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This week we look at chapter 23 Managing Critical State: Distributed Consensus for Reliability and chapter 24 Distributed Periodic Scheduling with Cron from Site Reliability Engineering: How Google Runs Production Systems.

Things to monitor with a distributed consensus system

• The number of members running in each consensus group, and the status of each process (healthy or not healthy)
• Persistently lagging replicas
• Whether or not a leader exists
• Number of leader changes
• Consensus transaction number - in a healthy system, this number will be incremented over time
• Number of proposals seen and number of proposals agreed upon
• Throughput and latency
• Latency distributions for proposal acceptance
• Distributions of network latencies observed between parts of the system in different locations
• The amount of time acceptors spend on durable logging
• Overall bytes accepted per second in the system

Projects mentioned

• Apache ZooKeeper™
• Consul
• etcd

 

Articles from this week's chapters

• Harvest, Yield, and Scalable Tolerant Systems
• Brewer’s Conjecture and the Feasibility of
  Consistent, Available, Partition-Tolerant Web
  Services
• CAP Twelve Years Later: How the "Rules" Have Changed
• Existential Consistency:
  Measuring and Understanding Consistency at Facebook
• The trouble with timestamps
• F1: A Distributed SQL Database That Scales
• Impossibility of Distributed Consensus With One Faulty Process
• The Part-Time Parliament
• In Search of an Understandable Consensus Algorithm
  (Extended Version)
• Zab: High-performance broadcast for primary-backup systems
• Mencius: Building Efficient Replicated State Machines for WANs
• ZooKeeper Recipes and Solutions
• ZooKeeper: Wait-free coordination for Internet-scale systems
• The Chubby lock service for loosely-coupled distributed systems
• Stumbling over consensus research: Misunderstandings and issues
• Paxos for System Builders: An Overview
• Implementing Fault-Tolerant Services Using the State Machine
  Approach: A Tutorial
• Spanner: Google’s Globally-Distributed Database
• The Google File System
• Bigtable: A Distributed Storage System for Structured Data
• MapReduce: Simplified Data Processing on Large Clusters
• Unreliable Failure Detectors for Reliable Distributed Systems
• Paxos Replicated State Machines as the Basis of a High-Performance Data Store
• Photon: Fault-tolerant and Scalable Joining of Continuous Data Streams
• High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
• Paxos Quorum Leases: Fast Reads Without Sacrificing Writes
• Fast Paxos
• Classic Paxos vs. Fast Paxos: Caveat Emptor
• Egalitarian Paxos
• Tuning Paxos for High-Throughput with Batching and Pipelining
• Paxos made live: an engineering perspective
• Practical Byzantine Fault Tolerance
• WHAT TAKES US DOWN?
• Large-scale cluster management at Google with Borg