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Scale Testing RHCS with
10,000,000,000+ Objects
Karan Singh
Sr. Solution Architect
Cloud Storage & Data Services BU
1
2
Rare View cluster with 10B Objects
3
● RHT tested 1 Billion Objects in Feb 2020 !! (What’s Next ?)
○ https://www.redhat.com/en/blog/scaling-ceph-billion-objects-and-beyond
Why 10 Billion ? Motivations
● Other Object Storage Systems aspire to scale to Billions of objects one day
○ Ceph can do it today, but can we Test ?
● Object Storage is getting popular for for Data Lake use cases
● Educate and Motivate Communities, Customers and Partners
“RHCS delivered Deterministic Performance
at scale for both Small and Large object size workloads”
4
Executive Summary

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- 발표자: netmarble 한승진 - 설명: https://event.openinfradays.kr/2018/session2/50_track1

openinfra days korea 2018cephobject storage
5
● 10,000,000,000+ Objects Ingested (and retrieved)
● 100,000+ Buckets
● 100,000 Objects / Bucket
● 318 HDDs / 36 NVMe devices
● 5.0 PB RAW capacity
● ~500 Test Runs
Defining Scale
6
● 6 x RHCS Nodes
○ 53 x 16TB HDDs
■ Seagate Exos E 4U106
○ 6 x Intel QLC 7.6 TB
○ 2 x Intel Xeon Gold 6152
○ 256GB
○ 2 x 25GbE
● 6 x Client Nodes
○ 2 x 25GbE
HW & SW Inventory
● RHEL 8.1
● RHCS 4.1
○ Containerized Deployment
○ 2 x RGWs per RHCS node
○ EC 4+2
○ S3 Access Mode
○ 100K Objects / Bucket
● COSBench for workload generation
○ 6 x Drivers
○ 12 x Workers
■ 64 x Threads each
7
Test Lab Architecture
RHCS 4.1 Cluster
2 x 25 Gb
Mellanox MSN2010
COSBench Workers
2 x 25 Gb
2 x 25 Gb
2 x 25 Gb
2 x 25 Gb
2 x 25 Gb
2 x 25 Gb
2 x 25 Gb
25 GbE Bonded Ports
• Isolated Network
2 x 25 Gb
2
x 25
Gb
2 x 25 Gb
2 x 25 Gb
Internet10 GbE Mgmt. Ports
8
● Object Sizes
○ 64KB (Small Objects)
○ 128MB (Large Objects)
Workload Selection
● Access Pattern
○ 100% PUT
○ 100% GET
○ 70% GET, 20% PUT, 5% LIST, 5% Delete
● Degraded State Simulation
○ 1 x HDD Down
○ 6 x HDDs Down
○ 53 x HDDs Down (1 Node Failure)

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Small Object Performance : Operations Per Sec
● Average Cluster Performance
○ ~17,800 S3 PUT Ops
○ ~28,800 S3 GET Ops
● Avg Single HDD OSD Perf.
○ 60 S3 PUT Ops
○ 90 S3 GET Ops
10
Small Object Performance Dissection
Deep-Scrubbing
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Cluster’s spacial capacity (%used)
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Minor decline due to NVMe to HDD
Bluestore Metadata Spill Over
DC power outage
~48+ Hours
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Small Object Performance Dissection
● Bluestore uses RocksDB
● RocksDB uses Level Style Compaction
○ L0: in memory
○ L1: 256MB
○ L2: 2.56 GB
○ L3: 25.6 GB
○ L4: 256 GB
○ L5: 2.56 TB
○ L6: 25.6 TB
L5 could not fit in Flash, hence spilled over to HDD
Bluestore and RocksDB Details
https://www.redhat.com/en/blog/scaling-ceph-billion-objects-and-beyond

