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Sep. 19. 2017 WebDB Forum Tokyo
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Yasuharu Goto
Dragon: A Distributed Object Storage @Yahoo! JAPAN
(English Ver.)

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About me
• Yasuharu Goto
• Yahoo! JAPAN (2008-)
• Software Engineer
• Storage, Distributed Database Systems (Cassandra)
• Twitter: @ono_matope
• Lang: Go
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Agenda
• About Dragon
• Architecture
• Issues and Future works
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Dragon

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Object Storage
• What is Object Storage?
• A storage architecure that manages files not as files but as objects.
• Instead of providing features like file hierarchy, it provides high availability and scalabiliity.
• (Typically) provides REST API, so it can be used easily by applications.
• Populer products
• AWS: Amazon S3
• GCP: Google Cloud Storage
• Azure: Azure Blob Storage
• An essential component for modern web development.
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Dragon
• A distributed Object Storage developed at Yahoo! JAPAN.
• Design Goals:
• High { performance, scalability, availability, cost efficiency }
• Written in Go
• Released in Jan/2016 (20 months in production)
• Scale
• deployed in 2 data centers in Japan
• Stores 20 billion / 11 PB of objects.
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Use Cases
• 250+ users in Y!J
• Various usage
• media content
• user data, log storage
• backend for Presto (experimental)
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• Yahoo! Auction (image)
• Yahoo! News/Topics (image)
• Yahoo! Display Ad Network (image/video)
• Yahoo! Blog (image)
• Yahoo! Smartphone Themes (image)
• Yahoo! Travel (image)
• Yahoo! Real Estate (image)
• Yahoo! Q&A (image)
• Yahoo! Reservation (image)
• Yahoo! Politics (image)
• Yahoo! Game (contents)
• Yahoo! Bookstore (contents)
• Yahoo! Box (user data)
• Netallica (image)
• etc...

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S3 Compatible API
• Dragon provides an S3 compatible API
• aws-sdk, aws-cli, CyberDuck...
• Implemented
• Basic S3 API (Service, Bucket, Object, ACL...)
• SSE (Server Side Encryption)
• TODO
• Multipart Upload API (to upload large objects up to 5TB)
• and more...
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Performance(with Riak CS/reference)
• Dragon: API*1, Storage*3, Cassandra*3
• Riak CS: haproxy*1, stanchion*1, Riak (KV+CS)*3
• Same Hardware except for Cassandra and Stanchion.
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0
500
1000
1500
2000
2500
3000
3500
1 5 10 50 100 200 400
Requests/sec
# of Threads
GET Object 10KB Throughput
Riak CS
Dragon
0
100
200
300
400
500
600
700
800
900
1000
1 5 10 50 100 200 400
Requests/sec
# of Threads
PUT Object 10KB Throughput
Riak CS
Dragon

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Why?

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Why did we build a new Object Storage?
• Octagon (2011-2017)
• Our 1st Generation Object Storage
• Up to 7 PB / 7 Billion Objects / 3,000 Nodes at a time
• used for personal cloud storage service, E-Book, etc...
• Problems of Octagon
• Low performance
• Unstable
• Expensive TCO
• Hard to operate
• We started to consider alternative products.
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Requirements
• Our requirements
• High performance enough for our services
• High scalability to respond to rapid increase in data demands
• High availability with less operation cost
• High cost efficiency
• Mission
• To establish a company-wide storage infrastructure
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Alternatives
• Existing Open Source Products
• Riak CS
• Some of our products introduced it, but it did not meet our performance requiremnt.
• OpenStack Swift
• Concerns about peformance degration when object count increases.
• Public Cloud Providers
• cost inefficient
• We mainly provides our services with our own DC.
We needed a high scalable storage system which runs on-premise.
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Alternatives
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OK, let’s make it by ourselves!
• Existing Open Source Products
• Riak CS
• Some of our products introduced it, but it did not meet our performance requiremnt.
• OpenStack Swift
• Concerns about peformance degration when object count increases.
• Public Cloud Providers
• cost inefficient
• We mainly provides our services with our own DC.
We needed a high scalable storage system which runs on-premise.

