Big Data Processing Technologies
Chentao Wu
Associate Professor
Dept. of Computer Science and Engineering [email protected]
Schedule
• lec1: Introduction on big data and cloud computing
• Iec2: Introduction on data storage
• lec3: Data reliability (Replication/Archive/EC)
• lec4: Data consistency problem
• lec5: Block storage and file storage
• lec6: Object-based storage
• lec7: Distributed file system
• lec8: Metadata management
Collaborators
Contents
Distributed File System (DFS)
1
File System & Operating Systems
File System Component
The File Systems Evolution
•
File systems evolved over time•
Starting with local file system over time, additional file systems appeared focusing on specialized requirements such as data sharing, remote file access, distributed file access, parallel file access, HPC, archiving, etc.The File Systems Taxonomy
File System Types
•
Local File System Host-based, single operating system
Co-located with application server
Many types with unique formats, feature mix
•
Shared (SAN and Clustered) File Systems Host-based file systems
Hosts access all data
Co-located with application server for performance
•
Distributed File System Remote, network-access
Semantics are limited subset of local file system
Cooperating file servers
Can include integrated replication
Clustered DFS/Wide Area File System
Evaluating File Systems (1)
•
Does it fit the Application Characteristics Does the application even support the file system?
Is it optimized for the type of operations that are important to the application?
•
Performance & Scalability Does the file system meet the latency and throughput requirements?
Can it scale up to the expected workload and deal with growth?
Can it support the number of files and total storage needed?
•
Data Management What Kind of features does it include? Backup, Replication, Snapshots, Information Lifecycle Management (ILM), etc.
Evaluating File Systems (2)
•
Security Does it conform to the security requirements of your company?
Does it integrate with your security services?
Does it have Auditing, Access control and at what granularity?
•
Ease of Use Does it require training the end users or changing applications to perform well?
Can it be easily administered in small and large deployments?
Does it have centralized monitoring, reporting?
How hard is it to recover from a software or hardware failure and how long does it take?
How hard is it to upgrade or downgrade the software and is it live?
Application Characteristics
•
Typical applications (A) OLTP
(B) Small Data Set
(C) Home Directory
(D) Large Scale Streaming
(E) High Frequency Metadata Update (small file create/delete)
Performance & Scalability
•
Performance Throughput
Read/write access patterns
Impact of data protection mechanisms, operations
•
Scalability Number of files, directories, file systems
Performance, recovery time
Simultaneous and active users
Data Management (1)
•
Backup Performance
Backup vendors; local agent vs. network-based
Data deduplication backup once
•
Replication Multiple read-only copies
Optimization for performance over network
Data deduplication transfer once
•
Quotas Granularity： User/Group/Directory tree quotas
Extended quota features
Ease of set up
Local vs. external servers
Data Management (2)
•
Information Lifecycle Management (ILM) Lots of features, differing definitions
Can enforce compliance and auditing rules
Cost & performance vs. impact of lost/altered data
Security Considerations (1)
•
Authentication Support and to what degree
•
Authorization Granularity by access types
Need for client-side software
Performance impact of large scale ACL changes
•
Auditing Controls
Audit log full condition
Login vs. login attempt vs. data access
Security Considerations (2)
•
Virus scanning Preferred vendor supported?
Performance & scalability
External vs. file server-side virus scanning
•
Vulnerabilities Security & data integrity vulnerabilities vs. performance
Compromised file system (one client, one file server)
Detection
Packet sniffing
Ease of Use
•
End-User Local file system vs. Distributed File System
•
Deployment & Maintenance Implementation
Scalability of management
File system migration
Automatic provisioning
Centralized monitoring, reporting
Hardware failure recovery
Performance monitoring
Distributed File System
•
A distributed file system is a network file system whose clients, servers, and storage devices are dispersed among the machines of a distributed system or intranet.Distributed File System (NAS & SAN Environment)
Key Characteristics of DFS
•
Often purpose-built file servers•
No real standardization for file sharing across Unix (NFS) and Windows (CIFS)•
Scales independently of application services•
Performance limited to that of a single file server•
Reduces (not eliminate) islands of storage•
Replications sometimes built in•
Global namespace through external service•
Strong network security supported•
Etc.DFS Logical Data Access Path
•
Using Ethernet as a networking protocol between nodes, a DFS allows a single file system to span across all nodes in the DFS cluster, effectively creating a unified Global Namespace for all files.Contents
Google File System (GFS)
2
Why build GFS?
