Designing Data-Intensive Applications, Chapter 1

• Recently I've acquired Martin's DDIA book, and in the following days I will be sharing some notes about the chapters
• Also reading it? Feel free to connect, let's talk about it!

Reliability

• Roughly, "continuing to work correctly, even when things go wrong"
  • Things go wrong -> fault
  • Systems that anticipate and can cope with faults are called fault-tolerant or resilient
  • Fault =/= Failure
    • Fault: one component deviating from spec
    • Failure: the whole system stops providing the required service to the user
  • ❌ Reduce fault probability to zero ❌
  • ✅ Design fault-tolerance mechanisms that prevent faults from causing failures ✅
    • There are cases where prevention is better, though, as with security matters

Hardware faults

• Crashed hard disks, faulty RAM, lack of maintainance, etc
  • First response: adding redundancy to individual hardware components
• ⬆ applications' computing demands ⬆ rate of hardware faults
  • And in common cloud services, flexibility and elasticity are prioritized over single-machine reliability
  • Software fault tolerance techniques!

Software faults

• Harder to anticipate
• Correlated across nodes
  • Which causes more system failures than uncorrelated hardware faults
• Examples include:
  • Software bugs given a particular input
  • Runaway processes (infinite loops)
  • Downtime in some service that the system depends on
  • Cascading faillures
• Prevention includes:
  • Carefully thinking about interactions and assumptions about the system
  • Automated/manual testing
  • Process isolation
  • Measuring, monitoring and analyzing system behavior in production

Human errors

• How do we make our systems reliable, in spite of unreliable humans?
  • Designing systems in a way that minimizes opportunities for error. Eg: abstractions, APIs and admin dashboards (high-level interface)
  • Non-production environments (so people can explore and experiment safely)
  • Automated/manual testing to cover corner cases
  • Easy rollback options
  • Deploying code gradually + feature flags(trunk-based development)
  • Detailed monitoring and observability. Eg: performance metrics and error rates
  • Good management practices and training
• Reliability is important for big or small companies (potential lost of revenue and damage to reputation)
  • Balance between reliability and costs

Scalability

• Ability to cope with increased load
  • If the system grows in a particular way, what are our options for coping with the growth?

Load

• Actually, first we have to consider what load is
• Load parameters depend on the system architecture
  • Request per second to a web server
  • Ratio of reads to writes in database
  • Number of Simultaneosly active users in a chat room
  • Hit rate on cache, etc
• Twitter key load parameter: distribution of followers per user (fan out load)
  • SELECT all tweets in home timeline requests VS timeline cache

Performance

• Batch processing -> throughput (records processed/second or time to complete a job)
• Online systems -> response time (request -> response)
• We should think response time as a distribution of values, not a single number
  • Since in every request, the response time will be different
  • Random latency, loss of packet, garbage collection pause, mechanical vibrations, etc
• Average response time is NOT a good metric
  • It does not tell how many users experienced that delay
• Always use percentile: sort it from fatest to slowest and take the median (half of the requests return less/longer than X)
  • Also known as p50
• To know how bad the outliers are: p95, p99, p999
  • Also known as tail latencies
• If p95 = 1.5 seconds, 5 out of 100 requests take 1.5 seconds or more
• Tail latencies are important, since they affect users' experience of the service
  • 100ms increase in response time reduces sales by 1% (Amazon)
  • 1 sec slowdown reduces customer satisfaction metrics by 16%
• These percenetiles are used when defining SLAs and SLOs
  • "Server considered up if has a median response time of X ms and a p99 under 1s"
• Monitoring response times:
  • Ongoing basis
  • Window of response times of requests in the last 10 minutes
  • Every minute, calculate median and percentiles and plot those metrics in a graph

Coping with load

• Rethink the architecture on every order of magnitude load increase
• Scaling up (moving to a more powerful machine) vs scaling out (distributing load across machines)
  • Elastic systems (automatically add computing resources as load increases
  • Useful if increase of load is unpredictable
• Architecture of large systems is highly specific to the application
  • There is no such thing as a one-size-fits-all architecture
  • 100,000 1kB requests/sec vs 3 2GB requests/min
  • An architecture that scales well will be built around the load parameters
  • Iterate quickly on features VS scale to hypothetical future load
  • However, scalable architectures are built from general-purpose building-blocks (arranged in familiar patterns)

Maintainability

• Software cost is due mainly in maintainance, not in initial development process
  • Fixing bugs, investigating failures, adapting to new platforms, modifying use cases, repaying technical debt, adding new features, etc

Operability

• Good software can not run reliably with bad operations
• Operations squad responsibilities include:
  • System health monitoring/restoring from bad state
  • Tracking cause of problems (eg. failures or degrade performance)
  • Keep everything up to date
  • Anticipating future problems and solving them
  • Establishing good practices and development tools
  • Performing complex management tasks (such as platform migrations)
  • Maintaining system security
  • Writing docs about the system
• Good operability of data systems include:
  • Good monitoring (visibility)
  • Automation and integration with standard tools
  • Good documentation of operations
  • Good and predictable default behavior but with options to override it
  • Self-healing

Simplicity

• Simple and expressive code instead of bloated and complex
• Symptoms of complexity:
  • Tight coupling of modules
  • Tangled dependencies
  • Inconsistent naming
  • Hacks to solve performance issues, etc
• When the system is harder for developers to understand and reason about (hidden assumptions, unintended consequences, unexpected interactions), the risk of introducing new bugs is increased
• Making system simpler -> removing accidental complexity
  • Through abstractions (façade) #### Evolvability
• Constant flux: learn new facts, new use cases emerge, business priorities change, user request new features, architecture changes, etc
• Agile development
• The ease of modifying a system is linked with its simplicity
  • Easy-to-understand systems are easier to modify than complex ones