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Architecture

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Architecture Overview
• Dragon consists of 3 components: API Nodes, Storage Cluster and MetaDB.
• API Node
• Provides S3 compatible API and serves all user requets.
• Storage Node
• HTTP file servers that store BLOBs of uploaded objects.
• 3 nodes make up a VolumeGroup. BLOBs in each group are periodically synchronized.
• MetaDB
• Apache Cassandra cluster
• Stores metadata of uploaded objects including the location of its BLOB.
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Architecture
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API Nodes
HTTP (S3 API)
BLOB
Metadata
Storage Cluster
VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
Meta DB

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Architecture
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API Nodes
HTTP (S3
API)
BLOB
Metadata
Storage Cluster
API and Storage nodes are witten in Go
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VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
Meta DB

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Architecture
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API Nodes
BLOBStorage Cluster
VolumeGroup: 01
StorageNode
1
HDD4
HDD3
StorageNode
2
HDD4
HDD3
StorageNode
3
HDD4
HDD3
VolumeGroup: 02
StorageNode
4
HDD4
HDD3
StorageNode
5
HDD4
HDD3
StorageNode
6
HDD4
HDD3
API Nodes periodically fetch and cache VolumeGroup configuration from MetaDB.
Meta DB
id hosts Volumes
01 node1,node2,node3 HDD1, HDD2
02 node4,node5,node6 HDD1, HDD2
volumegroup configuration

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Upload
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API Nodes
Meta DB
VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
① HTTP PUT
key: bucket1/sample.jpg,
size: 1024bytes
blob: volumegroup01/hdd1/...,
PUT bucket1/sample.jpg
② Metadata
1. When a user uploads an object, the API Node first randomly picks a VolumeGroup and transfers
the object’s BLOB to the nodes in the VolumeGroup using HTTP PUT.
2. Stores the metadata including its BLOB location into the MetaDB.

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Download
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API Nodes
Meta DB
VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
② HTTP GET
key: bucket1/sample.jpg,
size: 1024bytes
blob: volumegroup01/hdd1/...,
PUT bucket1/sample.jpg
① Metadata
1. When a user downloads an Object, the API Node retrieves its metadata from the MetaDB.
2. Requests a HTTP GET to a Storage holding the BLOB based on the metadata and transfer the
response to the user.

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Failure Recovery
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API Nodes
Meta DB
VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
When a Hard Disk fails...

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Failure Recovery
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API Nodes
Meta DB
VolumeGroup: 01
StorageNode
1
HDD2
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
The drive will be replaced and data that should be in the drive will be recovered by transferring from
the other StorageNodes in the VolumeGroup.
HDD1

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Scaling out
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API Nodes
Meta DB
When you add capacity to the cluster...
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VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
id hosts Volumes
01 node1,node2,node3 HDD1, HDD2
02 node4,node5,node6 HDD1, HDD2
volumegroup Configuration

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Scaling out
API Nodes
Meta DB
• ... simply set up a new set of StorageNodes and update the VolumeGroup configuration.
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VolumeGroup: 01
StorageNode
1
HDD2
HDD1
StorageNode
2
HDD2
HDD1
StorageNode
3
HDD2
HDD1
VolumeGroup: 02
StorageNode
4
HDD2
HDD1
StorageNode
5
HDD2
HDD1
StorageNode
6
HDD2
HDD1
VolumeGroup: 03
StorageNode
7
HDD2
HDD1
StorageNode
8
HDD2
HDD1
StorageNode
9
HDD2
HDD1
id hosts Volumes
01 node1,node2,node3 HDD1, HDD2
02 node4,node5,node6 HDD1, HDD2
03 node7,node8,node9 HDD1, HDD2
volumegroup Configuration