• Node failures happen frequently
• Files are huge – multi-GB
• Most files are modified by appending at the end
Random writes (and overwrites) are practically non-existent
• High sustained bandwidth is more important than low latency
Place more priority on processing data in bulk
Typical workloads on GFS
•
Two kinds of reads: large streaming reads & small random reads Large streaming reads usually read 1MB or more
Oftentimes, applications read through contiguous regions in the file
Small random reads are usually only a few KBs at some arbitrary offset
•
Also many large, sequential writes that append data to files Similar operation sizes to reads
Once written, files are seldom modified again
Small writes at arbitrary offsets do not have to be efficient
•
Multiple clients (e.g. ~100) concurrently appending to a single file e.g. producer-consumer queues, many-way merging
Interface
•
Not POSIX-compliant, but supports typical file system operations:create, delete, open, close, read, and write
•
snapshot: creates a copy of a file or a directory tree at low cost•
record append: allow multiple clients to append data to the same file concurrently At least the very first append is guaranteed to be atomic
GFS Architecture (1)
GFS Architecture (2)
•
Very important: data flow is decoupled from control flow Clients interact with the master for metadata operations
Clients interact directly with chunkservers for all files operations
This means performance can be improved by scheduling expensive data flow based on the network topology
•
Neither the clients nor the chunkservers cache file data Working sets are usually too large to be cached, chunkservers can use Linux’s buffer cache
The Master Node (1)
•
Responsible for all system-wide activities managing chunk leases, reclaiming storage space, load-balancing
•
Maintains all file system metadata Namespaces, ACLs, mappings from files to chunks, and current locations of chunks
all kept in memory, namespaces and file-to-chunk mappings are also stored persistently in operation log
•
Periodically communicates with each chunkserver in HeartBeat messages This let’s master determines chunk locations and assesses state of the overall system
Important: The chunkserver has the final word over what chunks it does or does not have on its own disks – not the master
The Master Node (2)
•
For the namespace metadata, master does not use any per-directory data structures – no inodes! (No symlinks or hard links, either.)
Every file and directory is represented as a node in a lookup
table, mapping pathnames to metadata. Stored efficiently using prefix compression (< 64 bytes per namespace entry)
•
Each node in the namespace tree has a corresponding read-write lock to manage concurrency Because all metadata is stored in memory, the master can
efficiently scan the entire state of the system periodically in the background
Master’s memory capacity does not limit the size of the system
The Operation Log
•
Only persistent record of metadata•
Also serves as a logical timeline that defines the serialized order of concurrent operations•
Master recovers its state by replaying the operation log To minimize startup time, the master checkpoints the log periodically
The checkpoint is represented in a B-tree like form, can be directly mapped into memory, but stored on disk
Checkpoints are created without delaying incoming requests to master, can be created in ~1 minute for a cluster with a few million files
Why a Single Master? (1)
•
The master now has global knowledge of the whole system, which drastically simplifies the design•
But the master is (hopefully) never the bottleneck Clients never read and write file data through the master; client only requests from master which chunkservers to talk to
Master can also provide additional information about subsequent chunks to further reduce latency
Further reads of the same chunk don’t involve the master, either
Why a Single Master? (2)
•
Master state is also replicated for reliability on multiple machines, using the operation log and checkpoints If master fails, GFS can start a new master process at any of these replicas and modify DNS alias accordingly
“Shadow” masters also provide read-only access to the file system, even when primary master is down
They read a replica of the operation log and apply the same sequence of changes
Not mirrors of master – they lag primary master by fractions of a second
This means we can still read up-to-date file contents while master is in recovery!
