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High Throughput
Low Latency
https://github.com/Netflix/EVCache
User Request
The Hidden Microservice
Distributed
Memcached
Tunable
Replication
High
Resilience
Topology
Aware
Data
Chunking
Additional
Functionality
Ephemeral Volatile Cache
Architecture
Eureka
(Service Discovery)
Server
Memcached
Prana (Sidecar)
Monitoring & Other Processes
Client Application
Client Library
Client
Architecture
● Complete bipartite graph between clients and servers
● Sets fan out, gets prefer closer servers
● Multiple full copies of data
Use Case: Lookaside cache
Application (Microservice)
Service Client Library
Client Ribbon Client
S S S S. . .
C C C C. . .
. . .
Data Flow
Use Case: Primary Store
Offline / Nearline
Precomputes for
Recommendation
Online Services
Offline Services
. . .
Online Client Application
Client Library
Client
Data Flow
Usage @ Netflix
11,000+ AWS Server instances
30+ Million Ops/sec globally
Over 1.5 Million cross-region replications/sec
130+ Billion objects stored globally
70+ distinct caches
170+ Terabytes of data stored
MAPBruce Wobbe
Senior Software Engineer
What is Map?
● Merchandising Application Platform
● It is the final aggregation point for the data before it
is sent to the device.
● The Home page assembled here.
Why Map Uses EVCache?
● Map utilizes EVCache to cache the home page for
each customer.
● The Home Page is cached from 1-10 hours
depending on device (some rows updated more
often).
● EVCache provides extremely quick and reliable
access to data.
How Is The Data Stored?
● Map stores the data as individual records in
EVCache. Each home page row is a record in
EVCache.
● The normal access pattern is to request 1-6 rows at
a time.
● Each record is given a TTL.
Advantages of EVCache For Map
● High hit rate: 99.99%
● Clean up is automatic (Let the TTL handle it for you.)
● Solid (Map was using Cassandra as a backup for the
data, but EVCache proved so solid we removed the
Cassandra backup)
● Super fast (Average latency under 1ms)
Confidence
● We’ve never have seen cases of data corruption.
● When we have problems, EVCache is the last
place I'd look for a cause.
The Stats
Peak Per Region RPS:
● Total = Reads-Writes-Touches = 616K
● Reads = 275K
● Touches = 131K
● Writes = 210K
● Peak Data Per Region
● Total = 7.7 Tbytes
Future
● As a common use case is for devices to never hit
the cache after initial load, MAP would like to see a
cheaper way to store the data for infrequent
accessing users.
MonetaScott Mansfield & Vu Nguyen
Moneta
Moneta: The Goddess of Memory
Juno Moneta: The Protectress of Funds for Juno
● Evolution of the EVCache server
● EVCache on SSD
● Cost optimization
● Ongoing lower EVCache cost per stream
● Takes advantage of global request patterns
Old Server
● Stock Memcached and Prana (Netflix sidecar)
● Solid, worked for years
● All data stored in RAM (Memcached)
● Became more expensive with expansion / N+1 architecture
Optimization
● Global data means many copies
● Access patterns are heavily region-oriented
● In one region:
○ Hot data is used often
○ Cold data is almost never touched
● Keep hot data in RAM, cold data on SSD
● Size RAM for working set, SSD for overall dataset
Cost Savings
3 year heavy utilization reservations (list price)
r3.2xlarge x 100 nodes ≅ $204K / yr
Working set 30%
i2.xlarge x 60 nodes ≅ $111K / yr
~46% savings
New Server
● Adds Rend and Mnemonic
● Still looks like Memcached
● Unlocks cost-efficient storage & server-side intelligence
external internal
Rend
https://github.com/netflix/rend
Rend
● High-performance Memcached proxy & server
● Written in Go
○ Powerful concurrency primitives
○ Productive and fast
● Manages the L1/L2 relationship
● Server-side data chunking
● Tens of thousands of connections
Rend
● Modular to allow future changes / expansion of scope
○ Set of libraries and a default
● Manages connections, request orchestration, and
backing stores
● Low-overhead metrics library
● Multiple orchestrators
● Parallel locking for data integrity
Rend in Production
● Serving some of our most important personalization data
● Two ports
○ One for regular users (read heavy or active management)
○ Another for "batch" uses: Replication and Precompute
●● Maintains working set in RAM
● Optimized for precomputes
○ Smartly replaces data in L1
external internal
Mnemonic
Mnemonic
● Manages data storage to SSD
● Reuses Rend server libraries
○ handle Memcached protocol
● Mnemonic core logic
○ implements Memcached operations into
RocksDB
Mnemonic Stack
Why RocksDB for Moneta
● Fast at medium to high write load
○ Goal: 99% read latency ~20-25ms
● LSM Tree Design minimizes random writes to SSD
○ Data writes are buffered Record A Record B
...
memtables
● SST: Static Sorted Table
How we use RocksDB
● FIFO Compaction Style
○ More suitable for our precompute use cases
○ Level Compaction generated too much traffic to SSD
● Bloom Filters and Indices kept
in-memory
How we use RocksDB
● Records sharded across many RocksDB’s on aws instance
○ Reduces number of SST files checked--decreasing latency
...