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Why not Consistent Hash?
• Dragon’s distributed architecture is based on mapping managed by the DB.
• Q. Why not Consistent Hash?
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quoted from: http://docs.basho.com/riak/kv/2.2.3/learn/concepts/clusters/
• Consistent Hash
• Data is distributed uniformly by hash of key
• Used by many existing distributed systems
• e.g. Riak CS, OpenStack Swift
• No need for external DB to manage the map

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Why not Consistent Hash?
• A. Able to add storage capacities without Rebalancing
• It heavily consumes Disk I/O, bandwidth, and often takes a long time.
• eg. Adding 1 node into 10 node * 720TB cluster which is 100% utilized requires transfering
655TB. 655TB/2Gbps = 30 days
• Scaling hash-based DB to more than 1000 nodes with large nodes is very challenging.
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655TB
(720TB*10Node)/11Node = 655TB

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Other Pros/Cons
• Pros
• We can scale out MetaDB and BLOB Storage independently.
• Backend Storage Engine is pluggable.
• We can easily add or change the storage technology/class in the future
• Cons
• We need external Database to manage the map
• BLOB load would be non-uniform
• We’ll rebalance periodically.
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Storage Node

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Storage Hardware
• High density Storage Servers for cost efficiency
• We need to make use of the full potential of the hardware.
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https://www.supermicro.com/products/system/4U/6048/SSG-6048R-E1CR90L.cfm

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Storage Configuration
• HDDs are configured as independent logical volumes instead of RAID
• Reason 1: To reduce time to recover when HDDs fail.
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VolumeGroup
StorageNode
HDD4
HDD3
HDD2
HDD1
StorageNode
HDD4
HDD3
HDD2
HDD1
StorageNode
HDD4
HDD3
HDD2
HDD1

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Storage Configuration
• Reason 2: RAID is slow for random access.
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Configure Requests per sec
Non RAID 178.9
RAID 0 73.4
RAID 5 68.6
Throughput for random access work load.
Served by Nginx. 4HDDs. Filesize: 500KB
2.4x Faster
than RAID 0

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File Persistence Strategy
• Dragon’s Storage Nodes use one file per BLOB.
• Strategy to increase robustness by using stable filesystem (ext4).
• But, it is known that file systems can not handle large numbers of files well.
• It is reported that Swift has poor writing performance as the number of files increases.
• To get over this problem, Dragon uses a unique technique.
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ref.1: “OpenStack Swiftによる画像ストレージの運用” http://labs.gree.jp/blog/2014/12/11746/
ref.2: “画像システムの車窓から｜サイバーエージェント 公式エンジニアブログ” http://ameblo.jp/principia-ca/entry-12140148643.html

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File Persistence Strategy
• Typical approach: Write files into directories evenly which are created in advance
• Swift writes files in this manner.
• As the number of files increases, the number of seeks increases and the write throughput decreases.
• Cost for updating dentries increases.
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(256dirs)
... 256 dirs01 02 03 fe ff
Seek count and throughput when randomly writing 3 million files in 256 directories.
Implemented as a smple HTTP server. Used ab, blktrace, seekwatcher for measurement.
photo2.jpgphoto1.jpg photo4.jpgphoto3.jpg
Hash function

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Dynamic Partitioning
• Dynamic Partitioning Approach
1. Create a sequentially numbered directories (partitions). API Nodes upload files into the latest directory.
2. Once the number of files in the partition reaches a threshold (1000 here), the Storage Node creates the
next partition and informs the API nodes about it.
• Keep the number of files in the directory constant by adding directories at any time.
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When # of files/dir exceeds approximately 1000, Dragon creates a next directory and uploads there.
0 1 0 New
Dir!
1
1000
Files!
2

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Dynamic Partitioning
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• Comparison with hash strategy. Green is Dyamic Partitioning.
• Even if file count increases, seek count does not increase, throughput is stable
Writing Files in Hash Based Strategy (blue) and Dynamic Partitioning (green)

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Microbenchmark
Confirmed the maintenance of writing
throughput up to 10 Million files for
single HDD.
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Writing throughput when creating up to 10 Million files.
We syncd and dropped cache after each creating 100,000 files.

