2016/11/10

Devoxx 2016 - day 4: notes (2016/11/10)

Programming your body with chip implants

Pär Sikö

chip: 12mm / 2mm -- same as for pets
  • rfid: entrance system
  • nfc: smartcards: 1kb
issues
  • battery -- energy harvesting
  • communication technology
    • active

Optional - The Mother of All Bikesheds

Stuart Marks

Optional
  • java 8 (java.util)
  • non-null ref (present) or empty (absent)
  • primitives: OptionalInt, Long etc
  • never use "null" as ref in Optional
  • "limited mech for returntypes where null will very likely return errors" e.g. streams api: Optional prevents NPE's in chained calls
  • issue NoSuchElementException
usage of Optional
  • never call "Optional.get()" when you can prove that the Optional is present
  • prefer alternatives to Optional.isPresent() / .get()
    • use: orElse() / orElseGet() / orElseThrow()
  • Optional.filter() predicate
  • Optional.ifPresent(): (<> isPresent) executes lambda if present
  • other methods
    • empty()
    • of()
    • flatMap()
    • ...
  • stream of Optional: .filter(Optional::isPresent).map(Optional::get).collect() -- filters present Optionals & extract values
misuses:
  • simple nullchecks - avoid Optional.isNullable. chainsgh
  • too complex constucts: Optional chains should be avoided
  • Optional.get() "attractive nuisance" -- will be deprecated
  • do not use Optional for
    • fields
    • method parameters
    • collections
    • replacing every null
  • Optional adds extra objects -- check performance issues
  • no identity-sensitive operations (e.g. serialization)

A Crash Course in Modern Hardware

Cliff Click

  • classic Von Neumann Architecture
  • throughput / core +10% / year (single-threaded)
  • CISC: easier to program, but harder to optimize (pagefaults)
  • RISC: simpler, but faster execution
  • walls:
    • power wall
    • ILP wall (branch prediction, speculative execution)
      • pipelining
        • better throughput, but latency remains
      • cache misses: stall -- performance = cache misses
      • branch predictions: 95% success
      • Itanium: static ILP: not much gain for huge effort
      • x86: limited by cache misses / branch mispredicts
      • locality is critical
    • memory wall
      • memory is larger, but latency is still high (DRAM)
      • SRAM for caches
        • requires data locality
        • cache layers
      • "memory is the new disk"
      • faster memory
        • relax coherency constraints
        • better throughput
    • speed of light
  • flat clock rates (15y)
    • hyper-threading: same limits (cache misses)
    • more cores
      • challenges:
        • chips reorder
        • concurrency is hard
        • immutable data
        • missing toolsets

The ISS position in real time on my mobile in less than 15mn ? Yes, we can.

Audrey Neveu

  • api.open-notify.org
  • ionic + cordova
  • server-sent events: push technology: text-only
    • streamdata.io for streaming the server-sent-events
    • JSON-patch RFC-6902 for changes demo
  • ionic start iss.io maps (=template)
  • ionic serve --lab
  • bower.json --> bower install

graph databases and the "panama papers"

Stefan Armbruster

panama papers: 2,6 TB data
property graph model
  • nodes: entities (can have name/value properties
  • relationships: type + direction (=semantic)
neo4j usecases
  • internal
    • network / it operations
    • data management
  • customer facing
    • real-time recommendations
    • graph based search
    • identity/access management
neo4j:
  • graph database -- easy to draw structure
  • solves relational pains (logical vs table model)
  • open source
  • easy to use
  • ACID
  • scalable (3.1)
  • syntax
    • patterns: (:Person{name:"Dan"})-:KNOWS>(:Person{name:"an"})
    • clauses CREATE / MERGE / SET/DELETE..
    • MATCH > WHERE <>
      • ORDER BY <>
      • paginationSKIP / LIMIT
    • LOAD CSV
  • demo

A JVM does That?

Cliff Click

 services -- "Virtual"
  • high quality GC
  • high quality machine code gen
  • uniform threading / memory model
  • type safety
  • ...
Illusion:
  • infinite mem -- gc pauses
    • jvm optimizes
  • byte code is fast:
    • JIT brings back expected cost model (gcc -O2 level)
    • JIT requires profiling
  • virtual calls are slow: java makes them fast
    • inline caches
  • partial programs are fast: requires deoptimization, reprofile, reJIT
  • consisten memory model: every machine has different memory models -- JVM handles this
  • consistent thread model: JVM imporves locking etc
  • Locks are fast
  • quick time access: difficult on hardware / multiple threads *
    • gettimeofday in java
wishes for the future:
  • tail calls
  • Integer as cheap as int
  • BigInteger as cheap as int
  • atomic multi-address update (software transactional memory)
  • thread priorities: on linux -- only as root
  • finalizers: "eventually" runs -- might be never (no timeliness guarantees)
  • soft/phantom refs: difficult to maintain in GC 
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