Chunks and Chunkservers
•
Files are divided into fixed-size chunks, which has an immutable, globally unique 64-bit chunk handle By default, each chunk is replicated three times across multiple chunkservers (user can modify amount of replication)
•
Chunkservers store the chunks on local disks as Linux files Metadata per chunk is < 64 bytes (stored in master)
Current replica locations
Reference count (useful for copy-on-write)
Version number (for detecting stale replicas)
Chunk Size
•
64 MB, a key design parameter (Much larger than most file systems.)•
Disadvantages: Wasted space due to internal fragmentation
Small files consist of a few chunks, which then get lots of traffic from concurrent clients
This can be mitigated by increasing the replication factor
•
Advantages: Reduces clients’ need to interact with master (reads/writes on the same chunk only require one request)
Since client is likely to perform many operations on a given chunk, keeping a persistent TCP connection to the chunkserver reduces network overhead
Reduces the size of the metadata stored in master → metadata can be entirely kept in memory
Consistency Model
•
Terminology: consistent: all clients will always see the same data, regardless of which replicas they read from
defined: same as consistent and, furthermore, clients will see what the modification is in its entirety
•
Guarantees:Data Modification in GFS
•
After a sequence of modifications, if successful, then modified file region is guaranteed to be defined and contain data written by last modification•
GFS applies modification to a chunk in the same order on all its replicas•
A chunk is lost irreversibly if and only if all its replicas are lost before the master node can react, typically within minutes even in this case, data is lost, not corrupted
Record Appends
•
A modification operation that guarantees that data (the “record”) will be appended atomically at least once – but at the offset of GFS’s choosing The offset chosen by GFS is returned to the client so that the application is aware
•
GFS may insert padding or record duplicates in between different record append operations•
Preferred that applications use this instead of write Applications should also write self-validating records (e.g.
checksumming) with unique IDs to handle padding/duplicates
GFS Write Control and Data Flow (1)
•
If the master receives a modification operation for a particular chunk: Master finds the chunkservers that have the chunk and grants a chunk lease to one of them
This server is called the primary, the other servers are called secondaries
The primary determines the serialization order for all of the chunk’s modifications, and the secondaries follow that order
After the lease expires (~60 seconds), master may grant primary status to a different server for that chunk
The master can, at times, revoke a lease (e.g. to disable modifications when file is being renamed)
As long as chunk is being modified, the primary can request an extension indefinitely
If master loses contact with primary, that’s okay: just grant a new lease after the old one expires
GFS Write Control and Data Flow (2)
• 1. Client asks master for all chunkservers (including all secondaries)
• 2. Master grants a new lease on chunk, increases the chunk version number, tells all replicas to do the same. Replies to client. Client no longer has to talk to master
• 3. Client pushes data to all servers, not necessarily to primary first
• 4. Once data is acked, client sends write request to primary. Primary decides serialization order for all incoming modifications and applies them to the chunk
GFS Write Control and Data Flow (3)
• 5. After finishing the modification, primary forwards write request and serialization order to secondaries, so they can apply modifications in same order. (If primary fails, this step is never reached.)