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Eventual Consistency
• To achieve high availability, writing to Storage Nodes uses eventual consistency with Quorum.
• Uploads succeed if writing to the majority of 3 nodes is successful.
• Anti-Entropy Repair process synchronizes failed nodes periodically.
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VolumeGroup: 01
StorageNode
1
HDD4
HDD3
HDD2
HDD1
StorageNode
2
HDD4
HDD3
HDD2
HDD1
StorageNode
3
HDD4
HDD3
HDD2
HDD1
API Nodes
OK

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Anti-Entropy Repair
• Anti-Entropy Repair
• Process to compare data between nodes, detect data that is not replicated and recover the
consistency.
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Node B Node C
file1
file2
file3
file4
Node A
file1
file2
file3
file4
file1
file2
file4
file3

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Anti-Entropy Repair
• Detect and correct inconsistency of Storage Nodes in a partition unit.
1. Calculate the hash of the names of the files in a partition.
2. Compare the hashes between nodes in a VolumeGroup. There are inconsistencies if the hashes do not match.
3. If the hashes do not match, compare the files in the partition and transfer missing files.
• Comparing process is IO efficient as we can cache the hash and the update is concentrated in the latest
partition.
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HDD2
01 60b725f...
02 e8191b3...
03 97880df...
HDD2
01 60b725f...
02 e8191b3...
03 97880df...
HDD2
01 60b725f...
02 e8191b3...
03 10c9c85c...
node1 node2 node3
file1001.data
-----
file1003.data
file1001.data
file1002.data
file1003.data
file1001.data
file1002.data
file1003.data
transfer file1002.data to node1

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MetaDB

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Cassandra
• Apache Cassandra
• High Availability
• Linear Scalability
• Low operation cost
• Eventual Consistency
• Cassandra does not support ACID transactions
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Cassandra
• Tables
• VolumeGroup
• Account
• Bucket
• Object
• ObjectIndex
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Object Table
• Object Table
• Table to retain Object Metadata
• size, BLOB location, ACL, Content-Type...
• Distributed evenly within the cluster by the partition key which is composed of (bucket, key).
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bucket key mtime status metadata...
b1 photo1.jpg uuid(t2) ACTIVE {size, location, acl...,}
b1 photo2.jpg uuid(t1) ACTIVE {size, location, acl....}
b3 photo1.jpg uuid(t3) ACTIVE {size, location, acl....}
Partition Key

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PUT Object
• Update matadata
• Within each partition, metadata is clustered in descending order by UUIDv1 based on creation time.
• When an object is overwritten, the metadata of the latest version is inserted into the top of the partition.
• Since we keep records of multiple versions, no inconsistency occurs even if the object is overwritten
concurrently.
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Clustering Column
bucket key mtime status metadata...
b1 photo2.jpg
uuid(t5) ACTIVE {size, location, acl...,}
uuid(t4) ACTIVE {size, location, acl...,}
uuid(t1) ACTIVE {size, location, acl...,}
b1 photo2.jpg uuid(t1) ACTIVE {size, location, acl....}
PUT b1/photo2.jpg (time: t4)
PUT b1/photo2.jpg (time: t5)
photo2.jpg reaches consistency. (t5 wins)

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GET Object
• Retrieving Metadata
• Retrieve the first row of the partition with SELECT query
• Since the partition is sorted by the creation time, the first row always indicates the current
state of the object.
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bucket key mtime status metadata...
b1 photo1.jpg
uuid(t5) ACTIVE {size, location, acl...}
uuid(t3) ACTIVE {size, location, acl....}
b1 photo2.jpg uuid(t1) ACTIVE {size, location, acl....}
Partition Key Clustering Column
SELECT * FROM bucket=‘b1’ AND key= ‘photo1.jpg’ LIMIT 1;
(time:t5)

