• Pedestal
• Hiccup
• Hyperion
• core.async
• Mustache

https://github.com/nathanmarz/specter

• simple data structures allow for threaded macro passes through map
• complex graphs will be much more difficult
• map of vectors of maps
• vector in vector out
• define MAP-VALS
• signature become a set of transformation based on the data structures
• navigation through a datascructure is it's own structure
• navigate then transformation as a general approach
• adding to set is navigating to an empty subset
• navigation to a subsequence
• filterer srange
• precompilation of the selction path before the transformation can improve performation

Reasonable approach for a general solution of graph navigation.

• Boeing: fuel effeciency and gate turns (short-haul)
• New sensors in the 737
• Architecture
• Data managment: functional reactive programming
• DSL parsing; instaparse
• JVM already installed

Issues:

• Circular dependencies between Trapperkeeper serivces between message
• Messaging: publisher phase publisher logic go through a notification channel via pub/sub then to several taps with filters

Validation:

• identifying memory leaks is critical since minimum uptime is 7 days on a small footprint
• YourKit https://www.yourkit.com/

Consulting:

• Transducers
• in 1.7 there were a number of threading macros passe through (map #())
• in 1.8 this now becomes (into [])
• move from clojure.lang.PersistentQueue
• volitility in the ecosystem (e.g., Lamina, hug)
• mustache templates couldn't keep up with large files
• wrap library use in case the library needs to be swapped
• new developer onboarding difficult for OO programmers
• immmutable state difficult initially for new developers

Process:

• consiseness helped with code reviews
• no need to have Clojure developers on the simulator component for the REST end-point
• deftest and assertion checks
• only mandated requirement was 70% code coverage
• pairing was the key way to get new engineers onboarded
• schema or type checking

• Financial data
• Datomic
• Schemas and queries are data
• Prismatic https://github.com/plumatic/schema
• type-clojure
• entities

• defrecord
• records behave like maps
• get, assoc, dissoc all provide access
• don't use protocols
• defrecord can be used to contrain the code
• reify and specify
• everything in ojureScript is a deftype

• eDiscovery
• Lawyers filer (redaction of private information, company secrets, client)
• Produce documents

Requires that one import then search documents.

Constraint checks:

• retries in queue
• disk space
• monitoring
• cleanup on tasks
• monitoring

Core features:

• ClojureScript
• React/OM
• DataScript
• core/async

Issues:

• expensive remote calls
• multiple remote calls
• overfetching
• synchronizing loads

Tests:

• How much of this is required http://brewhouse.io/blog/2015/05/13/emberjs-an-antidote-to-your-hype-fatigue.html
• Look at the http://netflix.github.io/falcor/ for overfetching
• Look at http://redux.js.org/

• http://www.dollabs.com/

• modeling language
• backends
• visualization
• Physical Systems
• Learning: HMM, MDP, POMDP
• https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language

Visualization

• https://github.com/dollabs/planviz

• https://github.com/Day8/re-frame
• reactions from re-frame / posh that are an updated computation
• http://tonsky.me
• DataScript: (defrecord Datom [e a v tx added])
• https://writings.quilt.org/2014/05/12/distributed-systems-and-the-end-of-the-api/

http://michael.steindorfer.name/publications/oopsla15.pdf

• 32 nodes
• Node has a 32 elements
• Take the hash of "foo" then partition
• (partition 2 (str ( hash "foo")))
• Issue: complexity of key value pair or sub node reference

http://cider.readthedocs.org/en/latest/installation/