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Using AWS To Build
A Scalable Machine Data Analytics Service
Christian Beedgen
November 13, 2013
Who Am I
• Co-Founder & CTO, Sumo Logic since 2010– Cloud-based Machine Data Analytics Service
– Applications, Operations, Security
• Server guy, Chief Architect, ArcSight, 2001-2009– Major SIEM player in the enterprise space
– Log Management for security & compliance
Everything You Know Is Wrong
Everything You Know Is Wrong
Agenda
• Introduction To Logs & Logging
• Why We Are Building This Service
• Architecture Of The Service
• Deployment Automation
• Loosely Coupled Components
• Lessons Learned
• Cost & Business Value
Introduction To Logs & Logging
What Is Machine Data?
• Actually, Machine Generated Data
Curt Monash:
“Data that was produced
entirely by machines OR
data that is more about
observing humans than
recording their choices.”
Daniel Abadi:
"Machine-generated data is
data that is generated as a
result of a decision of an
independent computational
agent or a measurement of
an event that is not caused
by a human action."
Examples Of Machine Data
• Computer, network, and other equipment logs
• Satellite and similar telemetry (espionage or science)
• Location data, RFID chip readings, GPS system output
• Temperature and other environmental sensor readings
• Sensor readings from factories, pipelines, etc.
• Output from many kinds of medical devices
What Are Logs?
• Logs are a kind of Machine Data
• Time-stamped bits and pieces of text
• Whispers & utterances of your infrastructure
• Written to disk to a log file by applications
• Sent over the network by devices
A Wealth Of Information
• Like Twitter for your infrastructure
• Machine data analytics…
• …is sentiment analysis for machines
• Free data of tremendous value
• Don’t forget to manage and analyze it
Or Else…
Anatomy Of A Log
Anatomy Of A Log
• Timestamp with time zone!
Anatomy Of A Log
• Timestamp with time zone!
• Log level
Anatomy Of A Log
• Timestamp with time zone!
• Log level
• Host ID & module name (process/service)
Anatomy Of A Log
• Timestamp with time zone!
• Log level
• Host ID & module name (process/service)
• Code location or class
Anatomy Of A Log
• Timestamp with time zone!
• Log level
• Host ID & module name (process/service)
• Code location or class
• Authentication context
Anatomy Of A Log
• Timestamp with time zone!
• Log level
• Host ID & module name (process/service)
• Code location or class
• Authentication context
• Key-value pairs
Use Cases
• Availability & Performance– Prevent downtime by proactive analytics, alerting
– Reduce MTTR by having all required data at your fingertips
• Application Release– Derive metrics from development and staging systems pre-deploy
– Baseline and compare after post-deploy quickly shows errors
• Security & Compliance– Compliance starts with having all security related logs in one place
– Analytics across all data facilitates detecting breaches and problems
Use Case Customer Examples Metric
Security &
Compliance
Apigee reduced compliance
audit costs by ~50%
Availability and
Performance
Ink saves nearly $500K
annually
Application
Release
Intaact reduced errors
by 4X
Customer Metrics
Machine Data Is Big Data
• Volume– Machine Data is voluminous and will continue to grow
– Our own application creates 1TB/logs per week easily
• Velocity– Machine Data occurs in real-time, and it is time-stamped
– Needs to be processed in real-time as well
• Variety– Machine Data is unstructured, or poly-structured at best
– Some standard schema, but sure enough not for you applications
Why We Are Building This Service
We Need To Evolve
We Need To Evolve
Legacy Products Fall Short
• Volume leads to scalability issues– Every Log Management system will fail – I have seen it
– Why should you bother with scaling yet one more system?
• Velocity challenges processing pipelines– What good are dashboards if they are not real-time?
– Streaming query engines are absolute must
• Variety isn’t being embraced– All data should be allowed into the system
– No vendor will ever know your application’s log schema
AWS Enables Innovation
• Attending Werner’s talk at Stanford in 2008
• First parking lot discussion
• This can apply to our space!
