Modeling, estimating, and predicting Ceph

Lars Marowsky-BréeDistinguished Engineer, Architect Storage & HA

lmb@suse.com

What is Ceph?

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From 10,000 Meters

[1] http://www.openstack.org/blog/2013/11/openstack-user-survey-statistics-november-2013/

• Open Source Distributed Storage solution

• Most popular choice of distributed storage for

OpenStack[1]

• Lots of goodies‒ Distributed Object Storage

‒ Redundancy

‒ Efficient Scale-Out

‒ Can be built on commodity hardware

‒ Lower operational cost

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From 1,000 Meters

Object Storage(Like Amazon S3) Block Device File System

Unified Data Handling for 3 Purposes

●RESTful Interface●S3 and SWIFT APIs

●Block devices●Up to 16 EiB●Thin Provisioning●Snapshots

●POSIX Compliant●Separate Data and Metadata●For use e.g. with Hadoop

Autonomous, Redundant Storage Cluster

Unreasonable expectations

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Unreasonable questions customers want answers to

• How to build a storage system that meets my requirements?

• If I build a system like this, what will its characteristics be?

• If I change XY in my existing system, how will its characteristics change?

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How to design a Ceph cluster today

• Understand your workload (?)

• Make a best guess, based on the desirable properties and factors

• Build a 1-10% pilot / proof of concept‒ Preferably using loaner hardware from a vendor to avoid early

commitment. (The outlook of selling a few PB of storage and compute nodes makes vendors very cooperative.)

• Refine and tune this until desired performance is achieved

• Scale up‒ Ceph retains most characteristics at scale or even improves (until it

doesn’t), so re-tune

Software Defined Storage

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Legacy Storage Arrays

• Limits:‒ Tightly controlled

environment

‒ Limited scalability

‒ Few options

‒ Only certain approved drives

‒ Constrained number of disk slots

‒ Few memory variations

‒ Only very few networking choices

‒ Typically fixed controller and CPU

• Benefits:‒ Reasonably easy to

understand

‒ Long-term experience and “gut instincts”

‒ Somewhat deterministic behavior and pricing

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Software Defined Storage (SDS)

• Limits:‒ ?

• Benefits:‒ Infinite scalability

‒ Infinite adaptability

‒ Infinite choices

‒ Infinite flexibility

‒ ... right.

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Properties of a SDS System

• Throughput

• Latency

• IOPS

• Single client or aggregate

• Availability

• Reliability

• Safety

• Cost

• Adaptability

• Flexibility

• Capacity

• Density

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Architecting a SDS system

• These goals often conflict:‒ Availability versus Density

‒ IOPS versus Density

‒ Latency versus Throughput

‒ Everything versus Cost

• Many hardware options

• Software topology offers many configuration choices

• There is no one size fits all

A multitude of choices

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Network

• Choose the fastest network you can afford

• Switches should be low latency with fully meshed backplane

• Separate public and cluster network

• Cluster network should typically be twice the public bandwidth‒ Incoming writes are replicated over the cluster network

‒ Re-balancing and re-mirroring utilize the cluster network

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Networking (Public and Internal)

• Ethernet (1, 10, 40 GbE)‒ Can easily be bonded for availability

‒ Use jumbo frames if you can

‒ Various bonding modes to try

• Infiniband‒ High bandwidth, low latency

‒ Typically more expensive

‒ RDMA support

‒ Replacing Ethernet since the year of the Linux desktop

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Storage Node

• CPU‒ Number and speed of cores

‒ One or more sockets?

• Memory

• Storage controller‒ Bandwidth, performance, cache size

• SSDs for OSD journal‒ SSD to HDD ratio

• HDDs‒ Count, capacity, performance

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Adding More Nodes

• Capacity increases

• Total throughput increases

• IOPS increase

• Redundancy increases

• Latency unchanged

• Eventually: network topology limitations

• Temporary impact during re-balancing

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Adding More Disks to a Node

• Capacity increases

• Redundancy increases

• Throughput might increase

• IOPS might increase

• Internal node bandwidth is consumed

• Higher CPU and memory load

• Cache contention

• Latency unchanged

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OSD File System

• btrfs‒ Typically better write

throughput performance

‒ Higher CPU utilization

‒ Feature rich

‒ Compression, checksums, copy on write

‒ The choice for the future!

• XFS‒ Good all around choice

‒ Very mature for data partitions

‒ Typically lower CPU utilization

‒ The choice for today!

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Impact of Caches

• Cache on the client side‒ Typically, biggest impact on performance

‒ Does not help with write performance

• Server OS cache‒ Low impact: reads have already been cached on the client

‒ Still, helps with readahead

• Caching controller, battery backed:‒ Significant benefit for writes

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Impact of SSD Journals

• SSD journals accelerate bursts and random write IO

• For sustained writes that overflow the journal, performance degrades to HDD levels

• SSDs help very little with read performance

• SSDs are very costly‒ ... and consume storage slots -> lower density

• A large battery-backed cache on the storage controller is highly recommended if not using SSD journals

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Hard Disk Parameters

• Capacity matters‒ Often, highest density is not

most cost effective

‒ On-disk cache matters less

• Reliability advantage of Enterprise drives typically marginal compared to cost‒ Buy more drives instead