• 6. All secondaries reply back to the primary once they finish the
modifications
• 7. Primary replies back to the client, either with success or error
If write succeeds at primary but fails at any of the secondaries, then we have inconsistent state
→ error returned to client
Client can retry steps (3) through (7)
Contents
Hadoop File System (HDFS)
3
Hadoop History
•
Dec 2004 – Google GFS paper published•
July 2005 – Nutch uses MapReduce•
Feb 2006 – Starts as a Lucene subproject•
Apr 2007 – Yahoo! on 1000-node cluster•
Jan 2008 – An Apache Top Level Project•
May 2009 – Hadoop sorts Petabyte in 17 hours•
Aug 2010 – World’s Largest Hadoop cluster at Facebook 2900 nodes, 30+ PetaByte
Hadoop Commodity Hardware
•
Typically in 2 level architecture Nodes are commodity PCs
20-40 nodes/rack
Uplink from rack is 4 gigabit
Rack-internal is 1 gigabit
Goals of Hadoop Distributed File System (HDFS)
•
Very Large Distributed File System 10K nodes, 1 billion files, 100 PB
•
Assumes Commodity Hardware Files are replicated to handle hardware failure
Detect failures and recovers from them
•
Optimized for Batch Processing Data locations exposed so that computations can move to where data resides
Provides very high aggregate bandwidth
•
User Space, runs on heterogeneous OSBasic of HDFS
•
Single Namespace for entire cluster•
Data Coherency Write-once-read-many access model
Client can only append to existing files
•
Files are broken up into blocks Typically 128 - 256 MB block size
Each block replicated on multiple DataNodes
•
Intelligent Client Client can find location of blocks
Client accesses data directly from DataNode
HDFS Architecture (1)
HDFS Architecture (2)
Namenode Metadata
•
Meta-data in Memory The entire metadata is in main memory
No demand paging of meta-data
•
Types of Metadata List of files
List of Blocks for each file & file attributes
•
A Transaction Log Records file creations, file deletions, etc.
Datanode
•
A Block Server Stores data in the local file system (e.g. ext3)
Stores meta-data of a block (e.g. CRC32)
Serves data and meta-data to Clients
Periodic validation of checksums
•
Block Report Periodically sends a report of all existing blocks to the NameNode (heartbeats)
•
Facilitates Pipelining of Data Forwards data to other specified DataNodes
Block Placement
•
Current Strategy One replica on local node
Second replica on a remote rack
Third replica on same remote rack
Additional replicas are randomly placed
•
Clients read from nearest replica•
Pluggable policy for placing block replicas Co-locate datasets that are often used together
Block Replication
HDFS Read
•
To read a block, the client requests the list of replica locations from the NameNode•
Then pulling data from a replica on one of the DataNodesData Pipelining
•
Client writes block to the first DataNode The first DataNode forwards the data to the next
•
DataNode in the Pipeline, and so on When all replicas are written, the Client moves on to write the next block in file
•
Not good for latency sensitive applicationsHDFS Write
•
To write a block of a file, the client requests a list of candidate DataNodes from the NameNode, and organizes a write pipeline.Namenode failure
•
A Single Point of Failure•
Transaction Log stored in multiple directories A directory on the local file system
A directory on a remote file system (NFS/CIFS)
•
This is a problem with 24 x 7 operations AvatarNode comes to the rescue
NameNode High Availability Challenges
•
DataNodes send block location information to only oneNameNode
•
NameNode needs blocklocations in memory to serve clients
•
The in-memory metadata for 100 million files could be 60 GB, huge!NameNode High Availability AvatarNode
•
Active-Standby Pair Coordinated via zookeeper
Failover in few seconds
Wrapper over NameNode
•
Active AvatarNode Writes transaction log to filer
•
Standby AvatarNode Reads transactions from filer
Latest metadata in memory
Rebalancer
•
Goal: % disk full on DataNodes should be similar Usually run when new DataNodes are added
Cluster is online when Rebalancer is active
Rebalancer is throttled to avoid network congestion
•
Disadvantages Does not rebalance based on access patterns or load
No support for automatic handling of hotspots of data
HDFS RAID
•
Triplicate every data block•
Background encoding Combine third replica of blocks from a single file to create parity block
Remove third replica
•
RaidNode Auto fix of failed replicas
•
Reed Solomon encoding for old filesHDFS Command
•
HDFS Administrator Command•
HDFS Shell CommandContents
Microsoft Azure and Ali DFS
4
Microsoft Azure Storage
• Blobs – File system in the cloud
• Tables – Massively scalable structured storage
• Queues – Reliable storage and delivery of messages