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DELETE Object
• Request Deletion of object
• Insert row with deletion status without deleting the row immediately.
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bucket key mtime status metadata...
b1 photo1.jpg
uuid(t5) ACTIVE {size, location, acl...}
uuid(t3) ACTIVE {size, location, acl....}
b1 photo2.jpg
uuid(t7) DELETED N/A
uuid(t1) ACTIVE {size, location, acl....}
DELETE b1/photo1.jpg (time: t7)
Partition Key Clustering Column

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GET Object (deleted)
• Retrieving Metadata (in case of deleted)
• If the retrieved latest row has DELETED status, the object is considered deleted logically and
returns error
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bucket key mtime status metadata...
b1 photo1.jpg
uuid(t5) ACTIVE {size, location, acl...}
uuid(t3) ACTIVE {size, location, acl....}
b1 photo2.jpg
uuid(t7) DELETED N/A
uuid(t1) ACTIVE {size, location, acl....}
SELECT * FROM bucket=‘b1’ AND key= ‘photo2.jpg’ LIMIT 1;
(time:t7)
Partition Key Clustering Column

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Object Garbage Collection
• Garbage Collection (GC)
• Periodically deletes metadata and the linked BLOBs of overwritten or deleted Objects.
• Full scan of Object table
• The second and subsequent rows of each partition are garbage. GC Deletes them.
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bucket key mtime status metadata...
b1 photo1.jpg
uuid(t5) ACTIVE {size, location, acl...}
uuid(t3) ACTIVE {size, location, acl....}
b1 photo2.jpg
uuid(t7) DELETED N/A
uuid(t3) ACTIVE {size, location, acl...,}
uuid(t1) ACTIVE {size, location, acl....}
Garbage
Garbage
Garbage
full scan
Upload 0 byte tomstone files to delete the BLOB
Partition Key Clustering Column

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Object Garbage Collection
• GC completed
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bucket key mtime status metadata...
b1 photo1.jpg uuid(t5) ACTIVE {size, location, acl...}
b1 photo2.jpg uuid(t7) DELETED N/A
GC completed
We achieved Concurrency control on Eventual Consistency Database by using partitioning and UUID
clustering.
Partition Key Clustering Column

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Issues and Future Plans

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ObjectIndex Table
• ObjectIndex Table
• Objects in bucket are sorted and stored in ObjectIndex table in asc order by key name for ListObjects API
• Since the partitions get extremely large, objects in a bucket are split into 16 partitions.
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bucket hash key metadata
bucket1 0
key0001 ...
key0003 ...
key0012 ...
key0024 ...
... ...
bucket1 1
key0004 ...
key0009 ...
key0011 ...
... ...
bucket1 2
key0002 ...
key0005 ...
... ...
... ... ... ...
key metadata
key0001 ...
key0002 ...
key0003 ...
key0004 ...
key0005 ...
key0006 ...
key0007 ...
key0008 ...
... ...
Retrieve 16 partitions and merge them to respond
ObjectIndex Table
Partition Key Clustering Column

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Issues
• ObjectIndex related problems
• Some API requests cause a lot of queries to Cassandra, resulting in high load and high
latency.
• Because of Cassandra’s limitation, the # of Objects in Bucket is restricted to 32 Billion.
• We’d like to eliminate constraints on the number of Objects by introducing a mechanism
that dynamically divides the index partition.
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Future Plans
• Improvement of Storage Engine
• WAL (Write Ahead Log) based Engine?
• Erasure Coding?
• Serverless Architecture
• Push notification to messaging queues such as Kafka, Pulsar
• Integration with other distributed systems
• Hadoop, Spark, Presto, etc...
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Wrap up
• Yahoo! JAPAN is developing a large scale object storage named “Dragon”.
• “Dragon” is a highly scalable object storage platform.
• We’re going to improve it to meet our new requirements.
• Thank you!