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Presented at JAX London 2013 Hailo, the taxi app, has served more than 5 million passengers in 15 cities and has taken fares of $100 million this year. I'm going to talk about how that rapid growth has been powered by a platform based on Cassandra and operational analytics and insights powered by Acunu Analytics. I'll cover some challenges and lessons learned from scaling fast!
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Hailo and NoSQL
David Gardner, Architect at Hailo
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1. Why choose NoSQL
2. A whistle-stop tour of Cassandra
3. Adoption of Cassandra at Hailo
What this talk is about
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What is Hailo?
Hailo is The Taxi Magnet. Use Hailo to get a cab wherever you are, whenever you want.
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• The world’s highest-rated taxi app – over 11,000 five-star reviews
• Over 500,000 registered passengers
• A Hailo hail is accepted around the world every 4 seconds
• Hailo operates in 15 cities on 3 continents from Tokyo to Toronto in nearly 2 years of operation
Facts and figures
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• Hailo is a marketplace that facilitates over $100M in run-rate transactions and is making the world a better place for passengers and drivers
• Hailo has raised over $50M in financing from the world's best investors including Union Square Ventures, Accel, the founder of Skype (via Atomico), Wellington Partners (Spotify), Sir Richard Branson, and our CEO's mother, Janice
Hailo is growing
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Why choose NoSQL?
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“NoSQL DBs trade off traditional features to better support new and emerging use cases”
Andy Gross, Riak
http://www.slideshare.net/argv0/riak-use-cases-dissecting-the-solutions-to-hard-problems
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• More widely used, tested and documented software
• Ad-hoc querying
• Talent pool with direct experience
What are we trading off?
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• High availability
• Scalability
• Operational simplicity
What do we get back in return?
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Cassandra 101
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Consistent hashing Vector clocks * Gossip protocol Hinted handoff Read repair http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
Columnar SSTable storage
Append-only Memtable
Compaction
http://labs.google.com/papers/bigtable-osdi06.pdf
Amazon Dynamo + Google Big Table
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coordinator node
Client
tokens are integers from 0 to 2127
three replicas (RF=3)
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Consistency level (CL)
Level Description
ONE 1st Response
QUORUM N/2 + 1 replicas
LOCAL_QUORUM N/2 + 1 replicas in local data centre
EACH_QUORUM N/2 + 1 replicas in each data centre
ALL All replicas
How many replicas must respond to declare success?
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Big Table
• Sparse column based data model • SSTable disk storage • Append-only commit log • Memtable (buffer and sort) • Immutable SSTable files • Compaction
http://research.google.com/archive/bigtable-osdi06.pdf http://www.slideshare.net/geminimobile/bigtable-4820829
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Plus timestamp, used for Last Write Wins (LWW) conflict resolution
Name
Value
Column
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we can have millions of columns
* theoretically up to 2 billion!
Name
Value
Column
Name
Value
Column
Name
Value
Column
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Name
Value
Column
Name
Value
Column
Name
Value
Column
Row Key
Row
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Column Family
Column Row Key Column Column
we can have billions of rows
Column Row Key Column Column
Column Row Key Column Column
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Write Memtable
SSTable SSTable Commit Log
Memory
Disk
buffers writes and sorts data
flush on time or size trigger
immutable
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Cassandra at Hailo
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Hailo launched in London in November 2011
• Launched on AWS
• Two PHP/MySQL web apps plus a Java backend
• Mostly built by a team of 3 or 4 backend engineers
• MySQL multi-master for single AZ resilience
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Why Cassandra?
• A desire for greater resilience – “become a utility” Cassandra is designed for high availability
• Plans for international expansion around a single consumer app Cassandra is good at global replication
• Expected growth Cassandra scales linearly for both reads and writes
• Prior experience I had experience with Cassandra and could recommend it
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The path to adoption
• Largely unilateral decision by developers – a result of a startup culture
• Replacement of key consumer app functionality, splitting up the PHP/MySQL web app into a mixture of global PHP/Java services backed by a Cassandra data store
• Launched into production in September 2012 – originally just powering North American expansion, before gradually switching over Dublin and London
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One year on...