• Drives – Durable NTFS volumes for Windows Azure applications
Windows Azure Storage Stamps
Storage Stamp LB
Storage Location
Service
Access blob storage via the URL: http://<account>.blob.core.windows.net/
Data access
Partition Layer Front-Ends
Stream Layer
Intra-stamp replication
Storage Stamp LB
Partition Layer Front-Ends
Stream Layer
Intra-stamp replication Inter-stamp (Geo) replication
Storage Stamp Architecture – Stream Layer
• Append-only distributed file system
• All data from the Partition Layer is stored into files (extents) in the Stream layer
• An extent is replicated 3 times across different fault and upgrade domains
• With random selection for where to place replicas for fast MTTR
• Checksum all stored data
• Verified on every client read
• Scrubbed every few days
• Re-replicate on disk/node/rack failure or checksum mismatch
M
Extent Nodes (EN) Paxos
M
Stream M
Layer
(Distributed File System)
Storage Stamp Architecture – Partition Layer
• Provide transaction semantics and strong consistency for Blobs, Tables and Queues
• Stores and reads the objects to/from extents in the Stream layer
• Provides inter-stamp (geo) replication by shipping logs to other stamps
• Scalable object index via partitioning
M
Extent Nodes (EN) Paxos
M M Partition
Server Partition
Server Partition
Server Partition Server Partition
Master Lock
Service
Partition Layer
Stream Layer
Storage Stamp Architecture – Front End Layer
• Stateless Servers
• Authentication + authorization
• Request routing
M
Extent Nodes (EN) Paxos
Front End
Layer FE
M M Partition
Server Partition
Server Partition
Server Partition Server Partition
Master
FE FE FE FE
Lock Service
Partition Layer
Stream Layer
Storage Stamp Architecture – Request
M
Extent Nodes (EN) Paxos
Front End
Layer FE
Incoming Write Request
M M Partition
Server Partition
Server Partition
Server Partition Server Partition
Master
FE FE FE FE
Lock Service
Ack
Partition Layer
Stream Layer
Partition Layer – Scalable Object Index
• 100s of Billions of blobs, entities, messages across all accounts can be stored in a single stamp
• Need to efficiently enumerate, query, get, and update them
• Traffic pattern can be highly dynamic
• Hot objects, peak load, traffic bursts, etc
• Need a scalable index for the objects that can
• Spread the index across 100s of servers
• Dynamically load balance
• Dynamically change what servers are serving each part of the index based on load
Scalable Object Index via Partitioning
• Partition Layer maintains an internal Object Index Table for each data abstraction
• Blob Index: contains all blob objects for all accounts in a stamp
• Table Entity Index: contains all entities for all accounts in a stamp
• Queue Message Index: contains all messages for all accounts in a stamp
• Scalability is provided for each Object Index
• Monitor load to each part of the index to determine hot spots
• Index is dynamically split into thousands of Index RangePartitions based on load
• Index RangePartitions are automatically load balanced across servers to quickly adapt to changes in load
Partition Layer – Index Range Partitioning
Account Name
Container Name
Blob Name
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…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
…….. …….. ……..
zzzz zzzz zzzzz
• Split index into
RangePartitions based on load
• Split at PartitionKey boundaries
• PartitionMap tracks Index RangePartition assignment to partition servers
• Front-End caches the
PartitionMap to route user requests
• Each part of the index is assigned to only one Partition Server at a time
Storage Stamp
Partition Server Partition
Server
Account Name
Container Name
Blob Name richard videos tennis
……… ……… ………
……… ……… ………
zzzz zzzz zzzzz
Account Name
Container Name
Blob Name harry pictures sunset
……… ……… ………
……… ……… ………
richard videos soccer
Partition Server
Partition Master
Front-End Server
PS 2 PS 3
PS 1
A-H: PS1 H’-R: PS2 R’-Z: PS3
A-H: PS1 H’-R: PS2 R’-Z: PS3
Partition Map Blob Index
Partition Map Account
Name
Container Name
Blob Name
aaaa aaaa aaaaa
……… ……… ………
……… ……… ………
harry pictures sunrise
A-H
H’-R R’-Z
Each RangePartition – Log Structured Merge Tree
Checkpoint File Table
Checkpoint File Table
Checkpoint File Table
Blob Data Blob Data Blob Data Commit Log Stream
Metadata log Stream
Writes Read/Query
Stream Layer
• Append-Only Distributed File System
• Streams are very large files
• Has file system like directory namespace
• Stream Operations
• Open, Close, Delete Streams
• Rename Streams
• Concatenate Streams together
• Append for writing
• Random reads
Stream Layer Concepts
Extent E2 Extent E3
Block Block Block Block Block Block Block Block
Block
• Min unit of write/read
• Checksum
• Up to N bytes (e.g. 4MB)
Extent
• Unit of replication
• Sequence of blocks