Key: ABC
Key: XYZ
RocksDB’s
FIFO Limitation
● FIFO Style Compaction not suitable for all use cases
○ Very frequently updated records may prematurely push
out other valid records
SST
Record A2
Record B1
Record B2
Record A3
Record A1
Record A2
Record B1
Record B2
Record A3
Record A1
Record B3Record B3
Record C
Record D
Record E
Record F
Record G
Record H
SST SST
time
● Future: Custom Compaction or Level Compaction
Moneta Perf Benchmark
● 1.7ms 99%ile read latency
○ Server-side latency
○ Not using batch port
● Load: 1K writes/sec, 3K reads/sec
● Instance type: i2.xlarge
Open Source
https://github.com/Netflix/EVCache
https://github.com/Netflix/rend
https://github.com/Netflix/dynomite
Problems
● Cassandra not a speed demon (reads)
● Needed a data store:
o Scalable & highly available
o High throughput, low latency
o Active-active multi datacenter replication
o Advanced data structures
o Polyglot client
Observations
Usage of Redis increasing:
o Not fault tolerant
o Does not have bi-directional replication
o Cannot withstand a Monkey attack
What is Dynomite?
● A framework that makes non-distributed data
stores, distributed.
o Can be used with many key-value storage engines
Features: highly available, automatic failover, node
warmup, tunable consistency, backups/restores.
Dynomite @ Netflix
● Running >1 year in PROD
● ~1000 nodes
● 1M OPS at peak
● Largest cluster: 6TB source of truth data
store.
● Quarterly production upgrades
Completing the Puzzle
Dyno Dynomite-manager
Dynomite Ecosystem
Features
● Multi-layered Healthcheck of Dynomite node
● Token Management and Node configuration
● Dynomite Cold Bootstrap (warm up)
o after AWS or Chaos Monkey terminations
● Backups/Restores
● Exposes operational tasks through REST API
● Integration with the Netflix OSS Ecosystem
Where can I find it?
https://github.com/Netflix/dynomite-manager
https://github.com/Netflix/dynomite
https://github.com/Netflix/dyno
Thomson Reuters
About us
Canadian Head of Architecture. SOA, Cloud,
DevOps Practitioner. Drummer v1, Wine v0.5.
Working on all aspects of NetflixOSS and AWS.
Brazilian Principal Software Architect, SOA
Expert, DevOps Practitioner, Blogger, Terran SC2
Player. Working with Chaos / Perf Engineering
and NetflixOSS.
@diego_pacheco diegopacheco
http://diego-pacheco.blogspot.com.br/
ilegra.com
@samsgro samsgro
https://www.linkedin.com/in/sam-sgro-4217845
2015
Technology
POC
2016
First Commercial Apps
“Project
Neon”
First
Release
2017...
“Project
Neon”
Platform
Business Use Case
Why Dynomite?
Performance
Accumulated Latency per 1k operations
Infrastructure Changes
Dynomite Manager
● Submitted 2 Dynomite PRs to Netflix to improve integration with the Dynomite Manager
Eiddo: TR’s git-based network property server
● Converted this to talk to the Dynomite Manager
Ribbon: Eureka-integrated client load balancer
● Cache service instrumentation around Dyno, observables
Docker: Fun with Containers
● Simple image, ease of use, developer testing
Dynomite-manager
Issues and Challenges
Dynomite-manager
DEMO Architecture
Acknowledgements
Diego Pacheco
Maksim Likharev
Yurgis Baykshtis
Sam Sgro
Latency
Performance
2 to 3 years
Database Development
RESP server
Redis API
Hash Conf
Sharding
SnitchAnti-entropy
Dynomite
RESP API
Storage APIs / RESP Client
Redis Client
Gossip Telemetry
RESP protocol
RESP protocol
Central Command
Partitioning
2 to 3 years
Database Development
3 weeks
vs.
Dynomite + RocksDB
Dynomite + RocksDB
DevOps
RESP server
Redis API
Hash Conf
Sharding
SnitchAnti-entropy
Dynomite
RESP API
Storage API
Storage APIs / RESP Client
Redis Client
Gossip Telemetry
● Infrastructure
● Tooling
● Automation
● Deployment
Central Command
Application Development
Redis API
Simple, Composable, Powerful
Single Server Redis
Distributed Cache
Key/Value + Key/Data Structures
String
Integer
Float
List
Set
Sorted Set
Hash
Commands
Data model and query reuse
Function Benefits
Database
engineers
● Order of magnitude faster development
● Framework for rapid development
DevOps ● Efficiency gains
● Common infrastructure
● Reuse tools, scripts, and more
Application
developers
● Increase development velocity
● Single API for cache and database
● Query and data model reuse
Beyond Key/Value
10 001 12 002
Smith 001 Jones 002
Joe 001 Mary 002
Thank you
@DynomiteDB
www.dynomitedb.com
Netflix Open Source Meetup Season 4 Episode 2

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