodes Network: 10G moving to 25G Data Disks: 24T-72T over 12 HDDs CPU: 8 cores with 64G memory I/O: Network: ~20MB/s sustained, peaks of 10x HDFS read: 20 rq/s sustained, peaks of 3x HDFS write: large variation, peaks of 10x 16
- 17. 2. Twitter Hadoop on cloud VMs Durable storage: cloud object store Scratch storage: a. with HDFS over cloud object store b. with HDFS on cloud block store c. with HDFS on local disks 1. Hadoop-as-a-Service (HaaS) from the cloud provider Cloud architectural options 17
- 18. 2. Functional Test Gridmix: IO + Compute ● Capture of real production cluster workload (1k-5k jobs) ● Replays reads, writes, shuffles, compute Testing plan 1. Baseline Tests ● TestDFSIO: low level IO read/write ● Teragen: measure maximum write rate ● Terasort: read, shuffle, write 18
- 19. HDFS configurations tested Availability ● Critical data: 2 regions ● Other data: 2 zones Each type of Object, Block and Local Storage Dataset consistency Test cloud provider choices: 1. object store 2. object store with external consistency service 19
- 21. GCP HaaS: DataProc config ● Hadoop 2.7.2 ● Performance tests with 800 vCPUs: ○ 100 x n1-standard-8 (8 VCPU, 30G memory) ○ 200 x n1-standard-4 (4 VCPU, 30G memory) ● Scale test with 8000 vCPUs: ○ 1000 x n1-standard-8 (8 vCPU, 30G memory) ● Modeled average CPU and average to peak CPU. ● No preemptible instances in initial work ● Similar to on premise hardware SKUs 21 Decided to use DataProc for evaluation.
- 22. Durable Storage Scratch Storage HDFS Speedup vs on premise (normalized by IO-per-core) Cloud Storage Local SSD 3 x 375G SSD ~2x (but expensive) Cloud Storage PD-HDD 1.5TB PD-HDD ~1x None PD-HDD 1.5TB PD-HDD ~1x DataProc 100 x n1-standard-8 Results Tuned Compute Engine instance types to get the optimum balance of network : cores : storage (this changes over time) 22
- 23. Durable Storage Scratch Storage HDFS Speedup vs on premise (normalized by IO-per-core) Cloud Storage Local SSD 2 x 375G SSD ~2x (but expensive) Cloud Storage PD-HDD 1.5TB PD-HDD 1.4x DataProc 200 x n1-standard-4 Results 23
- 24. Benchmark Findings 1. Application Benchmarks are critical Total job time is composed of multiple steps. We found variation both better and worse at each step. Recommendation: You should rely on an application benchmark like GridMix rather than micro-benchmarks. 2. Can treat network storage like local disk Both Cloud Storage and PD offered nearly as much bandwidth as typical direct attached HDDs on premise 24
- 25. Functional Test Findings 1. Live Migration of VMs was not noticeable during Hadoop testing. It was during other Twitter platform testing of Compute Engine (cache at very high rps of small objects) 2. Cloud Storage checksum vs HDFS checksum. Fixed via HDFS-13056 in collaboration with Google 3. fsync() system call on Local SSD was slow (fixed) 25
- 27. + Leads to the fastest migration + Limits duplication of costs during migration period - Introduces significant tech debt post-migration - Requires a major rearchitecture post-migration to capture benefits of cloud - Concerns around overall cost, risk, and distraction of this approach at Twitter scale Life-and-Shift everything Disqualified Lift-and-Shift *Everything* 27
- 28. ● Separable with fewer dependencies ● Standard open source software: ○ Continue to develop in house and run on premise ○ Reduces lock-in risk ● Rearchitecting is achievable ○ Not a lift-and-shift ● Data in Cloud Storage: ○ Enables broader diversity of data processing frameworks and services ● Long-term bet on Google’s Big Data ecosystem Hadoop to Cloud was Interesting 28
- 29. Separate Hadoop Compute and Storage ● Scaling the dimensions independently ● Makes it easy to run multiple clusters and processing frameworks over the same data ● Virtual network and project primitives provide segmentation of access and cost structures. ● State is preserved in Cloud Storage therefore deployments, upgrades, and testing are simpler ● Can treat storage as a commodity Enables 29
- 30. 1. Cold Cluster ● Storage: Cloud Storage ● Compute: Limited ephemeral Dataproc an option ● Scaling: mostly storage driven 2. Ad-Hoc Clusters ● Storage: Cloud Storage ● Compute: Compute Engine and Twitter build of Hadoop (long running clusters) ● Scaling: mixture, with spiky compute Twitter Hadoop Rearchitected for Cloud 30
- 31. Twitter production Hadoop remains on premise ● Not as separable from other production workloads ● Focusing on non-production workloads limits our risk ● Regular compute-intensive usage patterns ● Benefits more from purpose built hardware ● Fewer processing frameworks are needed 31
- 32. Twitter Strategic Benefits ● Next-generation architecture with numerous enhancements: ○ security, encryption, isolation, live migration ● Leverage Google’s capacity and R&D ● Larger ecosystem of open source & cloud software ● Long-term strategic collaboration with Google ● Beachhead that enable teams across Twitter to make tactical cloud adoption decisions What does this do overall for Twitter? 32
- 33. Infrastructure benefits ● Large-scale ad-hoc analysis and backfills ● Cloud Storage avoids HDFS limits ● Offsite Backup ● Increases availability of cold data Twitter Functional Benefits Platform benefits ● Built-in compliance support (e.g. SOX) ● Direct chargeback using Project ● Simplified retention ● GCP services such as BigQuery, Spanner, Cloud ML, TPUs, etc 33
- 34. Finding: At Twitter Scale, Cloud has limits ● Cloud providers have limits for all sorts of things and we often need them increased. ● Cloud HaaS do not generally support 10K node hadoop clusters ● Dynamic scaling down < O(days) is not yet feasible / cost-effective with current Hadoop at Twitter scale ● Capacity planning with cloud providers is encouraged for O(10K) vCPU deltas and required for O(100K) vCPU deltas 34
- 35. What we are working on now ❏ Finalizing bucket & user creation and IAM designs ❏ Building replication, cluster deployment, and data management software ❏ Hadoop Cloud Storage connector improvements continue (open source) ❏ Retention and “directory” / dataset atomicity in GCS 35 ✓ Foundational network (8x100Gbps) ✓ Copy cluster ✓ Copying PBs of data to the cloud ✓ Early Presto analytics use case: up to 100K-core Dataproc cluster querying 15PB dataset in Cloud Storage
- 37. 3. Ensure migration plan captures benefits Lift-and-shift may not deliver value in all cases. Substantial iteration is required to balance tactical migration work with long-term strategy. 2. Compare application benchmark costs Compare the cost of running an application using benchmark results. Don’t just look at pricing pages. e.g. the network is hugely important to performance. 1. Run the most informative tests Application-level benchmarking (e.g. GridMix) Scale testing Recommendations 37
- 38. 2. Cloud adoption is complex Finding separable workloads can be a challenge. Architectural choices are non-obvious. Methodical evaluation is well-worth the effort. 1. Separate compute and storage is a real thing The better the network, the less locality matters. Life gets much easier when Compute can be stateless. You can treat PD like direct attached HDDs. Conclusions 3. Very early in this process and lots more to come We’re excited to be gaining experience with the platform and learning from everyone. 38