• Further breakdown of functionality into Go/Java SOA
• Migrating all online databases to Cassandra
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Development perspective
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“Cassandra just works”
Dom W, Senior Engineer
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Use cases
1. Entity storage
2. Time series data
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CF = customers
126007613634425612: createdTimestamp: 1370465412 email: [email protected] givenName: Dave familyName: Gardner locale: en_GB phone: +447911111111
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Considerations for entity storage
• Do not read the entire entity, update one property and then write back a mutation containing every column
• Only mutate columns that have been set
• This avoids read-before-write race conditions
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CF = stats_db
2013-06-01: 55374fa0-ce2b-11e2-8b8b-0800200c9a66: {“action”:”… a48bd800-ce2b-11e2-8b8b-0800200c9a66: {“action”:”… b0e15850-ce2b-11e2-8b8b-0800200c9a66: {“action”:”… bfac6c80-ce2b-11e2-8b8b-0800200c9a66: {“action”:”…
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CF = stats_db
LON123456: 13b247f0-ce2c-11e2-8b8b-0800200c9a66: {“action”:”… 20f70a40-ce2c-11e2-8b8b-0800200c9a66: {“action”:”… 2b44d3b0-ce2c-11e2-8b8b-0800200c9a66: {“action”:”… 338a22f0-ce2c-11e2-8b8b-0800200c9a66: {“action”:”…
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Considerations for time series storage
• Choose row key carefully, since this partitions the records
• Think about how many records you want in a single row
• Denormalise on write into many indexes
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Analytics
• With Cassandra we lost the ability to carry out analytics eg: COUNT, SUM, AVG, GROUP BY
• We use Acunu Analytics to give us this abilty in real time, for pre-planned query templates
• It is backed by Cassandra and therefore highly available, resilient and globally distributed
• Integration is straightforward (HTTP POST)
NSQ Acunu C* events
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AQL
SELECT SUM(accepted), SUM(ignored), SUM(declined), SUM(withdrawn) FROM Allocations WHERE timestamp BETWEEN '1 week ago' AND 'now’ AND driver='LON123456789’ GROUP BY timestamp(day)
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Get a picture of driver supply
SELECT COUNT DISTINCT(driverId) FROM driverLocs WHERE timestamp BETWEEN '1 day ago' AND 'now' GROUP BY timestamp(hour) SELECT COUNT FROM driverLocs WHERE timestamp BETWEEN '1 day ago' AND 'now' GROUP BY latitude(0.01), longitude(0.01)
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Operational perspective
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“Allows a team of 2 to achieve things they wouldn’t have considered before Cassandra existed”
Chris H, Operations Engineer
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3 clusters
6 machines per region
3 regions (stats cluster is a long story)
Operational Cluster
Stats Cluster
ap-southeast-1 us-east-1 eu-west-1
us-east-1 eu-west-1
AZ1
eu-west-1
AZ1
AZ2 AZ2
AZ3 AZ3
AZ1
us-east-1
AZ1
AZ2 AZ2
AZ3 AZ3
AZ1
ap-southeast-1
AZ1
AZ2 AZ2
AZ3 AZ3
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AWS VPCs with Open VPN links
3 AZs per region
m1.large machines
Provisoned IOPS EBS
Operational Cluster
Stats Cluster
~ 1TB/node
~ 200GB/node
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Backups
• SSTable snapshot
• Used to upload to S3, but this was taking >6 hours and consuming all our network bandwidth
• Now take EBS snapshot of the data volumes
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Encryption
• Requirement for NYC launch
• We use dmcrypt to encrypt the entire EBS volume
• Chose dmcrypt because it is uncomplicated
• Our tests show a 1% performance hit in disk performance, which concurs with what Amazon suggest
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Datastax Ops Centre is a quick win
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Multi DC
• Something that Cassandra makes trivial
• Would have been very difficult to accomplish active-active inter-DC replication with a team of 2 without Cassandra
• Rolling repair needed to make it safe (we use LOCAL_QUORUM)
• We schedule “narrow repairs” on different nodes in our cluster each night
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Compression
• Our stats cluster was running at ~1.5TB per node
• We didn’t want to add more nodes
• With compression, we are now back to ~600GB
• Easy to accomplish
• `nodetool upgradesstables` on a rolling schedule
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Management perspective
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“The days of the quick and dirty are over”
Simon V, EVP Operations
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Technically, everything is fine…
• Our COO feels that C* is “technically good and beautiful”, a “perfectly good option”
• Our EVPO says that C* reminds him of a time series database in use at Goldman Sachs that had “very good performance”
…but there are concerns
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People who can attempt to query MySQL
People who can attempt to
query Cassandra
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Lessons learned
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There might be a gulf in experience
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10 Average years experience per team member
MySQL Cassandra
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Lesson learned
• Have an advocate - get someone who will sell the vision internally
• Learn the theory - teach each team member the fundamentals
• Make an effort to get everyone on board
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Things can drift into failure
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Lesson learned
• Be pro-active with Cassandra, even if it seems to be running smoothly
• Peer-review data models, take time to think about them
• Big rows are bad - use cfstats to look for them
• Mixed workloads can cause problems - use cfhistograms and look out for signs of data modeling problems
• Think about the compaction strategy for each CF
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EBS is terrible
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Lessons learned
• EBS is nearly always the cause of Amazon outages
• EBS is a single point of failure (it will fail everywhere in your cluster)
• EBS is slow
• EBS is expensive
• EBS is unnecessary!
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Management need to know the trade offs
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Lessons learned
• Keep the business informed – explain the tradeoffs in simple terms
• Sing from the same hymn sheet
• Make sure there solutions in place for every use case from the beginning
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People who can attempt to query MySQL People who can
attempt to query Cassandra
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Conclusions
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We like Cassandra
• Solid design
• HA characteristics
• Easy multi-DC setup
• Simplicity of operation
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Lessons for successful adoption
• Have an advocate, sell the dream
• Learn the fundamentals, get the best out of Cassandra
• Invest in tools to make life easier
• Keep management in the loop, explain the trade offs
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The future
• We will continue to invest in Cassandra as we expand globally
• We will hire people with experience running Cassandra
• We will focus on expanding our reporting facilities
• We aspire to extend our network (1M consumer installs, wallet) beyond cabs
• We will continue to hire the best engineers in London, NYC and Asia
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Questions?