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Small Object Performance : Latency
● Average Cluster Latency
○ 510 ms S3 PUT Latency
○ 27 ms S3 GET Latency
14
Large Object Performance : Bandwidth
COSBench config
experimentation
Missed running 100% GET Tests
● Average Cluster Performance
○ ~10.7 GB/s S3 PUT BW
○ ~11.6 GB/s S3 GET BW
● Avg Single HDD OSD Perf.
○ 34 MBps S3 PUT BW
○ 37 MBps S3 GET BW
15
Performance during Degraded State
Did not had time to
execute this test case
Total 318 HDDs Storage Failure (%) PUT Perf Drop (%) GET Perf Drop (%)
6 HDDs Failed 2 6 8
53 HDDs Failed 17 21 25
16
● I needs X Ops and Y GBps for S3 workload ? How to Size ?
Sizing Guidance
Single HDD OSD Performance ( with 4% Flash for Bluestore )
S3 Access 100% PUT 100% GET
Small Object (64K) 60 Ops 90 Ops
Large Object (128M) 34 MBps 37 MBps
● Use 2 RGWs Instances per Ceph Node
● RHT recommendation of 4% for Bluestore is good at scale as well
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most of your 4% Bluestore Flash allocation
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IT organizations of the future (and present) are faced with managing infrastructure that spans multiple private data centers and multiple public clouds. Emerging tools and operational patterns like kubernetes and microservices are easing the process of deploying applications across multiple environments, but the achilles heel of such efforts remains that most applications require large quantities of state, either in databases, object stores, or file systems. Unlike stateless microservices, state is hard to move. Ceph is known for providing scale-out file, block, and object storage within a single data center, but it also includes a robust set of multi-cluster federation capabilities. This talk will cover how Ceph's underlying multi-site capabilities complement and enable true portability across cloud footprints--public and private--and how viewing Ceph from a multi-cloud perspective has fundamentally shifted our data services roadmap, especially for Ceph object storage.

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17
● Our testing showed RHCS achieving deterministic performance at scale for
both Small and Large Object sizes, PUT and GET operations, before hitting
resource saturation, capacity limits
● Performance during failure scenarios found to be acceptable
Summary
● Undoubtedly RHCS can scale a lot more than what we tested
○ 10 Billion objects are just Tested Maximum, This is NOT A LIMIT
Download the full performance report http://red.ht/10billion
linkedin.com/company/red-hat
youtube.com/user/RedHatVideos
facebook.com/redhatinc
twitter.com/RedHat
18
Red Hat is the world’s leading provider of enterprise
open source software solutions. Award-winning
support, training, and consulting services make
Red Hat a trusted adviser to the Fortune 500.
Thank you
Download the full performance report at
http://red.ht/10billion

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Ceph scale testing with 10 Billion Objects