• Datacenter as API
• Massive power up to scraggly devs
AWS Enables Sumo Logic
• Entering an existing market – Existing & established competition, some of it huge
– Catch up & differentiate at the same time
• A Big Data service– Scaling on premise is hard and leaves the hard part to the customer
– Now we build one single system to deal with all customers
• This data is important– Regulatory compliance is among the big drivers for collecting it
– HA & DR concerns all over the place Amazon S3
Deployment Architecture - Before
Deployment Architecture - After
Architecture Of The System
Development Approach
• Developed in Scala because we like it
• Many small cohesive modules, low coupling
• Maven-based build system
• Layers of modules combined into applications
• Different applications for different concerns
• Internal Service-Oriented Architecture
• Communication via documented protocols
Basic Concerns
• Data ingestion
– Receiving data
– Raw storage
– Full-text indexing
• Data analysis
– Interactive analytics
– Scheduled queries
– Machine learning
– Continuous query
evaluation
Concerns Map To Clusters
• A cluster is multiple instances of the same application
• Deployed on multiple Amazon EC2 instances
• Deployed across multiple availability zones
• Instances within a cluster are oblivious of each other
• Receive from upstream, talk to downstream
• Receive from message bus, or talk RPC
Ingestion Path
Receiver Bus Index
Raw
CQ
S3
Receiver
• HTTPS endpoint behind Elastic Load Balancing
• Decompress messages from Collector
• Extract timestamps from messages
• Aggregate messages per-customer into blocks
• Flush blocks to message bus
• Ack to Collector
• “Statelessly stateful”/”Statefully stateless”
Receiver
Raw
• Receive message blocks from message bus
• Encrypt message blocks
• Different key for every day for every customer
• Flush encrypted message blocks to Amazon S3
• Copy blocks as CSV to customer’s Amazon S3 bucket
• Ack to message bus
• Fully stateless
Raw
Index
• Receive message blocks from message bus
• Cache message block on disk and ack to message bus
• Add message blocks to Lucene indexes
• Deal with wildly varying timestamps
• Flush index shards to Amazon S3
• Update meta data database with index shard info
• Stateful
Index
Continuous Query
• Receive message blocks from message bus
• Evaluate each message against all search expressions
• Push matching messages into respective pipelines
• Ack to message bus
• Flush results periodically for pickup by client
• Persist checkpoints periodically to Amazon S3
• Stateful, with checkpoint recovery
CQ
Analytics Path
Query
Service
CQ
S3
Query
• Fully distributed streaming query engine
• Materialize messages matching search expression
• Push messages through a pipeline of operators
• First stage – non-aggregation operators
• Second stage – aggregation operators
• Present both raw message results as well as aggregates
• Results update periodically for interactive UI experience
Query
Deployment Automation
Why Deployment Automation
• Add 1 part developers, 1 part Datacenter-as-API, stir…
• Aim for fully integrated continuous deployment
• Checkin unit test integration test deployment
• Jenkins automates it all – using AWS instances
• Deployment doesn’t mean production
• Nite Stag Long Prod deployments
• There are humans involved as well!
Automation Enables Scale
• The goal is 100% - accept no less
• Why U need automation– Number of deployments grows (staging, per-developer)
– Number of AWS resources per deployment grows
– Number of operators/developers grows
– Frequency of deployments, changes increases
Current Deployment Stats
• 4 Deployments running 24/7, 50 for development
• 20+ clusters per deployment
• 25+ software components deployed
• Hundreds of instances in production
• Less than 10 minutes to deploy from scratch
• Less than 4 minutes to restart hundreds of components
dsh: Another AWS deployment tool
• Model-driven, describe desired state, run to make it so
• High performance due to parallelization
• Covers all layers of the stack – AWS, OS, Sumo Logic
• Easy to use and extend, scriptable CLI
• Developer-friendly, Scala-based, high-level APIs
Example session
Sie Ist Ein Model & Sie Sieht Gut Aus
• Model contains concepts– Deployment
– Cluster
– AWS Resources (Amazon S3, Amazon Elastic Load Balancing, Amazon
DynamoDB, Amazon RDS, etc.)
– Software assemblies
– AWS configuration (IAM users, security groups, etc.)
• Human-readable names: prod-index-5
Model Snippet
Model Snippet
Differential Deployment
• Start by finding existing resources– Use tagging where it is available
– Name prefixes (“prod_xxx”) where it isn’t (security groups, IAM, …)
• Fix differences to model– Start “missing” instances
– Change security group rules, missing IAM users
• Proceed with caution– Never delete anything that holds data
– Amazon EBS, Amazon DynamoDB, Amazon S3, Amazon RDS
Example Of Tag Usage
Making It Fast
• Parallelize all the things– Upload to Amazon S3 while booting instances while creating IAM users
while setting up security groups while…
– Hyper-concurrent rolling restarts
Hyper, Hyper
Making It Fast
• Parallelize all the things– Upload to Amazon S3 while booting instances while creating IAM users
while setting up security groups while…
– Hyper-concurrent rolling restarts
• Fast enough for development– Write new code or fix a bug, compile locally
– Push code to development deployment and make it live
• Optimize data transfers– Use Amazon S3 hashes to only transfer new files
– Only upload changed JARs
Making It Reliable
• Check prerequisites before you even try– Does Prod account have room for this many instances?
– Do I have the required permissions for the AWS APIs?
– Any model discrepancies I can’t automatically resolve? Too many Amazon EBS volumes?