• Size limit (e.g. 1GB)
• Sealed/unsealed
Stream
• Hierarchical namespace
• Ordered list of pointers to extents
• Append/Concatenate
Block Block Block Block Block Block Block
Extent E4
Stream //foo/myfile.data Ptr E1 Ptr E2 Ptr E3 Ptr E4
Extent E1
Creating an Extent
SMStream SM Master
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN
Create Stream/Extent
Allocate Extent replica set
Primary Secondary A Secondary B
EN1 Primary
EN2, EN3 Secondary
Replication Flow
SMSMSM
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN
Append
Primary Secondary A Secondary B
Ack
EN1 Primary
EN2, EN3 Secondary
Providing Bit-wise Identical Replicas
• Want all replicas for an extent to be bit-wise the same, up to a committed length
• Want to store pointers from the partition layer index to an extent+offset
• Want to be able to read from any replica
• Replication flow
• All appends to an extent go to the Primary
• Primary orders all incoming appends and picks the offset for the append in the extent
• Primary then forwards offset and data to secondaries
• Primary performs in-order acks back to clients for extent appends
• Primary returns the offset of the append in the extent
• An extent offset can commit back to the client once all replicas have written that offset and all prior offsets have also already been completely written
• This represents the committed length of the extent
Dealing with Write Failures
Failure during append
1. Ack from primary lost when going back to partition layer
• Retry from partition layer can cause multiple blocks to be appended (duplicate records)
2. Unresponsive/Unreachable Extent Node (EN)
• Append will not be acked back to partition layer
• Seal the failed extent
• Allocate a new extent and append immediately
Stream //foo/myfile.dat Ptr E1 Ptr E2 Ptr E3 Ptr E4
Extent E5
Ptr E5
Extent E1 Extent E2 Extent E3 Extent E4
Extent Sealing (Scenario 1)
SMStream SM Master
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN 4
Append
Primary Secondary A Secondary B
Ask for current length
120 120
Sealed at 120
Seal Extent
Seal Extent
Extent Sealing (Scenario 1)
SMStream SM Master
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN 4
Primary Secondary A Secondary B
Sync with SM 120
Sealed at 120
Seal Extent
Extent Sealing (Scenario 2)
SMSMSM
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN 4
Append
Primary Secondary A Secondary B
Ask for current length 120
Sealed at 100
Seal Extent
100 Seal Extent
Extent Sealing (Scenario 2)
SMSMSM
Paxos
Partition Layer
EN 1 EN 2 EN 3 EN 4
Primary Secondary A Secondary B
Sync with SM
Sealed at 100
Seal Extent
100
Providing Consistency for Data Streams
SMSMSM
EN 1 EN 2 EN 3
Primary Secondary A Secondary B
Partition Server
Network partition
• PS can talk to EN3
• SM cannot talk to EN3
• For Data Streams, Partition Layer only reads from offsets returned from successful appends
• Committed on all replicas
• Row and Blob Data Streams
• Offset valid on any replica
Safe to read from EN3
Providing Consistency for Log Streams
SMSMSM
EN 1 EN 2 EN 3
Primary Secondary A Secondary B
Partition Server Check commit length
• Logs are used on partition load
• Commit and Metadata log streams
• Check commit length first
• Only read from
• Unsealed replica if all replicas have the same commit length
• A sealed replica Check commit lengthSeal Extent
Use EN1, EN2 for loading
Network partition
• PS can talk to EN3
• SM cannot talk to EN3
Design Consideration (1)
• Multi-Data Architecture
• Use extra resources to serve mixed workload for incremental costs
• Blob -> storage capacity
• Table -> IOPS
• Queue -> memory
• Drives -> storage capacity and IOPS
• Multiple data abstractions from a single stack
• Improvements at lower layers help all data abstractions
• Simplifies hardware management
• Tradeoff: single stack is not optimized for specific workload pattern
Design Consideration (2)
• Append-only System
• Greatly simplifies replication protocol and failure handling
• Consistent and identical replicas up to the extent’s commit length
• Keep snapshots at no extra cost
• Benefit for diagnosis and repair
• Erasure Coding
• Tradeoff: GC overhead
• Scaling Compute Separate from Storage
• Allows each to be scaled separately
• Important for multitenant environment
• Moving toward full bisection bandwidth between compute and storage
• Tradeoff: Latency/BW to/from storage
Design Consideration (3)
• Automatic load balancing
• Quickly adapt to various traffic conditions
• Need to handle every type of workload thrown at the system
• Built an easily tunable and extensible language to dynamically tune the load balancing rules
• Need to tune based on many dimensions
• CPU, Network, Memory, TPS, GC load, Geo-Rep load, Size of partitions, etc.