‒ Consider validation matrices for small/medium NAS servers as a guide

• RPM:‒ Increase IOPS & throughput

‒ Increases power consumption

‒ 15k drives quite expensive still

• SMR/Shingled drives‒ Density at the cost of write

speed

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Other parameters

• IO Schedulers‒ Deadline, CFQ, noop

• Let’s not even talk about the various mkfs options

• Encryption

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Impact of Redundancy Choices

• Replication:‒ n number of exact, full-size

copies

‒ Potentially increased read performance due to striping

‒ More copies lower throughput, increase latency

‒ Increased cluster network utilization for writes

‒ Rebuilds can leverage multiple sources

‒ Significant capacity impact

• Erasure coding:‒ Data split into k parts plus m

redundancy codes

‒ Better space efficiency

‒ Higher CPU overhead

‒ Significant CPU and cluster network impact, especially during rebuild

‒ Cannot directly be used with block devices (see next slide)

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Cache Tiering

• Multi-tier storage architecture:‒ Pool acts as a transparent write-back overlay for another

‒ e.g., SSD 3-way replication over HDDs with erasure coding

‒ Can flush either on relative or absolute dirty levels, or age

‒ Additional configuration complexity and requires workload-specific tuning

‒ Also available: read-only mode (no write acceleration)

‒ Some downsides (no snapshots), memory consumption for HitSet

• A good way to combine the advantages of replication and erasure coding

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Number of placement groups

● Number of hash buckets per pool● Data is chunked &

distributed across nodes

● Typically approx. 100 per OSD/TB

• Too many:‒ More peering

‒ More resources used

• Too few:‒ Large amounts of data per

group

‒ More hotspots, less striping

‒ Slower recovery from failure

‒ Slower re-balancing

From components to system

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More than the sum of its parts

• If this seems straightforward enough, it is because it isn’t.

• The interactions are not trivial or humanly predictable.

• http://ceph.com/community/ceph-performance-part-2-write-throughput-without-ssd-journals/

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Hiding in a corner, weeping:

When anecdotes are not enough

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The data drought of big data

• Isolated benchmarks

• No standardized work loads

• Huge variety of reporting formats (not parsable)

• Published benchmarks usually skewed to highlight superior performance of product X

• Not enough data points to make sense of the “why”

• Not enough data about the environment

• Rarely from production systems

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Existing efforts

• ceph-brag‒ Last update: ~1 year ago

• cephperf‒ Talk proposal for Vancover OpenStack Summit, presented at

Ceph Infernalis Developer Summit

‒ Mostly focused on object storage

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Measuring Ceph performance(you were in the previous session by Adolfo, right?)

• rados bench‒ Measures backend performance of the RADOS store

• rados load-gen‒ Generate configurable load on the cluster

• ceph tell osd.XX

• fio rbd backend‒ Swiss army knife of IO benchmarking on Linux

‒ Can also compare in-kernel rbd with user-space librados

• rest-bench‒ Measures S3/radosgw performance

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Standard benchmark suite

• Gather all relevant and anonymized data in JSON format

• Evaluate all components individually, as well as combined

• Core and optional tests‒ Must be fast enough to run on a (quiescent) production cluster

‒ Also means: non-destructive tests in core only

• Share data to build up a corpus on big data performance

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Block benchmarks – how?

• fio‒ JSON output, bandwidth, latency

• Standard profiles‒ Sequential and random IO

‒ 4k, 16k, 64k, 256k block sizes

‒ Read versus write

‒ Mixed profiles

‒ Don’t write zeros!

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Block benchmarks – where?

• Individual OSDs

• All OSDs in a node‒ On top of OSD fs, and/or raw disk?

‒ Cache baseline/destructive tests?

• Journal disks‒ Read first, write back, for non-destructive tests

• RBD performance: rbd.ko, user-space

• One client, multiple clients

• Identify similar machines and run a random selection

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Network benchmarks

• Latency (various packet sizes): ping

• Bandwidth: iperf3

• Relevant links:‒ OSD – OSD, OSD – Monitor

‒ OSD – Rados Gateway, OSD - iSCSI

‒ OSD – Client, Monitor – Client, Client – RADOS Gateway

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Other benchmark data

• CPU loads:‒ Encryption

‒ Compression

• Memory access speeds

• Benchmark during re-balancing an OSD?

• CephFS (FUSE/cephfs.ko)

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Environment description

• Hardware setup for all nodes‒ Not always trivial to discover

• kernel, glibc, ceph versions

• Network layout metadata

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Different Ceph configurations?

• Different node counts,

• replication sizes,

• erasure coding options,

• PG sizes,

• differently filled pools, ...

• Not necessarily non-destructive, but potentially highly valuable.

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Performance monitoring during runs

• Performance Co-Pilot?

• Gather network, CPU, IO, ... metrics

• While not fine grained enough to debug everything, helpful to spot anomalies and trends‒ e.g., how did network utilization of the whole run change over

time? Is CPU maxed out on a MON for long?

• Also, pretty pictures (such as those missing in this presentation)

• LTTng instrumentation?

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Summary of goals

• Holistic exploration of Ceph cluster performance

• Answer those pesky customer questions

• Identify regressions and brag about improvements

• Use machine learning to spot correlations that human brains can’t grasp‒ Recurrent neural networks?

• Provide students with a data corpus to answer questions we didn’t even think of yet

Thank you.

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Questions and Answers?

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