Using AWS to Build a Scalable Big Data Management & Processing Service (BDT401) | AWS re:Invent 2013
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This document summarizes Christian Beedgen's presentation on using AWS to build a scalable machine data analytics service. The presentation covers the architecture of Sumo Logic's service, which ingests machine-generated log data from customers in near real-time and performs analytics. It discusses how the service is built as loosely coupled microservices deployed across AWS with automation. Challenges of scaling such a distributed system are also addressed.
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Using AWS to Build a Scalable Big Data Management & Processing Service (BDT401) | AWS re:Invent 2013
- 1. Using AWS To Build A Scalable Machine Data Analytics Service Christian Beedgen November 13, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
- 2. 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
- 3. Everything You Know Is Wrong
- 4. Everything You Know Is Wrong
- 5. 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
- 6. Introduction To Logs & Logging
- 7. 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."
- 8. 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
- 9. 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
- 11. 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
- 12. Or Else…
- 13. Anatomy Of A Log
- 14. Anatomy Of A Log • Timestamp with time zone!
- 15. Anatomy Of A Log • Timestamp with time zone! • Log level
- 16. Anatomy Of A Log • Timestamp with time zone! • Log level • Host ID & module name (process/service)
- 17. Anatomy Of A Log • • • • Timestamp with time zone! Log level Host ID & module name (process/service) Code location or class
- 18. Anatomy Of A Log • • • • • Timestamp with time zone! Log level Host ID & module name (process/service) Code location or class Authentication context
- 19. 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
- 20. 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
- 21. Customer Metrics 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
- 22. 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
- 23. Why We Are Building This Service
- 24. We Need To Evolve
- 25. We Need To Evolve
- 26. 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
- 27. 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
- 28. 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
- 29. Deployment Architecture - Before
- 30. Deployment Architecture - After
- 31. Architecture Of The System
- 32. 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
- 33. Basic Concerns • Data ingestion – Receiving data – Raw storage – Full-text indexing • Data analysis – Interactive analytics – Scheduled queries – Machine learning – Continuous query evaluation
- 34. 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
- 36. 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
- 37. Raw • • • • • • • 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
- 38. Index • • • • • • • 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
- 39. Continuous Query • • • • • • • CQ 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
- 41. Query • • • • • • • 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
- 43. 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!
- 44. 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
- 45. 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
- 46. 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
- 47. Example session
- 48. 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!
- 49. Model Snippet
- 50. Model Snippet
- 51. 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
- 52. Example Of Tag Usage
- 53. 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
- 54. Hyper, Hyper
- 55. 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
- 56. 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)
- 57. 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
- 58. 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
- 59. 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
- 60. 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
- 61. 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
- 63. 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!
- 64. 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)
- 65. 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
- 66. The Perils Of Horizontal Scale Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index
- 67. The Perils Of Horizontal Scale 1 1 1 1 1 Index Index Index Index Index 1 1 1 1 1 Index Index Index Index Index 1 1 1 1 1 Index Index Index Index Index 1 1 1 1 1 Index Index Index Index Index 1 1 1 1 1 Index Index Index Index Index
- 68. The Perils Of Horizontal Scale 1 1 1 1 1 2 1 2 1 2 1 2 1 2 1 Index Index Index Index Index 2 1 2 1 2 1 2 1 2 1 Index Index Index Index Index 2 1 2 1 2 1 2 1 2 1 Index Index Index Index Index 2 1 2 1 2 1 2 1 2 1 Index Index Index Index Index 2 Index 2 Index 2 Index 2 Index 2 Index
- 69. The Perils Of Horizontal Scale 1 5 1 5 1 5 1 5 1 5 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 4 1 8 5 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 2 3 Index 6 7 4 8 4 8 4 8 4 8 4 8
- 70. The Perils Of Horizontal Scale 1Index 1Index Index Index Index 1Index 1Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index
- 71. The Perils Of Horizontal Scale 1Index 1Index 2Index 2Index 2Index 1Index 1Index 2Index 2Index 2Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index Index
- 72. The Perils Of Horizontal Scale 1Index4 3 1Index4 3 2Index5 3 2Index5 3 2Index6 3 1Index4 3 1Index4 3 2Index5 3 2Index5 3 2Index6 3 7 Index 7 Index 5 Index 8 5 Index 8 5Index6 8 7 Index 7 Index 5 Index 8 5 Index 8 5Index6 8 7Index 7Index 5Index 8 5Index 8 5Index6 8
- 73. Customer Partitioning • • • • • Each cluster elects a leader node via Zookeeper Leader runs the partitioning logic Set[Customer], Set[Instance] à Map[Instance, Set[Customer]]! Partitioning written to Zookeeper Example: indexer node knows which customer’s message blocks to pull from message bus
- 74. Lessons Learned
- 75. 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 ! http://aws.typepad.com/aws/2012/03/amazon-s3-performance-tips-tricks-seattle-hiring-event.html • 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
- 76. 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
- 77. Cost & Business Value
- 78. 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
- 79. 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
- 80. One More Thing
- 81. 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
- 83. Please give us your feedback on this presentation BDT401 As a thank you, we will select prize winners daily for completed surveys! Thank You
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