• Achieving consistently low append latencies
• Ended up using journaling
• Efficient upgrade support
• Pressure point testing
Ali Pangu Architecture
Paxos M
Master-1
Paxos
Master-2
Paxos M
Master-n
……
ChunkServer-1 ChunkServer-i ChunkServer-n Name SpaceChunk Storage
M M
M M M
M M
……
Ali Pangu - Hybrid Read/Write Mode
SSD1 SSD2
HDD HDD
HDD
client dump
write/read
read
•
SSD High IOPS
Limited Endurance
•
HDD High throughput
Low IOPS
Pangu Functions
API
Data Security Availability &
Performance Access type
Management &
Maintaince
• Multi-Master
• Multiple Replication
• Error Detection
• Flow control
• Hybrid Storage
• Data Aggregation
• Hotspot avoidance
• Blacklist
• Capability Security Access
• Quota Management
• Disk Automatic online
• Dynamic Scaling
• Online Monitoring
• Offline Analytics
• Directory/File Structure
• Create/Open/Close/Delete/Rename … Operations
• Support Batch Processing/Asynchronous API
• Random Access
• Append-Only
Write Mode for Multiple Replications
• Chain Replication
• Direct Replication • Primary-Secondary Replication
• Checksum
• Garbage Collection
Contents
Project 3
5
Distributed File System Design (1)
• Design a Mini Distributed File System (Mini-DFS), which contains
• A client
• A name server
• Four data servers
Name Server
Data Server-1
Data Server-2
Data Server-3 App/Client
Data Server-4
Distributed File System Design (2)
•
Mini-DFS is running through a process. In this process, the name server and data servers are different threads.•
Basic functions of Mini-DFS Read/write a file
Upload a file: upload success and return the ID of the file
Read the location of a file based on the file ID and the offset
File striping
Slicing a file into several chunks
Each chunk is 2MB
Uniform distribution of these chunks among four data servers
Replication
Each chunk has three replications
Replicas are distributed in different data servers
Distributed File System Design (3)
•
Name Server List the relationships between file and chunks
List the relationships between replicas and data servers
Data server management
•
Data Server Read/Write a local chunk
Write a chunk via a local directory path
•
Client Provide read/write interfaces of a file
Distributed File System Design (4)
•
Mini-DFS can show Read a file (more than 7MB)
Via input the file and directory
Write a file (more than 3MB)
Each data server should contain appropriate number of chunks
Using MD5 checksum for a chunk in different data servers, the results should be the same
Check a file in (or not in) Mini-DFS via inputting a given directory
By inputting a file and a random offset, output the content
Distributed File System Design (5)
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Bonus points Add directory management
Write a file in a given directory
Access a file via “directory + file name”
Recovery
Delete a data server (three data servers survive)
Recover the data in the lost data server
Redistribute the data and ensure each chunk has three replicas