  • 1. Scale Testing RHCS with 10,000,000,000+ Objects Karan Singh Sr. Solution Architect Cloud Storage & Data Services BU 1
  • 2. 2 Rare View cluster with 10B Objects
  • 3. 3 ● RHT tested 1 Billion Objects in Feb 2020 !! (What’s Next ?) ○ https://www.redhat.com/en/blog/scaling-ceph-billion-objects-and-beyond Why 10 Billion ? Motivations ● Other Object Storage Systems aspire to scale to Billions of objects one day ○ Ceph can do it today, but can we Test ? ● Object Storage is getting popular for for Data Lake use cases ● Educate and Motivate Communities, Customers and Partners
  • 4. “RHCS delivered Deterministic Performance at scale for both Small and Large object size workloads” 4 Executive Summary
  • 5. 5 ● 10,000,000,000+ Objects Ingested (and retrieved) ● 100,000+ Buckets ● 100,000 Objects / Bucket ● 318 HDDs / 36 NVMe devices ● 5.0 PB RAW capacity ● ~500 Test Runs Defining Scale
  • 6. 6 ● 6 x RHCS Nodes ○ 53 x 16TB HDDs ■ Seagate Exos E 4U106 ○ 6 x Intel QLC 7.6 TB ○ 2 x Intel Xeon Gold 6152 ○ 256GB ○ 2 x 25GbE ● 6 x Client Nodes ○ 2 x 25GbE HW & SW Inventory ● RHEL 8.1 ● RHCS 4.1 ○ Containerized Deployment ○ 2 x RGWs per RHCS node ○ EC 4+2 ○ S3 Access Mode ○ 100K Objects / Bucket ● COSBench for workload generation ○ 6 x Drivers ○ 12 x Workers ■ 64 x Threads each
  • 7. 7 Test Lab Architecture RHCS 4.1 Cluster 2 x 25 Gb Mellanox MSN2010 COSBench Workers 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 25 GbE Bonded Ports • Isolated Network 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb 2 x 25 Gb Internet10 GbE Mgmt. Ports
  • 8. 8 ● Object Sizes ○ 64KB (Small Objects) ○ 128MB (Large Objects) Workload Selection ● Access Pattern ○ 100% PUT ○ 100% GET ○ 70% GET, 20% PUT, 5% LIST, 5% Delete ● Degraded State Simulation ○ 1 x HDD Down ○ 6 x HDDs Down ○ 53 x HDDs Down (1 Node Failure)
  • 9. 9 Small Object Performance : Operations Per Sec ● Average Cluster Performance ○ ~17,800 S3 PUT Ops ○ ~28,800 S3 GET Ops ● Avg Single HDD OSD Perf. ○ 60 S3 PUT Ops ○ 90 S3 GET Ops
  • 10. 10 Small Object Performance Dissection Deep-Scrubbing effect Cluster’s spacial capacity (%used) 70% to 90% Minor decline due to NVMe to HDD Bluestore Metadata Spill Over DC power outage ~48+ Hours
  • 12. 12 Small Object Performance Dissection ● Bluestore uses RocksDB ● RocksDB uses Level Style Compaction ○ L0: in memory ○ L1: 256MB ○ L2: 2.56 GB ○ L3: 25.6 GB ○ L4: 256 GB ○ L5: 2.56 TB ○ L6: 25.6 TB L5 could not fit in Flash, hence spilled over to HDD Bluestore and RocksDB Details https://www.redhat.com/en/blog/scaling-ceph-billion-objects-and-beyond
  • 13. 13 Small Object Performance : Latency ● Average Cluster Latency ○ 510 ms S3 PUT Latency ○ 27 ms S3 GET Latency
  • 14. 14 Large Object Performance : Bandwidth COSBench config experimentation Missed running 100% GET Tests ● Average Cluster Performance ○ ~10.7 GB/s S3 PUT BW ○ ~11.6 GB/s S3 GET BW ● Avg Single HDD OSD Perf. ○ 34 MBps S3 PUT BW ○ 37 MBps S3 GET BW
  • 15. 15 Performance during Degraded State Did not had time to execute this test case Total 318 HDDs Storage Failure (%) PUT Perf Drop (%) GET Perf Drop (%) 6 HDDs Failed 2 6 8 53 HDDs Failed 17 21 25
  • 16. 16 ● I needs X Ops and Y GBps for S3 workload ? How to Size ? Sizing Guidance Single HDD OSD Performance ( with 4% Flash for Bluestore ) S3 Access 100% PUT 100% GET Small Object (64K) 60 Ops 90 Ops Large Object (128M) 34 MBps 37 MBps ● Use 2 RGWs Instances per Ceph Node ● RHT recommendation of 4% for Bluestore is good at scale as well ○ Increase “max_bytes_for_level_base” (default 256MB) such that you can get most of your 4% Bluestore Flash allocation ● Embrace Co-located & Containerized Storage Demons ● Go big on osd_memory_target if you can (8-10 GB is good to have) ○ Not a silver bullet, but can give you a ballpark number
  • 17. 17 ● Our testing showed RHCS achieving deterministic performance at scale for both Small and Large Object sizes, PUT and GET operations, before hitting resource saturation, capacity limits ● Performance during failure scenarios found to be acceptable Summary ● Undoubtedly RHCS can scale a lot more than what we tested ○ 10 Billion objects are just Tested Maximum, This is NOT A LIMIT Download the full performance report http://red.ht/10billion
  • 18. linkedin.com/company/red-hat youtube.com/user/RedHatVideos facebook.com/redhatinc twitter.com/RedHat 18 Red Hat is the world’s leading provider of enterprise open source software solutions. Award-winning support, training, and consulting services make Red Hat a trusted adviser to the Fortune 500. Thank you Download the full performance report at http://red.ht/10billion