• Handle common failures automatically– No m1.large in us-east-1b? Move Amazon EBS volumes to us-west-1c and
try there
– Hitting the AWS API rate limit? Throttle and try again
– SSH didn’t come up on the instance? Kill it and launch another
– Eventual consistency in AWS– query until it has the expected state (tags)
Making It Secure
• Different AWS accounts
– Per developer
– Production
• account.xml
– All credentials for one AWS
account (AWS keys, SSH
keys)
– Password-protected
• IAM
– One user per Sumo
component
– Minimal IAM policy
– Inject AWS credentials
• Security Groups
– Part of the model
– Minimal privileges
Making It Safe
• Let mistakes happen at most once
• Add safeguards to prevent operator mistakes
• Type in the deployment name before deleting anything
• Disallow risky operations in production (shutdown Prod)
• Don’t allow –SNAPSHOT code to be deployed in production
Making It Easy
• Automate best practices– Distribute instances over availability zones evenly
– Register instances in Elastic Load Balancing and match AZs to
instances
– Tag all resources consistently
• Consistent naming– Generate SSH with logical names
Making It Affordable
• Developers forget to shut stuff down– Deployment reaper automatically shuts down deployments
– Daily cost emails
• Per-team budgets– Manager responsible to
keep within budget
Pitfalls
• Base AMI plus scripted installation prevents auto scaling
• Security group updates cause TCP disconnects
• This is fixed in the VPC stack, however
• Parallelism can cause stampedes (for example,
Amazon DynamoDB)
• Tagging API rate limits are easy to hit
Loosely Coupled Components
Loose Coupling In The Large
• A deployment is made up of many things
• Some of these things need to talk to each other
• Some of these things come and go
• Don’t pass in a huge list of static dependencies
• Start each application with one parameter
$ bin/receiver prod.service-registry.sumologic.com
Service Registry
• Service Registry is a concept, enables discovery
• A client-side library accessing a Zookeeper cluster
• Services are abstracted into types
• Application provides and consumes different services
• Sumo Logic services (RPC)
• Third-party services (message bus)
• AWS services (Amazon ElastiCache, Amazon RDS)
The Perils Of Horizontal Scale
• Scaling out a multi-tenant processing system
• 1000s of customers, 1000s of machines
• Parallelism is good, but locality has to be considered
• 1 customer distributed over 1000 machines is bad
• No single machine getting enough load for that customer
• Batches & shards will become too small
• Metadata and in-memory structures grow out of
proportion
The Perils Of Horizontal Scale
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The Perils Of Horizontal Scale
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The Perils Of Horizontal Scale
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The Perils Of Horizontal Scale
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The Perils Of Horizontal Scale
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1 3 4 1 3 4 2 3 5 2 3 5 2 3 6
7 7 5 8 5 8
1 3 4 1 3 4 2 3 5 2 3 5 2 3 6
7 7 5 8 5 8
7 7 5 8 5 8
5 8
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Customer Partitioning
• Each cluster elects a leader node via Zookeeper
• Leader runs the partitioning logic
•
• Partitioning written to Zookeeper
• Example: indexer node knows which customer’s message
blocks to pull from message bus
Set[Customer], Set[Instance] Map[Instance, Set[Customer]]
Lessons Learned
Some Tips On AWS S3
• Use the TransferManager class from the AWS Java SDK– Multi-part uploads and downloads
– Multi-threaded, overall latency reduction
• Use random prefixes for keynames in Amazon S3 buckets– Amazon S3 partitions by keyname prefix
• Endpoint URL for Amazon S3– s3.amazonaws.com might go to Virginia, or Pacific Northwest (!)
– If you are in us-east, use s3-external-1.amazonaws.com instead
http://aws.typepad.com/aws/2012/03/amazon-s3-performance-tips-tricks-seattle-hiring-event.html
Elastic Block Store
• RAID-0 makes Amazon EBS faster– Use LVM RAID-0 if heavy I/O is required
– Align stripe sizes with file system block sizes
• Snapshotting Amazon EBS volumes– Snapshots eat performance
– Even for volumes with provisioned IOPS
• Overlapping snapshots– Can be scheduled too close together, like every minute
– I/Os start taking 30+ seconds
Cost & Business Value
Somebody Has To Pay For Lunch
• On-demand resources are very sexy
• Automation gives developers their own sandbox
• Compute is the most easily incurred cost
• You need an automated reaper
• Or just raise another round…
Elasticity Is Not An Arbitrary Need
• At least in our system, there’s baseline load
• At least in our system, the cost is in compute
• Alert-based scaling can be safe & effective
• Measure your spend with tools that are out there
• We actually use Sumo Logic for that!
• Look for a moving average of resource consumption
• Buy Reserved Instances, don’t fret the instance types
One More Thing
Amazon CloudTrail
• Logs! From AWS! The eagle has landed!
• Amazon CloudTrail logs your API activity to Amazon S3
• Sumo Logic will read from Amazon S3, allow analysis
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