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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 à 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 à unit test à integration test à deployment •  Jenkins automates it all – using AWS instances •  Deployment doesn’t mean production •  Nite à Stag à Long à 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

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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] à 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… J

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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As a thank you, we will select prize winners daily for completed surveys!

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